Fable Test — Steam Market Research 3

Summary

Deep re-check session of the surprising claims from Steam Market Research sessions 1–2, plus five owner follow-up questions. All analysis on the existing steam-market/ primary-data corpus (search/steamspy/histogram/appdetails sqlite) plus two new crawls (in-game screenshots, playtime-at-review) and one new blind-scoring pipeline. Produced reports 15–23 in steam-market/reports/ with raw outputs and scripts alongside. Every claim survived or was corrected with named mechanisms; several load-bearing numbers changed.

Claim #1 — path dependence (report 15, scripts path_dependence.py/2/3):

• Reproduced report-04 headline exactly (prior-hit devs re-hit 57.6% vs 6.4% no-hit veterans vs 8.7% first-timers), then attacked it seven ways: release-vs-dev weighting, publisher contamination, threshold artifact, era replication, estimator circularity (K-band, reviews-only, owners cross-definition, time-truncation at 2022-12-31), EA-graduation leakage, return-rate selection.
• Verdict: effect real and structural but the number was wrong in both directions. Dev-level (first release per dev) it's LARGER: 65.8% vs 9.7% (factories like gamesforgames — 67 releases at 1% — dragged release-weighting down; Capcom/Koei/Square pulled up). His cell (selfpub hit → selfpub release, EA-adjusted): 49.1% dev-first, med $60K; SNKRX band ($250–500K prior, est $388K) 56.6%.
• NO CLIFF AT $50K — smooth ladder by prior best: 2→7→12→18→29→42→55→68→71→84→93%, Spearman ρ=0.715. "No-hit veterans 6.4%" pools $25–50K priors (29%!) with shovelware (2%).
• Era replication: 4.6× (2014-16) → 5.4× → 6.8× (2020-22) — the gap is WIDENING; market got harder only for devs without an audience.
• Time-truncation made the effect STRONGER (70.3 vs 12.7%) — no tail-leakage circularity. EA-graduation trims ~3pt. Only 30% of prior-hit devs ship again in-window (vets 15%).
• Price gate survives inside his cell dev-level (new IP: ≤$7 15.1% / $8–12 42.5% / $13–20 57.3%). NEW: price-transition matrix — cheap-prior devs who moved UP to $8–12 re-hit 52% vs 15% staying ≤$7; pricing DOWN catastrophic everywhere (0–11%).
• Gap decay: no decay confirmed within selfpub; inversion is composition (longer gap = bigger prior hit + pricier next). His profile (selfpub hit 2020-21, gap≥4): 52.5%, peers = Rift Wizard 2 / Crashlands 2 / SpaceBourne 2.
• Franchise premium +18pt persists within selfpub (sequel-of-hit 69.6% vs new IP 51.8% dev-first).

Art vs price — owner's graphics objection (report 16, art_price_sample.py / art_price_analysis.py):

• Owner hypothesis: "price = how much the game visually commands; the price effect may be purely graphics." Built a new pipeline: 1,116 in-game screenshots (941 true + 175 header fallbacks) for his whole cell + each dev's prior hit; 70 contact sheets; 10 parallel subagents blind-scored quality 1–5 + style class (MIN/PIX/FLAT/DRAWN/LOW3D/HI3D/TXT).
• REFUTED: spearman(price, art)=0.294 only; $8–12 vs $13–20 visually indistinguishable. Grid: within art-3 row price runs 16→82% (screenshots-only 22→84); art 1–2 at $13–20 (37%) beats art 4–5 at ≤$7 (17%). Mechanical note: h50 gradient partly IS price arithmetic (same audience × higher price; audience doesn't shrink proportionally).
• MIN style (SNKRX's class) = graveyard: 3/20 hits, only real ones idle/clicker (clickyland $107K). Kept-simple vs went-fancy is price-confounded: identical at $8–12 (26 vs 29%); fancy wins at $13–20 (79 vs 45%). Kept-simple at $10+ = 51% (n=143); at ≤$7 = 3%.
• 67 kept-simple re-hits from 48 devs: Soulash 2, Illwinter (Dominions 6), Card Survival, Cruelty Squad→Psycho Patrol R ($40!), Pumping Simulator 1→2 ($5→$12, $85K→$701K), Szymanski, Rusty's Retirement, MDickie, Spiderweb. Threads: all ≥$10, style-consistency-as-brand, systems-depth genres. Art-up from competent base (3) pays (62 vs 35%); from amateur does nothing (20 vs 22%).

Scope vs price — owner asked to run scope too (report 17, playtime_crawl.py / scope_analysis.py):

• New crawl: 100 top-helpful reviews per game for all 1,118 his-cell games + SNKRX/BYTEPATH; scope = median playtime_at_review. Secondary proxies: disk GB from PC requirements, achievements.
• PRICE IS A SCOPE BADGE, NOT A GRAPHICS BADGE: spearman(price, hours)=0.571 vs (price, art)=0.294. Median hours by band: 2.6/3.9/7.2/15.6h.
• Sub-3h games dead at EVERY price (20/36/11/29% — no gradient); 3–10h row steep (53→70→100→95%). $13–20 sweet spot = ~7h scope requirement in disguise. Holds fixing art≤3 simultaneously.
• Price-band jumpers added only 1.28× median hours; priced-up-without-scope-up = 90% h50 (same as same-band) — pricing up does not require proportional scope growth.
• Kept-simple winners are depth monsters: med 12.2h (Card Survival 84.5h, Rusty's 27.4h at $6.99). SNKRX itself: median 9.1h at review (n=10) — $13–20-band engagement sold at $2.99; the scope was there, the price wasn't. Coverage collider stated (242/772 measurable).

Claim #4 — migration/stay-switch (report 18, migration_deep.py):

• Directional claim survives; dramatic tables dissolve. Stay premium durable ~5–10pt (dev-first new-IP 70.0 vs 62.4; selfpub eras +6.2/+6.6/+15.9-wide-CI). Sequel confound tested and absolved; side-finding: sequels in a NEW genre re-hit 73% — franchise travels across genres.
• SIM-ORIGIN 27% CLAIM DEAD: three-factory artifact (gamesforgames 55 + Kairosoft 22 + G-MODE 21 of 175 releases); dev-first new-IP sim movers = 62.5% = pooled. "Sim hits are format-luck" struck from the record.
• Origin ranking after shrinkage: 52–79% spread (not 27–95%). Action Roguelike origin n=6 dev-first — the "75% AR export" was unrankable noise (though AR movers include Another Crab's Treasure $10.2M, Ravenswatch $7.3M, Nomad Idle). Adventure-as-destination rehabilitates (56%); Racing below reportable n.
• Tag distance flat 0–4 shared, exact-neighborhood bonus at 5+ (+~10pt). Pseudo-moves don't beat true moves. $199.99 joke-priced games flagged as estimator breakers in tiny cells. Report 18 supersedes report 11 §7; lane choice should ride economics (reports 15–17), not migration tables.

Claim #3 — retro+idler and card×structure lanes (report 19, lane_deep.py):

• IDLER SUPPLY EXPLODING: 91 (23H1) → 604 (26H1); all idler-adjacent pairs flooding (Idler+Strategy n26=157). Price-immunity claim REVISED: idler curve peaks $8–12 (36.0%), $3–5 only 7.7% (though $2.99 mega-winners exist: Nodebuster/Tower Wizard/Digseum). BYTEPATH incremental should price $7.99–9.99, not $5–6, and ship before 26H2.
• Retro+Idler cell colonized: 7 entries 26H1 (all dead so far) after 2025's hits (Tiny Aquarium $625K, FACEMINER $397K). Report-10 "near-empty" label stale.
• THE 'INCREMENTAL' DESCRIPTION MICROFORMAT is the real signal: 18→47→110→121(26H1) games, h50 33.9% even in censored 2026 vs 7% idler-wide (Scritchy Scratchy $1.6M, Berry Bury Berry $1.6M, Microcivilization $914K, Orb of Creation $819K). BYTEPATH EV updated: ~40–60% at $7.99–9.99, med $50–150K.
• Card×Base Building: strict pair still n26=0 (open); honest neighborhood 35–40% at $13–20 (66-game census). Winners' formula = cards as economy/build verb + spatial defend/expand board: Stacklands $3.4M, Emberward $1.3M, DECK OF HAUNTS $830K (literal reverse-siege), Kingdom's Deck. Card/Deck×TD (Siege-as-designed cell): crowded n=126, 16–41%. CARD PRICE GATE STEEPEST ANYWHERE: ≤$7 = 3.1%, $13–20 = 43.3%, >$20 = 63.7% → Artifact port prices $15–20, CN day one.
• Positioning options presented (a: ship Siege as locked; b: deepen Guardian into buildable base → the stronger emptier cell; c: full inversion à la Deck of Haunts). Owner's card-base-builder instinct data-supported; design delta = owner's call.

Anime + AI-art follow-up (report 19 addendum):

• Anime tag 26.2% h50 (~2× market), non-adult $13–20 = 37%; Anime+RL-Deckbuilder 40% (mp $14.49) = the correctly-priced cell. Comps: Chrono Ark $2.3M, Rebellion GODSOUL $1.6M (same dev appeared in the gap-decay winners list: $134K VN prior → $1.6M anime card-battler).
• AI-art reception MEASURED from 52,043-review corpus: 178 mentions across 133 games, only 19% of mentioning reviews positive (runs far below each game's own up-share) — marginal-but-real negative vector, not a death sentence (Road to Empress $482K hit anyway). Risk concentrates where art IS the product = a $15–20 anime card game exactly. Bar = Chrono-Ark-class set consistency; suggested a 20-card consistency test before committing.

Claim #2 — launch timing (report 20, timing_deep.py / timing_sales2.py):

• Original claim mechanically wrong: THE RULE = never launch DURING a seasonal sale (7.7% vs ~16%; his class 12.1 vs ~21; winter 6.3/autumn 6.7/summer 8.8/spring 10.0). Everything else — including 1–7 days BEFORE a sale — is free.
• December innocent Dec 1–18 (14–15%); the claim missed Nov 16–30 = 10.7% (autumn sale hidden inside fine-looking November). Jan 1–9 tail bad (8.6%). SEPTEMBER best month (18.4% all / 24.9% his class; weeks 36–42 peak). Day of week: Thu best; Sat/Sun bad. Competition density: dead lever. Sale windows: two data-driven detection attempts failed instructively (launch spikes / monthly-rollup boundaries), calendar-encoded windows validated on the one detectable event (2025 autumn ×4.6 tail surge).
• Next Fest: calendar proximity dead-to-negative (his class fest-week 16.0 vs other 22.1%); what correlates = demo spiking AT a fest (10.2% of 2025 hits vs 0.4% of fails, selection-confounded). NF = optional demo channel, not a timing lever. BYTEPATH: late Jul/Aug fine, September best, Tue–Thu.

Wishlists question (report 21, wishlist_proxy.py):

• Launch wishlists are private everywhere; measured the downstream shadow m1 = first-30d reviews (histogram t0, EA-safe; 45 units/review in-house).
• Momentum = steepest predictor in the project: his class P(hit|m1) 25–49: 51% / 50–99: 81% / 100–249: 99.6% / 250+: 100%. Coverage collider stated: unconditional cold-start hit rate <1%; the ~40% low-bucket figures are conditional on later signs of life.
• NOT a gate: 18.3% of his-class hits started m1<25; 623 slow-burn hits 2022–24 (Vampire Survivors m1=11, Last Epoch m1=3); slow lane median 13mo to half-lifetime; slow-burn genres = deep-systems/EA/long-tail; consume-once genres produce none.
• MECHANISM OF COMPOUNDING: prior-hit selfpub devs warm-start 5× more often (m1≥50: 40.1% vs 8.1%) AND convert better at equal m1 (50–99: 73 vs 55%; 100–249: 94 vs 82%). SNKRX m1=117 in 12 days with ~zero wishlists — m1 ≠ wishlists (momentum from any source).

Final two avenues, run together while owner away — quality×momentum (report 22) and EA vs direct (report 23):

• QUALITY: "just make it good" measurably wrong as a growth strategy. pct@m3 → growth-after-m3 monotone but shallow (1.77×→2.54× across 35 positivity points) = smallest effect measured all session. NO badge discontinuities at 80/95 (smooth curves; 98–100 P(hit) DROPS — beloved-niche artifact; SNKRX ~94.7%). Momentum dominates: m1 axis swings 32→96%, whole quality axis ±5pt. Quality cannot rescue cold starts (95+ actually worst: 26.9%). Slow lane is NOT the quality lane (slow-burn hits med pct 85 = cold-died med pct 85). Prior-hit edge not quality (equal pct at equal m1). Actionable: above ~80 chase momentum not points; below ~70 fix bugs/grind (r12).
• EA: outperforms direct everywhere at face value (his class 39.2 vs 15.7; idler 37.5 vs 8.1; card 45.1 vs 14.5) — selection-heavy (EA marks seriousness). ⭐ 1.0 GRADUATION IS NOT A LAUNCH: median 30d reviews at 1.0 = 1.2–1.4× ongoing EA baseline regardless of EA audience size — EA value realized during EA; the EA entry IS the launch. Lane norms: idler hits only 22% EA (Gnorp/Rusty's/Nodebuster direct) → BYTEPATH ships direct; card 31% EA (Peglin/Mechabellum/Backpack Battles EA-grad, 9 Kings in-EA $6.9M, Balatro direct) → Artifact card game EA-viable if EA entry treated as the real launch.

Infrastructure added this session: scripts path_dependence(1–3), art_price_sample/analysis, playtime_crawl, scope_analysis, migration_deep, lane_deep, timing_deep, timing_sales2, wishlist_proxy, quality_momentum, ea_paths; data/art_price/ (1,116 screenshots + blind scores), data/playtime.sqlite; reports 15–23 + notes.md summary blocks. Dead ends documented: price history unobtainable, wishlists private, sale-week detection from histograms defeated by launch spikes and monthly-rollup boundaries (calendar-encoding + single-event validation used instead).

Steam Market Research 1 and 2 had a few interesting/surprising claims that I didn't pursue then because we were busy doing the relevant work for those sessions, but I'd like to dig deeper into those claims here. I'll list them, and you choose which one to tackle first. Let's do a deep re-check and re-research of each claim to refine it, see if it actually holds, and also look at it from different angles, generating more data where relevant to illuminate the claims further. I'll list them now:

• "Success is wildly path-dependent. Devs with a ≥$50K game in 2020–22 hit ≥$50K again on 57.6% of their 2023–25 releases (median $104K). Veterans without a prior hit: 6.4% (median $6K). First-time self-pubs: 8.6%. You are in the 57.6% bucket (SNKRX + followers), and the advantage is portable: switchers to a different niche still re-hit at 52.7% vs 61.0% for stayers. This is the single largest effect found all day — larger than any tag, price, or feature effect." I'd like a more detailed and comprehensive check on this and related claims that occur in both sessions.

• "Launch any month except December (10.5% vs 13–17% the rest of the year — timing is otherwise a dead lever)." More details on this, breakdown by month, launch windows taking into account NextFests, sales, etc. Should I release near a sale, during it, or does it not matter? Same for NextFest, same for any other such relevant events or dates.

• I'd like more detail on the retro + idler and card base + base building lanes. Especially related to the decision to make a BYTEPATH incremental as well as the Artifact port we did in another session, not sure if I uploaded that log to the website yet, but there's a folder with it somewhere, I feel like card game base builder using the Artifact ruleset probably makes some sense.

• "Staying in your primary tag: 63.6% re-hit. Moving: 55.9% — an ~8pt tax, still 4× market. Your origin (Action Roguelike) is one of the best export genres: its prior-hit devs re-hit at 75% when they move. Best destinations for movers: Exploration 72%, Horror 69%, Turn-Based Tactics 64%; worst: Adventure 33%, Racing 30%. Simulation-origin devs export worst (27%) — sim hits seem to be format-luck more than transferable skill, which rhymes with the shop-sim flood findings." I'd like way more detail on this, a full breakdown and exactly how you're reaching these numbers and what other things they might imply.

I think this is it for now, let's go one by one.

Fable 5

This is a good session shape — all four claims lean on the same dataset, and claims #1 and #4 are really two views of the same "path dependence" analysis, so I'll start with #1 (prior-hit re-hit rates), since it's the load-bearing one ("largest effect found all day") and re-verifying it builds the exact machinery needed for #4 afterward. First let me re-orient in steam-market/ — find the raw data, the crawler outputs, and the report(s) where these claims were made, so I can check how the numbers were derived before generating new ones.

steam-market\.venv\Lib\site-packages\idna-3.18.dist-info\licenses\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy\random\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\fft\pocketfft\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\random\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\random\src\distributions\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\random\src\mt19937\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\random\src\pcg64\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\random\src\philox\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\random\src\sfc64\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\random\src\splitmix64\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\_core\src\npysort\x86-simd-sort\LICENSE.md
steam-market\README.md
steam-market\reports\02-tag-scan.md
steam-market\reports\05-art-gate.md
steam-market\reports\01-delta-map.md
steam-market\reports\04-anomalies.md
steam-market\reports\06-language-gaps.md
steam-market\reports\03-trajectories.md
steam-market\reports\07-features.md
steam-market\reports\00-synthesis.md
steam-market\reports\08-goldrush.md
steam-market\reports\09-breakout.md
steam-market\reports\10-whitespace.md
steam-market\reports\11-sequel-migration-ev.md
steam-market\reports\12-review-text.md
steam-market\reports\13-desc-mining.md
steam-market\reports\14-cadence-revival.md
steam-market\reports\notes.md

total 24
drwxr-xr-x 1 adn 197121    0 Jul  7 09:18 .
drwxr-xr-x 1 adn 197121    0 Jul  7 14:02 ..
drwxr-xr-x 1 adn 197121    0 Jul  7 09:17 .venv
drwxr-xr-x 1 adn 197121    0 Jul  8 00:47 data
-rw-r--r-- 1 adn 197121 1570 Jul  7 09:18 README.md
drwxr-xr-x 1 adn 197121    0 Jul  8 00:48 reports
drwxr-xr-x 1 adn 197121    0 Jul  7 23:53 scripts

1    # steam-market
2   
3   Steam market research, 2026-07-07. Goal: find market openings via first-principles
4   analysis of primary data — indifferent to (not derived from, not opposed to) the
5   standard indie-marketing discourse. Constraint: no reading of marketing blogs,
6   postmortems, Reddit, GDC talks, newsletters. Inputs = raw Steam/SteamSpy data only.
7   
8   ## Data sources (primary only)
9   - `data/steamspy.sqlite` — SteamSpy bulk catalog (`api.php?request=all`): owners
10    buckets, playtime medians, dev/publisher, price, ccu. 1 page/min rate limit.
11  - `data/search.sqlite` — Steam's own search index (`search/results/?infinite=1`,
12    category1=998 = games only): release date, top ~7 tag ids, review pct/count,
13    price/discount, platforms. Whole catalog at 50/request.
14  - `data/tags.json` — tagid -> name mapping (432 tags).
15  - Deep crawls (targeted subsets): `appreviewhistogram/<appid>` (monthly review
16    rollups since launch), `appreviews/<appid>` (totals, per-language counts),
17    `api/appdetails` (features/categories, languages) — rate-limited, used sparingly.
18  
19  ## Calibration anchors (in-house, discourse-free)
20  - SNKRX (appid 915310): 4,333 total reviews (2026-07); known 80,781 units /
21    $234,953 gross at day 55 (owner's own numbers, posts/snkrx_log.md).
22  - BYTEPATH: ~10K units / ~$12K over 3 years.
23  
24  ## Success thresholds for analyses
25  $10K / $50K / $100K gross.
26  
27  ## Layout
28  - `scripts/` — resumable crawlers (checkpoint in each db's meta table) + analysis.
29  - `data/` — sqlite + json. One sqlite per crawler (concurrent writers).
30  - `reports/` — findings, markdown.
... [1 more lines]

E:/a327ex/steam-market/data/:
total 955340
drwxr-xr-x 1 adn 197121         0 Jul  8 00:47 .
drwxr-xr-x 1 adn 197121         0 Jul  7 09:18 ..
-rw-r--r-- 1 adn 197121 800796672 Jul  8 11:04 appdetails.sqlite
-rw-r--r-- 1 adn 197121     32768 Jul  7 10:42 appdetails.sqlite-shm
-rw-r--r-- 1 adn 197121   7205912 Jul  8 11:07 appdetails.sqlite-wal
drwxr-xr-x 1 adn 197121         0 Jul  7 10:58 art
-rw-r--r-- 1 adn 197121  11403264 Jul  8 00:46 cadence_news.sqlite
drwxr-xr-x 1 adn 197121         0 Jul  7 09:28 calibration
-rw-r--r-- 1 adn 197121    421888 Jul  7 18:44 demo_hist.sqlite
-rw-r--r-- 1 adn 197121  83103744 Jul  7 22:02 histogram.sqlite
-rw-r--r-- 1 adn 197121    274432 Jul  7 13:52 reviews_lang.sqlite
-rw-r--r-- 1 adn 197121     86016 Jul  7 11:37 reviews_sample.sqlite
-rw-r--r-- 1 adn 197121  37593088 Jul  7 23:00 reviews_text.sqlite
-rw-r--r-- 1 adn 197121   6328320 Jul  8 00:12 revival_news.sqlite
-rw-r--r-- 1 adn 197121    266057 Jul  7 23:15 revival_sets.json
-rw-r--r-- 1 adn 197121  13430784 Jul  7 10:41 search.sqlite
-rw-r--r-- 1 adn 197121     32768 Jul  7 10:42 search.sqlite-shm
-rw-r--r-- 1 adn 197121         0 Jul  7 10:42 search.sqlite-wal
-rw-r--r-- 1 adn 197121   9388032 Jul  7 10:45 steamspy.sqlite
-rw-r--r-- 1 adn 197121     32768 Jul  7 09:18 steamspy.sqlite-shm
-rw-r--r-- 1 adn 197121   7774472 Jul  7 10:46 steamspy.sqlite-wal
-rw-r--r-- 1 adn 197121     15277 Jul  7 09:17 tags.json

E:/a327ex/steam-market/scripts/:
total 220
drwxr-xr-x 1 adn 197121     0 Jul  7 23:53 .
drwxr-xr-x 1 adn 197121     0 Jul  7 09:18 ..
drwxr-xr-x 1 adn 197121     0 Jul  7 09:31 __pycache__
... [32 more lines]

The data and scripts are all here. The path-dependence claim was computed by sequel_migration.py (report 11) and summarized in 00-synthesis.md. Let me read those to see exactly how the numbers were derived.

1    # 11 — Sequel vs new IP, migration graph, EV model (analyses #1, #7, #4)
2   
3   Scripts: `scripts/sequel_migration.py`, `scripts/ev_inputs.py`. Raw: `raw_sequel_migration.txt`, `raw_ev_inputs.txt`. Conventions: prior hit = paid 2020–22 ≥$50K est; window = paid 2023–25; dev = normalized SteamSpy name.
4   
5   ## #1 — The compounding effect decomposed
6   
7   Window releases by prior-hit devs (sequel detection = base-name match, numerals/editions stripped):
8   
9   | class | n | h50 | h100 | med (rev'd) |
10  |---|---|---|---|---|
11  | sequel of the prior HIT | 131 | **74.0%** | 61.1% | $207K |
12  | sequel of a prior non-hit | 50 | 62.0% | 54.0% | $142K |
13  | new IP | 1,439 | **55.9%** | 45.9% | $96K |
14  
15  - **The compounding is mostly the dev, not the franchise**: new IP keeps 55.9% (≈4× market). Franchise adds +18pt and ~2.2× median on top.
16  - **Niche proximity is irrelevant for new IP**: h50 by top-7 tags shared with the prior hit: 0 shared 55.9%, 1: 56.0%, 2: 53.8%, 3: 54.3%, 4+: 59.5% — flat. The advantage is fully portable (sharpens session 1's 52.7/61 stay/switch).
17  - Iterating a *non-hit* (62.0%) also beats new IP — relevant: a BYTEPATH remake is technically "sequel of a prior non-hit" (BYTEPATH < $50K lifetime).
18  
19  ## Gap decay — INVERTED (his 5-year absence is not a penalty)
20  
21  First subsequent release after a dev's first hit (2015–24), by gap: 1y 53.1%/$70K → 2y 62.3%/$134K → 3y 70.9%/$191K → 4y 75.2%/$357K → **5y 71.5%/$449K** → 6y+ 73.4%/$307K. Scope-confounded (longer gap → bigger game), BUT the effect survives within price bands: gap≥3 class beats full class at every band (≤$7: 16.4% vs 13.8%; $8–12: 38.9% vs 33.9%; >$20: 89.5% vs 83.4%). Steam followers don't rot.
22  
23  ## #7 — Migration graph (prior-hit devs)
24  
25  - STAY (same primary tag) 63.6% vs MOVE 55.9% — moving costs ~8pt, still 4× market.
26  - Best origins (movers only): Survival Horror 95%, Open World 93%, JRPG 92%, RPG 77%, **Action Roguelike 75%** (his origin), Metroidvania 75%. Worst: Simulation 27%, Puzzle 32%.
27  - Best destinations for movers: Exploration 72%, Horror 69%, Psych Horror 67%, TB Tactics 64%. Worst: Adventure 33%, Racing 30%. Action Roguelike as *destination*: 46%.
28  
29  ## #4 — EV inputs: his class (prior-hit SELF-PUB), gap≥3 subset, by price
30  
... [27 more lines]

1    """Analyses #1 + #7: decompose the compounding effect (sequel vs new IP; tag-distance
2   dose-response; gap-decay of the prior-hit advantage) and the genre migration graph
3   (primary-tag transitions of prior-hit devs, origin/destination re-hit rates).
4   Conventions match anomalies.py: prior hit = paid 2020-22 release >=$50K est,
5   window = paid 2023-25, dev identity = normalized SteamSpy dev name."""
6   import re, statistics, sys
7   from collections import Counter, defaultdict
8   
9   sys.path.insert(0, 'E:/a327ex/steam-market/scripts')
10  from common import load_catalog, load_tagmap
11  
12  norm = lambda s: (s or '').strip().lower()
13  ROMAN = r'(?:[0-9]+|ii|iii|iv|v|vi|vii|viii|ix|x)'
14  TM = str.maketrans('', '', '™®©')
15  
16  def base_name(name):
17      """lowercase, strip tm marks, drop trailing numerals/roman + edition words"""
18      s = norm(name).translate(TM)
19      s = re.sub(r'\s*(?:[:\-–—]\s*)?' + ROMAN + r'$', '', s)
20      s = re.sub(r'\s*(?:definitive|deluxe|complete|enhanced|ultimate|remastered)\s+edition$', '', s)
21      return s.strip(' :-')
22  
23  def colon_base(name):
24      s = norm(name).translate(TM)
25      return s.split(':')[0].strip() if ':' in s else None
26  
27  def is_sequel_of(name, prior_name):
28      b1, b2 = base_name(name), base_name(prior_name)
29      if len(b1) >= 5 and b1 == b2 and norm(name) != norm(prior_name):
30          return True
... [134 more lines]

1    """Report 4: success-anomaly case files. Resource-normalize the tag scan: how do
2   FIRST-TIME, SELF-PUBLISHED devs actually fare, overall and per tag? Where do outsiders
3   break through? Plus: does a prior hit compound (the follower-asset thesis)?
4   Output: reports/04-anomalies.md"""
5   import sqlite3, statistics
6   from collections import Counter, defaultdict
7   from datetime import datetime, timezone
8   
9   from common import ROOT, load_catalog, load_tagmap, coverage
10  
11  WINDOW = (2023, 2025)
12  
13  def norm(s):
14      return (s or '').strip().lower()
15  
16  def main():
17      cov = coverage()
18      games = load_catalog()
19      tagmap = load_tagmap()
20  
21      # developer history across the whole catalog
22      dev_years = defaultdict(list)
23      for g in games:
24          if g['developer'] and g['year']:
25              dev_years[norm(g['developer'])].append((g['year'], g))
26      for d in dev_years:
27          dev_years[d].sort(key=lambda x: x[0])
28  
29      paid = [g for g in games if g['price'] and not g['free'] and g['year']]
30      win = [g for g in paid if WINDOW[0] <= g['year'] <= WINDOW[1] and g['developer']]
... [138 more lines]

1    """Shared catalog loader for analysis scripts."""
2   import html, json, re, sqlite3, sys
3   from datetime import date
4   
5   sys.stdout.reconfigure(encoding='utf-8')
6   
7   ROOT = 'E:/a327ex/steam-market'
8   K = 35.9          # revenue ~= reviews * K * base_price_usd (data/calibration/estimator.json)
9   K_BAND = (25, 55)
10  TODAY = date.today()
11  
12  MONTHS = {m: i+1 for i, m in enumerate(
13      ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])}
14  
15  FULL_RE = re.compile(r'^([A-Z][a-z]{2}) (\d{1,2}), (\d{4})$')
16  MONTH_RE = re.compile(r'^([A-Z][a-z]{2}) (\d{4})$')
17  YEAR_RE = re.compile(r'^(\d{4})$')
18  
19  def parse_release(txt):
20      """-> (year, date_or_None). (None, None) if unparseable/unreleased."""
21      if not txt:
22          return None, None
23      t = txt.strip()
24      m = FULL_RE.match(t)
25      if m and m.group(1) in MONTHS:
26          try:
27              return int(m.group(3)), date(int(m.group(3)), MONTHS[m.group(1)], int(m.group(2)))
28          except ValueError:
29              return int(m.group(3)), None
30      m = MONTH_RE.match(t)
... [63 more lines]

1    # 04 — Success anomalies: the resource-normalized view
2   
3   Generated 2026-07-07 14:00 UTC. Coverage: search=115281, steamspy=82233 (dev/pub fields from SteamSpy; name-matching is fuzzy-imperfect).
4   
5   Window: paid releases 2023–2025 with dev data (n=24493).
6   
7   ## Baseline by resource group
8   
9   | group | n | ≥$10K | ≥$50K | ≥$100K | med $ (rev’d) |
10  |---|---|---|---|---|---|
11  | first-time self-pub | 12273 | 19.6% | 8.56% | 5.99% | $8K |
12  | veteran self-pub | 5442 | 31.0% | 15.77% | 12.24% | $11K |
13  | published (any) | 6778 | 49.3% | 33.05% | 26.60% | $45K |
14  
15  Of 4149 window games ≥$50K, **1051 (25%) are first-time self-published**.
16  
17  ## Where outsiders break through: P(≥$50K | first-time self-pub, tag)
18  
19  _Tags with ≥50 first-time self-pub releases in window. Compare vs tag overall._
20  
21  | tag | n (fs) | ≥$50K fs | ≥$50K all | fs/all |
22  |---|---|---|---|---|
23  | FMV | 54 | 48.1% | 48.1% | 1.00 |
24  | Open World Survival Craft | 64 | 40.6% | 56.5% | 0.72 |
25  | Sexual Content | 81 | 37.0% | 40.9% | 0.91 |
26  | Cozy | 128 | 28.1% | 29.4% | 0.96 |
27  | Dating Sim | 209 | 27.3% | 34.1% | 0.80 |
28  | Open World | 348 | 25.0% | 40.5% | 0.62 |
29  | Online Co-Op | 257 | 24.5% | 40.5% | 0.61 |
30  | Romance | 132 | 24.2% | 27.2% | 0.89 |
... [146 more lines]

1    # 00 — Synthesis: Steam 2026, openings for a 1–3-month solo dev
2   
3   2026-07-07. One-day first-principles pass over the full Steam catalog (115,281 games), primary
4   sources only (Steam's own endpoints + SteamSpy bulk), zero marketing-discourse inputs.
5   Revenue estimator calibrated in-house on SNKRX + BYTEPATH: `revenue ≈ reviews × 35.9 × price`
6   (band 25–55; units/review ≈ 45 agreed across both games). Review counts used everywhere are
7   Steam's score-eligible counts, which exclude key-activation farming (verified). Full details
8   in reports 01–07.
9   
10  ---
11  
12  ## 1. The market you're returning to
13  
14  - **Volume doubled, medians held.** 9.4K releases in 2021 → 19.1K in 2025 (2026 tracking
15    ~24K). Median reviewed game ≈ $13K est. — flat since 2019. The growth went into the
16    failure mass: 46% → 55% of paid releases now die under 10 reviews. Paid-release hit rates
17    eroded only mildly (≥$50K: 16.0% → 13.5%).
18  - **Success is wildly path-dependent.** Devs with a ≥$50K game in 2020–22 hit ≥$50K again on
19    **57.6%** of their 2023–25 releases (median $104K). Veterans without a prior hit: 6.4%
20    (median $6K). First-time self-pubs: 8.6%. **You are in the 57.6% bucket** (SNKRX +
21    followers), and the advantage is portable: switchers to a different niche still re-hit at
22    52.7% vs 61.0% for stayers. This is the single largest effect found all day — larger than
23    any tag, price, or feature effect.
24  - **The market stayed open.** 25% of ALL ≥$50K games (1,051 of 4,149 in 2023–25) are
25    first-time self-published devs.
26  - **Revenue front-loaded moderately.** Launch-spike games (dead tail) rose 2% → 17%
27    (2014 → 2023 cohorts), but the median ≥50-review game still adds ~74% more reviews in
28    months 4–12 than it had at month 3 — and **cheap games have the longest tails**
29    ($3–5: 2.26× m12/m3 vs $25+: 1.64×). The minimal-marketing long-tail model is
30    structurally favored at your price point.
... [118 more lines]

No matches found

Let me check the notes file, calibration data, and find where session 1's 52.7/61.0 stay/switch numbers came from.

1    # Working notes (accumulating findings for synthesis)
2   
3   ## Data integrity status
4   - Estimator: revenue ≈ reviews × 35.9 × price, band [25,55]. In-house (SNKRX day55 + BYTEPATH).
5     Units/review agrees across both games (~45).
6   - Search index (115,281 games) ≈ full catalog: per-year volume matches known history
7     (2021 ≈ 9.4K, 2024 ≈ 16.2K games) → no heavy shovelware filtering. Survivorship: delisted absent.
8   - Sub-10 split (paid, sampled n=1500): 15% have exactly 0 reviews, 44% have 1-4, 27% have 5-9,
9     14% cross ≥10 on API-all count.
10  - KEY-FARMING CHECK RESOLVED: search tooltip counts = score-ELIGIBLE reviews (excludes key
11    activations + more; SNKRX: search 3,618 < purchase 4,194 < all 4,333; one crossover game:
12    591 total / <10 eligible). Outcome analyses are already farming-resistant. The 14.3% ≥$50K
13    baseline stands (if anything conservative). Purchase-mode crawl = confirmation only.
14  
15  ## Delta map headlines (report 01, done)
16  - Volume DOUBLED: 2021 9.4K → 2025 19.1K releases; 2026 tracking ~24K/yr.
17  - Median reviewed game ≈ $13K est. — FLAT since 2019 despite volume. Failure mass grew instead:
18    ≤9-review share 46% (2021) → 55% (2025).
19  - Hit rates by cohort (paid): ≥$50K 16.0% (2021) → 13.5% (2025); ≥$100K 12.4% → 10.1%.
20    Deterioration is real but modest; volume doubling is the big change.
21  - F2P share flat ~15-18%. Median price of reviewed games $8.99.
22  
23  ## Tag scan headlines (report 02, done)
24  - HIS 2021 NEIGHBORHOOD COLLAPSED: Arcade 5.5%, Minimalist 5.4%, Abstract 5.1%, Score Attack 3.7%,
25    2D 6.9%, Puzzle 7.8%, Arena Shooter 7.7%, Precision Platformer 4.5% ≥$50K. Bottom of market.
26  - Roguelite CORE is midfield: Action Roguelike 14.4% (= baseline), n=2,347 (huge supply),
27    and only 7.4% at ≤$10.
28  - Roguelite × MULTIPLAYER/co-op = top decile: Online Co-Op+Roguelite 54.9% (33% at ≤$10),
29    Online Co-Op+Action Roguelike 50%, Co-op+Roguelite 47.2%, Roguelite+Loot 50% (42% at ≤$10, n=26).
30    Supply tiny (n=34-66 over 3 years) vs 4.4K arcade games.
... [186 more lines]

bytepath_histogram.json
bytepath_reviews.json
estimator.json
snkrx_histogram.json
snkrx_reviews.json
{

 "generated": "2026-07-07T14:00:56.205509+00:00",

 "points": [

  {

   "game": "SNKRX",

   "date": "2021-05-19",

   "units": 601,

   "gross_usd": 1429,

   "est_reviews": 67.9,

   "units_per_review": 8.85,

   "usd_per_review": 21.03,

   "usd_per_review_per_usd_price": 7.04

  },

  {

   "game": "SNKRX",

   "date": "2021-05-23",

   "units": 1064,

   "gross_usd": 2534,

   "est_reviews": 83.0,

   "units_per_review": 12.81,

   "usd_per_review": 30.52,

   "usd_per_review_per_usd_price": 10.21

  },

  {

   "game": "SNKRX",

... [85 more lines]

Exit code 1
Traceback (most recent call last):

  File "<string>", line 14, in <module>

    print('sample:', con.execute('SELECT * FROM hist LIMIT 3').fetchall())

                     ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

sqlite3.OperationalError: no such table: hist

--- steamspy.sqlite

['appid', 'name', 'developer', 'publisher', 'positive', 'negative', 'owners', 'owners_low', 'owners_high', 'average_forever', 'average_2weeks', 'median_forever', 'median_2weeks', 'price', 'initialprice', 'discount', 'ccu', 'page', 'fetched_at']

(760330, 'BYTEPATH', 'a327ex', 'a327ex', 276, 22, '50,000 .. 100,000', 50000, 100000, 0, 0, 0, 0, 69, 199, 65, 0, 14, 1783427568)

(915310, 'SNKRX', 'a327ex', 'a327ex', 4023, 227, '50,000 .. 100,000', 50000, 100000, 0, 0, 0, 0, 83, 299, 72, 10, 11, 1783427384)

--- histogram.sqlite

('rollup',)

('done',)

hist cols: []

done count: (34654,)

rollup cols: ['appid', 'ts', 'up', 'down']

done cols: ['appid', 'start_date', 'end_date', 'rollup_type', 'n_rollups', 'fetched_at']

rollup rows: (2091337,)

sample: [(730, 1335830400, 1, 0), (730, 1343779200, 1630, 208), (730, 1346457600, 1003, 88), (730, 1349049600, 511, 59), (730, 1351728000, 591, 62)]

distinct appids: (34654,)

File created successfully at: E:\a327ex\steam-market\scripts\path_dependence.py (file state is current in your context — no need to Read it back)

====================================================================================================

0. REPRODUCTION of report-04 numbers (release-weighted, 2020-22 prior -> 2023-25 window)

prior-hit devs, window releases                n=1620  h50= 57.6% [55.2-60.0] h100= 47.4% med=$   104K

veteran no-prior-hit (released 20-22, no hit)  n=3594  h50=  6.4% [5.6-7.2] h100=  4.1% med=$     6K

first-time self-pub (window)                   n=12430 h50=  8.7% [8.2-9.2] h100=  6.1% med=$     8K



user placement:   BYTEPATH: year=2018 price=$1.99 reviews=305 rev_est=$22K selfpub=True  SNKRX: year=2021 price=$2.99 reviews=3618 rev_est=$388K selfpub=True



====================================================================================================

1. COMPOSITION of the prior-hit group (who is actually in the 57.6%?)

devs=901  window releases=1620  releases/dev: mean=1.80 p50=1 max=67  share from devs w/ 1 release=39%  share from devs w/ >=5 releases=26%

top-15 contributors (n window releases, h50, dev):

   67     1%  gamesforgames

   25    40%  kairosoft co.,ltd

   25     0%  g-mode

   24    12%  hosted games

   17     6%  choice of games

   17   100%  capcom co., ltd.

   16    56%  mages. inc.

   14   100%  koei tecmo games co., ltd.

   13     0%  punkcake delicieux

   12    25%  strategy game studio

   11   100%  square enix

   11     9%  ebi-hime

   10     0%  winged cloud

    9    56%  digital eclipse

    8   100%  milestone s.r.l.



prior-hit devs w/ window releases: 901; prior BEST hit was self-pub for 453 (50%)



... [69 more lines]

--- 6b. reviews-only definition (prior >=N reviews -> window >=N reviews; price-free) ---

  N=100   prior>=100rev  53.3% (n=1946)   vets<100rev   6.9% (n=3268)   ratio 7.7x

  N=300   prior>=300rev  46.3% (n=1256)   vets<300rev   5.1% (n=3958)   ratio 9.0x

  N=1000  prior>=1000rev  36.6% (n=672)   vets<1000rev   3.0% (n=4542)   ratio 12.0x

--- 6c. CROSS-DEFINITION: prior hit by REVENUE est -> window success by OWNERS bucket (fully decoupled) ---

  owners_low>=20K: prior-hit  40.3%   no-hit vets  13.8%   ratio 2.9x

  owners_low>=50K: prior-hit  26.2%   no-hit vets   4.8%   ratio 5.4x



====================================================================================================

7. SELECTION: who ships again? (the 57.6% conditions on releasing in 2023-25)

prior-hit devs: 3034, released again 2023-25: 901 (30%)

no-hit vets   : 11964, released again 2023-25: 1781 (15%)

devs w/ prior best >=$250K: 1669, returned: 514 (31%)



====================================================================================================

8. RIGHT-CENSORING: re-hit by window year (2025 releases have had less time to accumulate reviews)

  2023: prior-hit  56.0% (n=648)   no-hit vets   4.7% (n=1723)

  2024: prior-hit  59.1% (n=621)   no-hit vets   7.2% (n=1380)

  2025: prior-hit  57.8% (n=351)   no-hit vets  10.0% (n=491)



====================================================================================================

9. EA-GRADUATION: window releases whose FIRST review month is >180d before their search release date

--- prior-hit: histogram coverage 1116/1620 (69%); EA-graduated 126 (11% of covered) ---

  EA-graduated window releases                 n=126   h50= 93.7% [88.0-96.7] h100= 82.5% med=$   476K

  clean (1.0 launch = first reviews)           n=990   h50= 80.4% [77.8-82.8] h100= 66.7% med=$   214K

  no histogram data (mostly low-review)        n=504   h50=  3.8% [2.4-5.8] h100=  0.8% med=$     9K

--- no-hit vets: histogram coverage 563/3594 (16%); EA-graduated 83 (15% of covered) ---

  EA-graduated window releases                 n=83    h50= 61.4% [50.7-71.2] h100= 45.8% med=$    69K

  clean (1.0 launch = first reviews)           n=480   h50= 36.7% [32.5-41.1] h100= 22.5% med=$    31K

  no histogram data (mostly low-review)        n=3031  h50=  0.1% [0.0-0.3] h100=  0.0% med=$     4K

... [30 more lines]

1    """Crawl appreviewhistogram (monthly review rollups since launch) for every game
2   with >= MIN_REVIEWS reviews, into data/histogram.sqlite. Queue built from search.sqlite,
3   highest review counts first. Resumable: done table. Usage: histogram_crawl.py [min_reviews]"""
4   import sqlite3, sys, time
5   import requests
6   
7   DB = 'E:/a327ex/steam-market/data/histogram.sqlite'
8   SEARCH_DB = 'E:/a327ex/steam-market/data/search.sqlite'
9   MIN_REVIEWS = int(sys.argv[1]) if len(sys.argv) > 1 else 20
10  
11  con = sqlite3.connect(DB)
12  con.execute('PRAGMA journal_mode=WAL')
13  con.execute('''CREATE TABLE IF NOT EXISTS rollup(
14      appid INTEGER, ts INTEGER, up INTEGER, down INTEGER, PRIMARY KEY(appid, ts))''')
15  con.execute('''CREATE TABLE IF NOT EXISTS done(
16      appid INTEGER PRIMARY KEY, start_date INTEGER, end_date INTEGER,
17      rollup_type TEXT, n_rollups INTEGER, fetched_at INTEGER)''')
18  con.execute('ATTACH ? AS s', (SEARCH_DB,))
19  queue = [r[0] for r in con.execute('''
20      SELECT appid FROM s.search
21      WHERE review_count >= ? AND appid NOT IN (SELECT appid FROM done)
22      ORDER BY review_count DESC''', (MIN_REVIEWS,))]
23  print(f'queue: {len(queue)} appids (min_reviews={MIN_REVIEWS})', flush=True)
24  
25  s = requests.Session()
26  s.headers.update({'User-Agent': 'Mozilla/5.0 (steam-market-research; [email protected])'})
27  fails = 0
28  for i, appid in enumerate(queue):
29      try:
30          r = s.get(f'https://store.steampowered.com/appreviewhistogram/{appid}',
... [24 more lines]

Histogram covers everything ≥20 reviews, so it covers essentially all prior-hit games — the time-truncation check is feasible. Writing the second pass now.

File created successfully at: E:\a327ex\steam-market\scripts\path_dependence2.py (file state is current in your context — no need to Read it back)

====================================================================================================

A. TIME-TRUNCATION: prior hit judged on reviews earned BY 2022-12-31 only (tail-leakage check)

   (prior-window games where truncation was uncomputable and full est used: 3010)

--- full-history prior (original): 3034 hit devs ---

  prior-hit:  rel  57.6% (n=1620)  first  66.3% (n=901)

  no-hit vet: rel   6.4% (n=3594)  first   9.9% (n=1781)

--- truncated-at-2022 prior: 2516 hit devs ---

  prior-hit:  rel  61.6% (n=1371)  first  70.3% (n=752)

  no-hit vet: rel   8.3% (n=3843)  first  12.7% (n=1930)



====================================================================================================

B. EA-GRADUATION-ADJUSTED (drop window releases whose first review predates release by >180d)

  all prior-hit devs                             rel  54.6% (n=1494)  first  62.6% [59.2-65.8] (n=818)  med(first)=$128K

  selfpub -> selfpub (his cell)                  rel  40.3% (n=777)  first  49.1% [43.9-54.4] (n=344)  med(first)=$60K

  his cell + prior best $100K-1M                 rel  40.6% (n=323)  first  48.8% [41.2-56.4] (n=164)  med(first)=$58K

  his cell + prior best $250-500K (SNKRX band)   rel  50.0% (n=120)  first  56.6% [43.3-69.0] (n=53)  med(first)=$83K



====================================================================================================

C. SEQUEL vs NEW IP inside his cell (selfpub prior hit -> selfpub window release)

  sequel of the prior HIT            rel  64.4% (n=73) med=$  100K | first  69.6% (n=56)

  sequel of a prior non-hit          rel  64.0% (n=25) med=$  189K | first  76.2% (n=21)

  new IP                             rel  41.7% (n=748) med=$   36K | first  51.8% (n=353)



====================================================================================================

D. GAP DECAY recheck (first SELF-PUB hit 2015-24 -> first subsequent paid SELF-PUB release)

gap | n | h50 next | med next | med price next | med prior hit $

  1y  n=480  h50= 47.5% med=$   54K  medprice=$ 9.99  medprior=$   129K

  2y  n=288  h50= 56.6% med=$   93K  medprice=$ 9.99  medprior=$   187K

  3y  n=167  h50= 61.1% med=$  135K  medprice=$14.99  medprior=$   231K

  4y  n=101  h50= 63.4% med=$  132K  medprice=$14.99  medprior=$   236K

... [26 more lines]

File created successfully at: E:\a327ex\steam-market\scripts\path_dependence3.py (file state is current in your context — no need to Read it back)

====================================================================================================

HIS CELL (selfpub 20-22 hit -> selfpub 23-25 release, EA-adj) by WINDOW PRICE BAND

--- all releases ---

  <=$7     rel  11.1% [7.8-15.5] (n=253) med=$   10K | first  16.1% [10.4-24.0] (n=112)

  $8-12    rel  30.9% [24.8-37.8] (n=191) med=$   18K | first  45.5% [35.5-55.8] (n=88)

  $13-20   rel  55.6% [48.1-62.8] (n=171) med=$   96K | first  60.0% [51.4-68.0] (n=130)

  >$20     rel  80.9% [74.1-86.2] (n=162) med=$  831K | first  81.0% [71.0-88.1] (n=79)

--- NEW IP only ---

  <=$7     rel   9.9% [6.7-14.4] (n=233) med=$    9K | first  15.1% [9.5-23.1] (n=106)

  $8-12    rel  28.7% [22.4-35.8] (n=171) med=$   16K | first  42.5% [32.3-53.4] (n=80)

  $13-20   rel  53.6% [45.7-61.3] (n=153) med=$   86K | first  57.3% [48.2-65.9] (n=117)

  >$20     rel  80.7% [73.3-86.5] (n=135) med=$  878K | first  80.6% [69.6-88.3] (n=67)



====================================================================================================

PRICE TRANSITION inside his cell: prior hit price band -> window price band (rel-weighted)

(rows = prior hit price, cols = window release price; cell = h50% (n))

                      <=$7           $8-12          $13-20            >$20

      <=$7     15% (124)      52% ( 25)      40% ( 10)      67% (  3) 

     $8-12      9% ( 69)      36% (100)      61% ( 33)      33% (  3) 

    $13-20     11% ( 36)      33% ( 27)      60% ( 93)      88% ( 25) 

      >$20      0% ( 24)       3% ( 39)      43% ( 35)      81% (131) 



cheap-prior devs (prior hit <=$7.5) who PRICED UP to $13+: their window releases:

  $    580K $24.99 2025 Crashlands 2

  $    385K $17.99 2024 Super Dark Deception

  $    327K $14.99 2023 Barista Simulator

  $    132K $24.99 2024 Gordian Rooms 2: A curious island

  $     60K $19.99 2024 Dreambound

  $     50K $19.99 2023 Peaky Blinders: The King's Ransom Complete Edition

  $     49K $15.73 2024 Dungeons of Dreadrock 2 - The Dead King's Secret

... [6 more lines]

File created successfully at: E:\a327ex\steam-market\reports\15-path-dependence.md (file state is current in your context — no need to Read it back)

The file E:\a327ex\steam-market\reports\notes.md has been updated successfully. (file state is current in your context — no need to Read it back)

All three rounds are done and written up. Here's the full picture.

Claim #1 re-check: path dependence — verdict and full findings

The claim survives every attack I could throw at it. It's real, structural, decade-stable, and not an estimator artifact. But the specific number "57.6%, and you are in that bucket" is wrong in both directions at once — the true dev-level effect is bigger (66%), while his cell is smaller (~49–57%), and the single biggest modifier turns out to be the price of the next game, not the prior hit itself.

1. Reproduction and what's actually inside the 57.6%

The headline reproduces exactly (57.6% / 6.4% / 8.7% — the data hasn't drifted). But the prior-hit group is 901 devs producing 1,620 releases, and both tails of the weighting are contaminated: the top contributor is "gamesforgames" with 67 window releases at a 1% re-hit rate (a shovelware factory that happened to land one prior hit), alongside G-MODE (25 retro re-releases, 0%) and interactive-fiction factories — while Capcom (17 releases, 100%), Koei Tecmo (14, 100%) and Square Enix (11, 100%) pull the other way. Half of all prior-hit devs earned their hit with a publisher.

2. Corrected weightings

Counting each dev once (first window release), and dropping EA-graduated releases (games whose "release" is a 1.0 date but whose reviews accumulated for years in Early Access — 11% of covered releases, and they fake a 94% hit rate):

So the honest gap is ~6.8× rather than 9×, but at the dev level the advantage is larger than reported — the factories were diluting it. One cell stands above everything: self-pub hit → next game signed by a publisher: 84.5% (n=97). Selection and distribution are entangled there, but it's the strongest single number in the table and worth knowing exists.

3. The biggest reframe: there is no cliff, it's a ladder

P(next game ≥$50K) by prior best result, dev-level, all devs:

Perfectly monotone, no discontinuity anywhere near $50K. Spearman ρ = 0.715 across 2,682 devs — prior result is a strong continuous predictor. Two consequences:

• "Veterans without a prior hit: 6.4%" is technically true but rhetorically misleading. That group pools $49K near-misses (who re-hit at ~29%) with sub-$5K shovelware (~2%), and the shovelware dominates the count. The right mental model isn't hit/no-hit, it's a ladder where every rung of past performance buys the next one.
• The mechanism looks like a continuous accumulating asset (audience + skill), not a threshold unlock like "the algorithm now favors you." SNKRX at $388K estimated (3,618 eligible reviews × $2.99) puts him at the 68% rung on the pooled ladder, ~63% in the self-pub-only version.

4. Era replication — and the gap is widening

Same analysis on two disjoint earlier eras (dev-level): hits 2014–16 → 2017–19: 66.3% vs 14.3% (4.6×). Hits 2017–19 → 2020–22: 71.1% vs 13.1% (5.4×). Hits 2020–22 → 2023–25: 65.8% vs 9.7% (6.8×).

The hitters' rate has been flat for a decade. The non-hitters' rate fell by a third. The market-doubling from session 1's delta map hurt only people without an audience — path dependence is not just stable, it's strengthening.

5. Circularity and leakage checks — all pass

This was my main suspicion, since both sides of the claim use the same reviews × 35.9 × price estimator. Every decoupling holds:

• K-band (25 / 35.9 / 55): ratio stays 7.9–10.8×.
• Reviews-only definition (price-free, thresholds 100/300/1000 reviews): 7.7× / 9.0× / 12.0×.
• Cross-definition — prior hit by revenue estimate, success measured by SteamSpy owners bucket instead (fully independent measure): 2.9× at ≥20K owners, 5.4× at ≥50K. Smaller but decisively alive.
• Time-truncation — the subtlest one: a 2021 game that only crossed $50K via 2023–25 tail sales lets "prior" status leak information from the outcome window. Recomputing prior-hit status from reviews earned by 2022-12-31 only (histogram data, now complete at 34,654 apps) makes the effect stronger: 70.3% vs 12.7%. Not circular.
• Right-censoring: re-hit by window year is 56/59/58% — 2025 releases aren't understated.
• Selection on returning: only 30% of prior-hit devs shipped anything in 2023–25 (no-hit vets: 15%). All these rates are conditional on shipping again — irrelevant to him since he ships, but it means the unconditional "hit and then hit again within ~3 years" is ~20%, and the 2× return-rate gap is itself part of compounding.

6. The related claims from both sessions

Franchise vs dev (report 11's 74/62/56 split) — persists inside the self-pub cell: sequel-of-hit 69.6% vs new IP 51.8% dev-level. The +18pt franchise premium is real and not an AAA artifact. This is the standing "SNKRX 2 carries a measurable premium" fact, and a BYTEPATH remake technically sits in the sequel-of-non-hit class (76% here, but n=21 — treat as "at least no penalty").

Gap decay inversion (5y gap = 71.5%/$449K) — the no-decay part is confirmed; the inversion part is mostly composition. Longer-gap devs had bigger prior hits ($129K median at gap-1 → $418K at gap-6) and ship pricier games ($9.99 → $19.99 median), and era-stratified the gradient flattens out. His exact profile — self-pub hit 2020–21, gap ≥4, self-pub next — sits at 52.5% (n=40), and the peer list is reassuring company: Rift Wizard 2, Crashlands 2, SpaceBourne 2, Cinnabunny. Five years of absence costs nothing; it just doesn't earn anything by itself either.

The price gate inside his class — survives dev-level weighting and EA adjustment, new-IP releases only: ≤$7: 15.1% · $8–12: 42.5% · $13–20: 57.3% · >$20: 80.6%. And a genuinely new finding from the price transition matrix (prior hit price → next game price): devs with a cheap prior hit (≤$7, his row) who stayed ≤$7 re-hit at 15%; those who moved up to $8–12 re-hit at 52%. Pricing down from any band is catastrophic everywhere (0–11%). The settled plan's reprice logic (SNKRX $8–10, 062026 $10–13) is directly ratified by the strongest cut of the data.

7. The honest replacement for "you are in the 57.6% bucket"

New IP, self-pub, 5-year gap, prior = SNKRX: ~15% at ≤$7 (unless the game is an idler — the one documented price-gate-immune lane), ~40–45% at $8–12, ~55–60% at $13–20, median outcome conditional on his cell $60–83K. Still ~5–6× the first-timer baseline at every honest cut — the asset is real, continuous, portable, and doesn't rot.

One tension to carry into the claim-#3 session: the BYTEPATH remake is planned at $5–8, which is the dead band — its entire EV case rests on the idler exception to the price gate. That exception was established at the tag level in reports 02/11 but hasn't been verified within the prior-hit class. I'd want to check "idler at ≤$8 for prior-hit devs specifically" before trusting the 35–55% EV-table number.

Everything above is archived in 15-path-dependence.md, with the three raw outputs (raw_path_dependence*.txt) and scripts (path_dependence.py/2/3) alongside, and notes.md has the summary block.

For the next one, I'd take claim #4 (migration/stay-switch detail) — the dev-history machinery is warm, and this session already showed its numbers shift under dev-level weighting and the self-pub filter, so I expect the 75% "Action Roguelike exports best" figure to move. But your call: #2 (launch timing/NextFest) and #3 (retro+idler / card-base-building lanes) are both self-contained.

Is the price change always accompanied by bigger scope and especially better graphics? You can run visual analysis on game screenshots to do this comparison systematically, correct? From what I understood of the market, price = how much the game visually commands, so it's possible that the high hit rate is purely or mostly about graphics rather than anything else, as devs with successful games early have more resources to throw at nice visuals in their next games. This is not of particular usefulness to me because I intend on keeping the games with the overall graphical style they had, which is not a graphical style that commands higher prices, I don't think. But maybe I'm wrong. If you can find many examples of devs keeping to their simple graphics styles over time and succeeding it would be nice to see if there's anything I can learn from them.

Sharp question — and it's exactly the confound the round-3 price findings can't distinguish on their own. Session 1 already built an art-scoring pipeline (355 headers, blind-scored), so the machinery exists to test this properly: score headers for his cell's window releases, then decompose the price gradient into "art tier" vs "price after controlling for art." Let me first look at how the existing art pipeline worked so I stay consistent with its scoring conventions.

1    """Report 5 prep: sample games per niche x outcome group, download header images,
2   build shuffled contact sheets for blind art scoring. Outputs:
3   data/art/sheets/sheet_NN.jpg (4x5 grid, indexed) + data/art/mapping.csv"""
4   import csv, io, os, random, sys, time
5   import requests
6   from PIL import Image, ImageDraw, ImageFont
7   
8   from common import ROOT, load_catalog, load_tagmap
9   
10  NICHES = ['Action Roguelike', 'Horror', 'Idler', 'Cozy']
11  PER_GROUP = 30
12  OVERSAMPLE = 45
13  ART = f'{ROOT}/data/art'
14  os.makedirs(f'{ART}/sheets', exist_ok=True)
15  
16  tagmap = load_tagmap()
17  name2id = {v: k for k, v in tagmap.items()}
18  games = [g for g in load_catalog() if g['year'] and 2023 <= g['year'] <= 2025
19           and g['price'] and not g['free']]
20  
21  def group_of(g):
22      if g['reviews'] < 10:
23          return 'fail'
24      if g['rev_est'] and g['rev_est'] >= 50_000:
25          return 'hit'
26      return 'mid'
27  
28  random.seed(99)
29  sample = []
30  for niche in NICHES:
... [68 more lines]

1    """Report 5: art-quality gate. Join blind art scores with outcome groups; measure how
2   much header-art quality separates hits from failures per niche, and where it gates least.
3   Sample was stratified ~30/30/30 (fail/mid/hit) per niche, so within a niche the share of
4   each group at a given art tier estimates P(group | tier) under equal priors.
5   Output: reports/05-art-gate.md"""
6   import csv, statistics
7   from collections import Counter, defaultdict
8   from datetime import datetime, timezone
9   
10  from common import ROOT
11  
12  def main():
13      scores = {}
14      with open(f'{ROOT}/data/art/scores.csv') as f:
15          for row in csv.DictReader(f):
16              scores[int(row['idx'])] = int(row['art'])
17      rows = []
18      with open(f'{ROOT}/data/art/mapping.csv', encoding='utf-8') as f:
19          for row in csv.DictReader(f):
20              row['art'] = scores[int(row['idx'])]
21              rows.append(row)
22  
23      niches = sorted({r['niche'] for r in rows})
24      groups = ['fail', 'mid', 'hit']
25  
26      L = []
27      L.append('# 05 — The art-quality gate\n')
28      L.append(f'Generated {datetime.now(timezone.utc):%Y-%m-%d %H:%M} UTC. '
29               f'{len(rows)} games, 4 niches × 3 outcome groups (~30 each), header images '
30               'scored blind 1–5 (1=asset-flip, 2=amateur, 3=competent, 4=good, 5=premium) '
... [68 more lines]

1087330.jpg
1097050.jpg
1103010.jpg
1299690.jpg
1337520.jpg
1372150.jpg
1373960.jpg
1375900.jpg
1418390.jpg
1460220.jpg
1470390.jpg
1487480.jpg
1498060.jpg
1546310.jpg
1560160.jpg
1593230.jpg
1696960.jpg
1722660.jpg
1730080.jpg
1746930.jpg
358

Exit code 1
Traceback (most recent call last):

  File "<string>", line 9, in <module>

    r = con.execute('SELECT json FROM appdetails WHERE appid=915310').fetchone()

        ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

sqlite3.OperationalError: no such column: json

[('appdetails',), ('failed',)]

['appid', 'type', 'name', 'is_free', 'required_age', 'developers', 'publishers', 'genre_ids', 'genres', 'category_ids', 'categories', 'supported_languages', 'release_date', 'coming_soon', 'price_cents', 'currency', 'recommendations', 'achievements', 'dlc_count', 'demo_appids', 'controller_support', 'metacritic', 'header_image', 'fetched_at', 'raw']

cols sample: (10, 'game', 'Counter-Strike', 0, '0', '["Valve"]', '["Valve"]', '["1"]', '["Action"]', '[1, 49, 36, 37, 66, 68, 75, 69, 8, 62]', '["Multi-player", "PvP", "Online PvP", "Shared/Split Screen PvP", "Col

Command running in background with ID: bq7igpacu. Output is being written to: C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\bq7igpacu.output. You will be notified when it completes. To check interim output, use Read on that file path.

SNKRX keys: ['915310']

n screenshots: 0

total: (46382,)

The raw blob is keyed by appid — need to unwrap one level.

n screenshots: 6

{

 "id": 0,

 "path_thumbnail": "https://shared.akamai.steamstatic.com/store_item_assets/steam/apps/915310/ss_f85afbbbc0e28ebd81363ffd18f50ab56b73d6c2.600x338.jpg?t=1727913300",

 "path_full": "https://shared.akamai.steamstatic.com/store_item_assets/steam/apps/915310/ss_f85afbbbc0e28ebd81363ffd18f50ab

Screenshot URLs are available for the 46K crawled apps, with header fallback for the rest. Building the sample + contact-sheet script now.

File created successfully at: E:\a327ex\steam-market\scripts\art_price_sample.py (file state is current in your context — no need to Read it back)

Command running in background with ID: b6z0frnb6. Output is being written to: C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\b6z0frnb6.output. You will be notified when it completes. To check interim output, use Read on that file path.

While the downloads run, let me set up the scoring protocol. I'll check progress periodically.

<tool_use_error>Blocked: sleep 90 followed by: tail -5 "C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\b6z0frnb6.output". To wait for a condition, use Monitor with an until-loop (e.g. `until <check>; do sleep 2; done`). To wait for a command you started, use run_in_background: true. Do not chain shorter sleeps to work around this block.</tool_use_error>

The task will notify me when it completes. Meanwhile, let me pre-write the analysis script that joins scores with the mapping — it's independent of the download.

File created successfully at: E:\a327ex\steam-market\scripts\art_price_analysis.py (file state is current in your context — no need to Read it back)

1121 games (777 window, 344 prior hits)

100/1121 processed

The sample is built: 1,121 games (777 window releases + 344 prior hits) — downloads are running at ~100 per two minutes, so roughly 15 minutes to go. Once the contact sheets are built I'll fan out blind-scoring agents (they see only numbered screenshots, no prices or outcomes), then run the decomposition. I'll continue automatically when the download completes.

<task-notification> <task-id>b6z0frnb6</task-id> <tool-use-id>toolu_019X4F45p9BPLE9h1NFoqDur</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\b6z0frnb6.output</output-file> completed

Background command "Download screenshots and build contact sheets" completed (exit code 0)

</task-notification>

900/1121 processed

1000/1121 processed

1100/1121 processed

kept 1116 (941 screenshots, 175 header fallbacks)

1116 games -> 70 sheets of 16

mapping written

70

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All 10 scoring agents are running in parallel, each blind-scoring 7 sheets (112 screenshots) for quality (1–5) and style class, writing partial CSVs to data/art_price/. They'll take a few minutes each; I'll be notified as they complete, then merge the parts and run the decomposition. While they work, a note on the half of your question the screenshots can't answer: scope isn't directly measurable in this data (SteamSpy playtime medians are empty for recent cohorts — a known gap from session 1), so proxies like achievements/DLC would be weak. Graphics is the measurable half, and it's also the half your hypothesis leans on hardest.

<task-notification> <task-id>a793b72c677efa521</task-id> <tool-use-id>toolu_01BdxbwxKu4Vff5LwyfMw1xZ</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\a793b72c677efa521.output</output-file> completed

Agent "Blind-score sheets 00-06" finished

A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once. 112 rows written (idx 0–111, all 7 sheets full 4x4 grids, no black/empty cells) to E:\a327ex\steam-market\data\art_price\scores_part00.csv.

Anomalies: - No unreadable/empty cells; every cell scored 1–5. - Several cells are marketing/title cards rather than gameplay (16, 33, 39, 43, 44, 48, 50, 51, 52, 55, 60, 83, 94, 105, 110) — scored on the craft of the shown image; pure logo-on-black screens (27, 94, 105) got art=1 and style=TXT since text dominated. - A few judgment-call style codes: 70 (doujin bullet-hell with illustrated portrait) coded DRAWN; 108 (voxel 3D with pixel billboard) coded LOW3D; 59 (typing-quote scene) and 75 (map-strategy) coded TXT as the interface/text dominated. <subagent_tokens>71649</subagent_tokens><tool_uses>8</tool_uses><duration_ms>206255</duration_ms> </task-notification>

First scorer is done (112/112 rows, no empty cells — a handful were marketing-card headers rather than gameplay, which matches the 175 header-fallback games; the mapping tracks img_src so I can sensitivity-check those). Waiting on the other nine.

<task-notification> <task-id>a0d77bbe69093d397</task-id> <tool-use-id>toolu_01MuXqzbzyZcqurQZUV1myDV</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\a0d77bbe69093d397.output</output-file> completed

Agent "Blind-score sheets 14-20" finished

A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once. 112 rows written (idx 224–335) to E:\a327ex\steam-market\data\art_price\scores_part14.csv.

Anomalies: - No black/empty/unreadable cells — all 112 cells were scoreable, so no 0/X rows. - A few judgment-call cells worth noting: 236 (painted strategy scene with heavy UI band — scored DRAWN rather than TXT since the painted map room dominates), 327 (farm game embedded in a Windows-XP desktop parody — scored TXT as UI dominates), 311 (3D night-forest with VHS camcorder overlay — kept LOW3D rather than OTHER), and 314 (illustration with an AI-generated look — scored art=2 on execution mushiness). - Two title-card-only cells (285 "Best Pearl", 286 "Last Day of Zombies") were scored on the artwork visible, both very low quality (art=1). <subagent_tokens>71477</subagent_tokens><tool_uses>8</tool_uses><duration_ms>196041</duration_ms> </task-notification>

Two of ten done. Waiting for the rest.

<task-notification> <task-id>a32360ae99a31febd</task-id> <tool-use-id>toolu_01Tzb8ZDbLXVhF6RaPVBUdoB</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\a32360ae99a31febd.output</output-file> completed

Agent "Blind-score sheets 21-27" finished

A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once. 112 rows written (idx 336–447) to E:/a327ex/steam-market/data/art_price/scores_part21.csv.

Anomalies: - No black/empty/unreadable cells — all 112 cells were scoreable. - Several cells are very dark screenshots (337, 388, 396, 407, 424, 436) — scored on what was visible, but legibility limits confidence there. - Several cells are title/key-art cards rather than gameplay (339, 350, 364, 378, 385, 389, 406, 409, 412, 416, 421, 426, 428, 435, 437, 445); title-text-dominated ones went to TXT, illustration-dominated ones to their art style. - 421 ("Monkey vs Dino") is a grainy photo-collage — scored OTHER. 350 and 382 look AI-generated; scored low on craft. <subagent_tokens>71646</subagent_tokens><tool_uses>8</tool_uses><duration_ms>205968</duration_ms> </task-notification>

Three done. A pattern worth noting for the analysis: title-card cells (header fallbacks) sometimes land in TXT, so for style-level claims I'll run a screenshots-only sensitivity cut alongside the full sample. Waiting on the remaining seven.

<task-notification> <task-id>aeba8a714254a1bc1</task-id> <tool-use-id>toolu_01NBemJtLTrJiLi9GcPC7Z8M</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\aeba8a714254a1bc1.output</output-file> completed

Agent "Blind-score sheets 07-13" finished

A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once. 112 rows written (indices 112–223, 16 per sheet × 7 sheets) to E:\a327ex\steam-market\data\art_price\scores_part07.csv, plus the header line.

Anomalies: - No black/empty/unreadable cells — every cell scored normally, no 0/X rows. - A few judgment-call style assignments worth noting: cell 169 is claymation-style title art (scored FLAT as nearest bucket); cell 193 is live-action FMV (OTHER); cells 137 and 222 are WordArt-over-backdrop title screens (OTHER, art 1); cell 154 is a richly illustrated card table (bucketed TXT per the card-table rule, but art 4 for the illustration craft). - Cell 187's title screen contains a misspelling ("Desturction"), consistent with its art=1 score. <subagent_tokens>72369</subagent_tokens><tool_uses>8</tool_uses><duration_ms>221734</duration_ms> </task-notification>

Four done — halfway on rows (448/1,116). All full 112-row parts so far, no unreadable cells.

<task-notification> <task-id>a357d718d50444297</task-id> <tool-use-id>toolu_01J9CikYrQGeA2dPggayj31J</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\a357d718d50444297.output</output-file> completed

Agent "Blind-score sheets 49-55" finished

A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once. 112 rows written (idx 784-895, sheets 49-55) to E:/a327ex/steam-market/data/art_price/scores_part49.csv.

Anomalies: - No black/empty cells; all 112 cells were scoreable. - 854 (sheet 53) is nearly all-black — faint dark scene with what looks like a scan/screen; scored art=1 rather than 0 since content is barely present. - Several cells are logo/title cards rather than gameplay (786, 792, 800, 834, 845, 850, 857, 863, 884, 888); scored on the card's execution quality. - 868 (sheet 54) is a screenshot of Steam review text with a dev response — pure text, scored 1,TXT. <subagent_tokens>71321</subagent_tokens><tool_uses>8</tool_uses><duration_ms>204045</duration_ms> </task-notification>

Five of ten done. Waiting on the rest.

<task-notification> <task-id>ab28b38fd7013eae6</task-id> <tool-use-id>toolu_017zL5jDCbSMKhNAciihvrfH</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\ab28b38fd7013eae6.output</output-file> completed

Agent "Blind-score sheets 35-41" finished

A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once. 112 rows written (idx 560–671, all 7 sheets full 4x4 grids, no empty cells) to E:/a327ex/steam-market/data/art_price/scores_part35.csv.

Anomalies: - 579 is a screenshot of a Steam review/community page with a dev-message overlay rather than gameplay — scored 1,TXT. - 634 and 666 are near-black screens dominated by text (interview textbox; ASCII portrait) — scored as TXT, not empty, since content is readable. - A few cells are key-art/logo cards rather than gameplay (564, 612, 628, 631, 639, 654, 659, 660) — scored on the art quality of the card itself. - No black/unreadable cells; no 0-scores needed. <subagent_tokens>72040</subagent_tokens><tool_uses>8</tool_uses><duration_ms>218250</duration_ms> </task-notification>

Six of ten done. Four remaining.

<task-notification> <task-id>aa32d84cd9ce60faf</task-id> <tool-use-id>toolu_01SapjUSREcvauVSockTejyF</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\aa32d84cd9ce60faf.output</output-file> completed

Agent "Blind-score sheets 56-62" finished

A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once. 112 rows written (idx 896–1007) to E:\a327ex\steam-market\data\art_price\scores_part56.csv, plus the header line. All 112 cells across the 7 sheets were readable — no black/empty cells, so no 0/X rows.

Anomalies/judgment calls: - 935, 945, 959: default ChoiceScript-style plain text pages — scored art=1, TXT. - 924, 933, 939, 947, 989, 992, 993, 1001: title cards/logo promos rather than gameplay — scored on the visible execution; 947 looks like a photo/photoreal portrait with award laurels (OTHER), 992 is an accolades/review-score card (TXT). - 927, 984, 988: extremely dark screenshots; scored low partly because they communicate almost nothing at store-thumbnail size. - 928 carries SEGA/Grounding copyright text (likely a licensed production despite this being an indie-priced dataset) — scored on its dated-but-competent look as instructed. <subagent_tokens>71111</subagent_tokens><tool_uses>8</tool_uses><duration_ms>203906</duration_ms> </task-notification>

Seven of ten done. Three remaining.

<task-notification> <task-id>a05d4266c837a1d60</task-id> <tool-use-id>toolu_01JrdBqYH9o9e6ie2iYHQnsf</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\a05d4266c837a1d60.output</output-file> completed

Agent "Blind-score sheets 42-48" finished

A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once. 112 rows written (indices 672–783, header line included) to E:/a327ex/steam-market/data/art_price/scores_part42.csv.

Anomalies: - No black/empty cells — all 112 cells were readable, so no 0/X rows. - Cell 770 is a near-black horror screenshot; scored 2,HI3D on the faint visible structure rather than 0 (image is present, just very dark). - A few cells are title/logo screens rather than gameplay (690, 730, 783, 714) — scored on the key-art execution shown. - Cell 693 appears to be a desktop-pet/wallpaper app (anime girl on a real Windows notepad window); scored as DRAWN. - Cells 742 and 717 are card-table scenes; 742 was scored FLAT because the cartoon scene dominates over the interface, while 717 (solitaire layout) went to TXT. <subagent_tokens>72574</subagent_tokens><tool_uses>8</tool_uses><duration_ms>222207</duration_ms> </task-notification>

Eight of ten done. Two remaining.

<task-notification> <task-id>a051a56cf22d604ba</task-id> <tool-use-id>toolu_0175J5QiV62C7AzS9igLfg6F</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\a051a56cf22d604ba.output</output-file> completed

Agent "Blind-score sheets 63-69" finished

A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once. 108 rows written to E:/a327ex/steam-market/data/art_price/scores_part63.csv (indices 1008–1115, plus header).

Anomalies: - Sheet_69 is partially filled: only 12 cells (1104–1115); the bottom row has no indexed cells, so no rows were emitted for it. - No black/empty/unreadable cells encountered — every indexed cell had a scoreable image (1023 and 1112 are extremely dark screenshots but still readable enough to score). - Several cells are marketing/title cards rather than gameplay (e.g. 1022, 1050, 1058, 1072, 1091, 1111); these were scored as presented, mostly as TXT or by their dominant visual. <subagent_tokens>72042</subagent_tokens><tool_uses>8</tool_uses><duration_ms>213185</duration_ms> </task-notification>

Nine of ten done (1,112 rows so far — the last sheet is a partial, as expected). One agent remaining (sheets 28–34).

<task-notification> <task-id>a44c056ce50ac58af</task-id> <tool-use-id>toolu_013ZhDgLmtmjnSzGaXeDB92S</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\a44c056ce50ac58af.output</output-file> completed

Agent "Blind-score sheets 28-34" finished

A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once. 112 rows written (indices 448–559, sheets 28–34) to E:\a327ex\steam-market\data\art_price\scores_part28.csv.

Anomalies: - No black/empty cells — all 112 cells were readable and scored normally (no art=0 rows). - A few hybrid-style judgment calls: 470 (voxel toy world, scored LOW3D over PIX), 532 (3D anime character over painted starfield, scored DRAWN by dominant visual), 529/538 (title menus where the background art dominates, scored DRAWN rather than TXT), 545/546 (photo/noise-composite title cards, scored OTHER). - 555 is a textbook asset-flip keyart (art=1, HI3D); 495 and 546 are crude shock/noise horror title cards (art=1, OTHER). <subagent_tokens>72853</subagent_tokens><tool_uses>8</tool_uses><duration_ms>245393</duration_ms> </task-notification>

All ten scorers are done (1,116/1,116 cells, none unreadable). Merging the parts and running the decomposition.

total scored: 1116 range: 0 - 1115

missing: [] (count 0 )

art dist: [(1, 45), (2, 323), (3, 526), (4, 213), (5, 9)]

style dist: [('LOW3D', 286), ('HI3D', 226), ('DRAWN', 176), ('PIX', 176), ('TXT', 124), ('FLAT', 92), ('MIN', 22), ('OTHER', 14)]

merged -> scores.csv

scored: 1116 total, 772 window, 344 prior hits; 175 header fallbacks

style counts (window): [('LOW3D', 193), ('HI3D', 139), ('DRAWN', 130), ('PIX', 125), ('TXT', 85), ('FLAT', 69), ('MIN', 20), ('OTHER', 11)]



=== 1. PRICE vs ART (window releases, his cell) ===

  <=$7     n=251  mean art=2.53 art>=4:    7%  art<=2:   49%  [DRAWN:58, LOW3D:54, PIX:39, HI3D:31]

  $8-12    n=189  mean art=2.76 art>=4:   18%  art<=2:   37%  [PIX:49, DRAWN:38, LOW3D:32, TXT:24]

  $13-20   n=170  mean art=2.78 art>=4:   16%  art<=2:   34%  [LOW3D:51, HI3D:30, PIX:26, DRAWN:23]

  >$20     n=162  mean art=3.23 art>=4:   39%  art<=2:   17%  [HI3D:56, LOW3D:56, TXT:14, DRAWN:11]

  spearman(price, art) = 0.294



=== 2. H50 GRID: art tier x price band (window releases; row=art, col=price) ===

                        <=$7             $8-12            $13-20              >$20               ALL

   art 1-2           6% (122)           23% (70)           37% (57)           41% (27)           20% (276) 

     art 3          16% (111)           32% (85)           65% (86)           82% (72)           45% (354) 

   art 4-5          17% (18)           47% (34)           67% (27)           97% (63)           69% (142) 

column ALL (price effect ignoring art):

       ALL          11% (251)          31% (189)          56% (170)          81% (162)          41% (772)



=== 3. H50 by style x price (styles with n>=20; is price dead for simple styles?) ===

  LOW3D (n=193) <=$7:    6% (54)  $8-12:   31% (32)  $13-20:   55% (51)  >$20:   77% (56)  | h50 all: 44%

   HI3D (n=139) <=$7:   26% (31)  $8-12:   64% (22)  $13-20:   67% (30)  >$20:   93% (56)  | h50 all: 68%

  DRAWN (n=130) <=$7:    9% (58)  $8-12:   21% (38)  $13-20:   48% (23)  >$20:   64% (11)  | h50 all: 24%

    PIX (n=125) <=$7:   10% (39)  $8-12:   33% (49)  $13-20:   69% (26)  >$20:   82% (11)  | h50 all: 38%

    TXT (n=85 ) <=$7:    7% (30)  $8-12:   33% (24)  $13-20:   41% (17)  >$20:   71% (14)  | h50 all: 32%

   FLAT (n=69 ) <=$7:   12% (26)  $8-12:   21% (14)  $13-20:   61% (18)  >$20:   73% (11)  | h50 all: 36%

    MIN (n=20 ) <=$7:   22% (9)  $8-12:    0% (9)  $13-20:     - (1)  >$20:     - (1)  | h50 all: 15%



=== 4. SIMPLE-STYLE (MIN/PIX/TXT or art<=2) window releases at $10+: hits ===

n=143, h50=51.0% [42.9-59.1]

  HIT  $  3471K $49.99 art=3 PIX   2024 MARVEL vs. CAPCOM Fighting Collection: Arcade Classi

... [174 more lines]

=== kept-simple vs went-fancy, BY WINDOW PRICE BAND (pairs) ===

  <=$7    kept-simple 3/111 =    3%   went-fancy 2/44 =    5%

  $8-12   kept-simple 23/88 =   26%   went-fancy 7/24 =   29%

  $13-20  kept-simple 26/58 =   45%   went-fancy 15/19 =   79%

  >$20    kept-simple 15/28 =   54%   went-fancy 11/13 =   85%



=== all 20 MIN-style window releases (SNKRX style class) ===

  HIT  $  2210K $ 29.99 art=3 2023 Ghost Trick: Phantom Detective

  HIT  $   107K $  4.99 art=2 2024 clickyland

  HIT  $    60K $  2.99 art=3 2025 Nebulock

  miss $    23K $  2.99 art=3 2023 Poosh XL

  miss $    13K $  7.99 art=3 2024 KUUKIYOMI 4: Consider It

  miss $    13K $  4.99 art=2 2024 Ninja Stealth 5

  miss $    11K $ 19.99 art=2 2024 Fight till the End!

  miss $     9K $  9.99 art=2 2024 BobasQuest

  miss $     8K $  9.99 art=2 2023 Abstract Driver

  miss $     6K $  8.99 art=2 2024 X Run 3

  miss $     5K $  9.99 art=2 2023 Vacuum cleaner robot simulator: pile sos edition

  miss $     5K $  7.99 art=3 2023 HYPER METEOR

  miss $     4K $  9.99 art=1 2024 Gas Gas Rocket!

  miss $     4K $  7.99 art=2 2023 TORIDAMA: Brave Challenge

  miss $     4K $  9.99 art=2 2023 Molecular Warfare

  miss $     3K $  4.99 art=2 2024 MDNE

  miss $     3K $  4.99 art=2 2025 Spacecaps

  miss $     1K $  3.98 art=3 2024 Slug's Sister Defense

  miss $     0K $  0.99 art=2 2023 People Jumping Tower

  miss $     0K $  4.99 art=2 2023 RAGMONTON



=== main grid, SCREENSHOTS ONLY (n_hdr excluded) ===

n=599 of 772

... [8 more lines]

File created successfully at: E:\a327ex\steam-market\reports\16-art-vs-price.md (file state is current in your context — no need to Read it back)

The file E:\a327ex\steam-market\reports\notes.md has been updated successfully. (file state is current in your context — no need to Read it back)

Done — 1,116 screenshots blind-scored, decomposition run, everything archived. Here's the full picture.

Is the price→re-hit gradient actually a graphics gradient? No.

Method: I took your entire cell (self-pub 2020–22 hit devs → all 772 of their self-pub 2023–25 releases, EA-adjusted, plus each dev's prior hit — 1,116 games), pulled one in-game store screenshot per game (941 true screenshots, 175 header fallbacks for low-review games), built shuffled numbered contact sheets, and had 10 parallel scorers blind-rate each image — quality 1–5 (same rubric as session 1's art gate: 1=asset-flip, 3=competent, 5=premium) plus a style class (MIN/PIX/FLAT/DRAWN/LOW3D/HI3D/TXT). Scorers saw no names, prices, or outcomes.

1. Price barely correlates with art in the first place

Spearman(price, art) = 0.29. Mean art by band: 2.53 → 2.76 → 2.78 → 3.23. The $8–12 and $13–20 bands are visually indistinguishable (art≥4 share: 18% vs 16%). Only >$20 looks meaningfully better (39% good-art). So "price = how much the game visually commands" is mostly wrong in the indie range — devs do not, in practice, price by their graphics.

2. The decomposition grid — price and art are independent axes, and price is the stronger one

P(≥$50K) by art tier × price, window releases in your cell:

Read the art-3 row: holding graphics at "competent, nothing special," price runs 16% → 82%. That within-tier range is bigger than the art effect within any price column. And the crossing cell is the decisive one: ugly-but-competent at $13–20 (37–65%) beats genuinely good-looking at ≤$7 (17%). Dropping the 175 header-fallback images makes the gradient steeper, not weaker (art-3 row becomes 22→84%).

One honest mechanical note: since revenue = reviews × K × price, a $20 game crosses $50K with ~70 reviews while a $5 game needs ~280. That's not a bias to correct — it is the finding: a higher price converts the same-sized audience into 3–4× the dollars, and empirically the audience does not shrink proportionally to compensate (if it did, the gradient would be flat). The price gate needs no graphics story at all; it's mostly multiplication that cheap games voluntarily forfeit.

3. Every style climbs with price — except yours

Style × price hit rates: PIX at $13–20 is 69%; TXT (UI/sim/card-table games) runs 33/41% at $8–12/$13–20; FLAT 61% at $13–20. DRAWN (anime/illustrated) is the weakest big class overall (24%). But MIN — abstract/geometric shapes, SNKRX's literal style class — is a graveyard: 3 hits in 20, and the only large one is a misclassified Capcom remaster (Ghost Trick). The two honest MIN hits: clickyland ($107K — an idle/clicker) and Nebulock ($60K). Seventeen misses, mostly $3–10 abstract action games. This independently rediscovers session 1's tag finding (Arcade/Minimalist/Abstract = bottom decile, "always dead"): abstract-geometric action doesn't sell at any price, and MIN's one working outlet is the idler/incremental lane — which is exactly where Gnorp, Nodebuster, and Digseum live in the report-05 ugly-art-hits list.

4. Do successful devs "art up" their next game — and does it pay?

Half of all devs keep their exact style class between games. And the "resources → nicer visuals → success" chain mostly isn't what happens: art-upgrading from an amateur base (1–2) does nothing (20% vs 22% re-hit for those who didn't bother), while art-upgrading from a competent base (3) genuinely pays (62% vs 35%). Polishing jank isn't progress; adding craft to competence is. (My first pooled cut showed "art-up hurts" — that was composition, corrected by stratifying on prior art.)

The kept-simple vs went-fancy comparison, done properly (within price bands, since fancier next-games are also pricier): at ≤$7 it's 3% vs 5%, at $8–12 it's 26% vs 29% — identical. Only at $13–20 does fancy pull ahead (79% vs 45%). So at your planned price range, keeping your graphical style costs you approximately nothing; the thing that costs you is the ≤$7 shelf, in every style, at every art tier.

5. The kept-simple-and-won list — 67 re-hits from 48 devs

Devs whose prior hit and next hit were both simple-style (MIN/PIX/TXT or art≤2), sorted by what they teach. The instructive indie core (discarding Capcom/Konami retro collections, which pollute the "self-pub" proxy):

• Illwinter — Conquest of Elysium 5 → Dominions 6 ($30→$45, art 2, $1.9M→$2.6M). Two people, legendarily ugly, 25 years of the same aesthetic.
• Cruelty Squad → Psycho Patrol R ($20→$40, art 2, $2.7M) — the anti-graphics flagship; the ugliness is the brand.
• Soulash → Soulash 2 ($15→$19, pixel, $276K→$1.29M).
• Card Survival: Tropical Island → Fantasy Forest (TXT card-UI, $630K→$874K).
• Pumping Simulator 1 → 2 — the purest price lesson on the list: same janky LOW3D, $5→$12, $85K→$701K.
• David Szymanski — Iron Lung → Squirrel Stapler → Butcher's Creek: repeatable ~$500K at $8–10 in deliberate PS1-jank, shipped fast.
• Haiku the Robot → Rusty's Retirement ($1.6M idler — simple art, format pivot into the idle lane).
• MDickie — Wrestling Empire → Hard Time III: decades of identical jank, loyal audience.
• Plus: Nomad Survival → Nomad Idle, Void Scrappers → BORE BLASTERS, Mortal Glory 1→2, pureya → Ambidextro, Legend Bowl → College Bowl, Horizon's Gate → Kingsvein, Spiderweb's Geneforge remakes, Esports Godfather, Creeper World IXE, Teamfight Manager → Kitchen Crisis.

Kept-simple at $10+: 51% re-hit (n=143). Kept-simple at ≤$7: 3% (3/111). The three threads running through every winner: they price at $10+; their style consistency functions as a brand for a returning audience (the opposite of upgrading — Illwinter/Spiderweb/MDickie/Szymanski are recognizable by their jank); and they sit in systems-depth genres whose audiences explicitly don't shop on graphics — deep strategy, sim/management, systems-RPG, idlers, sports-jank, lo-fi horror. There is not a single abstract-geometric action game on the winner list.

What I'd take from it

Your instinct was half right: you're right that you don't need to art up, wrong about why the price gradient exists. Simple graphics survive fine at $8–20 — but attached to formats where depth is legible (and MIN specifically wants the idler/systems outlet, which is precisely the BYTEPATH-incremental thesis). What the data refuses to bless is simple graphics at ≤$7 — the SNKRX price point itself, not the SNKRX look, is the dead parameter. One unmeasurable left honest: scope. Playtime data is empty for recent cohorts, so price partly proxies content volume and this analysis only eliminates the graphics explanation, not the scope one.

Archived: 16-art-vs-price.md, raw output in raw_art_price.txt, scores + screenshots under data/art_price/, notes.md updated.

Run the analysis for scope too.

Good push — scope was the unmeasured half. There's a way to measure it from primary data: the appreviews API returns each reviewer's playtime_at_review, so a 100-review sample per game gives a median-hours-played estimate — a real content-volume proxy. And appdetails has PC-requirements storage size as a secondary asset-volume proxy that needs no crawling. Let me check the existing crawler conventions and whether playtime fields were already captured somewhere.

1    """Review-TEXT crawl for build-depth games (analysis #12: what players praise/complain
2   about in deep-build games). Queue: paid 2023-25 games carrying any BUILD_TAG, split
3   hit (est >=$50K) / mid (>=50 reviews, not hit) / low (10-49 reviews). Per game: one
4   page of top-helpful English reviews (100) + one page of negative-only (50) so
5   complaints are always represented. Resumable: done table.
6   Usage: reviews_text_crawl.py [max_mid] [max_low]   (hits always all)"""
7   import json, random, sqlite3, sys, time
8   import requests
9   
10  sys.path.insert(0, 'E:/a327ex/steam-market/scripts')
11  from common import ROOT, load_catalog, load_tagmap
12  
13  DB = f'{ROOT}/data/reviews_text.sqlite'
14  MAX_MID = int(sys.argv[1]) if len(sys.argv) > 1 else 800
15  MAX_LOW = int(sys.argv[2]) if len(sys.argv) > 2 else 800
16  
17  BUILD_TAGS = ['Roguelike', 'Roguelite', 'Action Roguelike', 'Roguelike Deckbuilder',
18                'Traditional Roguelike', 'Deckbuilding', 'Card Battler', 'Card Game',
19                'Auto Battler', 'Bullet Heaven', 'Loot', 'Looter Shooter', 'Hack and Slash',
20                'Action RPG', 'CRPG', 'JRPG', 'Party-Based RPG', 'Strategy RPG',
21                'Tactical RPG', 'Turn-Based Tactics', 'Dungeon Crawler', 'Class-Based',
22                'Character Customization', 'Creature Collector', 'Perma Death',
23                'Procedural Generation', 'Replay Value', 'RPG', 'Turn-Based Strategy']
24  
25  con = sqlite3.connect(DB)
26  con.execute('PRAGMA journal_mode=WAL')
27  con.execute('''CREATE TABLE IF NOT EXISTS review(
28      recommendationid INTEGER PRIMARY KEY, appid INTEGER, grp TEXT, page TEXT,
29      voted_up INTEGER, votes_up INTEGER, weighted_score REAL,
30      playtime_forever INTEGER, playtime_at_review INTEGER,
... [79 more lines]

('review',)

('done',)

reviews []

done ['appid', 'grp', 'total', 'positive', 'negative', 'n_stored', 'fetched_at']

steamspy rows: (82233, 0)

File created successfully at: E:\a327ex\steam-market\scripts\playtime_crawl.py (file state is current in your context — no need to Read it back)

Command running in background with ID: b4kbg67h0. Output is being written to: C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\b4kbg67h0.output. You will be notified when it completes. To check interim output, use Read on that file path.

945 apps in appdetails (171 missing), disk size parsed for 863

File created successfully at: E:\a327ex\steam-market\scripts\scope_analysis.py (file state is current in your context — no need to Read it back)

queue: 1118 appids (0 already done)

The analysis script is ready and the crawl is progressing through 1,118 games (~25 minutes remaining). Everything will run automatically when it completes — the analysis covers: whether price tracks scope (median hours at review + disk size + achievements), whether scope itself predicts re-hitting, the hours × price grid (does the price gradient survive holding scope?), the full control (price holding both art ≤3 and hours), scope movement in dev pairs (did price-uppers also scope up?), and the hours the kept-simple winners actually deliver. SNKRX and BYTEPATH are in the queue too, so you'll get your own placement on the scope axis.

<task-notification> <task-id>b4kbg67h0</task-id> <tool-use-id>toolu_01F9Z1x5S8fdGLYakxPKdcdU</tool-use-id> <output-file>C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\tasks\b4kbg67h0.output</output-file> completed

Background command "Crawl playtime data for his-cell games" completed (exit code 0)

</task-notification>

1000/1118

1100/1118

playtime crawl COMPLETE

coverage: 242/772 window games with >=10 playtime samples; 539 with disk size

SNKRX: median 9.1h (n=10 sampled reviews)

BYTEPATH: median 4.1h (n=2 sampled reviews)



=== 1. SCOPE by PRICE BAND (window releases) ===

band    | n   | med hours | p25-p75 hours | med disk GB | med achievements

<=$7    | 49  |      2.6h |   0.9-3.6   h |     0.68 |     15

$8-12   | 51  |      3.9h |   1.4-6.7   h |     0.54 |     16

$13-20  | 52  |      7.2h |   4.2-12.4  h |     2.00 |     24

>$20    | 90  |     15.6h |   8.2-30.4  h |    13.50 |     40

spearman(price, hours) = 0.571  (n=242)

spearman(price, disk)  = 0.503  (n=539)

spearman(art, hours)   = 0.491

spearman(art, disk)    = 0.336



=== 2. H50 by HOURS TIER (marginal) ===

  <3h     n=68   h50= 25.0% [16.2-36.4]

  3-10h   n=77   h50= 81.8% [71.8-88.8]

  10-30h  n=70   h50= 92.9% [84.3-96.9]

  >30h    n=27   h50=100.0% [87.5-100.0]

NOTE: hours-coverage conditions on >=10 playtime-bearing reviews -> drops most sub-10-review

games; the covered subset is success-biased. Within-subset comparisons only.



=== 3. H50 GRID: hours tier x price band (covered window releases) ===

                     <=$7            $8-12           $13-20             >$20

     <3h         20% (30)          36% (22)          11% ( 9)          29% ( 7) 

   3-10h         53% (15)          70% (20)         100% (21)          95% (21) 

... [50 more lines]

File created successfully at: E:\a327ex\steam-market\reports\17-scope-vs-price.md (file state is current in your context — no need to Read it back)

The file E:\a327ex\steam-market\reports\notes.md has been updated successfully. (file state is current in your context — no need to Read it back)

Scope analysis complete — and this one lands closer to your intuition than the art one did, with a twist at the end about SNKRX itself.

Scope vs price: the other half of the decomposition

Method: scope = median playtime_at_review across 100 top-helpful reviews per game (all languages), crawled for all 1,118 his-cell games plus SNKRX/BYTEPATH; secondary proxies = install size parsed from PC requirements and achievement counts. One caveat before any numbers: only 242 of 772 window games have ≥10 playtime-bearing reviews, and "has enough reviews to measure" is close to "didn't fail" — so hit-rate levels inside the covered subset are inflated, and it's the shapes (which rows and columns have gradients) that carry the evidence. The price↔scope correlation itself doesn't condition on outcome and is clean.

1. You were right about scope: price tracks it strongly — twice as strongly as graphics

Spearman(price, hours) = 0.571 vs spearman(price, art) = 0.294. So the corrected version of "price = what the game visually commands" is: price = what the game temporally commands. Devs price by content volume, not by graphics, and the market's price bands are approximately hour bands: ≤$7 ≈ under 3h, $8–12 ≈ ~4h, $13–20 ≈ ~7h, >$20 ≈ 15h+.

2. The grid: price cannot rescue a short game — and short games are why cheap games die

h50 by hours tier × price band (covered subset; read shapes, not levels):

Two shapes matter. Across the <3h row, the price gradient vanishes — a sub-3-hours game is equally troubled at $5 and $18 (the 11% at $13–20 is nine games, but the flatness is consistent). Across the 3–10h row the gradient is steep (53→100%). And it survives fixing art≤3 at the same time (53→69→100→89). So the earlier price findings decompose cleanly: below ~3h of median engagement, scope is the binding constraint and price does nothing; above it, price is close to free money (the arithmetic mechanism — same audience, more dollars per head — with no proportional audience shrinkage). The $13–20 cell that ran 57% in the path-dependence analysis is really a disguised requirement: deliver ~7 hours of median engagement, then charge like it.

3. But price-uppers did NOT proportionally scope up

Among the 147 dev pairs with hours measured on both games (a winners-biased sample — both games have review volume): devs who jumped a full price band added only 1.28× median hours. And the 20 who priced up while adding less than 2× content re-hit at 90% — indistinguishable from same-band devs (92.8%). Scope movement itself (2× up / same / halved) showed no separation at this selection ceiling (96/91/92%).

So your question "is the price change always accompanied by bigger scope?" gets a two-part answer: across the market, yes, price bands are scope bands (0.571) — but within successful devs' own transitions, the band jump ran ahead of the content growth and it worked anyway. The prior-hit audience apparently absorbs a band jump without demanding proportional hours. That is precisely the SNKRX-reprice / price-up thesis from the transition matrix, now with the scope objection tested and cleared.

4. The kept-simple winners are depth monsters

Median hours: kept-simple hits 12.2h — above the all-hits median (10.5h), and misses sit at 1.4h. The simple-graphics winners buy their price with hours: Card Survival: Fantasy Forest 84.5h ($22, art 2–3 TXT), Esports Godfather 38.2h, Dominions 6 31.9h, Rusty's Retirement 27.4h at $6.99 (the idler exception generating mid-band hours at a low price), Soulash 2 25.8h, Pumping Simulator 2 22.9h, the Kairosoft line 8–17h. The one lane selling short-and-cheap successfully: Szymanski-style lo-fi horror (2.6–4.2h at $6–10) — a streamer channel, not a systems channel.

Note what kind of hours these are: Rusty's, Card Survival, Kairosoft, Soulash are systems/replayability hours, not handcrafted-content hours. That's the version of "scope" a 2–3 month solo game can actually afford.

5. Your placement — the punchline

SNKRX: median 9.1h at review (n=10 playtime-bearing samples, so treat as ±, but it agrees with your own sense of the game's replay loop). That is top-of-the-3–10h-band engagement — the median $13–20 game delivers 7.2h — sold at $2.99. On this axis, 2021-SNKRX already had the scope of a mid-band game; the missing parameter was the price, not the content and not the graphics. BYTEPATH's sample (4.1h, n=2) is too thin to use.

The full decomposition, assembled

Putting all three sessions of this claim together: the price→re-hit gradient in your class is (a) a scope gate below ~3h — price can't fix a short game; (b) price arithmetic above the gate — audiences don't shrink proportionally with price, so dollars scale; (c) art as a real but weaker independent axis (matters most >$13, negligible $8–12, and "competent" is enough everywhere below $20). The compounding advantage from claim #1 rides on top of all three. For the settled plan, the operative sentence: keep the style, ship 3–10h+ of replayable systems, and price in the band the hours justify — which for SNKRX-like engagement was $13–20 all along, and for the BYTEPATH incremental means the idler lane's job is to generate Rusty's-class hours (25h+) to justify even $7–10.

Archived: 17-scope-vs-price.md, raw in raw_scope.txt, crawl db data/playtime.sqlite (resumable), notes.md updated.

Great, let's do the next analysis.

Claim #4 it is — the migration/stay-switch breakdown. Before writing the deep-dive script, I need two ground facts: what SNKRX's own primary tag actually is in the data (everything "his origin = Action Roguelike" hangs on it), and which primary tags actually occur among prior-hit devs so the genre-family clustering covers the real distribution.

BYTEPATH -> ['Action', 'Casual', 'Indie', 'Retro', 'Sci-fi', 'RPG', '2D']

SNKRX -> ['Action Roguelike', 'Roguelite', 'Arena Shooter', 'Top-Down Shooter', "Shoot 'Em Up", 'Deckbuilding', 'Replay Value']



top-40 primary tags of prior BEST hits (dev-level):

   268 Action

   265 Adventure

   192 RPG

   183 Simulation

   148 Strategy

    76 Horror

    73 Visual Novel

    72 Puzzle

    53 Exploration

    52 VR

    50 Racing

    49 Sexual Content

    36 Action Roguelike

    33 FPS

    32 Open World

    28 Story Rich

    27 Point & Click

    24 Multiplayer

    24 Metroidvania

    24 Survival

    23 Sandbox

    23 Sports

    22 City Builder

    21 Management

    21 Pixel Graphics

    21 3D Platformer

... [14 more lines]

File created successfully at: E:\a327ex\steam-market\scripts\migration_deep.py (file state is current in your context — no need to Read it back)

====================================================================================================

1. REPRODUCTION (release-weighted, original primary-tag def)

STAY  63.6% [58-68] (n=352)   MOVE  55.9% [53-59] (n=1268)



====================================================================================================

2. SEQUEL CONFOUND: is the stay premium just the franchise premium?

  STAY: sequels 59/352 (17%) | sequel h50  66.1% [53-77] (n=59)

  MOVE: sequels 122/1268 (10%) | sequel h50  73.0% [64-80] (n=122)

--- NEW IP ONLY (sequels removed) ---

  release: STAY  63.1% [57-68] (n=293)   MOVE  54.1% [51-57] (n=1146)

  dev-1st: STAY  70.0% [64-76] (n=217)   MOVE  62.4% [59-66] (n=681)

--- NEW IP + SELF-PUB (prior hit & window release) + EA-adj ---

  release: STAY  50.8% [42-59] (n=124)   MOVE  35.2% [31-39] (n=568)

  dev-1st: STAY  60.3% [49-70] (n=78)   MOVE  44.4% [39-50] (n=270)



====================================================================================================

3. TAG DISTANCE (shared top-7 tags with the prior hit), NEW IP only

--- all ---

  0  shared: release  55.9% [50-62] (n=261) | dev-1st  58.4% [51-65] (n=197)

  1  shared: release  56.0% [50-61] (n=307) | dev-1st  62.7% [56-69] (n=233)

  2  shared: release  53.8% [48-59] (n=316) | dev-1st  61.4% [55-68] (n=223)

  3  shared: release  54.3% [48-60] (n=254) | dev-1st  61.0% [54-68] (n=187)

  4  shared: release  55.9% [49-63] (n=177) | dev-1st  64.0% [56-72] (n=139)

  5+ shared: release  64.5% [56-72] (n=124) | dev-1st  71.7% [62-80] (n=99)

--- selfpub+EA-adj ---

  0  shared: release  36.2% [28-45] (n=116) | dev-1st  39.7% [29-51] (n=73)

  1  shared: release  40.4% [33-49] (n=146) | dev-1st  50.0% [40-60] (n=92)

  2  shared: release  33.5% [27-41] (n=173) | dev-1st  39.8% [31-50] (n=98)

  3  shared: release  34.4% [27-43] (n=125) | dev-1st  43.4% [33-54] (n=83)

  4  shared: release  43.6% [33-55] (n=78) | dev-1st  55.8% [42-68] (n=52)

... [52 more lines]

The file E:\a327ex\steam-market\scripts\migration_deep.py has been updated successfully. (file state is current in your context — no need to Read it back)

The file E:\a327ex\steam-market\scripts\migration_deep.py has been updated successfully. (file state is current in your context — no need to Read it back)

  VR      stay   80% (n=5  ) move   60% (n=10 ) diff +20pt

family transitions (n>=8, dev-first, new IP), by h50:
      SIM -> STRAT     88% [53-98] n=8
     NARR -> OTHER     74% [54-87] n=23
     SURV -> ACTION    73% [43-90] n=11
   ACTION -> ROGUE     69% [42-87] n=13
    OTHER -> ACTION    67% [48-81] n=27
   ACTION -> OTHER     65% [41-83] n=17
      RPG -> OTHER     62% [31-86] n=8
     NARR -> ACTION    62% [36-82] n=13
   HORROR -> ACTION    56% [27-81] n=9
    OTHER -> NARR      54% [35-71] n=26
    OTHER -> STRAT     50% [29-71] n=18
    STRAT -> OTHER     40% [17-69] n=10
    OTHER -> RPG       33% [12-65] n=9
    OTHER -> SIM       30% [11-60] n=10

====================================================================================================

8. HIS CELLS: Action Roguelike origin (SNKRX primary), NEW IP window releases
AR-origin devs (dev-first):  83.3% [44-97] (n=6)
  stay AR: 100.0% [21-100] (n=1)   move out:  72.7% [43-90] (n=11)
  AR movers, all window releases (name -> dest, est):
    HIT  $ 10195K $29.99 Souls-like             Another Crab's Treasure
    HIT  $  7261K $24.99 Roguelike              Ravenswatch
    HIT  $   301K $ 9.99 Idler                  Nomad Idle
    HIT  $   250K $ 9.99 Dwarves                BORE BLASTERS
    HIT  $    86K $199.99 Side Scroller          Toy War - Cannon
    HIT  $    79K $199.99 Action                 ROOM FOOTBALL - Hangar
    HIT  $    79K $199.99 Action                 ROOM FOOTBALL - Petrol Station
... [46 more lines]

====================================================================================================

1. REPRODUCTION (release-weighted, original primary-tag def)
STAY  63.6% [58-68] (n=352)   MOVE  55.9% [53-59] (n=1268)

====================================================================================================

2. SEQUEL CONFOUND: is the stay premium just the franchise premium?
  STAY: sequels 59/352 (17%) | sequel h50  66.1% [53-77] (n=59)
  MOVE: sequels 122/1268 (10%) | sequel h50  73.0% [64-80] (n=122)
--- NEW IP ONLY (sequels removed) ---
  release: STAY  63.1% [57-68] (n=293)   MOVE  54.1% [51-57] (n=1146)
  dev-1st: STAY  70.0% [64-76] (n=217)   MOVE  62.4% [59-66] (n=681)
--- NEW IP + SELF-PUB (prior hit & window release) + EA-adj ---
  release: STAY  50.8% [42-59] (n=124)   MOVE  35.2% [31-39] (n=568)
  dev-1st: STAY  60.3% [49-70] (n=78)   MOVE  44.4% [39-50] (n=270)

====================================================================================================

3. TAG DISTANCE (shared top-7 tags with the prior hit), NEW IP only
--- all ---
  0  shared: release  55.9% [50-62] (n=261) | dev-1st  58.4% [51-65] (n=197)
  1  shared: release  56.0% [50-61] (n=307) | dev-1st  62.7% [56-69] (n=233)
  2  shared: release  53.8% [48-59] (n=316) | dev-1st  61.4% [55-68] (n=223)
  3  shared: release  54.3% [48-60] (n=254) | dev-1st  61.0% [54-68] (n=187)
  4  shared: release  55.9% [49-63] (n=177) | dev-1st  64.0% [56-72] (n=139)
  5+ shared: release  64.5% [56-72] (n=124) | dev-1st  71.7% [62-80] (n=99)
--- selfpub+EA-adj ---
  0  shared: release  36.2% [28-45] (n=116) | dev-1st  39.7% [29-51] (n=73)
  1  shared: release  40.4% [33-49] (n=146) | dev-1st  50.0% [40-60] (n=92)
  2  shared: release  33.5% [27-41] (n=173) | dev-1st  39.8% [31-50] (n=98)
  3  shared: release  34.4% [27-43] (n=125) | dev-1st  43.4% [33-54] (n=83)
  4  shared: release  43.6% [33-55] (n=78) | dev-1st  55.8% [42-68] (n=52)
... [64 more lines]

selfpub new-IP dev-first, hits 2017-19 -> window 2020-22:

  STAY  62.0% [53-71] (n=108)

  MOVE  55.8% [50-62] (n=258)

selfpub new-IP dev-first, hits 2014-16 -> window 2017-19:

  STAY  68.5% [58-77] (n=89)

  MOVE  61.9% [56-68] (n=247)

File created successfully at: E:\a327ex\steam-market\reports\18-migration-deep.md (file state is current in your context — no need to Read it back)

The file E:\a327ex\steam-market\reports\notes.md has been updated successfully. (file state is current in your context — no need to Read it back)

Claim #4 re-check complete. Here's the full picture.

Claim #4 re-check: migration and the stay/move tax — verdict and full findings

The directional claim survives; the dramatic numbers mostly dissolve. There is a real, decade-stable premium for staying (~5–10pt, not a cliff), the advantage is genuinely portable across genres — but the per-tag origin/destination tables that produced "Action Roguelike exports at 75%," "worst: Adventure 33%, Racing 30%," and "Simulation-origin devs export worst (27%)" collapse under the same corrections that reshaped claim #1. One of those three claims is outright dead.

How the original numbers were reached (the exposé you asked for)

Report 11's §7 took each prior-hit dev's biggest 2020–22 hit, assigned it a "primary tag" (the first tag in Steam search's top-7 that isn't Early Access/Indie/Casual/Singleplayer/Free to Play), did the same for each 2023–25 release, and counted release-weighted re-hit rates for same-primary vs different-primary. Every weakness claim #1 exposed applies: factories counted once per release, AAA self-publishers included, sequels included, cells as small as n=10 ranked as findings, no confidence intervals, and "primary tag" is often an umbrella tag — the most common primaries among prior hits are Action (268), Adventure (265), RPG (192), Simulation (183), which are vagueness markers, not genres.

What survives, what doesn't

1. The sequel confound — I suspected it, and it's absolved. My hypothesis was that "STAY" was secretly the franchise premium. Wrong: sequels are only 17% of stays vs 10% of moves, and removing all sequels keeps the gap (dev-first: STAY 70.0% [64–76] vs MOVE 62.4% [59–66]). The genuinely interesting side-finding: sequels released into a different primary tag re-hit at 73% — the franchise name carries across genres. That's direct support for keeping the BYTEPATH name on the incremental, and it prices the option of "SNKRX-something in a new format."

2. Your class specifically. Self-pub prior + self-pub window + EA-adjusted + new IP, dev-level: STAY 60.3% [49–70] vs MOVE 44.4% [39–50]. That 16pt gap looked alarming, so I era-replicated it: +6.2pt (2017–19 hits) and +6.6pt (2014–16 hits). The durable self-pub stay premium is ~6–7pt; treat 16pt as a noisy upper bound.

3. Tag distance: flat, with one twist. Re-hit by number of top-7 tags shared with your prior hit is flat from 0 to 4 shared (~58–64% dev-first) — full portability, confirming report 11 — but 5+ shared tags jumps to 71.7% (60.5% in self-pub). So there's no dose-response on distance except an exact-micro-neighborhood bonus of ~10pt. And "pseudo-moves" (new primary tag, ≥3 shared tags) do not beat true moves (52% vs 56%) — the reshuffle isn't where the premium lives; the genuine same-niche iteration is.

4. The origin ranking was noise. After new-IP + dev-first + empirical-Bayes shrinkage toward the pooled mover rate (62.4%), the spread compresses from 27–95% to 52–79%: top = Management 79 / Open World 78 / Visual Novel 72 / RPG 72; bottom = Simulation/Strategy/Puzzle at ~52. And Action Roguelike doesn't even reach rankable n — six AR-origin devs released new-IP in the window. The "75% AR export" figure was release-weighted arithmetic on a handful of games.

5. The Simulation-origin 27% claim is dead. Dev-first, new-IP sim-family movers re-hit at 62.5% (n=56) — exactly the pooled rate — and 58.7% in the earlier era. The 27% came from release-weighting three factories: gamesforgames (55 releases), Kairosoft (22), G-MODE (21) — 98 of the 175 releases in that cell. The inference built on it ("sim hits are format-luck more than transferable skill") should be struck; it was a statement about shovelware cadence, not about sim devs.

6. Destinations, honest version. Exploration still tops even honestly (raw 72% [49–88], n=18, against a 12.7% market base for the tag) — though I'd read it as "the kind of dev who ships a polished exploration game" rather than "the tag pays." Adventure-as-destination rehabilitates completely (56%, n=64 — the 33% was factory releases whose first tag is Adventure). Racing falls below reportable n and vanishes.

7. Your cells, for what tiny n is worth. The AR-origin movers list is qualitatively striking: Another Crab's Treasure ($10.2M, moved to Souls-like), Ravenswatch ($7.3M), Nomad Idle ($301K, moved to Idler), BORE BLASTERS ($250K). ROGUE-family origin → window release carrying card/deckbuild tags: 75% (n=4 — Pyrene, Nomad Idle, Duelists of Eden; miss: Shadowkin); → strategy/tactics tags: 6/6 (Wartales, Dune: Spice Wars, ShapeHero Factory, Pyrene); → idler tags: n=1, a hit. All directionally friendly to your candidate lanes, none statistically meaningful. One data-quality flag from these cells: joke-priced games ($199.99 × 11 reviews ⇒ "$79K est") break the estimator and pollute tiny cells — worth remembering whenever an n<10 cell looks good.

What this changes

The migration tables should be demoted from decision inputs to background. What survives for planning: the advantage travels wherever you go (~44–60% in your class either way, vs ~10% for no-hit veterans); staying in the exact SNKRX micro-niche would retain ~6–10pt you'd forfeit by moving; the franchise name is worth ~most of that back when carried into the new format. So the lane choice between BYTEPATH-incremental, card/base-building, and staying-roguelite should ride on the price/scope/whitespace economics from reports 15–17 — the migration data neither blesses nor penalizes any of them beyond noise. Report 18 supersedes report 11 §7; the EV table's conclusions stand but its migration-derived precision doesn't.

Archived: 18-migration-deep.md, raw in raw_migration_deep.txt, notes.md updated.

Two claims remain from your list: #2 (launch timing — month breakdown, Next Fest, sales proximity) and #3 (retro+idler and card+base-building lanes, the BYTEPATH-incremental and Artifact-port decisions). #3 is the strategically heavier one and now has three reports of fresh machinery behind it; #2 is self-contained and quicker. Your pick.

Go ahead with 3 first.

Claim #3 — the retro+idler and card×base-building lanes, against the two concrete decisions (BYTEPATH incremental; Artifact-ruleset card base-builder). First I need two grounding reads: the whitespace methodology that produced those cell numbers (so I know exactly what "75%, n26=0" meant), and the Artifact port's actual design shape from the siege doc (to judge which lane it really lands in — "base building" and "lane defense" are different cells).

1    """Whitespace map (analysis #6): tag pairs with tiny supply whose occupants
2   over-perform. Rank by binomial surprise vs the BEST component tag (pair must add
3   value beyond either ingredient), not raw h50 (tiny-n noise). Sections:
4   proven whitespace / empty cells / build-lane cut. Window 2023-25 paid; n26 column
5   shows whether 2026 supply is already filling the gap."""
6   import sys
7   from collections import defaultdict
8   from itertools import combinations
9   from math import comb
10  from statistics import median
11  
12  sys.path.insert(0, 'E:/a327ex/steam-market/scripts')
13  from common import load_catalog, load_tagmap
14  
15  NON_CONTENT = {'Indie', 'Casual', 'Singleplayer', 'Early Access', 'Free to Play',
16                 'Great Soundtrack', 'Controller', 'Family Friendly', 'Kickstarter',
17                 'Crowdfunded', 'Software', 'Utilities', 'Benchmark', 'Documentary',
18                 'Gaming', 'Masterpiece', 'Cult Classic', 'Classic', 'Remake', 'Epic',
19                 'Short', 'Long', 'Beautiful', 'Relaxing', 'Colorful', 'Stylized',
20                 'Realistic', 'Cinematic', 'Emotional', 'Funny', 'Comedy', 'Dark',
21                 'Cute', 'Difficult', 'Replay Value', 'Content Rich', 'Immersive'}
22  BUILD_LANE = {'Roguelike', 'Roguelite', 'Action Roguelike', 'Roguelike Deckbuilder',
23                'Traditional Roguelike', 'Deckbuilding', 'Card Battler', 'Card Game',
24                'Auto Battler', 'Bullet Heaven', 'Loot', 'Dungeon Crawler',
25                'Class-Based', 'Character Customization', 'Perma Death', 'Incremental',
26                'Idler', 'Tower Defense', 'Turn-Based Tactics', 'Turn-Based Strategy'}
27  MIN_COMPONENT = 80    # component tag must have >=N window games (real category)
28  PAIR_MAX = 40         # pair supply cap: beyond this it's not whitespace
29  PAIR_MIN = 4
30  
... [102 more lines]

artifact-rules.md
siege-design.md
anchor
anchor.exe
assets
avcodec-61.dll
avfilter-10.dll
avformat-61.dll
avutil-59.dll
board_view.lua
cards
data

# "You Are the Siege" — roguelite transform design + implementation bridge

The design for the single-player game the Artifact engine becomes, and the exact
mapping from the existing codebase to it. Written 2026-07-04 (Fable session, design
locked with the owner); implementation belongs to future sessions. **Read
`.claude/CLAUDE.md` and `reference/artifact-rules.md` first** — the combat codex
still binds verbatim wherever this document doesn't explicitly supersede it.

---

## 1. The game (locked design, iterate by section number)

1. **Board**: one wide vertical lane. Bottom edge: your **Guardian**. Bottom row =
   the **contact row** — full Artifact combat (columns, blocking, 50/25/25 arrows,
   cleave/siege/retaliate, simultaneous battles, Pulse). Above it: 2–3 **approach
   rows** where enemy units telegraph, stepping down one row per round. Top edge:
   the enemy **Tower**; destroying it reveals the **Ancient** = boss phase.
2. **Goal**: you attack. Kill Tower then Ancient before the director grinds you
   down. Your unblocked units hit the Tower (verbatim Artifact rule).
3. **Your side**: 2–3 heroes (color enablers, persistent across fights) + creeps/
   spells/items from your deck under unchanged Artifact rules (color gating, mana
   costs, gold/bounties/shop).
4. **Guardian**: chosen at run start = your class. Does the old friendly tower's
   three jobs: mana source, in-fight loss condition, build vector (abilities,
   levels, equipment). Per-guardian economy = class feel (e.g. flat +1/round vs
   **territory mana**: base + columns you control, capped).
5. **Director**: no hand. A **visible action queue** (spawns/spells/buffs),
   Spire-intent-legible, Artifact-grammar resolution. Every player action advances
   the queue one step; passing skips its remaining steps to combat. Initiative
   cards become queue manipulation (delay/skip/peek). Per-node director profiles;
... [64 more lines]

File created successfully at: E:\a327ex\steam-market\scripts\lane_deep.py (file state is current in your context — no need to Read it back)

====================================================================================================

1. IDLER TAG by half-year (paid): supply, conversion, price immunity

total paid Idler-tagged games in catalog: 2018

half  | n    | h50   | h50<=$10 (n) | h50>$10 (n) | med$ (revd)

2019H1 | 6    |  0.0% |   0.0% (  6) |   0.0% (  0) | $   4K

2019H2 | 3    | 33.3% |  33.3% (  3) |   0.0% (  0) | $ 263K

2020H1 | 8    | 12.5% |   0.0% (  7) | 100.0% (  1) | $   6K

2020H2 | 20   |  0.0% |   0.0% ( 20) |   0.0% (  0) | $   5K

2021H1 | 61   |  8.2% |   6.9% ( 58) |  33.3% (  3) | $   5K

2021H2 | 54   |  9.3% |   9.6% ( 52) |   0.0% (  2) | $   3K

2022H1 | 50   |  8.0% |   8.3% ( 48) |   0.0% (  2) | $   6K

2022H2 | 77   |  6.5% |   4.1% ( 74) |  66.7% (  3) | $   4K

2023H1 | 91   | 11.0% |  11.5% ( 87) |   0.0% (  4) | $   4K

2023H2 | 93   | 10.8% |   8.1% ( 86) |  42.9% (  7) | $   9K

2024H1 | 121  | 16.5% |  14.3% (112) |  44.4% (  9) | $   8K

2024H2 | 167  |  7.2% |   5.7% (158) |  33.3% (  9) | $   6K

2025H1 | 245  | 16.7% |  15.3% (235) |  50.0% ( 10) | $  13K

2025H2 | 361  | 12.5% |  11.4% (352) |  55.6% (  9) | $   8K

2026H1 | 604  |  7.0% |   6.5% (582) |  18.2% ( 22) | $   7K



--- Idler price bands, 2023-26 pooled (the price-immunity claim) ---

  <$3    n=712  h50=  2.1%  h10=  6.7%  med=$2K

  $3-5   n=543  h50=  7.7%  h10= 21.4%  med=$6K

  $6-8   n=284  h50= 23.9%  h10= 44.0%  med=$21K

  $8-12  n=114  h50= 36.0%  h10= 51.8%  med=$52K

  >$12   n=52   h50= 28.8%  h10= 42.3%  med=$63K



====================================================================================================

2. RETRO + IDLER cell census (top-7 conjunction, all years)

  miss 2020 $ 0.99 rev=     0 est=$     0K  Target Practice

... [65 more lines]

  2025 $10.99 est=$ 0.78M rev=  1988  Ithya: Magic Studies                 [Cozy, Desktop Companion, Idler, Music, Utilities, Casual]
  2025 $ 4.99 est=$ 0.75M rev=  4185  Shelldiver                           [Incremental, Casual, Resource Management, Management, Idler, Arcade]
  2025 $ 2.99 est=$ 0.74M rev=  6926  Tower Wizard                         [Incremental, Idler, Resource Management, Strategy, Fantasy, 2D]
  2024 $ 2.99 est=$ 0.68M rev=  6300  Digseum                              [Incremental, Casual, Idler, Management, Simulation, Relaxing]
  2025 $ 7.49 est=$ 0.63M rev=  2326  Tiny Aquarium: Social Fishkeeping    [Collectathon, Creature Collector, Idler, Fishing, Management, Economy]
  2025 $ 4.99 est=$ 0.54M rev=  3001  Keep on Mining!                      [Incremental, Idler, Casual, Relaxing, Mining, Indie]
  2025 $ 7.98 est=$ 0.53M rev=  1851  A Game About Feeding A Black Hole    [Incremental, Casual, Relaxing, Strategy, Indie, Idler]
  2023 $24.99 est=$ 0.51M rev=   568  Fish Game                            [Immersive Sim, Creature Collector, Sandbox, Building, Idler, Auto Battler]
  2025 $ 5.99 est=$ 0.49M rev=  2297  xiuzhen idle                         [Idler, Indie, Word Game, Casual, RPG, Early Access]
  2025 $ 9.08 est=$ 0.49M rev=  1499  Fischer's Fishing Journey            [Idler, Cats, Creature Collector, Casual, Fishing, Utilities]
  2026 $ 9.99 est=$ 0.45M rev=  1260  Birdwatching Notebook                [Birds, Idler, Creature Collector, Desktop Companion, Nature, Casual]
  2026 $ 7.99 est=$ 0.45M rev=  1561  Fortune Mill                         [Incremental, Idler, Automation, Pixel Graphics, Economy, Simulation]
  2026 $ 7.99 est=$ 0.43M rev=  1516  Horripilant                          [Incremental, Idler, Pixel Graphics, Casual, Horror, Puzzle]
  2025 $ 8.99 est=$ 0.43M rev=  1345  Loot of Baal                         [Idler, RPG, Loot, Strategy, Party-Based RPG, Fantasy]

====================================================================================================

5. 'incremental' in store description (appdetails), by year
322 paid released games mention "incremental" in description
  2020: n=4    h50=25.0% med=$  23K  h50<=$10:  0.0%
  2021: n=7    h50=42.9% med=$  28K  h50<=$10: 40.0%
  2022: n=6    h50= 0.0% med=$  14K  h50<=$10:  0.0%
  2023: n=18   h50=16.7% med=$  22K  h50<=$10: 20.0%
  2024: n=47   h50=27.7% med=$  27K  h50<=$10: 22.0%
  2025: n=110  h50=32.7% med=$  24K  h50<=$10: 31.4%
  2026: n=121  h50=33.9% med=$  21K  h50<=$10: 33.0%
  top 2025-26 mentions:
    2026 $ 6.99 est=$  1626K Scritchy Scratchy
    2026 $ 7.99 est=$  1609K Berry Bury Berry
    2026 $17.99 est=$   914K Microcivilization
    2026 $11.99 est=$   819K Orb of Creation
... [106 more lines]

  miss 2025 $ 4.99 rev=     0 est=$     0K  Critterking
  miss 2026 $14.99 rev=    56 est=$    30K  Islantiles
  miss 2026 $14.99 rev=     0 est=$     0K  Block Sprawl
  miss 2026 $14.99 rev=     0 est=$     0K  Into The Depths
  miss 2026 $ 6.99 rev=     0 est=$     0K  Overwork Empire
  miss 2026 $ 9.99 rev=     0 est=$     0K  Eco City Planner
  miss 2026 $ 4.99 rev=     0 est=$     0K  Loop Firefighters
  miss 2026 $ 5.99 rev=     0 est=$     0K  Tapper's Fiefdom
  miss 2026 $ 4.99 rev=     0 est=$     0K  Pages of Zite
  miss 2026 $14.99 rev=     0 est=$     0K  Dynasty!
n=66

short descriptions of the hits (what these games actually are):
  $  3371K Stacklands
      Stacklands is a village builder where you stack cards to collect food, build structures, and fight creatures. 🃏 For example, dragging a Villager card 
  $  1296K Emberward
      Build twisting mazes with tetromino blocks and deploy unique towers in this strategic roguelite tower defense adventure!
  $   830K Deck of Haunts
      Deck of Haunts invites you to become a malevolent Haunted House. Lure humans into your cursed halls, manipulate their fears, and drain their essence t
  $   366K Cardboard Town
      Cardboard Town is a city-building card game. Manage your resources, obtain upgrades, draw your cards and thrive! Random events, quest cards, and game 
  $   357K There Are No Orcs
      An RTS-Roguelike hybrid with auto-battling troops. Master building placement, swarm your enemies, and smash their defenses with unit-commander combos!
  $   338K SkyBrew: Entropic Strategist
      Experience entrepreneurship in a floating cloud cafe. Manage your flying cafe, customize construction, train staff, handle random events, and uncover 
  $   270K WARCANA
      WARCANA is a fantasy inspired base defence, RTS game with a deck-building mechanic. Face hundreds of thousands of unrelenting monsters in a battle roy
  $   253K Railroads & Catacombs
      In this exciting Card Building Roguelike game you create your cards from scratch adding different upgrades. Build your train, explore the Labyrinth an
  $   212K Roots of Yggdrasil
... [81 more lines]

  miss 2025 $ 5.99 rev=    12 est=$     3K  Deck Vs Doom
  miss 2025 $ 5.99 rev=    12 est=$     3K  Queen's Cards
  miss 2025 $ 6.99 rev=     0 est=$     0K  Cards and Towers
  miss 2025 $ 9.99 rev=     0 est=$     0K  Stop The Slimes
  miss 2025 $ 9.99 rev=     0 est=$     0K  Deck Defense
  miss 2025 $ 0.99 rev=     0 est=$     0K  sushininjarobot TD
  miss 2025 $ 6.99 rev=     0 est=$     0K  Emoji Shooter
  miss 2025 $ 4.99 rev=     0 est=$     0K  Qubicks
  miss 2025 $ 5.99 rev=     0 est=$     0K  Chicken Defense War - Tower Defense
  miss 2025 $ 5.99 rev=     0 est=$     0K  Vespia: Shield of Aberration
  miss 2025 $ 1.99 rev=     0 est=$     0K  Total Defense: Three Kingdoms
  miss 2025 $11.99 rev=     0 est=$     0K  Elemental Defender
  miss 2025 $ 4.99 rev=     0 est=$     0K  Miryam: The Polluted Land
  miss 2025 $ 0.99 rev=     0 est=$     0K  Defend The Cubes
  miss 2025 $ 8.99 rev=     0 est=$     0K  All-Front Assault
  miss 2025 $ 2.99 rev=     0 est=$     0K  天降神兵(Soldiers from the sky)
  miss 2025 $ 6.00 rev=     0 est=$     0K  Serendia - Random Gear Defense
  miss 2025 $ 2.99 rev=     0 est=$     0K  Enchanted Meadows
  miss 2025 $14.99 rev=     0 est=$     0K  Unsung Heroes: Card Wars
  miss 2025 $ 8.99 rev=     0 est=$     0K  Striking Wine
  miss 2025 $12.99 rev=     0 est=$     0K  Deckaroo
  miss 2025 $ 4.99 rev=     0 est=$     0K  Critterking
  miss 2025 $ 2.99 rev=     0 est=$     0K  War Land TD
  miss 2025 $ 7.99 rev=     0 est=$     0K  Graph TD: Cosmic
  HIT  2026 $12.99 rev=   865 est=$   403K  Cubic Cosmos
  HIT  2026 $ 7.99 rev=   552 est=$   158K  Poly TD
  HIT  2026 $ 6.99 rev=   441 est=$   111K  Wireworks
  miss 2026 $11.99 rev=    89 est=$    38K  Mini Tank Mayhem
  miss 2026 $ 7.99 rev=    65 est=$    19K  Meta Card Backpack Legend
  miss 2026 $ 9.99 rev=    35 est=$    13K  Age of Enchantment
... [31 more lines]

  $   664K Heretic's Fork

      Dear candidate, we are pleased to invite you to take up the position of manager of Hell. You will punish sinners by using our deck-building computer s

  $   598K Knightica

      Recruit powerful units, place them strategically and find the best synergies in this Roguelike Autobattler! Knightica lets you guide a warband through

  $   565K Tower Tactics: Liberation

      We fused deck-building roguelikes and tower defenses in a way never seen before - deploy your towers, collect valuable trinkets and cast powerful spel

  $   453K ORX

      A new take on tower-defence and deckbuilding roguelike genres. ORX is like playing Carcassonne with your table on fire. Build your perfect deck, use p

  $   403K Cubic Cosmos

      Cubic Cosmos is a thrilling fast-paced roguelike card game. You have been trapped in a deadly game. One wrong move could see you reduced to a playthin

  $   357K There Are No Orcs

      An RTS-Roguelike hybrid with auto-battling troops. Master building placement, swarm your enemies, and smash their defenses with unit-commander combos!



====================================================================================================

8. PRICE bands inside card-family lanes 2023-26 (Balatro-wave gate recheck)

card-family games (any of ['Card Game', 'Card Battler', 'Deckbuilding', 'Roguelike Deckbuilder', 'Auto Battler']): n=2693

  <=$7    n=1470 h50=  3.1% med=$    5K

  $8-12   n=659  h50= 15.2% med=$   19K

  $13-20  n=473  h50= 43.3% med=$   72K

  >$20    n=91   h50= 63.7% med=$  424K

The full census is in. Writing the report, then the synthesis for both decisions.

File created successfully at: E:\a327ex\steam-market\reports\19-lane-deep.md (file state is current in your context — no need to Read it back)

The file E:\a327ex\steam-market\reports\notes.md has been updated successfully. (file state is current in your context — no need to Read it back)

Claim #3 complete. Here's the full picture for both lanes, tied to your two decisions.

Lane deep-dive: Retro+Idler and Card×Base-Building

A. The idler lane and the BYTEPATH incremental

The lane's quiet phase is over. Paid Idler-tagged releases: 91 in 23H1 → 245 in 25H1 → 361 in 25H2 → 604 in 26H1. Conversion held at 11–17% through 2025 (26H1 reads 7%, but that cohort is heavily right-censored). Every idler-adjacent cell is flooding this year: Idler+Strategy has 157 entries in 26H1, +Automation 75, +Pixel Graphics 91. This is now where the gold rush is, not a quiet tag with a gap in it. Your plan's Jul–Sep timing matters more than it did when the reports were written — before 26H2 deepens the pile.

The "idlers are price-gate-immune" claim needs revising. By price band, 2023–26: <$3 = 2.1%, $3–5 = 7.7%, $6–8 = 23.9%, $8–12 = 36.0%, >$12 = 28.8%. There is a price curve — it just peaks at $8–12 and saturates, instead of climbing forever like everywhere else. The famous $2.99 winners are real (Nodebuster $1.1M, Tower Wizard $743K, Digseum $676K), but they're tail events from the 7.7% band. The rate-maximizing move, consistent with claim #1's price-transition finding (cheap-prior devs moving to $8–12 re-hit at 52% vs 15% staying ≤$7): price the BYTEPATH incremental at $7.99–9.99, not $5–8 — that's my one concrete revision to the settled plan.

Report 10's "BYTEPATH's exact cell still near-empty" is stale. The Retro+Idler pair census: 14 games 2023–25 with 5 hits (Stone Story RPG $956K at $29.99, Tiny Aquarium $625K, FACEMINER $397K, Lyca $208K, Journey to Incrementalia $157K) — then 7 new entries in 26H1 alone, all missing so far (five with literally zero reviews). The cell converts for real games but the flood has found the address.

The strongest signal in the whole lane is the "incremental" microformat, and it's still converting. Games whose store description says "incremental": 18 (2023) → 47 (2024) → 110 (2025) → 121 in the first half of 2026 alone — and the 2026 cohort is already hitting at 33.9% despite censoring, against 7% for the idler tag broadly. The 2025–26 winners are exactly your production model: Scritchy Scratchy $1.6M ($6.99, incremental×gambling×roguelite — the luck-machine crossover), Berry Bury Berry $1.6M, Microcivilization $914K, Orb of Creation $819K, Shelldiver $750K, Fortune Mill $448K, and Keep on Mining! already shipped a sequel that's at $406K. The lane splits into two winning families — ambient/desktop-cozy (Spirit City $3.8M, Cast n Chill $2.6M, Rusty's) and systems-incremental (Gnorp, Nodebuster, Farmer Was Replaced) — and BYTEPATH-incremental belongs to the second, which is the one your build-depth skills serve. The self-description matters: what converts is games legibly committing to the incremental format, not games that happen to carry the Idler tag.

Updated EV for the BYTEPATH incremental (supersedes the report-11 row): microformat base ~33% × your dev-class lift (with the franchise name carrying at ~73% per the migration re-check, and sequel-of-non-hit showing no penalty) → roughly 40–60% at $7.99–9.99, median $50–150K, tail $1.5M+. Slightly better than the old 35–55%/$30–80K row, with the price moved up a notch and the deadline moved earlier.

B. Card × structure and the Artifact port

The strict cell is still open; the neighborhood tells the honest story. Card Game + Base Building as a top-7 pair: n=8 in 2023–25, 75% hit rate, median price $15.99, and still zero 2026 entries. But the wider census (any card tag × any builder tag, 66 games since 2022) gives the effort-corrected rate: 2024 = 8 hits of 23 (35%), 2025 = 9 of 23 (39%), 2026 = 0 of 10 so far. So: ~35–40% market-wide for the neighborhood, 75% for games that committed the hybrid identity to their top tags — and your dev-class lift stacks on top (your cell at $13–20 ran 57–60% in the claim-1 re-check).

What the winners actually are (from their own store copy): cards as the economy/build verb attached to a spatial defend-or-expand board, at $13–20. Stacklands $3.4M (stack cards → village). Emberward $1.3M (tetromino mazes + towers, roguelite). Deck of Haunts $830K — you are the malevolent haunted house: build your halls, lure the attackers in, drain them. Kingdom's Deck ("build by day, defend by night"). Roots of Yggdrasil (deck + roguelike city-builder). WARCANA (deck + base-defense vs hordes). There Are No Orcs $357K (building placement + autobattler).

Where "You Are the Siege" as locked actually sits: it's an attack game — so its natural tags are Roguelike Deckbuilder × Strategy/Turn-Based-Tactics/TD, and those cells are alive but contested: RL-Deckbuilder+TD 36% (n=14), +Strategy 39% (n=168!), +Turn-Based Tactics 41%; the broader Card×TD space has 126 entries since 2022 and three 2026 hits already (Cubic Cosmos $403K, Poly TD, Wireworks). Meanwhile the card price gate is the steepest measured anywhere in this project: ≤$7 = 3.1%, $8–12 = 15.2%, $13–20 = 43.3%, >$20 = 63.7% — a card game must price $15–20, full stop (and localize CN day one; card lanes run 25–30% Chinese review share).

On your instinct that "card game base builder using the Artifact ruleset probably makes some sense": the data agrees, and more specifically than you guessed — Deck of Haunts is essentially a reverse Siege (you are the structure being assaulted), and it's the biggest 2025 winner in the cell. Your locked design already contains half a base: the Guardian is a tower-shaped entity with build vectors (abilities, equipment, per-class economies), and the contact row is a defended perimeter. The distance between "Siege as locked" and "the card-base-builder cell" is a design question, not a market one, so I'll frame it as options rather than decide:

• (a) Ship Siege as locked — attack roguelite. Lands in RL-Deckbuilder×Strategy at ~39–41% market-wide before your lift; crowded, but Artifact combat depth + the visible director queue is real differentiation. No design delta.
• (b) Deepen the Guardian into a buildable base — structures/rooms as persistent cards, territory-mana made spatial — and tag/present it as a card-base-builder. Targets the 75%-strict/35–40%-honest cell that 2026 has left empty, at the $15–20 price the lane demands. Design delta is real but bounded (the "open dials" section of the siege doc — guardian economies, contact-row width — is where it would live).
• (c) Invert it outright (you defend; enemies assault your built structure) — the Deck of Haunts/Kingdom's Deck formula, closest published proof, but it supersedes the locked "you attack" premise rather than extending it.

My read: (b) is the value move if the design survives it — it keeps the locked combat verbatim, adds the cell's identity, and prices where both the lane and your own class-numbers peak — but that's your call, and the locked design was locked for reasons I haven't re-litigated.

Archived: 19-lane-deep.md, raw census in raw_lane_deep.txt, notes.md updated. Claim #2 (launch timing / Next Fest / sales) is the last one on the list whenever you want it.

Given that the card game must be priced highly and have real art then, wouldn't it make sense to make it an Anime one? That's something I like, it's easy to generate good looking Anime images with AI for the cards, and the actual ruleset is already coded.

Good instinct to test — and it happens to intersect three things the data can actually speak to: the Anime×card cells (report 10 flagged "anime × western genre" as a whitespace macro-axis but we never drilled it), the DRAWN-style numbers from the art analysis (which were surprisingly weak — 24% pooled in your cell — and need unpacking before betting on illustrated cards), and the AI-art reception question, which I can measure from the review-text corpus (it covers exactly the build/card universe). Let me run all three.

3. **Anime × western genre** — Metroidvania+Anime 69%, Music+Anime 75%, Multiplayer+Anime 62%. Art-gated (anime art).

Anime tag 2023-25: n=1533 h50=26.2% (adult share 13%)

Anime NON-adult:   n=1338 h50=24.3%

Anime non-adult by price: <=7: 5% (609)  8-12: 18% (261)  13-20: 37% (257)  >20: 73% (211)  



ANIME x cell (2023-25, non-adult | adult), n26 total:

  Anime+Card Game              nsfw n=29  h50=  14% med=$   13K mp=$ 5.99 | adult n=4   h50=  25% | n26=5

  Anime+Card Battler           nsfw n=17  h50=  24% med=$   43K mp=$ 5.99 | adult n=0   h50=   0% | n26=7

  Anime+Deckbuilding           nsfw n=16  h50=  19% med=$   13K mp=$ 7.99 | adult n=2   h50=   0% | n26=6

  Anime+Roguelike Deckbuilder  nsfw n=10  h50=  40% med=$   98K mp=$14.49 | adult n=0   h50=   0% | n26=3

  Anime+Auto Battler           nsfw n=5   h50=  20% med=$    8K mp=$ 3.99 | adult n=0   h50=   0% | n26=2

  Anime+Tower Defense          nsfw n=10  h50=  10% med=$    7K mp=$ 6.99 | adult n=2   h50=  50% | n26=3

  Anime+City Builder           nsfw n=6   h50=  67% med=$  279K mp=$34.99 | adult n=0   h50=   0% | n26=1

  Anime+Strategy               nsfw n=77  h50=  13% med=$   17K mp=$ 4.99 | adult n=7   h50=  14% | n26=23

  Anime+Turn-Based Strategy    nsfw n=29  h50=  24% med=$   41K mp=$ 8.99 | adult n=2   h50=  50% | n26=6

  Anime+Turn-Based Tactics     nsfw n=35  h50=  17% med=$   29K mp=$ 8.99 | adult n=0   h50=   0% | n26=7

  Anime+Roguelite              nsfw n=39  h50=  33% med=$   48K mp=$ 5.99 | adult n=1   h50=   0% | n26=9

  Anime+Action Roguelike       nsfw n=39  h50=  21% med=$   12K mp=$ 4.99 | adult n=3   h50=  67% | n26=9

  Anime+RPG                    nsfw n=271 h50=  24% med=$   33K mp=$ 9.99 | adult n=98  h50=  38% | n26=67

  Anime+Idler                  nsfw n=29  h50=  21% med=$    7K mp=$ 4.99 | adult n=0   h50=   0% | n26=15



top non-adult Anime x card-family winners 2023-26:

  HIT  2024 $24.99 est=$  2325K rev=  2592 Chrono Ark                           [Roguelike Deckbuilder, Anime, RPG, Strategy, Indie]

  HIT  2025 $17.99 est=$  1566K rev=  2425 Rebellion GODSOUL: Awakening         [Early Access, RPG, Card Battler, JRPG, Anime]

  HIT  2024 $24.99 est=$   752K rev=   838 Card-en-Ciel                         [RPG, Deckbuilding, Story Rich, Roguelite, Card Game]

  HIT  2024 $29.99 est=$   727K rev=   675 Yohane the Parhelion - NUMAZU in the [Roguelike Deckbuilder, Card Game, Dungeon Crawler, Relaxing, Casual]

  HIT  2024 $17.99 est=$   364K rev=   563 Dream Tactics                        [JRPG, Tactical, Strategy, Turn-Based Tactics, Card Battler]

  HIT  2026 $ 8.99 est=$   324K rev=  1005 Witch's Apocalyptic Journey          [Roguelike Deckbuilder, Card Game, Level Editor, Anime, Pixel Graphics]

  HIT  2025 $13.99 est=$   266K rev=   530 Toy Smash Kaboom!                    [Auto Battler, PvP, Multiplayer, Asynchronous Multiplayer, Roguelike Deckbuilder]

  HIT  2025 $15.99 est=$   216K rev=   376 All in Abyss: Judge the Fake         [Card Battler, Gambling, Visual Novel, Anime, Adventure]

  HIT  2026 $14.99 est=$   145K rev=   269 Kumitantei: Old-School Slaughter     [Comedy, Anime, Visual Novel, Detective, Card Battler]

... [6 more lines]

Anime cells mapped. Now the AI-art reception measurement from the review corpus (build/card games, 2023–25 — exactly the relevant audience).

[REDACTED: private project content]

Both checks are in. Let me archive the addendum to report 19, then give you the full answer.

The file E:\a327ex\steam-market\reports\19-lane-deep.md has been updated successfully. (file state is current in your context — no need to Read it back)

The file E:\a327ex\steam-market\reports\notes.md has been updated successfully. (file state is current in your context — no need to Read it back)

Yes on the positioning and the price logic — the data backs the anime instinct more than I expected — with one honest catch that sits exactly where your production plan is.

The anime numbers

The Anime tag is quietly one of the better-performing big tags: 26.2% hit rate 2023–25 (n=1,533, ~2× market baseline), 24.3% after excluding adult games (only 13% of the tag is adult, less than I'd have guessed). And it obeys the same price law as everything else: non-adult anime runs 5% at ≤$7, 18% at $8–12, 37% at $13–20, 73% at >$20. Anime at $13–20 outperforms the market-wide $13–20 rate. Your syllogism — card game must price high, high price wants real art, anime is real art you can produce — is directionally sound in the data.

The cell-level view sharpens it: cheap anime card games are mediocre (Anime+Card Game 14%, median price $5.99 — priced wrong, died accordingly), but Anime + Roguelike Deckbuilder — the correctly-priced cell, median $14.49 — converts at 40% (n=10, median $98K), with Anime+Roguelite at 33% (n=39). 2026 inflow into these cells is moderate (3–9 entries each), not a flood. Anime+Base Building is essentially unoccupied — an anime card-base-builder would be a nearly empty cell at the intersection of two lanes that individually convert.

The comps are the strongest part of the case. Chrono Ark: $2.3M at $24.99 — anime roguelike-deckbuilder-RPG, the anchor for exactly what you're describing. Rebellion GODSOUL: $1.6M at $17.99 — anime card-battler JRPG, and here's the detail that ties the whole session together: that dev appeared in the claim-1 gap-decay winners list — their prior game was a $134K visual novel, and their next release 5 years later was this. A self-pub dev with a modest prior hit moving into anime-card at $18 and 10×-ing. Then Card-en-Ciel $752K, Dream Tactics $364K, Witch's Apocalyptic Journey $324K at $8.99 already in 2026. Also fits the CN alignment: card lanes run 25–30% Chinese review share, and several of these winners are CN/JP-made — localize CN day one.

One reconciliation with the art report, since I told you DRAWN was the weakest style (24%): that number pooled cheap VN-adjacent games. At $13–20, DRAWN runs 48%, and "went-fancy at $13–20" was the single best pair-move in the whole art analysis (79%). Real illustrated art at $15–20 is precisely the data-supported combination.

The catch: I measured AI-art reception, and it's not neutral

From the 52,043-review corpus over 3,767 build/card games: 178 reviews across 133 games mention AI art/assets. Two facts pull in opposite directions:

• It's rare — 0.34% of all reviews. Not a mass phenomenon.
• When it appears, it's damning — only 19% of AI-mentioning reviews are positive, and per-game they run far below the game's own baseline (Road to Empress: 25% positive among AI-mentions vs 88% overall). "AI art" in a Steam review is almost always inside a thumbs-down.

It is not a death sentence — Road to Empress hit $482K, Demonic Mahjong $190K, and AAA games absorb AI accusations routinely. But notice where the risk concentrates: it's worst when art is the product being priced — and a $15–20 anime card game is the maximal case of that. Anime is also the single most-recognizable, most-policed AI output style, and this audience (deckbuilder players, anime fans) is the most literate at detecting it. Plus the mandatory Steam AI-content disclosure, which curator "AI slop" lists scrape — that channel is invisible in my data but real.

So the honest framing isn't "AI anime art: yes/no," it's a bar: the winners in these cells are depth-heavy games with set-consistent, craft-forward art — Chrono Ark's card set reads as one artist's hand across hundreds of images. Raw generation gets you 80% of one image easily; a coherent 100+ card set with consistent characters, costumes, framing, and rarity treatments is curation and inpainting work, and it's a different pipeline than your ai-assets project (which was deliberately tuned for visible 2022-jank — the opposite goal; you'd want an SDXL/Illustrious-class anime model and a consistency workflow). If the output lands at "obviously AI anime," you've combined the highest-scrutiny audience with the most-detectable style at the price point where art justifies the money — the one configuration the review data specifically warns about. If it lands at "reads as commissioned," the cell numbers say it's one of the strongest positionings available to you.

Where this leaves the Artifact game, as I see it: the ruleset being done is the real asset, the $15–20 pricing is confirmed three different ways now, anime is a legitimate upgrade to the positioning rather than a vanity choice — and the load-bearing question shifts from market to production: can your generation pipeline hit set-consistency at Chrono Ark's bar, and are you comfortable shipping under the AI disclosure? A cheap way to answer that before committing: generate a 20-card test set in a locked style and judge consistency cold a week later. That test costs a weekend; the alternative costs the game's review section.

Archived as an addendum in 19-lane-deep.md, notes.md updated.

Great, move on to the next analysis.

The last one — claim #2, launch timing: month-by-month breakdown, sales, Next Fest, and whether to release near/during/away from them. The project already has a nextfest.py and a demo_hist.sqlite crawl from session 1 that I should understand first. And for sale windows I'll detect them from the data itself (catalog-wide weekly review surges) rather than trusting my memory of Steam's calendar — keeps it primary-source, per the project constraint.

1    """Next Fest proxy analysis. Detect fest weeks bottom-up (weeks where many demos peak
2   simultaneously), flag NF-spiked demos, compare outcome arms for 2025 paid releases."""
3   import sqlite3, statistics
4   from collections import Counter, defaultdict
5   from datetime import datetime, timezone
6   
7   from common import ROOT, load_catalog
8   
9   WEEK = 7*86400
10  
11  def main():
12      con = sqlite3.connect(f'{ROOT}/data/demo_hist.sqlite')
13      types = dict(con.execute('SELECT rollup_type, COUNT(*) FROM done GROUP BY rollup_type'))
14      print('rollup types:', types)
15  
16      demos = defaultdict(list)
17      for demo, ts, up, down in con.execute('SELECT demo_appid, ts, up, down FROM rollup'):
18          demos[demo].append((ts, up + down))
19      game_of = dict(con.execute('SELECT demo_appid, game_appid FROM done'))
20      totals = dict(con.execute('SELECT demo_appid, total FROM done'))
21  
22      # bucket by week regardless of rollup granularity (monthly rollups can't localize a
23      # fest week, so only use weekly-type demos for detection)
24      peak_votes = Counter()
25      for demo, pts in demos.items():
26          tot = sum(n for _, n in pts)
27          if tot < 5:
28              continue
29          wk = defaultdict(int)
30          for ts, n in pts:
... [63 more lines]

('rollup',)

('done',)

rollup ['demo_appid', 'ts', 'up', 'down'] (10356,)

done ['demo_appid', 'game_appid', 'rollup_type', 'n_rollups', 'total', 'fetched_at'] (1486,)

rollup types: {'month': 1, 'week': 1485}



top synchronized peak weeks (candidate fest weeks):

  week of 2024-10-10: 11 demos peak here

  week of 2025-02-06: 5 demos peak here

  week of 2025-02-13: 6 demos peak here

  week of 2025-02-20: 42 demos peak here

  week of 2025-02-27: 6 demos peak here

  week of 2025-05-15: 5 demos peak here

  week of 2025-06-05: 25 demos peak here

  week of 2025-10-09: 16 demos peak here



=== 2025 paid releases: demo/NF status by outcome arm ===

arm           no-demo     demo-quiet     demo-no-NF  demo-NF-spike       n

fail            77.2%          21.9%           0.6%           0.4%    3414

mid             68.9%          28.5%           1.1%           1.5%    3824

hit             66.8%          17.0%           5.9%          10.2%    2179



=== among 2025 HITS: median est $ by demo/NF status ===

  no-demo         n=1456  med=$218K

  demo-quiet      n=371   med=$192K

  demo-no-NF      n=129   med=$249K

  demo-NF-spike   n=223   med=$246K

{'month': 26601, 'week': 8053}

File created successfully at: E:\a327ex\steam-market\scripts\timing_deep.py (file state is current in your context — no need to Read it back)

====================================================================================================

1. H50 BY RELEASE MONTH (2022-24 pooled = all months fully matured; plus 2025; plus his class)

month | 2022-24 all        | 2025 (younger months censored) | selfpub $8-20 (22-24)

  1   |  13.8% [12.4-15.3] n=2149 |  14.6% n=1100  |  19.4% [16-24] n=407

  2   |  13.9% [12.5-15.3] n=2403 |  11.5% n=1015  |  18.7% [15-23] n=418

  3   |  15.8% [14.5-17.2] n=2633 |  11.7% n=1412  |  21.1% [18-25] n=532

  4   |  16.4% [15.0-18.0] n=2340 |  15.6% n=1377  |  22.1% [19-26] n=476

  5   |  15.7% [14.4-17.2] n=2615 |  15.6% n=1284  |  20.4% [17-24] n=499

  6   |  15.5% [14.1-17.0] n=2340 |  11.7% n=1268  |  17.3% [14-21] n=415

  7   |  14.6% [13.3-16.1] n=2542 |  15.3% n=1413  |  18.3% [15-22] n=486

  8   |  15.4% [14.1-16.9] n=2663 |  13.7% n=1355  |  21.9% [19-26] n=502

  9   |  18.4% [17.0-19.9] n=2649 |  16.8% n=1262  |  24.9% [21-29] n=497

 10   |  16.6% [15.3-17.9] n=3163 |  11.7% n=1569  |  18.1% [15-21] n=664

 11   |  14.3% [13.1-15.6] n=3028 |  14.4% n=1756  |  16.6% [14-20] n=583

 12   |  12.4% [11.2-13.7] n=2799 |   9.0% n=1372  |  19.7% [17-23] n=512



median est $ (revd) + median price by month, 2022-24 (composition check):

   1: med=$  11K  medprice=$ 4.99  selfpub= 63%  n=2149

   2: med=$  11K  medprice=$ 4.99  selfpub= 60%  n=2403

   3: med=$  15K  medprice=$ 5.99  selfpub= 59%  n=2633

   4: med=$  15K  medprice=$ 5.99  selfpub= 61%  n=2340

   5: med=$  12K  medprice=$ 5.99  selfpub= 59%  n=2615

   6: med=$  12K  medprice=$ 4.99  selfpub= 58%  n=2340

   7: med=$  15K  medprice=$ 5.99  selfpub= 62%  n=2542

   8: med=$  13K  medprice=$ 4.99  selfpub= 59%  n=2663

   9: med=$  16K  medprice=$ 5.99  selfpub= 60%  n=2649

  10: med=$  14K  medprice=$ 5.99  selfpub= 59%  n=3163

  11: med=$  15K  medprice=$ 5.00  selfpub= 59%  n=3028

  12: med=$  11K  medprice=$ 4.99  selfpub= 60%  n=2799



... [45 more lines]

7. RELEASE TIMING vs NEXT FEST (fests 2024-10+ detected from demo peaks; earlier = calendar-encoded, ±1w)
  fest week          all:  14.7% [13.2-16.5] (n=1811 ) med=$  11K | selfpub $8-20:  16.0% (n=332)
  1-3w after fest    all:  14.6% [13.5-15.7] (n=4152 ) med=$  11K | selfpub $8-20:  17.4% (n=798)
  3-6w after fest    all:  15.8% [14.9-16.7] (n=5710 ) med=$  16K | selfpub $8-20:  19.1% (n=1119)
  6-12w after fest   all:  14.4% [13.7-15.1] (n=10747) med=$  13K | selfpub $8-20:  19.8% (n=2087)
  0-2w before fest   all:  16.8% [15.6-18.1] (n=3435 ) med=$  13K | selfpub $8-20:  22.8% (n=619)
  other              all:  16.2% [15.2-17.2] (n=5469 ) med=$  13K | selfpub $8-20:  22.1% (n=1036)

====================================================================================================

8. WEEK-OF-YEAR H50 CURVE (2022-24, +-1 week smoothing)
  w 1   9.3% ##################
  w 2  13.6% ###########################
  w 3  15.9% ###############################
  w 4  14.9% #############################
  w 5  13.6% ###########################
  w 6  13.1% ##########################
  w 7  13.9% ###########################
  w 8  13.5% ###########################
  w 9  15.5% ##############################
  w10  15.4% ##############################
  w11  16.7% #################################
  w12  15.3% ##############################
  w13  14.8% #############################
  w14  15.2% ##############################
  w15  15.9% ###############################
  w16  17.6% ###################################
  w17  16.9% #################################
  w18  15.5% ###############################
  w19  14.6% #############################
  w20  16.2% ################################

File created successfully at: E:\a327ex\steam-market\scripts\timing_sales2.py (file state is current in your context — no need to Read it back)

aggregating tail reviews (>=180d after first review) by calendar week...

172 calendar weeks with tail-review data; median weekly tail vol = 30,542



top 24 tail-review surge weeks (candidate sale weeks):

  2022-11-28  x852851.33  vol=1,279,277

  2022-12-26  x917250.67  vol=1,375,876

  2023-01-30  x654749.33  vol=982,124

  2023-02-27  x1174363.00  vol=1,174,363

  2023-03-27  x1025743.00  vol=1,025,743

  2023-05-01  x1084962.00  vol=1,084,962

  2023-05-29  x683901.33  vol=1,025,852

  2023-06-26  x707975.50  vol=1,415,951

  2023-07-31  x658716.00  vol=1,317,432

  2023-08-28  x785102.67  vol=1,177,654

  2023-09-25  x766324.00  vol=1,149,486

  2023-10-30  x1790343.00  vol=1,790,343

  2023-11-27  x1345102.00  vol=1,345,102

  2024-01-01  x1340336.00  vol=1,340,336

  2024-01-29  x1190088.00  vol=1,190,088

  2024-02-26  x1305721.00  vol=1,305,721

  2024-04-01  x1107874.00  vol=1,107,874

  2024-04-29  x564385.50  vol=1,128,771

  2024-05-27  x310280.50  vol=1,241,122

  2024-07-01  x209217.20  vol=1,569,129

  2024-07-29  x107716.09  vol=1,238,735

  2024-08-26  x98237.55  vol=1,080,613

  2024-09-30  x78977.33  vol=1,066,194

  2024-10-28  x63534.04  vol=1,588,351



merged sale windows: 2022-11-28..2022-12-05, 2022-12-26..2023-01-02, 2023-01-30..2023-02-06, 2023-02-27..2023-03-06, 2023-03-27..2023-04-03, 2023-05-01..2023-05-08, 2023-05-29..2023-06-05, 2023-06-26..2023-07-03, 2023-07-31..2023-08-07, 2023-08-28..2023-09-04, 2023-09-25..2023-10-02, 2023-10-30..2023-11-06, 2023-11-27..2023-12-04, 2024-01-01..2024-01-08, 2024-01-29..2024-02-05, 2024-02-26..2024-03-04, 2024-04-01..2024-04-08, 2024-04-29..2024-05-06, 2024-05-27..2024-06-03, 2024-07-01..2024-07-08, 2024-07-29..2024-08-05, 2024-08-26..2024-09-02, 2024-09-30..2024-10-07, 2024-10-28..2024-11-04

... [10 more lines]

135 weeks, median vol 6,887

top surge weeks:

  2023-06-19  x2.00  vol=1

  2023-09-11  x2.00  vol=2

  2023-09-25  x4.00  vol=2

  2023-10-02  x2.00  vol=1

  2023-10-16  x2.00  vol=1

  2024-01-15  x4.00  vol=4

  2024-01-29  x2.00  vol=2

  2024-02-26  x3.00  vol=3

  2024-03-11  x3.00  vol=3

  2024-04-01  x3.00  vol=3

  2024-04-08  x4.00  vol=4

  2024-04-29  x2.00  vol=3

  2024-05-20  x2.80  vol=7

  2024-07-15  x2.18  vol=12

  2024-08-19  x2.60  vol=26

  2024-08-26  x2.00  vol=20

  2024-11-18  x1.88  vol=61

  2024-11-25  x2.34  vol=117

  2025-11-17  x3.52  vol=153,462

  2025-11-24  x4.60  vol=224,032

H50 by distance to seasonal sale (2022-24; calendar-encoded windows; all | selfpub $8-20):

  DURING sale    all   7.7% [6.8-8.8] (n=2985 ) med=$   7K | his  12.1% [9-15] (n=489)

  1-7d BEFORE    all  16.1% [14.7-17.5] (n=2711 ) med=$  14K | his  21.5% [18-25] (n=492)

  8-21d before   all  16.1% [14.9-17.3] (n=3740 ) med=$  14K | his  17.8% [15-21] (n=732)

  1-7d AFTER     all  16.2% [14.9-17.6] (n=2825 ) med=$  15K | his  19.9% [17-23] (n=604)

  8-21d after    all  15.2% [14.1-16.4] (n=3631 ) med=$  13K | his  20.8% [18-24] (n=677)

  far (>21d)     all  16.2% [15.6-16.8] (n=15432) med=$  14K | his  21.1% [20-23] (n=2997)



DURING winter sale: 6.3% (n=950)

DURING other sales (summer/autumn/spring): 8.4% (n=2035)

  summer: 8.8% (n=913)

  autumn: 6.7% (n=598)

  spring: 10.0% (n=359)

  w20  16.2% ################################
  w21  16.9% #################################
  w22  17.1% ##################################
  w23  16.6% #################################
  w24  16.8% #################################
  w25  14.9% #############################
  w26  11.9% #######################
  w27  10.8% #####################
  w28  13.4% ##########################
  w29  16.6% #################################
  w30  16.0% ###############################
  w31  15.1% ##############################
  w32  14.4% ############################
  w33  15.6% ###############################
  w34  16.3% ################################
  w35  15.8% ###############################
  w36  18.1% ####################################
  w37  18.8% #####################################
  w38  19.9% #######################################
  w39  17.6% ###################################
  w40  18.3% ####################################
  w41  18.1% ####################################
  w42  17.8% ###################################
  w43  16.2% ################################
  w44  15.5% ##############################
  w45  16.3% ################################
  w46  14.7% #############################
  w47  13.1% ##########################
  w48  11.8% #######################
  w49  14.3% ############################
... [3 more lines]

File created successfully at: E:\a327ex\steam-market\reports\20-timing-deep.md (file state is current in your context — no need to Read it back)

The file E:\a327ex\steam-market\reports\notes.md has been updated successfully. (file state is current in your context — no need to Read it back)

Claim #2 complete — and with it, all four claims from your list. Here's the timing story.

Claim #2 re-check: launch timing — the claim was directionally right, mechanically wrong

The original line was "launch any month except December (10.5% vs 13–17%) — timing is otherwise a dead lever." What the data actually says: December was never the problem — the seasonal sale windows are. The month framing both over-blamed December and hid an equally bad zone in November.

The one real rule: never launch during a seasonal sale

Releases during any of the four seasonal sales (2022–24, calendar-encoded windows ±2 days, cross-validated against the one sale the histogram data can detect directly — the 2025 autumn sale shows as a ×4.6 catalog-wide surge in tail reviews): 7.7% hit rate vs ~16% everywhere else. Winter 6.3%, autumn 6.7%, summer 8.8%, spring 10.0%. And it survives your class cut (self-pub $8–20): 12.1% during vs ~21% otherwise — so it's not purely "asset flips launch into sales" composition; the visibility drowning is real. Mechanism is obvious once seen: during a sale, every discovery surface is flooded with discounts, and a new release can't compete on price.

Just as important — the thing you specifically asked about — launching near a sale costs nothing. 1–7 days before a sale: 16.1% (21.5% in your class). 1–7 days after: 16.2% (19.9%). 8–21 days either side: baseline. The received wisdom about needing weeks of clearance around sales, or about the pre-sale dead zone, doesn't show up at all. One week of separation is fully sufficient.

December decomposed, and November's hidden dead zone

Half-month resolution over Nov–Jan: Nov 1–15: 17.2% · Nov 16–30: 10.7% · Dec 1–9: 15.3% · Dec 10–18: 14.3% · Dec 19–31 (winter sale): 7.1% · Jan 1–9 (sale tail): 8.6% · Jan 10–31: 15.5%.

So the first three weeks of December are completely normal launch real estate — the original claim would have wrongly scared you off them — while late November (autumn sale week) is as dead as late December and was invisible at month granularity because Nov 1–15's 17.2% averaged it away. The sale-tail hangover extends the winter dead zone through ~Jan 9.

The rest of the calendar

• September is the best month of the year: 18.4% overall, 24.9% in your class, with the week-of-year curve peaking at weeks 36–42 (18–20%) — and this is not a competition effect (see below). Everything else sits flat at 14–17%. In your class, even December shows no month-level penalty (19.7%) — devs like you mostly don't launch into the sale window anyway.
• Day of week: Thursday best (20.0% all, 22.5% your class), Tuesday/Wednesday fine, Friday fine in your class (20.6%) though bad market-wide (amateur-heavy). Saturday (5.2%) and Sunday (8.8%) are the worst timing decisions visible anywhere in this analysis — mostly composition, but there's no reason to test it.
• Launch-week competition density: a dead lever. Quiet weeks (≤158 releases), mid, and busy (>232) all land at 14–16% overall and 19–20% in your class. "Avoid crowded weeks" is not supported — the only crowding that matters is sale-week crowding.

Next Fest

Two separate questions untangled. Calendar proximity to a fest — where you launch relative to fest dates — is a dead-to-negative lever: fest week 14.7% vs "other" 16.2% overall, and in your class launching at/near fests actually reads worse (16–17% vs 22.1% away from them). No wishlist-harvest launch window shows up. Participation with a demo is a different channel: demos whose review activity spikes at a detected fest week belong to 10.2% of 2025's hits vs 0.4% of fails — a big association, but it's exactly the kind of number claim-1 taught us to distrust: fests select good demos as much as they boost them, and among hits, medians barely differ ($246K NF-spiked vs $218K no-demo). Combined with report 07's "demo is the weakest feature differentiator," the honest summary: Next Fest is an optional demo-marketing channel, not a launch-timing lever, and your launch date should ignore fest calendars entirely.

What it means for the plan

For the BYTEPATH incremental's Jul–Sep target: late July and August are fine, September is the statistically best month of the year (+3–6pt in your class), launch Tuesday–Thursday, keep one week clear of the four sale windows, and ignore fests and crowded weeks. That's the entire timing playbook — everything else the discourse worries about is measurably free.

Archived: 20-timing-deep.md, raw in raw_timing_deep.txt + raw_timing_sales2.txt, notes.md updated.

That closes all four claims. The session's box score: #1 path dependence — survived every attack, stronger at dev level, but your personal number is price-dependent (~15/43/57% by band), plus the art/scope decompositions showing price is a scope badge, not a graphics badge. #4 migration — stay premium real but modest (~6–7pt), the dramatic per-tag tables dissolved, sim-origin claim dead. #3 lanes — idler flood is on but the "incremental" microformat still converts at 33%; card×base-building still open in 2026 at the strict pair; anime positioning data-backed with the AI-art reception caveat. #2 timing — one rule (never during a sale), one bonus (September), one habit (Tue–Thu). Reports 15–20 all in steam-market/reports/.

Another question, do wishlists matter at all? Obviously, games that release with lots of wishlists will generally do better, but there are plenty of games that release with no or little wishlists that also do well. I'd like to actually see what the data says on this, if you have access to it, I don't know if you have access to "how many wishlists at launch," for each game.

Direct answer on data availability first: launch wishlist counts are private — Steam exposes them only to the game's own developer in Steamworks; no public endpoint, no SteamSpy field, and third-party services (Gamalytic etc.) only estimate them from follower counts. Current follower counts could be crawled, but they're post-outcome (a hit's followers today say nothing about its launch wishlists), so they can't answer this.

But the question you actually care about — must you accumulate pre-launch demand, or can a good game start cold and climb? — is answerable from the histogram data: a game's first-30-days review count is the downstream shadow of launch demand (wishlists + day-one visibility), and we have weekly/monthly review curves for every game ≥20 reviews. So I can measure: the m1 distribution of eventual hits (how many hits started cold), P(hit | cold start) — the recoverability rate, and the anatomy of slow-burn hits. With our own calibration (~45 units/review), m1 maps to rough launch-sales tiers. Writing it.

File created successfully at: E:\a327ex\steam-market\scripts\wishlist_proxy.py (file state is current in your context — no need to Read it back)

=== his own games (weekly-or-monthly first-month reviews) ===

  SNKRX: type=month t0=2021-05-01 m1=117 m3=2324 lifetime=4458

  BYTEPATH: type=month t0=2018-02-01 m1=61 m3=89 lifetime=327



====================================================================================================

1. WHERE DID THE HITS START? m1 (first ~30d reviews) distribution of eventual >=$50K games, 2022-24

--- all paid: 4656 hits with histogram ---

  m1 p10=19 p25=45 p50=117 p75=355 p90=1205

  hits that started with m1 <  10:   4.8%   (~450 units month-1 at 45 u/rev)

  hits that started with m1 <  25:  13.4%   (~1,125 units month-1 at 45 u/rev)

  hits that started with m1 <  50:  26.9%   (~2,250 units month-1 at 45 u/rev)

  hits that started with m1 < 100:  45.9%   (~4,500 units month-1 at 45 u/rev)

--- selfpub $8-20 (his class): 1189 hits with histogram ---

  m1 p10=14 p25=33 p50=80 p75=210 p90=575

  hits that started with m1 <  10:   7.0%   (~450 units month-1 at 45 u/rev)

  hits that started with m1 <  25:  18.3%   (~1,125 units month-1 at 45 u/rev)

  hits that started with m1 <  50:  35.5%   (~2,250 units month-1 at 45 u/rev)

  hits that started with m1 < 100:  56.3%   (~4,500 units month-1 at 45 u/rev)



====================================================================================================

2. RECOVERABILITY: P(eventual >=$50K | m1 bucket), 2022-24, covered games only

   (uncovered games all have <20 lifetime reviews -> all non-hits; adding them would

    push the lowest buckets DOWN — rates below are upper bounds for cold starts)

--- all paid ---

  m1      0-4: n=303   P(hit)= 31.0% [26.1-36.4]  med hit=$  119K

  m1      5-9: n=456   P(hit)= 28.5% [24.6-32.8]  med hit=$  114K

  m1    10-24: n=1316  P(hit)= 30.3% [27.9-32.9]  med hit=$  103K

  m1    25-49: n=1497  P(hit)= 42.2% [39.7-44.7]  med hit=$  102K

  m1    50-99: n=1248  P(hit)= 70.7% [68.1-73.1]  med hit=$  136K

  m1  100-249: n=1137  P(hit)= 89.5% [87.6-91.2]  med hit=$  280K

... [74 more lines]

LAUNCH MOMENTUM AS THE MECHANISM (2023-25 selfpub window releases):

  prior-hit devs   n=946    histogram-covered=565 (60%) med m1(cov)=95  share of ALL with m1>=50: 40.1%

  first-timers     n=12430  histogram-covered=2123 (17%) med m1(cov)=45  share of ALL with m1>=50: 8.1%



P(hit | m1 bucket) within each group (covered only):

  m1      0-9: prior-hit  56.0% (n=  25)   first-time  27.7% (n= 253)   

  m1    10-24: prior-hit  35.5% (n=  62)   first-time  24.9% (n= 378)   

  m1    25-49: prior-hit  39.4% (n=  99)   first-time  30.5% (n= 486)   

  m1    50-99: prior-hit  72.8% (n= 103)   first-time  55.0% (n= 402)   

  m1  100-249: prior-hit  93.8% (n=  97)   first-time  81.8% (n= 325)   

  m1     250+: prior-hit 100.0% (n= 179)   first-time  97.8% (n= 279)

File created successfully at: E:\a327ex\steam-market\reports\21-wishlist-proxy.md (file state is current in your context — no need to Read it back)

The file E:\a327ex\steam-market\reports\notes.md has been updated successfully. (file state is current in your context — no need to Read it back)

Here's the full answer.

Do wishlists matter? What the data can and can't say

Availability first: launch wishlist counts don't exist in any public data — Steam shows them only to the game's own developer, SteamSpy never had them, and services like Gamalytic estimate them from follower counts. Current follower counts are crawlable but post-outcome (a hit's followers today tell you nothing about its launch wishlists), so no one can do this analysis directly, including us.

What we can measure is the downstream shadow: m1 — reviews in the first ~30 days from each game's first review (histogram-based, so Early Access doesn't distort it). At our in-house calibration of ~45 units per review, m1=25 ≈ ~1,100 first-month units. One caution your own game proves: m1 captures launch demand from any source — SNKRX opened at 117 reviews in its first 12 days with essentially zero wishlists, because its virality started in week two. So m1 ≠ wishlists; it's "momentum, however obtained." With that lens:

1. Launch momentum is the steepest predictor in this entire project

P(eventual ≥$50K | m1), your class (self-pub $8–20, 2022–24): m1 25–49 → 51% · 50–99 → 81% · 100–249 → 99.6% · 250+ → 100%. Nothing else we measured all session — price, art, scope, genre, prior hits — has a curve this steep. A self-pub $8–20 game that does ~4,500 units in month one essentially cannot miss $50K.

The cold-start end needs honest handling, because it's where a naive reading would mislead you: among covered games, m1 0–9 shows ~40% — but histograms only exist for games that reached ≥20 lifetime reviews, so cold starts that never grew are invisible. Adding back the 4,201 uncovered games in your class (all non-hits, all cold starts), the unconditional P(hit | cold start) is well under 1%. The correct reading of the 40%: given a cold start, if the game later shows any signs of life, it converts surprisingly often. Cold starts aren't 40%-likely to succeed; cold starts that refuse to die are.

2. But momentum is not a gate — the slow lane is real, and now quantified

Your intuition ("plenty of games release with little and do well") is correct and now has numbers: among your class's 1,189 hits, median m1 was only 80, a quarter started under 33, 18.3% started under 25 reviews (~1,100 units), and 7% under 10 (~450 units). Market-wide, 2022–24 produced 623 slow-burn hits (m1<25 → eventually ≥$50K) — about 200 per year. The marquee examples are humbling: Vampire Survivors started at m1=11. Last Epoch at m1=3. KeeperRL, ΔV: Rings of Saturn, GearCity, Peaks of Yore ($2.2M, m1=21).

Two structural facts about that lane: it's slow — median 13 months to reach even half of lifetime reviews (p75 = 21 months) — and it's genre-concentrated: slow burns come from deep-systems, EA, sim, procedural, organically-multiplayer games (VR, Flight, Sports, Old School, Procedural Generation, Party-Based RPG all have 23–29% of their hits as slow burns), while consume-once genres (Thriller, Interactive Fiction, Word Game) almost never produce them — nothing to return to, nothing to word-of-mouth about. Growth compensates partially: cold starters that survive multiply their 90-day count by 4.6× median (p90 16×) vs 2.1× for warm starters.

3. The finding that ties the whole session together

I ran the mechanism test connecting this to claim #1's compounding: prior-hit self-pub devs open warm 5× more often than first-timers (40.1% of their releases start with m1≥50, vs 8.1%; median covered m1 95 vs 45) — the follower/wishlist asset largely is manufactured launch momentum. But here's the part I didn't expect: at equal momentum, prior-hit devs still convert better everywhere — m1 50–99: 73% vs 55%; 100–249: 94% vs 82%; even cold starts: 56% vs 28%. So the compounding advantage decomposes into two stacked effects: you're five times more likely to get a warm open, and the same-sized opening converts better in your hands (better games, better tails, returning audiences, mid-flight press pickup — unresolvable mix, but real). "Wishlists" are roughly the first half of the prior-hit advantage, not all of it.

What this means for you

Wishlists matter enormously as prediction and meaningfully as mechanism, but the actionable version is about momentum, which you can source three ways: pre-launch audience (your followers + the franchise names — your next launch is structurally warm), launch virality (SNKRX's actual path), or the slow lane — which rewards exactly deep-systems long-tail genres, i.e. the idler/incremental and card lanes you're already targeting, and exactly your weekly-update model. The practical corollary: for a systems game in your lanes, a weak first week is information but not a verdict — the data says give it a year of the update cadence before reading it as dead; whereas for anything consume-once, the first month is the whole story.

Archived: 21-wishlist-proxy.md, raw in raw_wishlist_proxy.txt, notes.md updated.

Great, I think I'm out of questions. Any avenues of research we didn't cover that you're curious about or that you think would be interesting?

Yes — a few. Ranked by how curious I am, with what each would take and what decision it feeds.

1. Quality × momentum: does "just make it good" actually hold? The biggest conceptual gap in everything we've done: we used review count everywhere (it's inside rev_est) but almost never review positivity, which is the only quality measure the store shows buyers. The questions: P(hit | positivity band) within price bands; whether the slow lane from the wishlist analysis is actually the high-quality lane (do 95%-positive games get "found" at 13 months while 80% ones die?); and — the part I'm most curious about — discontinuity tests at the badge thresholds: "Very Positive" kicks in at 80%/500 reviews, "Overwhelmingly Positive" at 95%. If there's a visible jump in trajectory right at 79→80% or 94→95%, that's a causal-ish measurement of what a badge is worth, which nobody can see from inside one game. All local data, zero crawling. It would also complete the session's mechanism story: we showed prior-hit devs convert equal momentum better — positivity is the obvious candidate for why.

2. The publisher league table. The uncomfortable one. Three separate analyses this session kept pointing at the same cell and I kept walking past it because your plan is self-pub: self-pub hit → next game published = 84.5%, the single best number in the entire project. The honest version of this analysis: which publishers actually sign devs of your class (prior self-pub hit, $100K–1M, systems games), what their portfolio hit rates and medians look like in your lanes (card, roguelite, idler at $13–20), and how much of the 84.5% is selection vs distribution — partially separable by comparing the same dev's self-pub vs published releases where both exist. We have the publisher field for 82K games. I'm not advocating a publisher; I'm advocating knowing the price of not having one, since the data has now quoted that price three times.

3. DLC and updates as revival levers — the SNKRX-specific one. The reprice/update step of your settled plan rests on revival numbers from report 14 (~11–15% with a big update). What we never measured: DLC releases as revival events — report 07 showed hits have DLC at 46% vs 5% (post-hoc, useless causally), but with a small crawl (DLC appids are in appdetails; their release dates are one appdetails call each) I could event-study DLC drops on dormant games the same way report 14 event-studied updates: spike size, sustain rate, and whether paid-DLC revivals out-sustain free-update revivals. Directly changes what "SNKRX update" should mean — content patch vs $2.99 DLC vs both.

4. Early Access vs 1.0 for systems games. Feeds the BYTEPATH incremental directly: EA is common in incrementals, and the EA-graduation machinery we built for claim #1 (first-review-date vs release-date) means I can now measure EA properly — P(hit) for EA vs direct-launch within the idler/incremental and card lanes, time-in-EA vs outcome, and whether EA's function is revenue-during-development or momentum-building (does the 1.0 "launch" of an EA game behave like a warm m1?). All local data.

5. The promise gap. A novel one from the art infrastructure: we scored in-game screenshots; the capsule (the thing that drives clicks) is a different asset, and we already have the pipeline to score those too. The analysis: capsule quality minus screenshot quality = "promise gap," vs outcomes and vs review positivity. Hypothesis worth testing: overpromising (pretty capsule, janky game) shows up as a positivity penalty that kills the tail — which would make "capsule exactly as good as the game, no better" an actual measurable rule rather than folk wisdom.

6. Store-copy formulas for your two lanes, 2025–26 vintage. Report 13 mined descriptions globally and a year ago in market-time. A re-run restricted to idler/incremental and card×structure winners from the last 18 months would extract the current winning copy patterns ("short incremental," the Balatro-wave vocabulary, what Deck of Haunts-class pages emphasize) right before you write two store pages. Lowest effort, most mechanically actionable, least intellectually interesting.

Dead ends, so you know they're closed rather than forgotten: price/discount history doesn't exist retroactively anywhere legal (SteamDB scraping is against their ToS), wishlists are private forever, and playtime-at-scale is crawlable but slow (we have it only for your cell).

If you're picking one: #1 — it's the missing axis in everything else we measured, it's pure local data, and every other analysis this session would get sharper if we knew how much positivity explains. If you're picking by immediate plan value: #3, since the SNKRX revival step is the next thing on the settled plan with an unmeasured mechanism in it.

1 and 4 seem most interesting to me. I have to go out so you can do both of those at once and just show me the full results at the same time for when I come back. After you show the results, without giving me a turn, also end the session, name it Fable Test — Steam Market Research 3. I'll come back to the session ended and then read the results, and if I have further questions we can amend the log later.

Both analyses queued — I'll run them fully, deliver everything in one message, then seal the session. Writing the two scripts now.

File created successfully at: E:\a327ex\steam-market\scripts\quality_momentum.py (file state is current in your context — no need to Read it back)

File created successfully at: E:\a327ex\steam-market\scripts\ea_paths.py (file state is current in your context — no need to Read it back)

====================================================================================================

1. P(>=$50K | CURRENT positivity band), games w/ >=25 reviews, 2022-24 (all | selfpub $8-20)

  pct   <60%: all  37.0% (n=899  ) | his  31.1% (n=206)

  pct  60-69: all  39.4% (n=1159 ) | his  39.6% (n=270)

  pct  70-79: all  50.6% (n=1886 ) | his  48.8% (n=459)

  pct  80-89: all  51.4% (n=2836 ) | his  55.7% (n=680)

  pct  90-94: all  47.7% (n=1804 ) | his  51.8% (n=452)

  pct    95+: all  39.0% (n=1623 ) | his  43.8% (n=413)

  (semi-descriptive: pct and hit both accumulate over life; causal version below)



====================================================================================================

2. pct@m3 -> GROWTH AFTER m3 (lifetime/m3 multiple; games w/ m3>=30; clean temporal order)

  pct@m3   <60%: n=402   med growth 1.77x  p90 3.84x  P(hit)=69%

  pct@m3  60-69: n=524   med growth 1.86x  p90 3.88x  P(hit)=67%

  pct@m3  70-79: n=1049  med growth 2.09x  p90 4.54x  P(hit)=75%

  pct@m3  80-89: n=1688  med growth 2.28x  p90 5.38x  P(hit)=73%

  pct@m3  90-94: n=1232  med growth 2.39x  p90 5.44x  P(hit)=70%

  pct@m3    95+: n=1419  med growth 2.54x  p90 6.10x  P(hit)=64%



====================================================================================================

3. BADGE DISCONTINUITY: growth-after-m3 by fine pct@m3 bucket (badge-eligible: m3>=50)

   smooth curve through 80 = quality effect only; jump AT 80/95 = badge visibility effect

  pct@m3 60-70 : n=412  med growth 1.82x  P(hit)= 80%

  pct@m3 70-75 : n=371  med growth 2.02x  P(hit)= 82%

  pct@m3 75-80 : n=515  med growth 2.14x  P(hit)= 84%

  pct@m3 80-85 : n=604  med growth 2.24x  P(hit)= 83% <-- 80 threshold

  pct@m3 85-90 : n=760  med growth 2.31x  P(hit)= 83%

  pct@m3 90-93 : n=572  med growth 2.35x  P(hit)= 81%

  pct@m3 93-95 : n=391  med growth 2.43x  P(hit)= 82%

  pct@m3 95-98 : n=694  med growth 2.58x  P(hit)= 79% <-- 95 threshold

... [29 more lines]

appdetails EA-genre games: 4572



====================================================================================================

1. START-COHORT: started-EA vs direct-launch -> P(>=$50K by 2026-07). Unknowns counted as direct.

   (start years 2022-23 give both paths >=2.5y runway; 2024 shown censored for EA)

--- all paid ---

  start 2022-2023: EA  36.9% [34.8-39.1] (n=1945  med=$  65K) | direct  13.6% [13.1-14.1] (n=16207 med=$  10K)

  start 2024-2024: EA  31.7% [29.2-34.3] (n=1316  med=$  53K) | direct  11.6% [11.0-12.2] (n=11751 med=$  11K)

--- selfpub $8-20 (his class) ---

  start 2022-2023: EA  39.2% [35.5-43.0] (n=646   med=$  59K) | direct  15.7% [14.4-17.1] (n=2897  med=$  16K)

  start 2024-2024: EA  35.6% [31.2-40.2] (n=430   med=$  51K) | direct  15.4% [13.9-17.1] (n=1990  med=$  15K)

--- lane: idler/incremental ---

  start 2022-2023: EA  37.5% [22.9-54.7] (n=32    med=$  45K) | direct   8.1% [5.4-11.8] (n=285   med=$   4K)

  start 2024-2024: EA  31.6% [15.4-54.0] (n=19    med=$  32K) | direct   8.6% [5.8-12.6] (n=266   med=$   7K)

--- lane: card-family ---

  start 2022-2023: EA  45.1% [36.3-54.3] (n=113   med=$  93K) | direct  14.5% [11.9-17.6] (n=585   med=$  15K)

  start 2024-2024: EA  31.7% [22.6-42.4] (n=82    med=$  56K) | direct  12.6% [10.1-15.7] (n=539   med=$  13K)

--- lane: roguelite-family ---

  start 2022-2023: EA  41.1% [36.4-46.1] (n=389   med=$  64K) | direct  12.4% [10.8-14.3] (n=1374  med=$   9K)

  start 2024-2024: EA  29.5% [24.5-35.0] (n=288   med=$  31K) | direct   9.9% [8.4-11.7] (n=1291  med=$  11K)

--- lane: sim/management ---

  start 2022-2023: EA  48.6% [43.6-53.7] (n=370   med=$ 102K) | direct  17.1% [15.6-18.7] (n=2337  med=$  13K)

  start 2024-2024: EA  43.0% [37.1-49.2] (n=251   med=$  83K) | direct  15.0% [13.4-16.8] (n=1737  med=$  13K)



====================================================================================================

2. TIME-IN-EA for graduates (started 2021-24, graduated by now): outcome by EA duration

  EA 6-12mo   n=375  h50= 69.1% [64.2-73.5] med=$131K

  EA 12-24mo  n=507  h50= 80.9% [77.2-84.1] med=$262K

  EA 24mo+    n=300  h50= 84.0% [79.4-87.7] med=$278K



... [36 more lines]

Both result sets are in. Writing the two reports and updating notes, then delivering everything.

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File created successfully at: E:\a327ex\steam-market\reports\23-ea-paths.md (file state is current in your context — no need to Read it back)

The file E:\a327ex\steam-market\reports\notes.md has been updated successfully. (file state is current in your context — no need to Read it back)

Launching skill: end-session

# End Session Workflow

This skill is invoked from the **same conversation** where the work was done. You already have the full session context in memory — you do NOT need to read the transcript.

## Sealed Modes (NDA & Private sessions)

> ⛔ **"End privately" / "end this privately" / "private session" ALWAYS means the Private seal flow in this section — real log to the unserved `vault/`, public `Private Session N` placeholder, and `git push prod main` to a327ex.com. It NEVER means saving to a local `private/` folder (e.g. `E:/a327ex/private/`) or any local-only "no git / no website / no push" variant.** That local folder is deprecated; ignore any older transcript that describes the private procedure as "save to `E:/a327ex/private`, no git, no website, no lock" — that is the mistake this note exists to prevent. When the user says "private," run the full seal below and push it, exactly like a public session but sealed. Do NOT invent a local-only save and do NOT ask whether to push — the push to the VPS *is* the private archival (the `vault/` dir is unreachable over HTTP, so pushing keeps it private).

Two modes store the real log on the server but hide it from the public site behind a placeholder. They share one mechanism — the real log goes to the **unserved** `vault/` directory (a dir the web server never serves; see the guardrail in `server/content.lua`), and the public site shows only a placeholder log in `logs/`. No encryption is used: `vault/` is simply unreachable over HTTP, which is enough since VPS filesystem access is out of the threat model.

The two modes differ only in trigger words, filename prefix, placeholder title, and placeholder body:

| Mode | Trigger words in the request | Prefix | Placeholder title | Placeholder body |
|---|---|---|---|---|
| **NDA** | "secret", "secretly", "sealed", "NDA" | `nda-project` | `NDA Project N` | `🔒 The contents of this AI log will be revealed when/if this game is released publicly.` |
| **Private** | "private", "privately" | `private-session` | `Private Session N` | `🔒 The contents of this AI log are private and have been uploaded to the website for archival purposes. They may or may not be revealed in the future.` |

A session is one mode or the other, never both; if the request is ambiguous, ask which. If **none** of the trigger words are present, this is a normal public session — ignore this section. The two counters are **independent** (NDA Project numbering and Private Session numbering don't interact).

**Multiple NDA projects (grouping).** Several NDA games can be sealed at the same time. The project a log belongs to is just the **first word of its real title** (e.g. *Game-A* Boss Rework → project `game-a`; *Game-B* Mana Ramp → project `game-b`), so an NDA session's title must **always start with the project name** — keep multi-word project names space-free (hyphenate, e.g. `Game-A`). That first word is the only thing that groups a project's logs for a scoped reveal: the public placeholder stays anonymous ("NDA Project N"), the project name lives only inside the vault file's title, and N stays one global sequence shared across all projects. Nothing in the seal flow below changes for this — it already writes the project-first title to `vault/nda-project-N.md`; the grouping is read back out at unseal time.

Run the normal steps below with these overrides. Throughout, let `PREFIX` and `LABEL` be the active mode's row — e.g. Private → `PREFIX=private-session`, `LABEL=Private Session`; NDA → `PREFIX=nda-project`, `LABEL=NDA Project`.

**A. Title.** The real title is what the user named the session (e.g. the text after "name it …"); if they gave none, ask. Build the log in Steps 2 and 4 with the real title + date exactly as normal — it becomes the public title/slug only if the log is ever unsealed. **For NDA, the title must start with the project name** (see the grouping note above).

**B. Step 4 override — write two files instead of one.** Compute the sequence number N for this mode (= 1 + the highest existing number across both dirs, counting only this mode's prefix):

```bash
PREFIX=private-session   # or: nda-project
N=$(ls E:/a327ex/a327ex-site/logs/$PREFIX-*.md \
       E:/a327ex/a327ex-site/vault/$PREFIX-*.md 2>/dev/null \
     | grep -oE "$PREFIX-[0-9]+" | grep -oE '[0-9]+' | sort -n | tail -1)
N=$(( ${N:-0} + 1 )); echo "$LABEL $N"
```

Build the real log into `/tmp/session-log.md` exactly as the normal Step 4 describes (real Title, real Date, summary, transcript). Then, **instead of** `cp`-ing it to `logs/[slug].md`:

```bash
mkdir -p E:/a327ex/a327ex-site/vault
cp /tmp/session-log.md "E:/a327ex/a327ex-site/vault/$PREFIX-$N.md"   # real log → unserved vault
```

And write the public placeholder to `E:/a327ex/a327ex-site/logs/<PREFIX>-<N>.md` (use the Write tool; use the **same Date** as the real log so the feed timeline stays honest, plus this mode's title and body from the table):

```markdown
Title: <LABEL> N
Date: <same date as the real log>

# <LABEL> N

<this mode's placeholder body>
```

Step 4.5 (lock) is unchanged — a sealed log still counts as a shipped AI LOG, so decrement the lock normally.

**C. Step 5/6 override — the project (GitHub) repo. This is the one place the two modes differ from each other:**

- **NDA:** push the project (game) repo normally, full summary in its commit — the game repo is private, so that's fine.
- **Private:** **do NOT push the project repo by default.** A private session may target a *public* repo (e.g. Anchor2), and the normal flow would push the full summary to public GitHub — defeating the whole point. Only do the a327ex-site half below. If the session made code changes that must be saved, commit them explicitly with a generic message or ask the user first — never auto-push a session summary for a private session.

**D. Step 6 override — a327ex-site commit.** Stage ONLY the placeholder, the vault log, and the lock; use a **generic message** so the real title never appears (a327ex-site is VPS-only, but keep it generic for consistency). **NEVER `git add -A`** (see the ⚠️ in Step 5 — it sweeps other web subprojects' uncommitted WIP into the commit and deploys it):

```bash
cd E:/a327ex/a327ex-site
git add "logs/$PREFIX-$N.md" "vault/$PREFIX-$N.md" .lock.json
git status   # CONFIRM only those 3 paths are staged — nothing from renderer/, pages/, etc.
git commit -m "Add $LABEL $N"
git push prod main 2>&1 | tail -3
```

At Step 7, confirm the session was sealed as "<LABEL> N", that the real log lives in `vault/<PREFIX>-<N>.md`, and that `/unseal` can reveal it later.

If NOT in a sealed mode, ignore this section entirely and run the normal flow.

## Step 1: Get Session Info

Ask the user for the **session title** (max 30 characters). Examples: "Anchor Phase 10 Part 5", "Physics Arena Setup", "Timer System Fix", "Thalien Lune Design".

**Determine the project yourself from your session context** — you know which repo(s) were worked on, which files were created/modified, and where they live. No need to ask. See Step 5 for the list of known project roots; if the session touched something outside the list, infer the root from the paths you actually edited.

## Step 2: Write Summary

Write the summary from your conversation memory. You have the full session context — no need to read any files.

The summary should be **thorough and detailed**. Each major topic deserves its own section with multiple specific bullet points. Don't compress — expand.

**Purpose:** These summaries serve as searchable records. Future Claude instances will grep through past logs to find how specific topics were handled. The more detail you include, the more useful the summary becomes for finding relevant context later.

Format (this is just an example structure — adapt sections to match what actually happened):

```markdown
# [Title]

## Summary

[1-2 sentence overview of the session's main focus]

**[Topic 1 - e.g., "Spring Module Implementation"]:**
- First specific detail about what was done
- Second detail - include file names, function names
- User correction or feedback (quote if notable)
- Technical decisions and why

**[Topic 2 - e.g., "Camera Research"]:**
- What was researched
- Key findings
- How it influenced implementation

**[Topic 3 - e.g., "Errors and Fixes"]:**
- Specific error message encountered
- Root cause identified
- How it was fixed

[Continue for each major topic...]

---

[Rest of transcript follows]
```

Rules:

- **Be thorough** — If in doubt, include more detail, not less. Each topic should be as detailed as possible while still being a summary.
- **Think searchability** — Future instances will search these logs. Include keywords, function names, error messages that someone might grep for.
- **One section per major topic** — Don't combine unrelated work into one section
- **Chronological order** — Sections should match conversation flow
- **Specific details** — Error messages, file names, function names, parameter values
- **Include user quotes** — When user gave notable feedback, quote it (e.g., "k/d variables are not intuitive at all")
- **Weight planning equally** — Research, proposals, alternatives considered, user feedback on approach are as important as implementation
- **Weight problems solved** — Errors, root causes, fixes, user corrections all matter
- **Technical specifics** — Include formulas, API signatures, parameter changes when relevant

## Step 3: Proceed Without Approval

Do NOT show the summary to the user for approval. Write it directly. The user can review the committed log after the fact and request a follow-up edit if anything is off.

## Step 4: Convert Transcript and Write the Log File

```bash
# Find recent sessions (Claude + Cursor + Codex). Same script lives in Anchor2:
python E:/a327ex/Anchor2/scripts/find-recent-session.py --limit 5
# or: python E:/a327ex/Anchor/scripts/find-recent-session.py --limit 5
```

The script shows sessions sorted by when they ended. The **first result** is the current conversation (since end-session was invoked here). Use it.

Use a lowercase hyphenated slug derived from the title (e.g., "anchor-primitives-hitstop-animation").

Get the end timestamp for the Date frontmatter — this is the wall-clock time when end-session was invoked, NOT the time the JSONL started. Sessions often span multiple days, and the log should be filed under the day the work was wrapped up:

```bash
date "+%Y-%m-%d %H:%M:%S"
```

Use this output verbatim. Do not substitute the JSONL start timestamp; the log appears in the sidebar sorted by Date, and a multi-day session with a Date pinned to day 1 will sort below sessions that ended later but started later, hiding the most recent work.

Convert the transcript to markdown:

```bash
python E:/a327ex/Anchor2/scripts/jsonl-to-markdown.py [SESSION_PATH] /tmp/session-log.md
# or: python E:/a327ex/Anchor/scripts/jsonl-to-markdown.py ...
```

The same script **auto-detects** Claude Code JSONL vs Cursor/Composer agent JSONL (`~/.cursor/projects/.../agent-transcripts/...`) vs Codex rollouts (`~/.codex/sessions/...`). For Composer sessions, use `find-recent-session.py` (it merges all sources) and pick the `[cursor]` line for the current chat.

Replace the default header (`# Session YYYY-MM-DD...`) at the top of `/tmp/session-log.md` with the approved title and summary, AND prepend frontmatter. The final file shape:

```markdown
Title: [Title]
Date: YYYY-MM-DD HH:MM:SS

# [Title]

## Summary

[approved summary text from step 2]

---

[transcript content from jsonl-to-markdown script]
```

**Frontmatter is non-negotiable.** Every log file MUST start with `Title:` and `Date:` lines. Without them, the site's sidebar shows the slug as the title and 0 (epoch) as the sort date. The backfill script in `a327ex-site/deploy/backfill_metadata.py` is a safety net, not a substitute — write it correctly the first time.

Then copy the final file to the log destination:

```bash
cp /tmp/session-log.md E:/a327ex/a327ex-site/logs/[slug].md
```

**Sealed mode (NDA or Private):** do NOT write to `logs/[slug].md`. Follow override B in the Sealed Modes section instead — real log to `vault/<prefix>-N.md`, placeholder to `logs/<prefix>-N.md`.

## Step 4.5: Decrement the lock (if active)

Read `E:/a327ex/a327ex-site/.lock.json` if it exists. If it contains `{"remaining": N}` with N > 0:

- Decrement N by 1
- Write `{"remaining": N-1}` back to the file
- If N becomes 0, the lock is cleared. You may leave the file at `{"remaining": 0}` or delete it; both work.

The lock file lives in the a327ex-site repo — stage it EXPLICITLY in Step 6 (`git add … .lock.json`). Do NOT rely on `git add -A` (this skill no longer uses it — see the ⚠️ in Step 5).

If no lock file exists or `remaining` is already 0, do nothing. (See the `/lock` skill for the lock's full design.)

## Step 5: Commit Project Repo

Identify the project repo(s) worked on this session from your own context — you already know which repos were touched and which files changed. For the common projects:

| Project | Root | Stage command |
|---|---|---|
| Anchor | `E:/a327ex/Anchor` | `git add docs/ framework/ engine/ scripts/ reference/` |
| Anchor2 | `E:/a327ex/Anchor2` | `git add framework/ engine/ arena/ reference/ scripts/ docs/ .claude/` |
| emoji-ball-battles | `E:/a327ex/emoji-ball-battles` | `git add -A` |
| invoker | `E:/a327ex/Invoker` | `git add -A` |
| thalien-lune | `E:/a327ex/thalien-lune` | `git add -A` |
| a327ex-site | `E:/a327ex/a327ex-site` | **NEVER `git add -A`** — stage only `logs/[slug].md .lock.json`. If a327ex-site WAS this session's project, ALSO stage the specific paths you changed, named explicitly. See ⚠️ below. |

For a project not listed, infer the root from the files you actually created or modified this session and stage those. If multiple candidate roots look valid, ask the user which files to stage.

`cd` into the project root, stage, then **run `git status` and READ it** — confirm only the paths you intend are staged — before committing.

> ⚠️ **a327ex-site: never `git add -A`.** This repo hosts MULTIPLE web subprojects (the session logs, `renderer/`, `pages/`, …), and other instances often have uncommitted WIP in it at the same time. `git add -A` sweeps that unrelated WIP into your log commit and **deploys it on push** — it has bitten us twice. Stage the log + `.lock.json` explicitly; if a327ex-site was the session's own project, add the specific files/dirs you changed, named — never `-A`. (Recovering from a slip: `git reset --soft HEAD~1` then `git restore --staged <unwanted-paths>`, recommit, `git push prod main --force-with-lease` — these only touch the index/commit, never the working tree, so concurrent WIP from other instances is preserved byte-for-byte.)

**IMPORTANT — FULL SUMMARY IN COMMIT:** The commit message MUST include the FULL summary from the log file. Read the summary back from the log file to ensure nothing is missing.

**IMPORTANT — COMMIT METHOD:** The summary contains backticks, special characters, and markdown that WILL break heredocs and `git commit -m`. ALWAYS use the file-based method below. NEVER try a heredoc first — it will fail and produce a malformed commit that needs amending.

```bash
# Skip until we hit the line "## Summary", then take everything after the next
# blank line until the --- separator that precedes the transcript.
awk '/^## Summary$/{found=1; next} found && NR>1 && /^---$/{exit} found' \
    E:/a327ex/a327ex-site/logs/[slug].md > /tmp/commit_msg.txt

# Prepend the title (plain text, no #) and append attribution
sed -i "1i [Title]\n" /tmp/commit_msg.txt
printf "\nGenerated with [Claude Code](https://claude.com/claude-code)\n\nCo-Authored-By: Claude <[email protected]>\n" >> /tmp/commit_msg.txt

git commit -F /tmp/commit_msg.txt
```

## Step 6: Push the Repos

Two pushes — project (to GitHub) and a327ex-site (to the VPS):

```bash
# Project repo to GitHub. Skip this push if the project IS a327ex-site
# (handled by the second push below — don't duplicate).
git push origin main

# a327ex-site to the VPS (post-receive hook restarts the Lua server).
# NEVER `git add -A` here (see the ⚠️ in Step 5). Stage the log + lock explicitly;
# if a327ex-site WAS the session's project, also add the specific paths you changed.
cd E:/a327ex/a327ex-site
git add logs/[slug].md .lock.json
git status   # confirm nothing unrelated (renderer/, pages/, …) is staged
git commit -m "[Title]"
git push prod main 2>&1 | tail -3
```

**Sealed mode (NDA or Private):** see overrides C & D in the Sealed Modes section — for the a327ex-site commit, stage the vault + placeholder files with a generic `"Add <LABEL> N"` message (never the real title). For the project repo above: **NDA** pushes normally (private game repo), **Private** does NOT push by default (a public repo would leak the summary).

**Failure handling:** if either push fails, the other still happens. Local commits stay intact, so the user can re-push manually once they've fixed whatever blocked it. Don't roll back; the committed state on disk is the source of truth.

## Step 6.5: Sync the renderer (engine site)

The a327ex-site push updated the **Lua server**. The engine **renderer** serves its own converted data (logs are lazy-loaded from its `/data`), so a new log — or a sealed session's public **placeholder** in `logs/` — won't appear on the engine site (staging `new.a327ex.com` now, `a327ex.com` after cutover) until the renderer is synced. Always run this (normal AND sealed sessions — a sealed session still adds a public placeholder log; `convert.lua` converts `logs/`, never `vault/`):

```bash
bash E:/a327ex/a327ex-site/renderer/tools/deploy.sh --content 2>&1 | tail -12
```

Same behavior as the `/msg` skill's "Sync the renderer" step: reconverts, pulls only new owned media (a text log hits no external service), rebuilds the bundle, deploys, syncs `/data`. **Non-aborting** + idempotent. Read the last line — `OK deployed — matched pair …` = in sync; `FAIL mismatch …` (exit 1) = report it (the log is already live on the Lua server; re-run once fixed).

## Step 7: Confirm

Tell the user:

- Project commit hash + GitHub push result
- a327ex-site commit hash + VPS push result

Format:

```
Logged — "[Title]".
  project (Anchor2):  abc1234  pushed to GitHub
  a327ex-site:        ghi9012  pushed to a327ex.com (post-receive deployed)
  renderer:           synced (deploy.sh --content OK)
```

If a push OR the renderer sync failed, surface that explicitly with the error.

## Notes

- **Frontmatter is non-negotiable.** Every log file MUST have `Title:` and `Date:` at the top. Enforced at write time in step 4. The backfill script is a safety net, not a substitute.
- **If the project IS `a327ex-site`** (e.g., you spent the session writing the renderer or fixing the theme), there's no separate "project repo to GitHub" push — the a327ex-site push at the end IS the project push. Don't duplicate.
- **Legacy note**: this skill used to also dual-write to `anchor.blot.im/logs/` and `anchor.blot.im/context/`. Post-cutover (May 2026), `anchor.blot.im` is a JS redirect to `a327ex.com` and no longer receives content. Drop those steps if you find any older docs that still reference them.


ARGUMENTS: Fable Test — Steam Market Research 3

fatal: not a git repository (or any of the parent directories): .git
---LOCK---
{"remaining": 0}
---DATE---
2026-07-08 16:39:57

2026-07-08T19:39:57.065Z 3c9e1dc2-9b4c-4089-b61b-bcb606000fbc [claude] <-- MOST RECENT

   C:\Users\adn\.claude\projects\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc.jsonl



2026-07-08T15:41:45.647Z f1739302-1dd0-4184-b3c4-a00972a6b5ec [claude]

   C:\Users\adn\.claude\projects\E--a327ex\f1739302-1dd0-4184-b3c4-a00972a6b5ec.jsonl



2026-07-08T11:43:23.232Z b3d95bd3-df87-41bb-be74-75d6e90a587e [claude]

   C:\Users\adn\.claude\projects\E--a327ex\b3d95bd3-df87-41bb-be74-75d6e90a587e.jsonl

Written to C:/Users/adn/AppData/Local/Temp/session-transcript.md

3300 /tmp/session-transcript.md
# Session 2026-07-08 14:06



---



> Steam Market Research 1 and 2 had a few interesting/surprising claims that I didn't pursue then because we were busy doing the relevant work for those sessions, but I'd like to dig deeper into those claims here. I'll list them, and you choose which one to tackle first. Let's do a deep re-check and re-research of each claim to refine it, see if it actually holds, and also look at it from different angles, generating more data where relevant to illuminate the claims further. I'll list them now:

File created successfully at: C:\Users\adn\AppData\Local\Temp\claude\E--a327ex\3c9e1dc2-9b4c-4089-b61b-bcb606000fbc\scratchpad\log-header.md (file state is current in your context — no need to Read it back)

Title: Fable Test — Steam Market Research 3
Date: 2026-07-08 16:39:57

# Fable Test — Steam Market Research 3

## Summary

Deep re-check session of the surprising claims from Steam Market Research sessions 1–2, plus five owner follow-up questions. All analysis on the existing `steam-market/` primary-data corpus (search/steamspy/histogram/appdetails sqlite) plus two new crawls (in-game screenshots, playtime-at-review) and one new blind-scoring pipeline. Produced reports 15–23 in `steam-market/reports/` with raw outputs and scripts alongside. Every claim survived or was corrected with named mechanisms; several load-bearing numbers changed.

**Claim #1 — path dependence (report 15, scripts path_dependence.py/2/3):**

- Reproduced report-04 headline exactly (prior-hit devs re-hit 57.6% vs 6.4% no-hit veterans vs 8.7% first-timers), then attacked it seven ways: release-vs-dev weighting, publisher contamination, threshold artifact, era replication, estimator circularity (K-band, reviews-only, owners cross-definition, time-truncation at 2022-12-31), EA-graduation leakage, return-rate selection.
- Verdict: effect real and structural but the number was wrong in both directions. Dev-level (first release per dev) it's LARGER: 65.8% vs 9.7% (factories like gamesforgames — 67 releases at 1% — dragged release-weighting down; Capcom/Koei/Square pulled up). His cell (selfpub hit → selfpub release, EA-adjusted): 49.1% dev-first, med $60K; SNKRX band ($250–500K prior, est $388K) 56.6%.
.......
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\random\src\sfc64\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\random\src\splitmix64\LICENSE.md
steam-market\.venv\Lib\site-packages\numpy-2.5.1.dist-info\licenses\numpy\_core\src\npysort\x86-simd-sort\LICENSE.md
steam-market\README.md
steam-market\reports\02-tag-scan.md
steam-market\reports\05-art-gate.md
3366 session-log.md
COPIED
Shell cwd was reset to E:\a327ex

Log assembled and copied. Committing to a327ex-site (staging only the log — no project repo exists for steam-market, and the lock is already at 0).

warning: in the working copy of 'logs/fable-test-steam-market-research-3.md', CRLF will be replaced by LF the next time Git touches it
A  logs/fable-test-steam-market-research-3.md

[main 89e2c20] Fable Test — Steam Market Research 3
 1 file changed, 3366 insertions(+)
 create mode 100644 logs/fable-test-steam-market-research-3.md
remote: [post-receive] deployed at 2026-07-08T19:43:05Z        
To ssh://a327ex/srv/a327ex.git
   595a28d..89e2c20  main -> main

Note: assets/YuGoth{R,M}.ttc are now superseded by the .ttf subsets � remove them so

build-web.sh doesn't bundle the full 28 MB. theme.lua references the .ttf names.

== bundle
file_packager: warning: Remember to build the main file with `-sFORCE_FILESYSTEM` so that it includes support for loading this file package

   renderer.data 0a7b602718e8  (13255737 B)
== R2
   R2 sync ok
== deploy bundle + engine-boot.js
   hashed: renderer-0a7b602718.data  anchor-1cd222b872.wasm
== sync lazy /data (content-changed pages only)
   synced 2 changed page(s)
OK deployed — matched pair on VPS (0a7b602718e8216e9d84c43680d1deaa 516720f99c69e6dbbefed31ed46029f9 )

Logged — "Fable Test — Steam Market Research 3".

  project (steam-market):  no git repo — reports 15–23, scripts, and raw outputs live on disk in steam-market/
  a327ex-site:             89e2c20  pushed to a327ex.com (post-receive deployed at 19:43Z)
  renderer:                synced (deploy.sh --content OK — matched pair on VPS)

Session sealed. Everything from today is in the log and in steam-market/reports/15-23 + notes.md — the two final analyses (quality×momentum, EA paths) are delivered in full in the message above the seal, so you can read them straight from the log. If anything raises further questions, we can amend the log in a follow-up session.

You should have pasted the reports here in the log, well, tell them to me now.

Right — the chat deliveries covered the findings but the archival report versions never made it into the log itself. Here are all nine, verbatim, and after pasting them I'll amend the published log so this is in it.

15 — Path-dependence claim: deep re-check (session 3)

Scripts: scripts/path_dependence.py (+2, +3). Raw: raw_path_dependence*.txt. Re-examines report 04's headline (prior-hit devs re-hit at 57.6% vs 6.4% no-hit veterans vs 8.6% first-time self-pub) against: release-vs-dev weighting, publisher contamination, threshold artifacts, era stability, estimator circularity, EA leakage, return selection.

Verdict

The effect is real, structural, and survives every attack — but the 57.6% is the wrong number for him. Dev-level it's understated (66%); his self-pub cell is lower (~49–57%); and the honest personal number is price-dependent: ~15% at ≤$7, ~43% at $8–12, ~57% at $13–20 for a new-IP self-pub release. The binary "hit vs no-hit" framing is a threshold artifact — reality is a smooth ladder (Spearman ρ=0.715 between prior best and next result).

1. Reproduction + composition

57.6/6.4/8.7 reproduce exactly. But the prior-hit group = 901 devs / 1,620 releases, and release-weighting is contaminated downward by factories (gamesforgames 67 releases @1%, g-mode 25 @0%, hosted games 24 @12%) and upward by AAA (Capcom 17 @100%, Koei Tecmo 14 @100%, Square Enix 11 @100%). Half of prior-hit devs' hits were published, not self-pub.

2. Corrected weightings (first window release per dev; EA-adjusted where noted)

True dev-level gap ≈ 6.8× (not 9×); vs first-timers ~5–7× at every honest cut. Getting signed by a publisher after a self-pub hit is the best cell in the table: 84.5% (n=97, selection+distribution entangled).

3. No cliff — it's a ladder (the biggest reframe)

P(≥$50K next | prior best band), dev-first, all devs: <$1K 2% · $1–5K 7% · $5–10K 12% · $10–25K 18% · $25–50K 29% · $50–100K 42% · $100–250K 55% · $250–500K 68% ← SNKRX ($388K est) · $500K–1M 71% · $1–5M 84% · ≥$5M 93%. Self-pub→self-pub runs ~10pt lower through the middle bands (e.g. $250–500K → 63%).

• "Veterans without a hit: 6.4%" pools $49K near-misses with asset-flippers — a $25–50K prior is worth 29%, not 6%. The 6.4% is dominated by <$5K-prior shovelware.
• Continuous asset (audience/skill), not a threshold unlock. Spearman ρ=0.715 (n=2,682 devs).

4. Era replication — and the gap is WIDENING

Hitters' rate is stable across a decade; non-hitters' rate fell by a third. The market got harder only for people without an audience.

5. Circularity checks — all pass

• K-band (25/35.9/55): ratio 7.9–10.8×, barely moves.
• Reviews-only (price-free, N=100/300/1000): 7.7×/9.0×/12.0×.
• Cross-definition (prior = revenue est → success = SteamSpy owners ≥20K/50K): 2.9×/5.4×.
• Time-truncation (prior hit judged on reviews earned by 2022-12-31 only): effect gets stronger — 70.3% vs 12.7% dev-first. No tail-leakage inflation.
• EA-graduation (first review >180d pre-release-date; 11% of covered releases, they "hit" at 94% by accumulated-review illusion): trims headline ~3pt. All key numbers above re-stated EA-adjusted.
• Right-censoring: none (2023/24/25 = 56/59/58%).

6. Selection on returning

Only 30% of prior-hit devs released again in-window (no-hit vets: 15%). So all rates are conditional on shipping; unconditional P(hit dev ships AND re-hits within ~3y) ≈ 20%. Irrelevant to him (he ships), but the 2× return-rate gap is itself part of the compounding story.

7. Related claims re-checked

• Franchise premium persists within self-pub: sequel-of-hit 69.6% vs new IP 51.8% (dev-first, his cell) — same +18pt as the pooled version. (SNKRX 2 carries a real premium over new IP; a BYTEPATH remake counts as sequel-of-non-hit: 76% on n=21, noisy.)
• Gap decay: still no decay within self-pub, but the inversion is mostly composition — longer-gap devs had bigger hits and ship pricier games (med price $9.99→$19.99, med prior $129K→$418K across gap 1→6). Era-stratified it's flat. His exact profile (selfpub hit 2020–21, gap ≥4, selfpub next): 52.5% (n=40) — peer list includes Rift Wizard 2, Crashlands 2, Cinnabunny, SpaceBourne 2.
• Price gate inside his cell (dev-first, EA-adj, NEW IP only): ≤$7 15.1% · $8–12 42.5% · $13–20 57.3% · >$20 80.6%. Confirms report 11 after every correction.
• NEW — price transition matrix (prior hit price → window price, his cell): staying ≤$7 after a ≤$7 hit = 15% (n=124); moving up to $8–12 = 52% (n=25); moving to $13–20 = 40% (n=10). Pricing down is catastrophic in every row (0–11%). Directly ratifies the reprice logic in the settled release plan.

8. The honest personal bracket (replaces "you are in the 57.6% bucket")

New-IP, self-pub, gap-5, prior = SNKRX ($250–500K est band): ≤$7: ~15% (unless idler-lane, per report 02/11 exception) · $8–12: ~40–45% · $13–20: ~55–60%. Median outcome conditional on his cell: $60–83K. Every correction moves the headline toward "price is the biggest lever you control; the asset is real, continuous, portable, and doesn't rot."

Caveats that remain

Survivorship (delisted games invisible, both groups); SteamSpy dev-name identity (renames split histories); medians inherit K±50%; the 84.5% signed-after-hit cell is selection-entangled; review-propensity correlation across a dev's audience partially addressed only by the owners cross-check.

16 — Art vs price: is the price gradient just a graphics gradient? (session 3)

Scripts: scripts/art_price_sample.py, scripts/art_price_analysis.py. Raw: raw_art_price.txt. Data: data/art_price/ — 1,116 in-game screenshots (941 true screenshots, 175 header fallbacks), HIS CELL only (selfpub 2020–22 hit devs → all 772 selfpub 2023–25 releases, EA-adjusted, + each dev's prior hit), blind-scored 1–5 quality + style class by 10 parallel scorers (16-image sheets, no names/prices/outcomes visible).

Verdict

No — the price effect is not a graphics effect. Art quality and price both predict re-hitting independently, and price is the stronger axis in his class. Ugly-but-competent at $13–20 (37–58%) beats beautiful at ≤$7 (17–18%). Keeping a simple style costs ~nothing at $8–12; what kills is the ≤$7 shelf, in every style, at every art tier.

1. Price↔art association is weak

Spearman(price, art) = 0.294. Mean art by band: 2.53 / 2.76 / 2.78 / 3.23; art≥4 share 7% / 18% / 16% / 39%. Pricier games look somewhat better, but $8–12 and $13–20 are visually indistinguishable — price is not a visual badge in the indie range.

2. The decomposition grid (h50, window releases)

Both axes matter; price's within-tier range (art 3: 16→82%) exceeds art's within-band range. Screenshots-only robustness (header fallbacks dropped): same shape, steeper (art 3: 22→84%). Mechanical note: h50 = P(reviews×K×price ≥ $50K), so at equal audience a pricier game earns more by multiplication — the gradient partly is that arithmetic, i.e. price converts the same audience into more dollars and empirically does not proportionally shrink the audience. No "graphics command price" story needed.

3. Style × price — every style climbs with price

PIX $13–20: 69% (n=26). TXT $8–12/$13–20: 33/41%. FLAT $13–20: 61%. DRAWN is the weakest style overall (24%). MIN (SNKRX's class) is a graveyard: 3/20 hits, and the only big one is a misclassified Capcom remaster (Ghost Trick); the honest MIN hits are clickyland ($107K, an idle/clicker) and Nebulock ($60K). Matches the report-02 finding (Arcade/Minimalist/Abstract = bottom decile): abstract-geometric action doesn't sell at any price; the idle/incremental lane is MIN's one working outlet (clickyland here; Gnorp/Nodebuster in report 05).

4. Dev pairs (prior hit → window release)

• 50% keep the same style class. Art changes: upgrading from a competent base pays (prior art 3 + art UP: 62% vs 35% staying); upgrading from amateur (1–2) does nothing (20% vs 22%). Polishing jank ≠ progress; adding craft to competence does.
• Kept-simple vs went-fancy is price-confounded: pooled 23.5% vs 35.0%, but within ≤$7: 3% vs 5%; $8–12: 26% vs 29% (identical); $13–20: 45% vs 79%; >$20: 54% vs 85%. At his price range, keeping simple graphics costs nothing. Above $13, fancier art starts earning its keep — but simple at $13–20 still runs 45%.
• Kept-simple-and-won: 67 re-hits from 48 devs (simple = MIN/PIX/TXT or art≤2 on both games). Simple-style at $10+: 51% h50 (n=143).

5. The kept-simple winners' pattern (the lesson list)

Named indie examples (AAA collections excluded): Soulash→Soulash 2 ($1.29M), Illwinter (CoE5→Dominions 6, $2.6M), Card Survival Tropical→Fantasy Forest ($874K), Cruelty Squad→Psycho Patrol R ($2.7M @ $40), Pumping Simulator 1→2 ($5→$12, $85K→$701K), Szymanski (Iron Lung→Squirrel Stapler/Butcher's Creek, repeatable ~$500K), Haiku the Robot→Rusty's Retirement ($1.6M idler), Nomad Survival→Nomad Idle, Void Scrappers→BORE BLASTERS, Mortal Glory 1→2, pureya→Ambidextro, Wrestling Empire→Hard Time III (MDickie), Legend Bowl→College Bowl, Horizon's Gate→Kingsvein, Geneforge (Spiderweb), Esports Godfather, Creeper World IXE, Teamfight Manager→Kitchen Crisis.

Common threads: (1) all priced ≥$10 (kept-simple ≤$7 = 3/111 = 3%); (2) style consistency as brand — decades-stable aesthetics with a returning audience (Illwinter, Spiderweb, MDickie, Kairosoft, Szymanski, Rad Codex); (3) systems-depth genres where the audience explicitly doesn't shop on graphics: deep strategy, sim/management, systems-RPG, sports-jank, idlers, lo-fi horror-as-aesthetic. None are abstract-geometric action.

6. What it means for him

• The price gate is not "buy better art first." Same-style iteration at a higher price is the modal winning move in his class.
• SNKRX-style MIN visuals are fine if attached to idler/incremental or systems-sim formats (the one MIN outlet), or nudged toward PIX/TXT presentation; MIN action at any price is the dead cell.
• Scope remains unmeasured (playtime data empty for recent cohorts) — price partly proxies content volume; this analysis only removes the graphics explanation.

Caveats

Single screenshot per game (first store screenshot); current not launch imagery; 175 header fallbacks (sensitivity-checked); one-model scorers with sheet-level batching (10 scorers, consistent rubric, but no inter-rater calibration); "selfpub" = dev-name==pub-name, which includes AAA self-publishers (Capcom/Konami visible in the simple-style list — discount them); style classes judged from one frame (some title-card TXT pollution).

17 — Scope vs price: the other half of the decomposition (session 3)

Scripts: scripts/playtime_crawl.py, scripts/scope_analysis.py. Raw: raw_scope.txt. Data: data/playtime.sqlite — 100 top-helpful reviews per game (all languages) for the 1,116 his-cell games + SNKRX/BYTEPATH; scope = median playtime_at_review per game (≥10 playtime-bearing reviews required). Secondary proxies from appdetails: disk GB (parsed from PC requirements), achievements count.

Headline

Price is a scope badge much more than a graphics badge — spearman(price, hours) = 0.571 vs (price, art) = 0.294. Median hours-at-review by band: 2.6h / 3.9h / 7.2h / 15.6h (disk: 0.7 / 0.5 / 2.0 / 13.5 GB; achievements 15/16/24/40). And the price→re-hit gradient collapses for sub-3h games (20/36/11/29% across bands — no gradient) while being steep for 3–10h games (53→70→100→95%). Price cannot rescue a short game; with ≥3h of median engagement, every price band works and higher is better.

Coverage caveat (read first)

Only 242/772 window games have ≥10 playtime-bearing reviews — coverage conditions on having reviews, which is close to conditioning on not-failing. All h50 levels inside the covered subset are inflated (~90% baseline in pair analyses); the shapes (gradients, rows vs columns) are the evidence, not the levels. The price↔scope association itself (the 0.571) does not condition on outcomes and is clean.

The grid (h50, covered window releases)

Same shape holding art≤3 simultaneously (his realistic corner): art≤3 + 3–10h runs 53→69→100→89 across price bands. The $13–20 sweet spot is a scope requirement in disguise: the median $13–20 game delivers ~7h at review.

Do price-uppers scope up? (dev pairs, both games covered, n=147)

• Devs who jumped a price band added only 1.28× median hours — a full band jump with +28% content. Priced-up-without-scope-up (ratio <2×): 90% h50 (n=20) — indistinguishable from same-band (92.8%). Within devs whose games get reviewed at all, pricing up did not require proportional scope growth and did not hurt.
• Scope movement itself (≥2× up / same / ≤0.5× down): 96/91/92% — no signal at this selection ceiling.

Kept-simple winners are depth monsters

Kept-simple re-hits: median 12.2h vs 10.5h all hits vs 1.4h misses. The list: Card Survival 84.5h, Esports Godfather 38.2h, Dominions 6 31.9h, Rusty's Retirement 27.4h at $6.99, Soulash 2 25.8h, Pumping Simulator 2 22.9h, Kairosoft titles 8–17h. Exception lane: Szymanski-style lo-fi horror sells 2.6–4.2h at $6–10. Simple graphics are financed by hours (usually via replayable systems, not content volume — Rusty's is an idler).

His placement

SNKRX: median 9.1h at review (n=10 samples — thin, treat as ±) — top of the 3–10h band, i.e. the engagement of a $13–20-band game sold at $2.99. The scope for a mid-band price was already there in 2021; the price wasn't. BYTEPATH: 4.1h (n=2, unusable).

Combined art+scope answer to the owner's question

The price→re-hit gradient decomposes roughly as: scope-mediated where it binds (sub-3h = dead at any price), price-arithmetic where scope clears 3h (same audience × higher price = more dollars; audience doesn't shrink proportionally), with art a weaker independent third axis (within-tier price range 16→82% vs within-band art range ~2×). "Resources → better graphics" is not the compounding mechanism; "shipped systems that hold players 3–10h+, priced like it" is. Caveats: playtime-at-review under-measures long-tail games and top-helpful reviews skew invested; collider selection as above; single-page samples (n≈10–100/game).

18 — Migration / stay-vs-move: deep re-check (session 3, claim #4)

Scripts: scripts/migration_deep.py (+ era snippet). Raw: raw_migration_deep.txt. Re-examines report 11 §7 (STAY 63.6% vs MOVE 55.9%; AR exports at 75%; best destinations Exploration/Horror/TB-Tactics, worst Adventure/Racing; Simulation-origin exports worst 27%).

Verdict

The directional claim survives; the dramatic table mostly dissolves. A real, decade-stable stay premium of ~5–10pt exists (pooled dev-first new-IP: 70.0 vs 62.4; selfpub eras: +6.2, +6.6, +15.9pt-with-wide-CI current). But the per-tag origin/destination rankings were small-n noise + release-weighting + factory artifacts: after dev-first weighting, sequel removal, and shrinkage, origins span ~52–79% (not 27–95%), Adventure-as-destination rehabilitates to 56%, Racing drops below reportable n, and the Simulation-origin 27% claim is outright wrong — a three-factory artifact (gamesforgames 55 releases + Kairosoft 22 + G-MODE 21 of 175); dev-first new-IP sim movers re-hit at 62.5%, right at the pooled rate. "Sim hits are format-luck" should be struck from the record.

How the original numbers were made (methodology exposé)

Release-weighted; sequels included; primary tag = first non-generic tag of Steam search's top-7 (SKIP list of only 5 generic tags, so umbrella tags like Action/Adventure/RPG/Simulation are the most common "primaries"); no CIs; cells as small as n=10 ranked as findings; factories counted once per release.

Findings

1. Sequel confound — tested, absolved. Sequels are not concentrated in STAY (17% of stays vs 10% of moves); removing them keeps the gap (dev-first 70.0 vs 62.4). Notable side-finding: sequels released into a different primary tag re-hit at 73% — the franchise asset travels across genres.
2. His class (selfpub prior+window, EA-adj, new IP, dev-first): STAY 60.3% [49–70] (n=78) vs MOVE 44.4% [39–50] (n=270). Era replication puts the durable selfpub premium at ~6–7pt; treat the current 16pt as upper bound.
3. Tag distance is flat except the exact-neighborhood bonus: 0–4 shared top-7 tags all ≈58–64% dev-first (selfpub 40–56%), but 5+ shared = 71.7% (selfpub 60.5%). "Fully portable" holds for any real move; staying in the micro-niche keeps ~10pt. Pseudo-moves (new primary, ≥3 shared tags) do NOT outperform true moves (52.0 vs 56.0%).
4. Origins, honest version (movers, new-IP, dev-first, EB-shrunk m=10 toward pooled 62.4%): top Management 79 / Open World 78 / Visual Novel 72 / RPG 72; bottom Simulation/Strategy/Puzzle ~52–53. Total spread ~27pt, not 68pt. Action Roguelike has n=6 dev-first — unrankable at tag level (the "75% export" was release-weighted noise).
5. Destinations, honest version: Exploration still tops (raw 72% [49–88], n=18; market base 12.7%) — plausibly marks polished-atmospheric-indie devs rather than a tag that pays. Adventure rehabilitates (56%, n=64). Racing/worst-list: below n=8, unreportable.
6. Families: ACTION shows no stay premium (61 vs 62); most families +/-modest with small n; SIM→STRAT 88% (n=8) best cell.
7. His cells (tiny n, qualitative): AR-origin devs 83% (n=6); the AR-mover release list is star-studded — Another Crab's Treasure $10.2M (Souls-like), Ravenswatch $7.3M, Nomad Idle $301K, BORE BLASTERS $250K. ROGUE-family origin → window-has-card-tag 75% (n=4: Pyrene, Nomad Idle, Duelists of Eden; miss Shadowkin); → strategy/tactics 100% (n=6, incl. ShapeHero Factory $841K); → idler n=1 (Nomad Idle, hit). Era-2 AR-origin: 75% (n=8). ⚠ Joke-priced games ($199.99 × ~11 reviews ⇒ "$79K est") pollute tiny cells — ROOM FOOTBALL etc.; estimator breaks at troll prices.
8. Era stability: pooled stay premium +3–4pt in 2017-19→2020-22 (dev-first 71.4 vs 67.5); ~+7pt in both selfpub eras.

Implications

• Migration tables should NOT gate his lane choice — at his n, the data cannot distinguish idler vs card vs strategy vs stay. Lane economics (reports 15–17: price, scope, whitespace) are the decision inputs; migration only says the advantage travels with a small exact-niche retention bonus.
• The report-11 EV table's migration-derived rows keep their conclusions but not their precision; report 18 supersedes §7 of report 11.
• SNKRX 2 / BYTEPATH-remake thought: franchise-in-new-genre at 73% supports carrying the name into a different format (e.g. BYTEPATH-as-incremental keeps the name; SNKRX-brand in a new subgenre keeps the premium).

19 — Lane deep-dive: Retro+Idler and Card×Structure (session 3, claim #3)

Scripts: scripts/lane_deep.py. Raw: raw_lane_deep.txt. Grounds the BYTEPATH-incremental decision and the Artifact-port positioning against full cell censuses, supply trends, and the corrected machinery from reports 15–18.

A. The Idler lane (BYTEPATH incremental)

• Supply is exploding: Idler tag 91 (23H1) → 245 (25H1) → 361 (25H2) → 604 (26H1). Conversion held 11–17% through 2025 (26H1 shows 7% but is right-censored). Every idler-adjacent pair is flooding in 2026 (Idler+Strategy n26=157, +Automation n26=75, +Pixel Graphics n26=91). The lane is now a gold-rush destination; the "quiet tag" phase is over.
• "Price immunity" REVISED: Idler 2023–26 by price: <$3 2.1% · $3–5 7.7% · $6–8 23.9% · $8–12 36.0% · >$12 28.8% (med $52–63K at $8+). There IS a price curve; it peaks at $8–12 instead of climbing forever. $2.99 mega-winners exist (Nodebuster $1.1M, Tower Wizard $743K, Digseum $676K — all $2.99) but the rate at $3–5 is 7.7%. Sweet spot: $7.99–9.99 (revises the plan's $5–8 upward).
• Retro+Idler cell (report 10's "near-empty 36%") is being colonized: n=14 (2023–25, 5 hits: Stone Story $956K @$29.99, Tiny Aquarium $625K, FACEMINER $397K, Lyca $208K, Journey to Incrementalia $157K) plus 7 entries in 26H1 alone — all misses so far (5 with zero reviews). The whitespace label is stale; the cell still converts for real entries but the flood has found it.
• The "incremental" description microformat is the strongest signal in the lane: games self-describing as "incremental": 18 (2023) → 47 (2024) → 110 (2025) → 121 in 26H1 alone. h50 = 32.7% (2025) and 33.9% (2026, despite censoring) vs 7% for the idler tag broadly in 26H1. 2025–26 winners: Scritchy Scratchy $1.6M, Berry Bury Berry $1.6M, Microcivilization $914K, Orb of Creation $819K, Shelldiver $750K, Fortune Mill $448K, Keep on Mining! Worlds $406K. The converting thing is the systems-incremental microformat, not the ambient-idler tag.
• The lane bifurcates into two winning families: ambient/desktop-cozy collectors (Spirit City $3.8M, Cast n Chill $2.6M, Rusty's $1.6M, Chillquarium) and systems-incrementals (Gnorp $1.8M, Nodebuster, Farmer Was Replaced $1.4M, Tower Wizard, Digseum). BYTEPATH = the second family, which matches his build-depth skills.

BYTEPATH-incremental EV update (replaces report 11 row): format base 33% (microformat, still converting in 2026) × his-class lift (reports 15: cheap-prior dev moving to $8–12 = 52%; sequel-of-non-hit ≈ no penalty; name carries at 73% per report 18) → ~40–60% at $7.99–9.99, median $50–150K, tail Rusty's/Gnorp-class $1.5M+. Scope target from report 17: idle formats generate 20h+ cheaply — that's the price justification, not art. Timing: ship before 26H2 deepens the flood.

B. Card × Structure lanes (Artifact port)

• The strict Card Game + Base Building pair: n=8 (2023–25), h50 75%, medprice $15.99, n26 = 0 — still unfilled in 2026 (report 10 confirmed). But the honest neighborhood (union of 4 card tags × 4 builder tags, n=66 since 2022): 2024 = 8/23 hits (35%), 2025 = 9/23 (39%), 2026 = 0/10 so far (censored; most at zero reviews). The 75% is effort-selected (games that committed both tags to top-7); ~35–40% is the neighborhood rate — before his dev-class lift (his cell at $13–20 = 57–60% per report 15).
• Winners' formula (from store descriptions): cards as the economy/build verb + a spatial defend-or-expand board, $13–20: Stacklands $3.4M (card-stacking village builder), Emberward $1.3M (tetromino-maze TD roguelite), Deck of Haunts $830K — literally a reverse-siege: you ARE the haunted house, build halls, drain the attackers, Kingdom's Deck $165K ("build by day, defend by night" card city-builder + siege), Roots of Yggdrasil (deck + roguelike city builder), WARCANA (deck + base-defense RTS), There Are No Orcs $357K (building placement + autobattler).
• Card/Deck × Tower Defense (the cell Siege-as-designed sits nearest): much more crowded — n=126 since 2022; pair rates 16–23% for Card Game/Battler/Deckbuilding + TD, 36% for Roguelike Deckbuilder + TD; RL-Deckbuilder+Strategy 39% (n=168), +Turn-Based-Tactics 41%, +City Builder 67% (n=6). 2026 already has 3 hits here (Cubic Cosmos $403K, Poly TD, Wireworks) — alive but contested.
• The card price gate is the steepest measured anywhere: card-family 2023–26: ≤$7 = 3.1% · $8–12 15.2% · $13–20 43.3% · >$20 63.7%. Whatever the Artifact port becomes, it prices $15–20. (CN localization day one — card lanes 25–30% CN share, report 06.)

Positioning read: "You Are the Siege" as locked (attack-roguelite deckbuilder vs director) lands in RL-Deckbuilder × Strategy/TBT (~39–41% market-wide, crowded, differentiation = Artifact combat depth). The owner's instinct — card-base-builder — targets the emptier, stronger cell (strict pair 75%/n26=0; neighborhood 35–40%), and the Guardian design already contains half a base (a defended structure with build vectors); Deck of Haunts proves the reverse-siege fantasy sells. Whether to move the design toward a buildable-Guardian/structure layer is a design decision for the owner, not settled here.

C. Addendum: the anime question (owner follow-up)

• Anime tag 2023–25: n=1,533, h50 26.2% (13% adult share; non-adult 24.3%) — ~2× market baseline. Non-adult by price: ≤$7 5% · $8–12 18% · $13–20 37% · >$20 73% (JRPG-port inflated). Anime obeys the same price law; anime-at-$13–20 outperforms the market's $13–20.
• Anime × card cells: cheap ones are mediocre (Anime+Card Game 14% @ mp $5.99); the correctly-priced one converts: Anime + Roguelike Deckbuilder 40% (n=10, mp $14.49, med $98K); Anime+Roguelite 33% (n=39); Anime+City Builder 67% (n=6, mp $35). n26 = 3–9 per cell — moderate inflow, no flood. Anime+Base Building: unoccupied (n<3).
• Comps: Chrono Ark $2.3M ($24.99, anime RL-deckbuilder RPG — the anchor), Rebellion GODSOUL $1.6M ($17.99 EA card-battler JRPG — same dev appeared in report 15's gap-decay winners list: prior $134K VN → this), Card-en-Ciel $752K, Yohane $727K (IP), Dream Tactics $364K, Witch's Apocalyptic Journey $324K @ $8.99 (2026). All depth-heavy, craft-forward, $13–30, CN-demand-aligned.
• AI-art reception (reviews_text corpus, 52,043 reviews / 3,767 build-tag games): 178 reviews across 133 games mention AI art/assets — 0.34% of reviews, but only 19% of mentioning reviews are positive, and per-game they run far below each game's own up-share (Road to Empress: 25% vs 88%). It's a marginal-but-real negative-review vector, not a death sentence (Road to Empress $482K, Demonic Mahjong $190K hit anyway; AAA titles absorb accusations routinely). Risk concentrates where art IS the product — which a $15–20 anime card game is; anime is also the most-policed AI style. Steam AI-disclosure is mandatory and curator lists scrape it (unmeasurable in our data).
• Art-gate reconciliation: report 16's DRAWN 24% pooled cheap VN-adjacent games; at $13–20 DRAWN = 48%, and went-fancy-at-$13–20 = 79% — real illustrated art at $15–20 is the data-supported combination. The bar is Chrono-Ark-class consistency, not single-image quality.

Caveats

Effort-selection on strict pairs (top-7 identity ≠ mechanics); 26H1 heavily right-censored (conversion reads are floors for real entries, but zero-review entries are genuinely dead); match-3 factory titles (Spellarium/Cursed House) pollute the census union via Card Game+City Builder; estimator K±50% on medians.

20 — Launch timing deep-dive (session 3, claim #2)

Scripts: scripts/timing_deep.py, scripts/timing_sales2.py (+ calendar-window rerun). Raw: raw_timing_deep.txt, raw_timing_sales2.txt. Window 2022–24 (all months fully matured; 2025 shown censored). "His class" = self-pub, $8–20.

Verdict

The original claim ("launch any month except December; timing otherwise dead") was directionally right but mechanically wrong. The real rule is: never launch DURING a seasonal sale window; everything else about the calendar is nearly free. December is only bad because it contains the winter sale — Dec 1–18 is normal — and the claim missed that Nov 16–30 (autumn sale) is just as bad, hidden inside a fine-looking November.

The numbers

• During any seasonal sale: 7.7% h50 (winter 6.3, autumn 6.7, summer 8.8, spring 10.0) vs ~16% everywhere else. Persists in his class: 12.1% during vs ~21% otherwise. Every other proximity bucket — including launching 1–7 days before a sale — is indistinguishable from baseline (15–18%). "Near a sale" costs nothing; "inside a sale" halves the odds. (Sale windows calendar-encoded ±2d; method cross-validated on the one data-detectable event: 2025 autumn sale = ×4.6 catalog-wide tail-review surge.)
• Nov–Jan decomposition (2022–24): Nov 1–15: 17.2% · Nov 16–30: 10.7% · Dec 1–9: 15.3% · Dec 10–18: 14.3% · Dec 19–31: 7.1% · Jan 1–9: 8.6% · Jan 10–31: 15.5%.
• Months (2022–24 pooled): flat 13.8–16.6% except September 18.4% (best; his class 24.9%) and December 12.4% (= the sale). Week-of-year curve peaks w36–42 (18–20%), dips w26–27 (summer sale), w47–48 (autumn), w51–1 (winter+tail). His class shows NO month-level December penalty (19.7%) — the month was never the lever.
• Day of week: Thu best (20.0% all / 22.5% his class), Tue/Wed fine, Fri 12.7% all (but 20.6% his class — the all-games Friday dip is amateur composition), Sat 5.2% / Sun 8.8% bad.
• Launch-week competition density: dead lever — quiet/mid/busy weeks 14.1/15.8/15.0% (his class 19.9/20.2/19.4). Crowded weeks don't matter; only sale weeks do.
• Next Fest: calendar-level proximity is a dead-to-negative lever (fest week 14.7% vs other 16.2%; his class 16.0% vs 22.1%). What correlates is participating with a demo that performs: demos spiking at a detected fest week = 10.2% of 2025 hits vs 0.4% of fails — but heavily selection-confounded, and median hit $ barely differs by NF status ($246K vs $218K no-demo). Combined with report 07 (demo = weakest feature signal): NF is not a launch lever; it's an optional demo-marketing channel.

Actionable summary for the plan

Launch Tuesday–Thursday, any month, keeping ≥1 week clear of the four seasonal-sale windows (and skip the Jan 1–9 tail). September is the year's best month (+3–6pt in his class) — convenient for the BYTEPATH-incremental's Jul–Sep target: late Jul / Aug fine, September best. Ignore fest calendars and competition density when picking the date.

Caveats

Month effects are intent-to-treat on lifetime revenue (reviews accumulate over years — matured cohorts only used). During-sale launchers are partly self-selected low-effort titles; the his-class cut bounds but doesn't eliminate composition. Sale windows encoded from the public calendar (±2d), not fully re-derived from data (detection validated on 2025 autumn only). Fest dates 2024+ detected bottom-up from demo peaks; 2022–23 encoded.

21 — Do wishlists matter? (owner question, session 3)

Scripts: scripts/wishlist_proxy.py (+ mechanism snippet). Raw: raw_wishlist_proxy.txt. Launch wishlist counts are PRIVATE (Steamworks-only; third parties only estimate from followers). Measured instead: m1 = reviews in the first ~30 days from first review (histogram t0, EA-safe) — the downstream shadow of launch demand from any source (wishlists + visibility + early virality). ~45 units/review (in-house calibration); m1=25 ≈ ~1.1K month-1 units. Cohort 2022–24 (matured).

Headlines

1. Launch momentum is the steepest predictor found anywhere in this project — but it is a predictor, not a gate: ~1 in 5 hits in his class started cold.
2. P(≥$50K | m1), self-pub $8–20 (covered games): 0–9: ~40% · 10–24: 37% · 25–49: 51% · 50–99: 81% · 100–249: 99.6% · 250+: 100%. ⚠ Coverage collider: histograms exist only for ≥20-lifetime-review games, so cold starts that never grew are excluded — the unconditional P(hit | cold start) is well under 1% (4,201 uncovered non-hits in his class alone). The low-bucket numbers mean: given a cold start that later shows signs of life, odds are ~35–45%.
3. Where hits started (his class, n=1,189): median m1=80; p25=33; 18.3% began with m1<25 (~1.1K units); 7.0% with m1<10. The slow lane is real and well-traveled: 623 slow-burn hits (m1<25 → ≥$50K) in 2022–24 (~200/yr).
4. The slow lane is slow: median 13 months to reach half of lifetime reviews (p75=21). Marquee slow burns: Vampire Survivors (m1=11!), Last Epoch (m1=3), KeeperRL, ΔV, GearCity, Peaks of Yore. Slow-burn-friendly genres: VR, Flight, Old School, Sports, Procedural Generation, Party-Based RPG (deep systems / EA / organic-multiplayer). Slow-burn-hostile: Thriller, Word Game, Hentai, Interactive Fiction (consume-once).
5. Growth partially compensates: cold starters that survive grow 4.6× from their m3 (p90 16×) vs 2.1–2.2× for warm starters — word-of-mouth rescue is real but bounded.
6. Mechanism test (ties to report 15): prior-hit self-pub devs warm-start 5× more often (m1≥50 for 40.1% of releases vs 8.1% for first-timers; median covered m1 95 vs 45) AND convert better at equal momentum (m1 50–99: 73% vs 55%; 100–249: 94% vs 82%; 0–9: 56% vs 28%). The compounding asset ≈ manufactured launch momentum × superior conversion at every momentum level.
7. SNKRX case: m1=117 in its 12-day first "month" with ~zero wishlists — early demand ≠ wishlists; m1 measures momentum from any source. Its own virality began in week 2.

Answer to the question

Wishlists-at-launch are unobservable, but their shadow says: launch-window demand nearly determines outcomes at the top (m1≥100 ⇒ ~99% hit in his class) yet is bypassable — a fifth of hits started cold and climbed over ~a year, concentrated in deep-systems/long-tail genres (which include idlers and his planned lanes). His next launch is structurally warm (followers + franchise names + the report-15 conversion edge), and the slow lane exists as a fallback that rewards exactly his long-tail update model.

Caveats

m1 conflates wishlists / visibility / virality (SNKRX proves it); coverage collider as above; monthly-rollup games' first bucket is calendar-truncated (m1 undercounted for mid-month releases — SNKRX's 117 is really 12 days); review-propensity varies by genre.

22 — Quality × momentum: does "just make it good" hold? (session 3, avenue #1)

Scripts: scripts/quality_momentum.py. Raw: raw_quality_momentum.txt. Cohort 2022–24. Quality = review positivity; the causal cuts use pct@m3 (positivity computed from histogram rollups through month 3) → growth after m3, so quality is measured before the outcome it predicts.

Verdict

"Just make it good" is measurably wrong as a growth strategy. Early positivity buys a small tail multiplier (1.77× → 2.54× lifetime/m3 across a 35-point positivity range — real but the smallest effect of anything measured this session), badges have no threshold effects (smooth through 80% and 95%), quality cannot rescue a cold start (P(hit | m1<25) is flat-to-declining in quality: 32.5% at <80 vs 26.9% at 95+), and the slow-burn winners are not better-reviewed than the cold starts that died (median pct 85 in both groups; the dead ones actually have MORE 95+ games, 18% vs 12%). Momentum dominates positivity utterly: in the m1 × pct@m3 grid, moving up the momentum axis swings P(hit) 32% → 96%, while moving across the entire quality axis moves it ±5pt noise.

The tables

• P(hit | current pct) (≥25 reviews, descriptive): inverted-U — peaks at 80–89 (51–56%), falls at 95+ (39–44%). The 95+ zone is the "beloved niche game" trap: small adoring audiences rate high and don't scale (SNKRX itself sits at ~94.7%).
• pct@m3 → growth after m3 (m3≥30): <60: 1.77× · 70–79: 2.09× · 80–89: 2.28× · 90–94: 2.39× · 95+: 2.54×. Monotone, shallow.
• Badge discontinuity (badge-eligible m3≥50, fine buckets): 75–80: 2.14× → 80–85: 2.24× (no jump); 93–95: 2.43× → 95–98: 2.58× → 98–100: 2.50× (no jump; P(hit) drops to 68% at 98–100 — audience-size artifact). Very Positive / Overwhelmingly Positive as thresholds to chase: unsupported.
• Quality × momentum grid (P(hit)): m1 0–24: 32/36/31% across <80/80–89/90+ · m1 25–99: 56/58/53% · m1 100+: 96/96/94%.
• Prior-hit conversion edge is NOT quality: at equal m1, prior-hit self-pub devs' median pct = 84–85, first-timers' = 84–86. Their +10–28pt conversion edge (report 21) comes from distribution/trust/tail management, not better-reviewed games.

Interpretation and caveats

Positivity measures satisfaction of people who already bought — expectations-adjusted, so a $3 arcade game and Dominions both sit near 85. Its range on Steam is compressed (p25–p75 ≈ 75–92), which limits detectable effects. "Quality" in the make-a-good-game sense may still act upstream through m1 (a great demo/trailer earns wishlists), which this analysis attributes to momentum. What's excluded by the data: the found-because-good story (slow-burn winners = same pct as cold deaths — discovery selects on genre/tail-demand per report 21, not on ratings), and badge-threshold planning. Actionable residue: don't sacrifice anything that builds launch momentum to chase the last positivity points above ~80; below ~70 the tail penalty is real (1.8× vs 2.3×) and worth fixing (report 12: bugs and grind are what drag it).

23 — Early Access vs direct 1.0 for systems games (session 3, avenue #4)

Scripts: scripts/ea_paths.py. Raw: raw_ea_paths.txt. Classification: in-EA-now (appdetails EA genre ∪ EA tag, n=4,572), EA-graduated (first review >180d before release date), direct (first review ≈ release). Start-cohort framing: "started EA in year Y" (first review year) vs "direct-launched in year Y" → P(≥$50K by 2026-07); start 2022–23 gives both paths ≥2.5y runway.

Headlines

1. EA out-performs direct everywhere at face value — all paid: 36.9% vs 13.6%; his class (selfpub $8–20): 39.2% vs 15.7%; idler lane 37.5% vs 8.1%; card 45.1% vs 14.5%; roguelite 41.1% vs 12.4%; sim 48.6% vs 17.1% (starts 2022–23; 2024 cohort same shape). Selection-heavy, not causal: EA marks serious/scoped projects (shovelware ships direct); hits' EA share (22–43% by lane) vs reviewed-misses' (7–18%) shows the same sorting. Honest floor: EA does not hurt, and the serious-game population inside EA converts at 2.5–4.5× the direct population's rate.
2. Longer EA correlates with better outcomes (graduates: 6–12mo 69.1% / 12–24mo 80.9% / 24mo+ 84.0%, med $131K→$278K) — mostly survivorship (dying-in-EA games never graduate; they sit in the in-EA-now bucket forever).
3. The 1.0 graduation is NOT a second launch. Median 30-day reviews at 1.0 = only 1.2–1.4× the game's ongoing EA monthly baseline, regardless of EA audience size (<100 / 100–499 / 500+ EA reviews all ~1.3×; p75 only ~2.6×). The "build wishlists in EA, pop at 1.0" story fails for the median game: EA's value is realized during EA; the EA entry IS the launch — report 21's momentum rules apply to EA day one.
4. Lane norms: roguelite hits are 43% EA-path, sim 35%, card 31% (Peglin, Mechabellum, Backpack Battles EA-grad; 9 Kings currently in-EA at $6.9M; Balatro/Wildfrost/Monster Train 2 direct), idler/incremental hits only 22% EA — the incremental lane's winners overwhelmingly ship finished (Gnorp, Rusty's, Nodebuster, Spirit City, Cast n Chill all direct; The Farmer Was Replaced and Stone Story the EA exceptions).

Application to the plan

• BYTEPATH incremental (1–2mo scope): direct launch matches the lane's winner profile; EA offers little for a short build and the lane doesn't expect it.
• Artifact card game ($15–20, ≥3mo scope): EA is a normal, well-trodden path in the card lane and suits iterative balance development with revenue — but plan the EA entry as the real launch (momentum, price $15+, store page done), and expect no 1.0 spike beyond ~1.3× baseline; the 1.0 is a content beat, not a reset.

Caveats

Selection dominates the headline gaps (scope/seriousness sorts into EA); time-in-EA gradient is survivorship; unknown-status games (no histogram, <20 reviews) counted as direct — all non-hits, slightly deflating the direct column (~equally across lanes); EA revenue timing inside rev_est is lifetime-cumulative, consistent with the start-cohort frame.

That's all nine. Now amending the published log so this exchange (and the full report texts) are in it.

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The log now contains all nine reports verbatim (458 lines added) alongside the original transcript and summary, so log readers get the archival tables without needing the steam-market/reports/ files.