Hits was diagnosed as a family mismatch: 84% of hits rows trade at 0.5, so
the stat rides on P(0), and a negative binomial has unbounded support and no
notion of opportunity at all. hits-v1 models it as the bounded conversion it
is -- N ~ the player's empirical at-bat distribution, hits|N ~ Binomial(N,q),
with the multiplier scaling q (conversion) and never N (opportunity).
STEP 0 confirmed the inputs before the model existed: 30/30 real ledger
players, 100% combined-input coverage. Every read goes through knownRate --
a row with no atBats is dropped, never counted as a 0-at-bat game.
It FIRES: 158/159 hits props (99.4%) on the live production snapshot, through
the real attachProjection path. Scoping by book IDENTITY rather than price
shape kept 94 out-of-promotion-band props on the board, 93 of them modelled --
59% that a price rule would have deleted.
And it LOST. Point-in-time replay (game log truncated strictly before each
row's game_date, real grade-time multiplier), hits-only, direction-aligned,
n=242: resolution champion 0.195 / ladder 0.048 / hits-v1 0.026. Paired
bootstrap on the same rows: hits-v1 - ladder = -0.022, CI95 excluding zero.
Not promoted.
The value is in what it eliminates. The family was wrong AND the mean was not
the constraint -- hits-v1 moved the line-0.5 mean 0.554 -> 0.581 toward a
0.598 base rate while resolution fell. What is left is per-prop
discrimination: the ladder's inputs, not its distribution.
The pre-registered fallback is recorded as WRONG rather than deleted. It said
hits might be genuinely low-resolution for anyone; the champion scores 0.276
on the identical 189 rows, so there is real signal and the ceiling claim was
the comfortable reading, not the honest one. Its own control refuted it, and
that control was already in hand when the branch was written.
hits-v1 stays wired as a challenger writing its own ledger columns so the
forward accrual can confirm the backtest. Champion, ladder, ranking,
calibration, reference ruler and the four accruing verdicts are byte-identical
-- the diff has zero deleted lines.
Tests 4,156 green (332 suites); web build exit 0.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9
proj_book_implied derived from raw book_odds — VIG-INCLUSIVE. A -110/-110 market
implies 52.4%/side (104.8% sum); fair is 50%. Comparing our P against raw book
overstates the book on both sides, biasing the handicapper test IN OUR FAVOR; on
juiced longshots (the Judge HR -18.5pt case) much of that "edge" was vig, not
disagreement.
Fix (fenced to proj-v1's stored comparison basis): proj_book_implied now derives
from DE-VIGGED FAIR via the grade's g.fair_prob — the SAME multiplicative de-vig
the triplet uses (utils/devig.js), so the basis matches the product's shown fair.
Expressed on the OVER basis (under props → 1 - fair) to match our stored P(≥rung);
traded-rung ladder book_implied likewise. HONEST-NULL where fair is uncomputable
(one-sided market, ~14%) — NEVER a raw-book fallback (that would recreate the vig
bias on a subset and mix two bases in one ledger). proj_factors records
book_implied_basis ('fair_multiplicative'|'none').
Phase 0 (prod-verified): fair reachable at store point (g.fair_prob on the grade,
no threading); 86% batting coverage; method = multiplicative/proportional.
Phase 2 FLAG: multiplicative de-vig mis-splits vig on juiced longshots (favorite-
longshot bias), so a longshot fair still carries known method bias — flagged
per-row (longshot_devig_caveat); a better de-vig (Shin/power) is a separate item.
Phase 3: version bumped proj-v1 → proj-v1.1 so pre-fix (raw-book) and post-fix
(fair) rows never silently mix — the projection model is byte-identical, only the
basis changed; pre-fix rows can't be recomputed (only the graded side's odds were
stored). Champion + arch-v1 + contact-v1 + proj-v1's other columns untouched.
proj suites 26/26.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VCNgGSt5qvcLxaeQqa7Zpj
The live fingerprint showed proj_book_implied null on every real row: grades
carry book_odds/locked_odds (e.g. -264) but NOT a de-vigged fair_prob, so keying
the book comparison off fair_prob yielded null. The book ODDS are exactly "the
book's implied probability" the handicapper test needs. Now proj_book_implied +
the traded rung's book_implied derive from americanToImplied(book_odds),
expressed on the OVER basis (under props → 1 - implied) so it's directly
comparable to our P(≥rung). Vigged (a known offset the ledger measures both
sides of). proj-v1 suites 24/24.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VCNgGSt5qvcLxaeQqa7Zpj
1. matchupRead fly-ball signal: the batter metrics `gb_pct_bb`/`fb_ld_pct` are
MISLABELED — they're exit velocities by batted-ball type (Judge fb_ld_pct =
100.3 mph, not a rate), not ground/fly RATES. Switched fly-ball lean to
avg_launch_angle (league p10/p50/p90 = 7.1/13.9/20.1°), the correct signal.
2. Absolute rate now fits the FULL season (recency-weighted), not a 20-game
window: the window under-sampled rare stats — Judge HR projected 0.11 vs his
0.28 season rate (a fake -32pt edge). Now point=0.27 (matches season); the
last-5-2x recency lean is preserved.
Post-fix induction (real statsapi logs + real statcast): Judge HR 0.27 (P>=1
0.235 vs book 0.42 -> flags the juiced over), Judge TB P>=2 0.548 vs 0.48
(+6.8pt), thin-hot 3-game P>=1 0.726 / P>=3 0.164 (credible low, thin high),
.300 hitter != 3.0. proj-v1 suites 23/23.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VCNgGSt5qvcLxaeQqa7Zpj
A THIRD challenger (after arch-v1, contact-v1), MLB batting v1. Champion is
market-relative P(stat>LINE); proj-v1 is ABSOLUTE — what the hitter will DO —
emitted as a full distribution from which the WHOLE LADDER (P≥1,P≥2,P≥3) derives.
Champion untouched; nothing claimed; the ledger decides per rung, per stat.
- projection/distribution.js — Bayesian Gamma-Poisson → negative-binomial
predictive. Admits over-dispersion; under-dispersion → Poisson approx
(conservative, documented). Uncertainty scales with sample by construction
(r=α): thin → WIDE (real mass on P≥1, honestly thin P≥3), thick → tight.
NEVER abstains — width carries the honesty.
- projection/matchupRead.js — the input the book doesn't use. HONEST FIDELITY:
pitcher repertoire is rich (97% pitch-mix) but hitters have NO pitch-type
performance, so TRUE repertoire-vs-profile is impossible today. This is the
COARSE version (arsenal buckets fastball/sinker/breaking + whiff/hard-hit
tendency × hitter whiff/chase/gb-fb/hard-hit) — beats generic L/R, derived +
documented + TESTED two-sided. A hitter pitch-type feed unlocks the true form.
- projectionChallenger.js — park RELATIVE to the player's own log exposure
(isHome→own park, away→opp park; Phase B's raw-multiply bug solved), recency-
weighted fit, per-factor breakdown (form/park/weather/platoon/matchup — show
your work), full rung set + book-implied per rung. Combined non-form
multiplier bounded.
- Wired after contact-v1, own try, flag PROJ_V1_ENABLED, reusing arch-v1's
already-computed park/weather/platoon (no duplicate env I/O). Own ledger
columns (migration 032, applied to prod): distribution, ladder, point, line,
our-P, book-implied, factor breakdown — measurable per rung/stat after settle.
Phase 0 (prod-verified): venue join via isHome; NB family; uncertainty-as-width;
coarse matchup honest fidelity; no lineup-slot (per-game rate, volume implicit).
Sanity: thin-hot → wide (credible low rung, thin high rung); .300 hitter ≠ 3.0;
matchup two-sided; champion byte-identical. proj-v1 suites 23/23; snapshot/
ledger/siblings 74 green. Forward-only, version-stamped, PROJ_V1_ENABLED kill.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VCNgGSt5qvcLxaeQqa7Zpj
builtbykev