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
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