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2 Commits
| Author | SHA1 | Message | Date | |
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 builtbykev
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83e9da3663 |
Consistency classifier: CV → index of dispersion for low-mean counts
The A/D investigation found CV (std/mean) is scale-broken on count data —
for a Poisson-ish stat cv ≈ 1/sqrt(mean), so EVERY stat with mean < 4 blew
past the boom_bust cutoff regardless of behavior. The S63 stopgap made those
return 'unknown', which silently ate a real +1.0 consistency signal on every
MLB batting prop — steady low-mean hitters never got their earned factor.
Fix, fenced to the low-mean branch of consistencyScore (the only branch that
was returning 'unknown'): classify with the index of dispersion (variance/mean,
Poisson baseline 1.0) — the scale-appropriate, UNBIASED statistic for counts.
mean ≥ 4 keeps the NBA-calibrated CV path BYTE-IDENTICAL (zero NBA blast
radius). This is a bug CORRECTION, not threshold loosening: the CV thresholds
and the engine1 ±1.0 delta are unchanged.
Bands (asymmetric around Poisson 1.0, since counts are naturally mildly
over-dispersed): iod<0.60 elite / <0.85 reliable (+1.0) / ≤1.30 volatile
(neutral) / >1.30 boom_bust (−1.0). Sample floor MIN_GAMES_FOR_IOD=8 so a
thin sample abstains ('unknown') — no small-sample guess.
Validated on real 10-game logs (two-sided): Kwan hits 0.67 / Alonso hits
0.78 → reliable (RECOVERED); Alonso TB 2.57 / Henderson hits 1.33 → boom_bust
(no false consistency); HR mean 0.1 → 1.0 → neutral. Direct engine1 proof: a
strong steady prop that grades B+ today reaches A- once the +1.0 fires; a
boom-bust bat stays B (no inflation). A- now emerges NATURALLY from a real
recovered factor. Standing two-sided test pins all three directions.
Forward-only (settled grades are locked in the ledger, never re-graded).
Emitting A- ≠ proving A- — the A-tier record accrues from emission, still
measurement-gated. Full unit suite green (4 pre-existing redis/timing flakes
pass in isolation); web build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VCNgGSt5qvcLxaeQqa7Zpj
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builtbykev
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1a94ef5fcf |
Revive the dead probability layer + restore grade range ON MERIT
Folds re-sequenced steps 1+2 into one change (Kev's call): same bug
family — features wired to sources that return null.
THE PROBABILITY LAYER WAS DEAD IN PRODUCTION. p_win/ev_pct/kelly/
model_odds/value were absent on 0/8 live grades because
gameLogService.getGameLogs returns null for MLB by construction and
depends on the offline Python service for NBA/WNBA, so meta.gameLogs was
[] for every sport. This was the S46 bug in a second location — that fix
gave featureCache an MLB branch (why grades still worked) but never the
estimator. featureCache.getStatRows now supplies normalized rows
([{date,[statType]:v}], most-recent-first) for every sport, feeding the
estimator AND consistency AND game_count_in_7d from one fetch.
VERIFIED on real props: p_win 25/25 WNBA, 8/8 MLB (was 0).
GRADE RANGE, ON MERIT — never by rescaling (permanent founder ruling:
minting A's without new information is a relabelled B sold as an A and
corrupts an append-only ledger).
- refreshTeamStats wired into runSnapshot — it had ZERO production
callers, so opp_rank_stat was permanently null and a +/-1.0 factor
could never fire. Test-env no-op (opsNotify precedent).
- L20 made SYMMETRIC: both branches were delta +1.0, so the season
baseline could only ever ADD. No negative path was a structural reason
D was unreachable. New l20_contradicts_* carries -1.0.
- game_count_in_7d derived from real logged dates (heavy_workload_7d).
- NOT wired, deliberately, with reasons inline: teamId (no team_id
column; getFeatures reads it top-level; factor also needs a starter-id
list) and season_type (ESPN 2 = REGULAR season; threading it raw would
fire veteran_in_playoffs in July). Dead code dressed as a fix is the
thing we are removing, not adding.
CALIBRATION GUARD (found by verifying, not assuming): consistency CV is
NBA-tuned; for a Poisson-ish stat cv ~ 1/sqrt(mean), so any stat with
mean < 4 auto-classifies boom_bust. First verification run showed 8/8 MLB
props boom_bust — a blanket -1.0 that dropped the board to all-C. Floored
at CONSISTENCY_MIN_MEAN=4 -> 'unknown' below. Absent beats wrong. MLB
low-count stats therefore still get no consistency factor: honest, not
fixed. Scale-free index-of-dispersion classifier is the open follow-up.
CONFIDENCE IS NOT A PROBABILITY: payloads carry confidence_basis:
'grade_band'. Corrected mlb-grade-degradation.md — its "25/25
grade<->confidence agreement" is a TAUTOLOGY (confidence is derived FROM
the letter, so it would report 25/25 even if every grade were wrong), not
a validation. Removed dead mlbGrader.js (referenced only by its own test)
and the stale computeFeatures comment claiming a penalty that never ran.
VERIFICATION (scripts/verify-grade-range.js, real props/logs/engine):
WNBA 25 props B 68%->32%, C 32%->64%, D 0->1 (4%); 11-step spread went
from 2 steps to 5 (C/C+/B-/D). The D is earned: Angel Reese assists o2.5,
p_win 0.365. Nothing flooded — grades got HARDER. A did not emit locally
because opp_rank_stat needs the Redis cache only prod populates (local
ceiling +3.0 vs the +4.5 A needs); reachability is proven arithmetically
and locked in tests. Prod A-emission is the outstanding fingerprint.
MARKETING HOLD: "A-RATED" (AccuracyBadge, TopSignals) is unsupported
until that fingerprint. Confirmed honest fallbacks render today —
/api/ledger/accuracy has B and C buckets only, so the badge shows
"MODEL · 63% HIT" and TopSignals self-hides. Nothing fabricated ships.
Suite 276/3286 green, web build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA
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