aaa41134d41831d8d7db278aa68f6f3d235610bc
4 Commits
| Author | SHA1 | Message | Date | |
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 builtbykev
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a80868c4eb |
S63 fingerprint: probability layer verified live (p_win/ev_pct/model_odds)
POST /api/analyze/prop on prod returns p_win 0.523, ev_pct -10.4, model_odds -109, confidence_basis grade_band, value false — every one of which was absent on 100% of grades before this change. The value triplet is whole (book -140 / fair -125 / model -109) and correctly refuses to call a -140 price value when the model gives it 52.3%. A-emission still pending the 01:00 UTC snapshot (opp_rank_stat populates only when refreshTeamStats runs in a snapshot). MARKETING HOLD on A-RATED copy stays until that passes. edge_pct scale remains broken (U-deg pt 2). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |
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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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builtbykev
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416639efe4 |
Grade collapse: mechanism traced — A is mathematically unreachable
Completes the diagnosis. Report only; no grade logic or thresholds changed.
The grade is an integer index (GRADE_SCALE, NEUTRAL_INDEX 3) moved by a
flat sum of +/-1.0 and +/-0.5 factor deltas, then clamped and rounded.
grade_thresholds.json is NOT an input mapper in the JS path — engine1
reads it BACKWARDS, taking the letter the index already produced and
looking up that band's midpoint to manufacture `confidence`. So
confidence is a cosmetic re-encoding of the letter: zero information
beyond it, and it can never disagree with it. There is no
data-sufficiency penalty in the live path (the one CLAUDE.md describes is
in mlbGrader.js, which is dead code).
Six of thirteen factors are wired to features nothing populates —
verified: refreshTeamStats has ZERO production callers (so opp_rank_stat
is permanently null, killing a +/-1.0), teamId/season_type/
game_count_in_7d are never passed (gameContext is built as {home_away}
and nothing else), and MLB consistency starves on the same dead
gameLogService path as Finding 2. Also verified: BOTH l20 branches are
delta +1.0 — there is no negative L20 contribution at all.
Arithmetic: an A needs sum >= +4.5; the live maximum is +3.0 (+2.0 on a
back-to-back, and MLB rest_days is 0 most days). D needs <= -1.51; the
live minimum is -1.5 and Math.round(1.5)=2, so it misses by one rounding
tick. Reachable band is index 2..6 = {C-,C,C+,B-,B}, which the adapter's
FOUR_LETTER_MAP (a 3->1 collapse) renders as exactly {C,B} — the observed
output, derived from first principles. Reachable confidences {42,47,52,
57,63} match the live values {47,52,57,63} exactly; C- is truncated by
gradeSlateService keeping the higher-confidence side.
mlb-grade-degradation.md's "25/25 grade<->confidence agreement" is a
TAUTOLOGY, not a validation — confidence is derived from the letter, so
it would report 25/25 even if every grade were wrong.
Recommends feeding the starving factors (restores A/D on merit) and
explicitly REJECTS re-scaling thresholds, which would mint A's without
adding information — every "A" would be a relabelled B.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA
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builtbykev
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3fe840ab83 |
Diagnose grade collapse + find the DEAD probability layer (report only)
Kev's call: investigate the B/C grade collapse before building. Report only — no grade logic, thresholds, or engine code touched. FINDING 1 — the collapse is real, live and structural. Across 604 ledger rows and both sports the engine has emitted exactly TWO grades (B, C) and NINE confidence values (63/57/55/52/47/45/35/25/20), ceiling 63. Still true today on both sports. Confidence does NOT determine the letter: conf 45 -> B while 47 and 52 -> C (non-monotonic), so the surfaced confidence is not the quantity the letter came from. Edge scale still broken: 311/604 rows exceed the frontend's sane cap of 40, 39 exceed 100, worst 620. FINDING 2 (bigger) — the entire probability layer is DEAD in production. Live /api/snapshot/mlb: p_win, kelly, ev_pct, model_odds and value are absent on 0/8 grades, while alt_lines (Desk-gated) IS present 8/8 — proving nothing is tier-stripped, they are simply never computed. Root cause: gameLogService.pythonPath returns null for MLB by construction and the Python service is offline for NBA/WNBA, so meta.gameLogs is [] for every sport; estimateProbability returns p_over null; every field guarded by `if (pWin != null)` is skipped. This is the S46 bug in a second location — that fix added an MLB branch to featureCache.gameLogFeatures (which is why grades/projections still work) but never to the estimator path. Consequences: EV — the Model Train's whole ranking signal — has never been computed on a live prop. Hero v2 matches nothing and always falls through to the recent-read fallback (live /api/hero-prop returns is_recent:true). Quarter-Kelly, sold on the pricing page and listed BUILT in PROMISE-AUDIT.md, never runs. The value triplet is a duet live. Recommend re-sequencing: revive the probability layer BEFORE G-a and C-led (C-led would persist a column of nulls; G-a's EV_FLEX_THRESHOLD would gate on a permanently-null value — Kev's EV_FLEX_ENFORCE=0 ruling accidentally prevented an outage). featureCache:206-226 already has both adapter branches and is the template. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA |