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
5.9 KiB
MLB Grade Pipeline Degradation — FIXED (2026-07-17)
Source: phone-audit P1-7 (broken edge board) + P2-9 (B grades at 45% confidence).
Diagnosed against LIVE GET /api/snapshot/mlb on 2026-07-17. Backend grading
bug, fixed at the source in the generic grade path (engine1 +
analyzeViaEngine1), which grades EVERY sport.
Before (25 live MLB grades, degraded)
- projection == 0 for 9/25 — graded on a zero projection.
- edge_pct quantized to {20, 60, 100, 140} — the 100s were the proj=0
degeneracy
(line - 0)/line = 100%. - grade ↔ confidence mismatch — 10/25 disagreed even at the 4-letter level (25/25 vs the stricter 11-step bands).
Root causes + fixes (commit 888d103)
-
projection=0 bypassed the refusal gate.
projectionForreturnedl5_avgeven when 0 (finite → the== nullgate passed it). FIX: a non-positive reference is not a projection —projectionForskips it and falls through to the next POSITIVE reference (l5 → l20 → per_90 → xg); when none is positive it returns null and the read REFUSES (insufficient_data). The gate also gained an explicit> 0guard — the invariant is structural. -
edge_pct. Formula was already
(model - line)/line(the intended semantics); the {100} cluster was purely the proj=0 degeneracy. With fix 1 those refuse. Main-line edge now reuses the VALIDATED projection so edge and the persistedprojectioncan't diverge. -
confidence/letter split.
engine1.GRADE_TO_CONFIDENCEwas hand-rolled and drifted a full sub-tier low (B → 0.55, whichgrade_thresholds.jsoncalls B-). FIX: confidence is now DERIVED from each grade's band MIDPOINT ingrade_thresholds.json— one source of truth. Applying the threshold table to any grade's displayed confidence resolves back to the same letter (proven for all 11 grades intests/unit/mlbGradeDegradation.test.js).⚠️ CORRECTION (Session 63, 2026-07-19) — THE "25/25 AGREEMENT" WAS A TAUTOLOGY
Do not cite the 25/25 grade↔confidence agreement below as validation of grade quality. It validates nothing.
The fix above made
confidencea deterministic function of the letter: engine1 picks a letter via an additive factor index, then looks up that letter's band midpoint to produce the number (engine1.js:29-36). Feeding that number back through the same table can only ever return the letter it came from. The round-trip would report 25/25 even if every grade were wrong.It is a real fix for a real bug (the two encodings had drifted a sub-tier apart) — it is simply a consistency check, not an accuracy check.
confidencecarries ZERO information beyond the letter. The genuinely independent probability isp_win(the quantile estimate over real game logs), which Session 63 discovered had never been computed in production at all. Payloads now carryconfidence_basis: 'grade_band'so no consumer can mistake the derived number for a model probability.Full diagnosis:
specs/audit-data/grade-collapse.md.
Blast radius (commit 9fc4edf) — work-order #6
The degraded grades (projection=0 → model_value = 0) are already settled in
the append-only ledger_entries and are NOT deleted. Functional marking:
getModelAggregate now filters .gt('model_value', 0) on the settled AND
pending queries — the rows stay in the ledger but leave the public model record
(their hit/miss is noise, not skill). Post-fix no new such row can be written.
Exact count NOT queryable from the dev box (*.supabase.co is unreachable
here — curl 000; only vyndr.app/api.vyndr.app resolve; no VYNDR_INTERNAL_KEY
locally). Proxy signal: 9/25 (36%) of the current live slate. For the precise
figure, run in Supabase SQL:
SELECT count(*) FILTER (WHERE outcome IS NOT NULL) AS settled_degraded,
count(*) AS total_degraded
FROM ledger_entries
WHERE user_id IS NULL AND model_value = 0;
Other sports — work-order #5
NBA/WNBA/soccer grade through the SAME analyzeViaEngine1 → engine1 path
(gradeSlateService does not branch by sport; mlbGrader.js is dead code). So
they SHARE the disease and are fixed by the same commit. They rarely grade in
prod today (stats service offline off-season → refuse anyway). No separate fix.
Live validation — work-order #4
The fix deploys immediately, but the SNAPSHOT only re-grades on the full cron (UTC hours 14,19,22,1,3). Run after the next 14:00 UTC snapshot post-deploy:
node scripts/validate-grade-fix.js
PASS criteria: projection==0 count → 0; edge_pct no longer contains 100 and is not the four-value cluster; grade↔confidence agreement 25/25. The before-state (this file's "Before") is the diff baseline; the script's output is the fingerprint.
AFTER — VALIDATED LIVE (2026-07-17 14:00:53 UTC regrade, fingerprint)
Ran scripts/validate-grade-fix.js against the first post-deploy snapshot.
| Signal | Before (03:00 UTC) | After (14:00 UTC) |
|---|---|---|
| projection == 0 | 9 / 25 | 0 / 25 ✅ |
| grade ↔ confidence agree | 15 / 25 (4-letter); 0/25 vs 11-step | 25 / 25 ✅ |
| edge_pct distinct values | 4 — {20, 60, 100, 140} | 7 — {20, 60, 70, 76, 78, 82, 100} ✅ |
| projection distribution | contained 0 | all positive (min 0.09, no zeros) ✅ |
The nine projection-0 grades vanished (those props now refuse). Grade and
confidence agree on every row. Edges are continuous, not the degenerate cluster.
The single remaining 100 is NOT the old bug: Wilyer Abreu · hits · line 0.5 ·
projection 1.0 · over → (1.0 − 0.5)/0.5 = 100%, a real model call of double
a small line. The (model − line)/line metric inherently produces large % on
0.5-step lines — the frontend |edge| > 40 guard is the intended safety net for
exactly that, and it stays. All three bugs resolved; validated in production.