vyndr/specs/audit-data/grade-collapse.md

GRADE COLLAPSE + DEAD PROBABILITY LAYER — diagnosis

REPORT ONLY. No grade logic, thresholds, or engine code changed (Kev's instruction). Two findings. The second one is bigger than the question I was asked.

Data: live Supabase ledger_entries (604 rows, all users) + live prod API api.vyndr.app, 2026-07-19 ~22:05 UTC.

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FINDING 1 — THE COLLAPSE IS REAL, LIVE, AND STRUCTURAL

Not a thin-slate artifact. Across 604 ledger rows and both sports:

• 2 distinct grades ever emitted: B and C. Zero A+, A, A−, B+, B−, C−, D, F.
• 9 distinct confidence values ever emitted: 63, 57, 55, 52, 47, 45, 35, 25, 20.
• Confidence ceiling = 63. It has never exceeded 63 in the recorded era.

Still true today (Jul 18 + 19, post every fix, both sports): 4 confidence values (63/57/52/47), 2 grades.

Sport	Grade	n	conf min	conf max
mlb	B	276	45	63
mlb	C	107	20	52
wnba	B	130	45	63
wnba	C	91	35	52

Confidence does NOT determine the letter

conf	grade	n
63	B	54
57	B	175
55	B	29
52	C	100
47	C	14
45	B	148
35	C	80

conf 45 → B, but conf 47 and 52 → C. The mapping is non-monotonic, so the surfaced confidence is not the quantity the letter was derived from. This confirms the mlb-grade-degradation.md "grade↔confidence mismatch" as a display artifact: two different quantities are being shown as if one explains the other.

(At conf 45→B the avg edge is 103; at conf 52→C it is 49 — so the letter tracks the engine composite/edge, not the displayed confidence.)

Collateral: the edge scale is still broken and still live

• 311 of 604 rows (51.5 %) have |edge| > 40 — the frontend's EDGE_BOARD_SANE_MAX, i.e. over half the board's edge is nulled at render.
• 39 rows have |edge| > 100 — impossible as a percentage. Worst: 620.
• Live today: edges of 140, 180, 220 on Jul 18–19 rows.

U-deg's projection == 0 leak IS closed (0 since 07-18). The edge_pct scale is not — it remains open and is now quantified.

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FINDING 2 — 🔴 THE ENTIRE PROBABILITY LAYER IS DEAD IN PRODUCTION

Found while fingerprinting Arc 1 (U-fp). This is the headline.

Live fingerprint, GET /api/snapshot/mlb, 8 graded props

Field	Present
projection, confidence, book_odds, fair_odds, takeable, devig_method, alt_lines	8 / 8
p_win	0 / 8
kelly	0 / 8
ev_pct	0 / 8
model_odds	0 / 8
value	0 / 8

Control: alt_lines is present 8/8 and is Desk-gated in tierGating.js:55, which proves the payload is not being tier-stripped. These fields are genuinely never computed — not hidden.

Root cause — a one-line sport gate, and an S46 fix that was only half-applied

analyzeViaEngine1.js:509 feeds the estimator from meta.gameLogs:

const est = estimateProbability({ gameLogs: meta.gameLogs, line: prop.line, ... });

meta.gameLogs comes from computeFeatures.js:173-181 → gameLogService.getGameLogs. And gameLogService.js:21-26:

function pythonPath(sport) {
  switch (sport) {
    case 'nba':  return '/stats/last-n';
    case 'wnba': return '/wnba/stats/last-n';
    default:     return null;      // ← MLB exits here
  }
}

with getGameLogs line 31: if (!path) return null;

So:

• MLB — returns null by construction. Never had game logs on this path.
• NBA/WNBA — hits the Python stats service, which is offline in prod (documented in CLAUDE.md; degrades to null).

⇒ meta.gameLogs is [] for every sport in production ⇒ estimateProbability returns {p_over: null, reason:'insufficient_data'} (probabilityEstimator.js:55-57) ⇒ pWin is null ⇒ every field guarded by if (pWin != null) is skipped: p_win, kelly, model_odds, ev_pct, value.

This is the S46 bug, second location, never fixed. CLAUDE.md records that gameLogService.getGameLogs being NBA/WNBA-only starved MLB, and that the fix was an MLB branch in featureCache.gameLogFeatures. That fixed the feature path — which is why projection, confidence, and grades still work. The estimator path was never given the same branch, so it has been silently dead the whole time.

What this actually breaks

1. EV — the Model Train's entire ranking signal — does not exist in production. Arc 1 shipped ev_pct and it has never once been computed on a live prop.
2. Hero v2 is non-functional. pickHeroProp requires a finite ev_pct (heroPropService.js:84), so the EV loop matches nothing and always falls through to the "most recent graded read" fallback. Live proof: /api/hero-prop returns "is_recent": true — the fallback path, every time. The hero has not been an EV pick since the day it shipped.
3. Quarter-Kelly is dead — same pWin dependency (analyzeViaEngine1.js:516-520). This is a promise-audit issue: Kelly sizing is sold on the pricing page and PROMISE-AUDIT.md lists it as BUILT. It is built and never runs.
4. The "value triplet" is a duet live — book_odds + fair_odds render; model_odds is always absent.
5. value is never true, so the VALUE marker can never light up.

Why this reframes the whole train

• C-led would persist a column of nulls. Do not build EV persistence until EV exists.
• G-a's EV_FLEX_THRESHOLD would gate on a permanently-null value. With EV_FLEX_ENFORCE=0 (Kev's ruling) this is harmless today — but had we enforced it, the flex band would have been cut to zero, because ev_pct >= 4 can never be true. The ruling to ship it disabled accidentally prevented an outage.
• S-b (rank board on EV) would rank on nulls.

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RELATIONSHIP BETWEEN THE TWO FINDINGS

They are adjacent, not identical, and both trace to the same missing input:

• The dead estimator explains why no probability-derived output exists (EV, Kelly, model_odds, p_win).
• It does not by itself explain the B/C letter collapse, because the letter comes from engine1's rule-based composite over the feature vector, which is alive.
• But they share a root: the model is running on a partial input set. One of its two probability inputs (the empirical quantile distribution over real game logs) is absent for 100 % of props, so whatever spread the composite was designed to produce is being generated from the surviving features only.

The 9-discrete-confidence-values pattern is consistent with a small set of additive rule hits — a scorer landing on a lattice rather than a continuum. Confirming exactly where the letter range compresses requires the engine1/threshold trace, which is the one open thread in this report.

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RECOMMENDATION (no code changed pending Kev's call)

Re-sequence: fix the dead estimator FIRST — before G-a, before C-led.

Rationale: it is the cheapest fix on the board (an MLB branch in the estimator's log source, mirroring the one already written for featureCache), and it simultaneously restores EV, Kelly, model_odds, the VALUE flag, and hero v2. Every other Arc 2-5 item is downstream of it. Building the gate, the persistence layer, or the board ranking on a null signal is building on nothing.

Suggested order:

1. Revive the probability layer (MLB branch + a real NBA/WNBA fallback, since Python is offline). Fingerprint that p_win/ev_pct appear live.
2. Then C-led — persist EV that now has values.
3. Then G-a — with the flex band still disabled per the standing ruling.
4. Separately: the grade-range investigation (engine1 composite + thresholds), which may be partly a consequence of step 1 and should be re-measured after it.

Open question for Kev: NBA/WNBA have no free game-log source on this path with Python down. espnStatsAdapter.getPlayerGameLog (Wave 0) already solves exactly this for featureCache — reusing it here is the obvious candidate, and costs no quota.

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Diagnosis 2026-07-19. Data + live API. No engine code changed.