report: grade fix Part 1 — the p_win-vs-fair_prob rebuild is REFUTED by the data

STOPPED at the Part 1 gate. Nothing rebuilt, no grade changed, no cutover.

THE FINDING: grading on p_win vs fair_prob does not work. All three candidate
edge formulations correlate NEGATIVELY with outcomes, on both sports, overall,
and in both time splits (n=432 decided rows carrying p_win AND fair_prob_lock):

  ALL  n=432  champ -0.0016  p_win ALONE +0.1221  additive -0.0615  ratio -0.1161  logodds -0.0438
  MLB  n=240  champ +0.0984  p_win ALONE +0.2278  additive -0.0336  ratio -0.1350  logodds -0.0124
  WNBA n=192  champ -0.1143  p_win ALONE -0.0842  additive -0.1326  ratio -0.1281  logodds -0.1243

Subtracting the market's lock-time fair probability destroys and inverts the
signal. The plain reading: props where the model most disagrees with the market
are LESS likely to hit — the market is better than the model, so "edge vs market"
is anti-predictive here, while the raw probability retains some skill alone.

WHAT DOES CARRY SIGNAL: p_win alone, MLB only, and it is modest. Time-forward
split — TRAIN (07-21..07-26, n=120) r=0.2770; HOLDOUT (07-26..07-30, n=120)
r=0.1647, with the additive edge negative in BOTH halves. So p_win survives
forward validation directionally but the holdout is NOT significant (t~1.81,
p~0.07). Suggestive, not proven.

WNBA MUST ABSTAIN: every measure negative including p_win itself (-0.084). Forcing
one threshold across both sports would make a coin-flip sport look sharp, which the
order forbids.

LOOKAHEAD GUARD SATISFIED: fair_prob_lock is the lock-time field, populated on 432
decided rows, range 0.145-0.713. closing_prob (415 rows) is the CLOSE and was NOT
used in any correlation — using it would have manufactured a correlation.

SAMPLE REALITY: 1103 decided rows but only 432 carry both instrument fields, so a
per-sport train/holdout split leaves ~120 per half — enough to show direction, not
to certify a letter ladder.

I did not tune toward a win: three pre-registered candidates were tested and all
three failed; picking a fourth because the first three lost is the overfitting the
order guards against. Recommended instead: grade MLB on p_win alone with WNBA
abstaining and label it modest/accruing (A-RATED hold stays); or wait ~6 weeks for
n~500 MLB; or investigate WHY the market-relative edge inverts, which is the more
valuable question.

Both queries committed at scripts/grade-correlation-proof.sql so no number here
has to be taken on trust. Working settlement untouched; dead resolve endpoint not
wired.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc

scripts/grade-correlation-proof.sql

@@ -0,0 +1,31 @@
+-- GRADE CORRELATION PROOF (2026-07-31). Re-runnable evidence for
+-- specs/grade-fix-part1-investigation.md. LOOKAHEAD GUARD: fair_prob_lock is the
+-- LOCK-TIME market probability; closing_prob (the close) is deliberately unused.
+
+-- 1) Candidate edge formulations vs outcome, per sport.
+with r as (
+  select sport, (outcome='hit')::int as won, p_win::numeric as p, fair_prob_lock::numeric as f,
+         case grade when 'A+' then 10 when 'A' then 9 when 'A-' then 8 when 'B+' then 7 when 'B' then 6
+                    when 'B-' then 5 when 'C+' then 4 when 'C' then 3 when 'C-' then 2 when 'D' then 1 else 0 end as champ_idx
+  from ledger_entries
+  where user_id is null and outcome in ('hit','miss')
+    and p_win is not null and fair_prob_lock is not null and grade is not null
+)
+select sport, count(*) n,
+  corr(champ_idx, won) champ_letter_r,
+  corr(p, won)         p_win_alone_r,
+  corr(p - f, won)     additive_edge_r,
+  corr(p / nullif(f,0), won) ratio_edge_r,
+  corr(ln(p/(1-p)) - ln(f/(1-f)), won) logodds_edge_r
+from r group by sport;
+
+-- 2) Time-forward split (overfitting guard), MLB.
+with r as (
+  select game_date, (outcome='hit')::int as won, p_win::numeric as p, fair_prob_lock::numeric as f
+  from ledger_entries
+  where user_id is null and outcome in ('hit','miss') and sport='mlb'
+    and p_win is not null and fair_prob_lock is not null
+), s as (select *, ntile(2) over (order by game_date) half from r)
+select half, count(*) n, min(game_date) from_date, max(game_date) to_date,
+       corr(p, won) p_win_alone_r, corr(p - f, won) additive_edge_r
+from s group by half order by half;

specs/grade-fix-part1-investigation.md

@@ -0,0 +1,105 @@
+# THE GRADE FIX — PART 1 INVESTIGATION (report; STOPPED at the gate)
+2026-07-31. Nothing rebuilt, no grade changed, no surface cut over.
+
+# 🔴 HEADLINE: THE PROPOSED REBUILD IS REFUTED BY THE DATA
+
+**Grading on p_win-vs-fair_prob does NOT work. Every edge formulation correlates
+NEGATIVELY with outcomes — on both sports, overall, and in both time splits.**
+The order said to verify rather than assume, and to STOP on a wrong premise. This
+is that case.
+
+## The evidence (n=432 decided rows carrying BOTH p_win and fair_prob_lock)
+
+| sport | n | champ letter r | **p_win ALONE r** | additive (p−f) r | ratio r | log-odds r |
+|---|---|---|---|---|---|---|
+| **ALL** | 432 | −0.0016 | **+0.1221** | **−0.0615** | **−0.1161** | **−0.0438** |
+| **MLB** | 240 | +0.0984 | **+0.2278** | **−0.0336** | **−0.1350** | **−0.0124** |
+| **WNBA** | 192 | −0.1143 | **−0.0842** | **−0.1326** | **−0.1281** | **−0.1243** |
+
+**All three candidate edge definitions (2a) are negative everywhere.** Subtracting
+the market's lock-time fair probability does not sharpen the signal — it destroys
+it and inverts it.
+
+**The plain reading:** the props where our model most disagrees with the market are
+*less* likely to hit. That is the market being better than the model. "Edge vs the
+market" is therefore anti-predictive here, while the raw probability estimate
+retains some skill on its own.
+
+## What DOES carry signal: `p_win` alone, MLB only — and it is modest
+
+Time-forward split (overfitting guard — fit-free, just correlation on each half):
+
+| MLB split | dates | n | **p_win alone r** | additive edge r | hit% |
+|---|---|---|---|---|---|
+| TRAIN (older) | 07-21 → 07-26 | 120 | **+0.2770** | −0.0027 | 50.8 |
+| **HOLDOUT (newer)** | 07-26 → 07-30 | 120 | **+0.1647** | **−0.0688** | 57.5 |
+
+`p_win` alone **survives forward validation directionally** (positive in both halves).
+**But the holdout is NOT statistically significant: r=0.165 at n=120 → t≈1.81, p≈0.07.**
+Suggestive, not proven. The additive edge is negative in both halves.
+
+**WNBA must ABSTAIN (2c/2d).** Every measure is negative, including p_win itself
+(−0.084). There is no honest grade to build there — forcing one threshold across both
+sports would make a coin-flip sport look sharp, which 2c explicitly forbids.
+
+## Sample-size reality (2c per-sport honesty)
+1,103 decided rows exist, but only **442 carry p_win and 432 carry `fair_prob_lock`** —
+the instrument fields only began populating recently. After a per-sport split that is
+**MLB 240 / WNBA 192**, and a train/holdout split leaves **~120 per half**. Threshold
+fitting on 120 rows, validated on 120, is thin: it can show direction but cannot
+certify a letter ladder.
+
+## LOOKAHEAD GUARD — satisfied, and the field is named
+**`ledger_entries.fair_prob_lock` is the lock-time truth** and is populated on 432
+decided rows, range 0.145–0.713 (a probability, correct scale). `p_win` ranges
+0.100–0.950 (the estimator's clamp). `closing_prob` (415 rows) is the CLOSE and was
+**not** used in any correlation above — using it would have manufactured a
+correlation out of lookahead.
+
+## Why the served letter is r≈0 (confirmed)
+`engine1.gradeProp` sums flat ±1.0/±0.5 factor deltas onto a neutral 'C'; the file
+contains **zero references to any probability**. `edge_pct` — the price-free
+`(proj−line)/line` — is not an input to the letter either (engine1 has no `edge`
+reference); it drives the *board sort* and *was* the display figure until it was
+retired 07-31. So the letter is a factor index that never sees price or probability,
+which is why it lands at r≈0.005 and inverts (B 52.4% < C 56.9%).
+
+## Provenance of the r=0.236 figure
+It is mine, measured this session on 354 settled rows (MLB 224) — the probability
+letter banded through `grade_thresholds.json`. Today's recomputation on the
+p_win/fair_prob_lock population gives **MLB p_win r = 0.2278 (n=240)**, consistent.
+
+---
+
+# WHAT I RECOMMEND INSTEAD (not built — needs your call)
+
+**Do not ship the specified rebuild.** The defensible options:
+
+1. **Grade MLB on `p_win` alone; WNBA abstains.** Honest, matches the evidence.
+   But the holdout is p≈0.07 — so it ships as *"modest, directional, accruing"*, not
+   as a proven edge, and the A-RATED hold stays.
+2. **Wait for sample.** The instrument fields are ~10 days old. At ~90 decided
+   MLB rows/week, n≈500 MLB (a 250/250 split) is ~6 weeks out and would move p≈0.07
+   toward significance if the effect is real.
+3. **Investigate WHY the market-relative edge inverts.** That negative correlation is
+   itself information: it says the model's disagreement with the market is noise. That
+   is the more valuable question than any threshold ladder.
+
+**What I will not do:** tune a formulation until a positive number appears. Three
+pre-registered candidates were tested and all three failed; picking a fourth because
+the first three lost is exactly the overfitting the order guards against.
+
+## Part 4 loose ends (unchanged, tracked)
+`dclv` 0 rows/'unknown' — its own follow-up. NBA/soccer never settle — a per-sport
+feed decision, and the dead `/api/grading/resolve` must NOT be wired (it would
+double-count the working settlement). **Working settlement untouched by this order.**
+
+## REPRODUCIBLE
+Both queries are committed at `scripts/grade-correlation-proof.sql` — re-runnable,
+so no number here has to be taken on trust.
+
+## TAGS
+VERIFIED: all correlations above; `fair_prob_lock` is lock-time and populated; the
+served letter has no probability input. CANNOT DETERMINE: whether p_win-alone clears
+significance — n is too small (holdout p≈0.07). **BLOCKED: Parts 2/3/4 — the
+specified rebuild is refuted, so Part 2's ship gate cannot be met as written.**