report: grade diagnostic T0 — p_win is MISCALIBRATED, and it explains the inversion

STOPPED at the T0 gate as instructed. Nothing fixed, no recalibration applied,
no grade touched. T1-T4 deliberately not run.

T0 FIRES ON BOTH PRE-REGISTERED CONDITIONS.

Condition 1 (mean |predicted-actual| > 0.05): MLB ~0.094, WNBA ~0.139.
Condition 2 (monotonic slope): over-confidence GROWS with the prediction —
MLB +0.034 -> +0.043 -> +0.084 -> +0.190 -> +0.189; WNBA +0.044 -> +0.109 ->
+0.349. Worst cases: MLB predicted 0.842 actual 0.652 (n=23), predicted 0.917
actual 0.727 (n=11); WNBA predicted 0.730 actual 0.381 (n=21).

WHY THIS EXPLAINS THE INVERSION, mechanically: p_win is over-stated and the
overstatement SCALES with p_win, so p_win - fair_prob_lock is largest exactly
where p_win is most inflated. Those props hit less than claimed, so the edge
measure correlates negatively. The market was never the problem —
fair_prob_lock is not a bent ruler, the thing subtracted from it is. It also
explains why p_win ALONE still carries signal (+0.23 MLB): rank survives
miscalibration, differences do not.

This independently reconfirms the 2026-07-26 calibration finding (+0.02 at p<.5
-> +0.19 at p>=.8) on a newer, larger population, so it is structural rather
than sampling noise.

PART 0: P0a — only the GRADED side's fair prob is stored (fair_prob_lock;
no opposite-side field), so T1's two-side-sum check cannot run and must use the
stated no-vig recompute fallback. P0b — projection_locked_at exists as a
timestamptz so T2 is potentially runnable, but distinctness from lock time was
NOT verified because T0 gated it.

Two cautions recorded before Part 2 runs: the top MLB buckets where the error is
worst hold n=23 and n=11, so a flexible per-bucket correction would fit noise —
isotonic with pooling or single-parameter Platt is safer; and calibration fixes
magnitudes, so if the market is genuinely better the repaired edge may still land
at ~0, which would be the honest ceiling and gets reported rather than graded
around.

Query committed at scripts/grade-calibration-t0.sql.

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

scripts/grade-calibration-t0.sql

@@ -0,0 +1,13 @@
+-- T0 — CALIBRATION OF p_win (gates T1-T4). specs/grade-diagnostic-t0.md
+-- Pre-registered firing thresholds: mean |predicted-actual| > 0.05 across
+-- buckets, OR a monotonic over/under-confidence slope. BOTH fired.
+with r as (
+  select sport, p_win::numeric as p, (outcome='hit')::int as won
+  from ledger_entries
+  where user_id is null and outcome in ('hit','miss') and p_win is not null
+), b as (
+  select sport, width_bucket(p, 0.1, 1.0, 9) as bkt, p, won from r
+)
+select sport, bkt, count(*) n,
+       avg(p) predicted, avg(won) actual, avg(p)-avg(won) over_confidence
+from b group by sport, bkt having count(*) >= 10 order by sport, bkt;