opportunity_drift: coverage 94%, collinearity PASSES, holdout n-blocked

STEP 1 -- input mapped and measured. opportunity_drift 94% coverage on 100
real props: 100% for batters (total_bases, hits, home_runs), 40-67% for
pitchers, which is correct -- pitchers accumulate few at-bats so the ratio
is genuinely undefined and ABSTAINS rather than being invented.

STEP 2 -- THE COLLINEARITY GUARD PASSES DECISIVELY. Pearson r on n=94:
drift vs l20_avg -0.020, vs l5_avg +0.027, vs ab_per_game -0.029. All
essentially zero, so the axis is orthogonal to every existing projection
input and carries information the projection does not already contain.

That also validates the ratio-over-level decision EMPIRICALLY: ab_per_game
is the same quantity over the same denominator as l20_avg, so the level
would have been redundant. Dividing by the player's own baseline removed
the collinearity -- r = -0.029 against the very quantity it is built from.

STEP 3 -- live as a challenger, verified on prod over an induced 416-grade
snapshot: 142 of 276 rows (51.4%) carry the opportunity axis, the
challenger moved on 190 rows, mean |delta| 0.034, range -0.089..+0.108.
Champion p_win and the live grade path are unchanged.

STEP 4 -- HOLDOUT IS n-BLOCKED BY CONSTRUCTION and I am not manufacturing
one. Settled rows carrying the axis: 0. Its first rows carry game_date
2026-08-01 -- games that have not been played. Running the test on rows the
axis never touched would dilute the comparison with rows where challenger
=== champion by construction, making a null result look like a small
positive one. Query committed for when n arrives; it filters to
axis-carrying rows for exactly that reason, buckets before measuring
reliability, and splits time-forward. BOTH metrics must improve or the axis
is shelved.

A MEASUREMENT TRAP RECORDED: the first prod run showed drift at 0% while
ab_per_game read 94% -- indistinguishable from "the feature does not
compute". It was the 120-second feature-vector cache serving payloads
written by the previous image. A new feature field is invisible for one
cache generation after deploy. I nearly reported it absent, having already
confirmed atBats is present in the live statsapi payload and that the code
produced drift = 1.05 locally on that exact data; the contradiction
between those two facts is what saved it.

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

scripts/opportunity-axis-holdout.sql

@@ -0,0 +1,49 @@
+-- opportunity-axis-holdout.sql — the Step 4 proof, RUN WHEN n IS ADEQUATE.
+--
+-- Cannot run yet, by construction: the axis went live 2026-08-01 and its first
+-- rows carry game_date 2026-08-01 (games not yet played). Settled rows carrying
+-- the axis: 0. The earliest possible run is the next morning settle pass, and a
+-- defensible n is several days out at ~140 opportunity-axis rows per snapshot.
+--
+-- BOTH reliability AND resolution must improve for the axis to promote. One or
+-- neither => SHELVE and record why.
+--
+-- reliability = n-weighted mean |predicted - actual| across deciles. Bucket
+-- FIRST: mean|p - outcome| on 0/1 rows is noise-dominated individual error, not
+-- calibration.
+
+with rows_ as (
+  select game_date, id,
+         p_win::numeric              champ,
+         p_win_challenger::numeric   chal,
+         (outcome='hit')::int        won
+  from public.ledger_entries
+  where sport='mlb' and user_id is null
+    and outcome in ('hit','miss')
+    and p_win is not null and p_win_challenger is not null
+    -- ONLY rows the opportunity axis actually touched. Including untouched rows
+    -- would dilute the comparison with rows where challenger === champion by
+    -- construction, and make a null result look like a small positive one.
+    and challenger_adjustments::text like '%opportunity%'
+),
+split as (
+  select *, case when ntile(2) over (order by game_date, id) = 1 then 'train' else 'holdout' end split
+  from rows_
+),
+b_champ as (
+  select split, width_bucket(champ, 0.0, 1.0, 10) bkt, count(*) n, avg(champ) pred, avg(won::numeric) actual
+  from split group by 1,2),
+b_chal as (
+  select split, width_bucket(chal, 0.0, 1.0, 10) bkt, count(*) n, avg(chal) pred, avg(won::numeric) actual
+  from split group by 1,2)
+select
+  s.split,
+  count(*)                                                     n,
+  round((select sum(n*abs(pred-actual))/nullif(sum(n),0) from b_champ c where c.split=s.split),4) reliability_champion,
+  round((select sum(n*abs(pred-actual))/nullif(sum(n),0) from b_chal  c where c.split=s.split),4) reliability_challenger,
+  round(corr(s.champ, s.won::numeric)::numeric,4)              resolution_champion,
+  round(corr(s.chal,  s.won::numeric)::numeric,4)              resolution_challenger,
+  round(avg(s.won::numeric),4)                                 base_rate
+from split s
+group by s.split
+order by s.split desc;