Revive the dead probability layer + restore grade range ON MERIT

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

src/services/intelligence/featureCache.js

@@ -199,6 +199,80 @@ function nbaGameLogFeatures(res, statType) {
   return out;
 }
 
+/**
+ * Session 63 — NORMALIZED PER-GAME STAT ROWS.
+ *
+ * The probability estimator (`probabilityEstimator.estimateProbability`) and the
+ * consistency scorer both read a game-log row as `row[statType]`. The ONLY
+ * producer wired to them was `gameLogService.getGameLogs`, which returns null for
+ * MLB by construction and depends on the offline Python service for NBA/WNBA —
+ * so `meta.gameLogs` was `[]` for every sport in production and every
+ * probability-derived output (p_win, ev_pct, kelly, model_odds, value) was
+ * silently skipped, along with the ±1.0 consistency factor.
+ *
+ * This is the S46 fix applied to the SECOND location: same adapters, same maps
+ * (no new stat map — the three-map-split rule stands), emitting rows in the shape
+ * those two consumers already expect:
+ *
+ *     [{ date, [statType]: value }, ...]   MOST-RECENT-FIRST
+ *
+ * Most-recent-first matters: the estimator treats `values.slice(0, 5)` as the
+ * recency window. Returns [] (never null) when no real log exists — absent beats
+ * a fabricated distribution.
+ */
+async function getStatRows(playerName, sport, statType) {
+  const sp = String(sport || '').toLowerCase();
+  const rows = [];
+  const push = (date, value) => {
+    if (value == null || !Number.isFinite(Number(value))) return;
+    rows.push({ date: date || null, [statType]: Number(value) });
+  };
+
+  try {
+    if (sp === 'mlb') {
+      const mlbStats = require('../adapters/mlbStatsAdapter');
+      const res = await mlbStats.getPlayerStats(playerName);
+      const logs = (res && res.found && Array.isArray(res.last10)) ? res.last10 : [];
+      // MLB logs are chronological (most recent LAST) — reverse to match.
+      for (const g of [...logs].reverse()) push(g && g.date, mlbStatValue(g && g.stat, statType));
+      return rows;
+    }
+
+    // NBA/WNBA — Python service first (it's the richer source when it's up),
+    // then the FREE ESPN per-athlete gamelog. Same order as gameLogFeatures.
+    const pyLogs = await gameLogs.getGameLogs(playerName, sp, 20);
+    if (Array.isArray(pyLogs) && pyLogs.length) {
+      // Python rows are already flat + most-recent-first.
+      for (const r of pyLogs) push(r && r.date, statFromGameLog(r, statType));
+      return rows;
+    }
+
+    if (sp === 'nba' || sp === 'wnba') {
+      const espnStats = require('../adapters/espnStatsAdapter');
+      const res = await espnStats.getPlayerGameLog(playerName, sp);
+      const logs = (res && res.found && Array.isArray(res.last10)) ? res.last10 : [];
+      const field = NBA_LOG_FIELD[statType];
+      if (!field) return rows;   // unmapped stat → no rows, never a guess
+      // ESPN last10 is most-recent-first already.
+      for (const g of logs) push(g && g.date, statFromGameLog(g && g.stat, field));
+    }
+    return rows;
+  } catch (e) {
+    console.warn('[featureCache] getStatRows failed:', e.message);
+    return [];
+  }
+}
+
+/** Games played in the trailing `days` window, from normalized rows. Powers the
+ *  `heavy_workload_7d` factor, whose feature nothing populated. */
+function gameCountInWindow(statRows, days = 7, now = Date.now()) {
+  if (!Array.isArray(statRows)) return null;
+  const cutoff = now - days * 86_400_000;
+  const dated = statRows.filter((r) => r && r.date && !Number.isNaN(new Date(r.date).getTime()));
+  if (dated.length === 0) return null;
+  return dated.filter((r) => new Date(r.date).getTime() >= cutoff).length;
+}
+
 async function gameLogFeatures(playerName, sport, statType) {
   // MLB game logs come from the FREE statsapi.mlb.com (Session 46) — the Python
   // gameLogService only covers NBA/WNBA, so MLB props had no recent/season
@@ -401,6 +475,8 @@ function getCacheStats() {
 
 module.exports = {
   getFeatures,
+  getStatRows,
+  gameCountInWindow,
   clearCache,
   getCacheStats,
   // Internal helpers exported for unit tests + Engine 2 reuse.