diff --git a/scripts/calibrate-four-stats.js b/scripts/calibrate-four-stats.js new file mode 100644 index 0000000..41891bc --- /dev/null +++ b/scripts/calibrate-four-stats.js @@ -0,0 +1,267 @@ +#!/usr/bin/env node +'use strict'; + +/** + * calibrate-four-stats — Phases 2, 3, 4 and 7. + * + * ── ONE SIDE PER PROP, OR THE MEASUREMENT IS MEANINGLESS ───────────────── + * 97.6% of snapshot props carry BOTH the over and the under. Their p_wins sum to + * ~1 and their outcomes are complementary, so any calibration statistic over the + * raw population is pinned to 0.5 by symmetry. Measured that way the counter + * looks perfectly calibrated (+0.0002 on hits); deduped to the model-PICKED side + * it is +0.0868. Same rows, opposite conclusion. + * + * ── DATE-CLUSTERED, PER THE ORDER ──────────────────────────────────────── + * A day's offensive environment is a real shared component, so uncertainty is + * clustered on the game DATE rather than the game. That is the honest unit for a + * systematic-bias claim and it is a much harder bar than game-clustering. + * + * SUPABASE_URL=... node scripts/calibrate-four-stats.js + */ + +require('dotenv').config(); +const fs = require('fs'); +const path = require('path'); +const { createClient } = require('@supabase/supabase-js'); +const cal = require('../src/services/model/calibration'); +const { knownNumber } = require('../src/utils/known'); + +const SB_URL = process.env.SUPABASE_URL; +const SB_KEY = process.env.SUPABASE_SERVICE_ROLE_KEY || process.env.SUPABASE_SERVICE_KEY; +const BOX = path.join(process.cwd(), '.seq-cache', 'batting-lines.json'); +const STATS = ['hits', 'total_bases', 'rbi', 'runs']; +const PAGE = 1000; +/** The order's deploy floor: dates, not games. */ +const MIN_DATE_CLUSTERS = 40; + +const FIELD = { hits: (b) => b.hits, total_bases: (b) => b.totalBases, rbi: (b) => b.rbi, runs: (b) => b.runs }; +const mean = (xs) => (xs.length ? xs.reduce((a, b) => a + b, 0) / xs.length : null); +const brier = (ps, ys) => mean(ps.map((p, i) => (p - ys[i]) ** 2)); + +async function page(sb, table, select, apply) { + const out = []; + for (let from = 0; ; from += PAGE) { + const { data, error } = await apply(sb.from(table).select(select)) + .order('id', { ascending: true }).range(from, from + PAGE - 1); + if (error) throw error; + if (!data || data.length === 0) break; + out.push(...data); + if (data.length < PAGE) break; + } + return out; +} + +const isPreGame = (capturedAt, gameDate) => { + const et = new Date(new Date(capturedAt).getTime() - 4 * 3600 * 1000); + const d = et.toISOString().slice(0, 10); + return d < gameDate || (d === gameDate && et.getUTCHours() < 19); +}; + +function makeRnd(seed) { + let s = seed >>> 0; + return () => { s ^= s << 13; s >>>= 0; s ^= s >>> 17; s ^= s << 5; s >>>= 0; return s / 4294967296; }; +} + +/** Paired bootstrap on the Brier difference, resampling DATES. */ +function dateClusteredCI(rows, cumulativeTests = 1, iters = 3000) { + const byDate = new Map(); + for (const r of rows) { + if (!byDate.has(r.date)) byDate.set(r.date, []); + byDate.get(r.date).push(r); + } + const keys = [...byDate.keys()]; + const rnd = makeRnd(20260807); + const diffs = []; + for (let it = 0; it < iters; it += 1) { + const raw = []; const adj = []; const ys = []; + for (let i = 0; i < keys.length; i += 1) { + for (const r of byDate.get(keys[Math.floor(rnd() * keys.length)])) { + raw.push(r.p); adj.push(r.pc); ys.push(r.won); + } + } + diffs.push(brier(adj, ys) - brier(raw, ys)); + } + diffs.sort((a, b) => a - b); + const tests = Math.max(1, Math.round(cumulativeTests)); + const alpha = 0.05 / tests; + const q = (x) => diffs[Math.floor(Math.min(diffs.length - 1, Math.max(0, x * (diffs.length - 1))))]; + return { ci: [round4(q(alpha / 2)), round4(q(1 - alpha / 2))], date_clusters: keys.length, ci_level: round4(1 - alpha) }; +} + +async function main() { + const sb = createClient(SB_URL, SB_KEY, { auth: { persistSession: false } }); + const lines = JSON.parse(fs.readFileSync(BOX, 'utf8')).lines; + + const snaps = await page(sb, 'model_snapshots', + 'id, game_date, captured_at, stat, player_key, line, side, p_win, refused, grade', + (q) => q.eq('sport', 'mlb').in('stat', STATS)); + + // ── ONE SIDE PER PROP: the side the model picked (its higher p_win). ── + const picked = new Map(); + const refusedProps = new Map(); + for (const r of snaps) { + if (!isPreGame(r.captured_at, r.game_date)) continue; + const k = [r.game_date, r.stat, r.player_key, r.line].join('|'); + if (r.refused || knownNumber(r.p_win) === null) { + if (!refusedProps.has(k)) refusedProps.set(k, r); + continue; + } + const prev = picked.get(k); + if (!prev || knownNumber(r.p_win) > knownNumber(prev.p_win)) picked.set(k, r); + } + + const resolve = (r) => { + const b = lines[`${r.game_date}|${r.player_key}`]; + const L = knownNumber(r.line); + if (!b || L === null || !r.side) return null; + const v = knownNumber(FIELD[r.stat](b)); + if (v === null) return null; + const over = v > L; + return { over, won: (String(r.side).toLowerCase() === 'under' ? !over : over) ? 1 : 0, realized: v }; + }; + + const out = { deploy_floor_date_clusters: MIN_DATE_CLUSTERS, per_stat: {}, refusal_accuracy: {} }; + + for (const stat of STATS) { + const rows = []; + for (const r of picked.values()) { + if (r.stat !== stat) continue; + const res = resolve(r); + if (!res) continue; + rows.push({ date: r.game_date, p: knownNumber(r.p_win), won: res.won }); + } + rows.sort((a, b) => String(a.date).localeCompare(String(b.date))); + const dates = [...new Set(rows.map((r) => r.date))].sort(); + + if (rows.length < 100 || dates.length < 3) { + out.per_stat[stat] = { n: rows.length, date_clusters: dates.length, decision: 'REFUSE', reason: 'too few rows or dates to split point-in-time' }; + continue; + } + + // POINT-IN-TIME: fit strictly on earlier dates, evaluate on later ones. + // + // The cut is placed by ROW COUNT rather than by date index. Props are not + // spread evenly across dates -- hits concentrate in the later ones -- so a + // 60%-of-DATES cut left only 143 rows to fit on, under the 200 the fitter + // needs. Splitting on cumulative rows keeps the split strictly temporal + // (every fit date precedes every eval date) while giving both sides enough + // to work with. + const perDate = new Map(); + for (const r of rows) perDate.set(r.date, (perDate.get(r.date) || 0) + 1); + let acc = 0; let cut = dates[dates.length - 1]; + for (const d of dates) { + acc += perDate.get(d) || 0; + if (acc >= rows.length * 0.45) { cut = d; break; } + } + const fit = rows.filter((r) => r.date < cut); + const ev = rows.filter((r) => r.date >= cut); + if (fit.length < 50 || ev.length < 50) { + out.per_stat[stat] = { n: rows.length, date_clusters: dates.length, decision: 'REFUSE', reason: 'time split leaves too little on one side' }; + continue; + } + + const iso = cal.fitIsotonic(fit.map((r) => ({ p: r.p, won: r.won }))); + // NULL IS NOT A PREDICTION. fitIsotonic returns null below its minimum and + // applyIsotonic then returns null per row -- and (null - 1)**2 === 1 while + // (null - 0)**2 === 0, so a "Brier score" computed over nulls is silently + // just the win rate. That is exactly the Number(null) === 0 breach this + // codebase keeps having to catch, and it produced a fake 0.5567 for hits. + if (!iso) { + out.per_stat[stat] = { + n: rows.length, date_clusters: dates.length, fit_n: fit.length, eval_n: ev.length, + decision: 'REFUSE', reason: `no calibration map could be fitted on ${fit.length} fit rows`, + }; + continue; + } + const scored = ev.map((r) => ({ ...r, pc: cal.applyIsotonic(iso, r.p) })) + .filter((r) => knownNumber(r.pc) !== null); + if (scored.length < 50) { + out.per_stat[stat] = { + n: rows.length, date_clusters: dates.length, + decision: 'REFUSE', reason: `only ${scored.length} eval rows could be mapped`, + }; + continue; + } + const ys = scored.map((r) => r.won); + const bRaw = brier(scored.map((r) => r.p), ys); + const bCal = brier(scored.map((r) => r.pc), ys); + const { ci, date_clusters, ci_level } = dateClusteredCI(scored, 1); + + // CERTIFIED BAND: p_win deciles where held-out |predicted - actual| is small. + const bands = []; + for (let lo = 0.3; lo < 0.95; lo += 0.1) { + const slice = scored.filter((r) => r.p >= lo && r.p < lo + 0.1); + if (slice.length < 25) continue; + const pred = mean(slice.map((r) => r.pc)); + const act = mean(slice.map((r) => r.won)); + bands.push({ range: [round2(lo), round2(lo + 0.1)], n: slice.length, calibrated_pred: round4(pred), actual: round4(act), err: round4(pred - act) }); + } + const certified = bands.filter((b) => Math.abs(b.err) <= 0.05).map((b) => b.range); + + const improves = bCal < bRaw && ci[1] < 0; + const enoughDates = date_clusters >= MIN_DATE_CLUSTERS; + + // PHASE 4 — bias SHAPE across the p_win range (diagnostic only). + const shape = []; + for (let lo = 0.3; lo < 0.95; lo += 0.1) { + const slice = rows.filter((r) => r.p >= lo && r.p < lo + 0.1); + if (slice.length < 25) continue; + shape.push({ range: [round2(lo), round2(lo + 0.1)], n: slice.length, bias: round4(mean(slice.map((r) => r.p)) - mean(slice.map((r) => r.won))) }); + } + + out.per_stat[stat] = { + n: rows.length, + date_clusters: dates.length, + bias_pre: round4(mean(rows.map((r) => r.p)) - mean(rows.map((r) => r.won))), + fit_n: fit.length, eval_n: ev.length, split_at: cut, + brier_raw: round4(bRaw), + brier_calibrated: round4(bCal), + brier_delta: round4(bCal - bRaw), + ci_date_clustered: ci, + ci_level, + eval_date_clusters: date_clusters, + certified_bands: certified, + band_detail: bands, + bias_shape: shape, + decision: improves && enoughDates ? 'DEPLOY' : 'REFUSE', + reason: improves && enoughDates ? 'held-out Brier improves, date-clustered, and the date floor is met' + : (!enoughDates + ? `date-clusters ${date_clusters} < ${MIN_DATE_CLUSTERS} — the honest unit for a systematic-bias claim` + : 'held-out Brier does not improve at the date-clustered interval'), + }; + } + + // ── PHASE 7 — REFUSAL ACCURACY ── + // The model passed on these. A pass is CORRECT when there was genuinely + // nothing to call: the over lands near a coin flip rather than at an + // exploitable rate. + for (const stat of STATS) { + const refs = []; + for (const r of refusedProps.values()) { + if (r.stat !== stat) continue; + const res = resolve({ ...r, side: 'over' }); + if (res) refs.push(res.over ? 1 : 0); + } + const graded = []; + for (const r of picked.values()) { + if (r.stat !== stat) continue; + const res = resolve({ ...r, side: 'over' }); + if (res) graded.push(res.over ? 1 : 0); + } + out.refusal_accuracy[stat] = { + refused_n: refs.length, + refused_over_rate: refs.length ? round4(mean(refs)) : null, + graded_over_rate: graded.length ? round4(mean(graded)) : null, + refused_distance_from_coinflip: refs.length ? round4(Math.abs(mean(refs) - 0.5)) : null, + graded_distance_from_coinflip: graded.length ? round4(Math.abs(mean(graded) - 0.5)) : null, + }; + } + + console.log(JSON.stringify(out, null, 2)); + process.exit(0); +} + +const round4 = (v) => (v == null || !Number.isFinite(v) ? null : Math.round(v * 10000) / 10000); +const round2 = (v) => Math.round(v * 100) / 100; + +main().catch((e) => { console.error(e); process.exit(1); }); diff --git a/scripts/settle-model-snapshots.js b/scripts/settle-model-snapshots.js new file mode 100644 index 0000000..baa612e --- /dev/null +++ b/scripts/settle-model-snapshots.js @@ -0,0 +1,249 @@ +#!/usr/bin/env node +'use strict'; + +/** + * settle-model-snapshots — pay the standing debt. + * + * 71,192 snapshot rows have never carried an outcome. They are the retention + * table built for exactly this kind of replay, and until they are settled every + * measurement in this programme runs on the far smaller ledger slice. + * + * ── OUTCOME IS SIDE-ALIGNED, NOT RAW ───────────────────────────────────── + * The order specifies `outcome = 1[realized > line]`. That is the OVER + * perspective, and it would be backwards for every under-side prop — `p_win` is + * side-aligned (verified: TB mean p_win 0.5698 against a 0.5074 side-won rate), + * so a raw over-indicator would silently invert the target on the under rows and + * make calibration measure the wrong thing. + * + * So: `actual_value` stores the realized stat (raw, unopinionated) and `outcome` + * stores whether the GRADED SIDE won. Deviation from the literal order, stated + * because it changes the number. + * + * ── INTEGRITY (hard-fail) ──────────────────────────────────────────────── + * conservation settled + unresolvable + orphaned == candidates + * no dupes one write per snapshot id + * no orphans a settled row must have matched a real box score + * prediction-time logging captured_at must PRECEDE the game date; a row + * logged after the fact is not a prediction and is refused + * + * node scripts/settle-model-snapshots.js # dry run, verifies only + * SETTLE_WRITE=1 node scripts/settle-model-snapshots.js + */ + +require('dotenv').config(); +const fs = require('fs'); +const path = require('path'); +const axios = require('axios'); +const { createClient } = require('@supabase/supabase-js'); +const { nameKey } = require('../src/utils/playerName'); +const { knownNumber } = require('../src/utils/known'); + +const SB_URL = process.env.SUPABASE_URL; +const SB_KEY = process.env.SUPABASE_SERVICE_ROLE_KEY || process.env.SUPABASE_SERVICE_KEY; +const WRITE = process.env.SETTLE_WRITE === '1'; +const BOX_CACHE = path.join(process.cwd(), '.seq-cache', 'batting-lines.json'); +const STATS = ['hits', 'total_bases', 'rbi', 'runs']; +const PAGE = 1000; + +/** Realized value per stat, from the box-score batting line. */ +const FIELD = Object.freeze({ + hits: (b) => knownNumber(b.hits), + total_bases: (b) => knownNumber(b.totalBases), + rbi: (b) => knownNumber(b.rbi), + runs: (b) => knownNumber(b.runs), +}); + +const get = async (url) => (await axios.get(url, { timeout: 45_000 })).data; + +/** Eastern first pitch, conservatively. Anything at or after this is in-game. */ +const FIRST_PITCH_ET_HOUR = 19; + +/** + * Was this row logged BEFORE the games it grades? + * + * The pipeline runs on UTC cron hours, so a 01:00-UTC cycle is 21:00 the + * PREVIOUS evening in Eastern -- same game date, three hours into the slate. + */ +function isPreGame(capturedAt, gameDate) { + if (!capturedAt || !gameDate) return false; + const cap = new Date(capturedAt); + if (Number.isNaN(cap.getTime())) return false; + const et = new Date(cap.getTime() - 4 * 3600 * 1000); // EDT + const etDate = et.toISOString().slice(0, 10); + if (etDate < String(gameDate)) return true; // day before, fine + if (etDate > String(gameDate)) return false; // day after, post-game + return et.getUTCHours() < FIRST_PITCH_ET_HOUR; +} + +async function pool(items, fn, n = 6) { + const out = []; let i = 0; + await Promise.all(Array.from({ length: n }, async () => { + while (i < items.length) { + const idx = i; i += 1; + try { out[idx] = await fn(items[idx]); } catch { out[idx] = null; } + } + })); + return out.filter(Boolean); +} + +async function page(sb, table, select, apply) { + const out = []; + for (let from = 0; ; from += PAGE) { + // STABLE ORDER. model_snapshots is a LIVE table -- the snapshot cron writes + // to it at 14/19/22/1/3 UTC -- and an unordered .range() walk over a table + // being appended to returns overlapping pages. The integrity gate caught + // exactly that on the first run. + const { data, error } = await apply(sb.from(table).select(select)) + .order('id', { ascending: true }) + .range(from, from + PAGE - 1); + if (error) throw error; + if (!data || data.length === 0) break; + out.push(...data); + if (data.length < PAGE) break; + } + return out; +} + +/** Box-score batting lines for a date range, cached. */ +async function battingLines(dates) { + if (fs.existsSync(BOX_CACHE)) { + const c = JSON.parse(fs.readFileSync(BOX_CACHE, 'utf8')); + if (dates.every((d) => c.dates.includes(d))) return c.lines; + } + const games = []; + for (const d of dates) { + try { + const s = await get(`https://statsapi.mlb.com/api/v1/schedule?sportId=1&date=${d}`); + for (const day of s.dates || []) { + for (const g of day.games || []) { + if (String(g.status && g.status.detailedState) === 'Final') { + games.push({ pk: g.gamePk, date: g.officialDate || d }); + } + } + } + } catch { /* absent day */ } + } + console.error(`[settle] ${games.length} final games across ${dates.length} dates`); + + const lines = {}; + const loaded = await pool(games, async (g) => { + const box = await get(`https://statsapi.mlb.com/api/v1/game/${g.pk}/boxscore`); + const out = []; + for (const side of ['home', 'away']) { + const t = box.teams[side]; + if (!t) continue; + for (const id of t.batters || []) { + const pl = t.players[`ID${id}`]; + const b = pl && pl.stats && pl.stats.batting; + if (!b || b.atBats == null) continue; // did not bat -> absent, not zero + out.push({ + date: g.date, + key: nameKey(pl.person && pl.person.fullName), + name: pl.person && pl.person.fullName, + gamePk: g.pk, + hits: b.hits, totalBases: b.totalBases, rbi: b.rbi, runs: b.runs, atBats: b.atBats, + }); + } + } + return out; + }); + for (const arr of loaded) for (const r of arr) { + const k = `${r.date}|${r.key}`; + // A doubleheader gives two lines; sum them — the prop covers the day. + if (!lines[k]) lines[k] = { ...r, games: 1 }; + else { + lines[k].hits += r.hits; lines[k].totalBases += r.totalBases; + lines[k].rbi += r.rbi; lines[k].runs += r.runs; lines[k].atBats += r.atBats; + lines[k].games += 1; + } + } + fs.mkdirSync(path.dirname(BOX_CACHE), { recursive: true }); + fs.writeFileSync(BOX_CACHE, JSON.stringify({ dates, lines })); + return lines; +} + +async function main() { + const sb = createClient(SB_URL, SB_KEY, { auth: { persistSession: false } }); + + const snaps = await page(sb, 'model_snapshots', + 'id, game_date, captured_at, stat, player_key, player_name, line, side, p_win, refused, outcome', + (q) => q.eq('sport', 'mlb').in('stat', STATS).is('outcome', null)); + console.error(`[settle] ${snaps.length} unsettled snapshot rows`); + + const dates = [...new Set(snaps.map((r) => r.game_date))].sort(); + const lines = await battingLines(dates); + + const counts = { candidates: snaps.length, settled: 0, unresolvable: 0, orphaned: 0, post_hoc_logged: 0 }; + const updates = []; + const seenIds = new Set(); + + for (const s of snaps) { + // Belt and braces: ordered pagination should make this impossible, and a + // duplicate would double-count a prediction in every downstream measurement. + if (seenIds.has(s.id)) throw new Error(`INTEGRITY: duplicate snapshot id ${s.id}`); + seenIds.add(s.id); + + // A row logged after first pitch is not a prediction. + // + // Measured: cycles at ET 21:00/22:00/23:00 on the game date (10,738 rows) + // were captured DURING or AFTER the games they grade, and a further 664 the + // following morning. Games start ~19:05 ET, so the honest cutoff is ET + // first pitch on the game date -- not a UTC date compare, which both keeps + // post-game 01:00-UTC rows and discards legitimate pre-dawn ones. + if (!isPreGame(s.captured_at, s.game_date)) { + counts.post_hoc_logged += 1; counts.unresolvable += 1; continue; + } + const line = knownNumber(s.line); + if (line === null || !s.side) { counts.unresolvable += 1; continue; } + + const b = lines[`${s.game_date}|${s.player_key}`]; + if (!b) { counts.orphaned += 1; continue; } + + const realized = FIELD[s.stat](b); + if (realized === null) { counts.unresolvable += 1; continue; } + + // SIDE-ALIGNED, so it matches how p_win is expressed. + const over = realized > line; + const won = String(s.side).toLowerCase() === 'under' ? !over : over; + updates.push({ id: s.id, outcome: won ? 'hit' : 'miss', actual_value: realized }); + counts.settled += 1; + } + + // CONSERVATION — hard fail. + const acc = counts.settled + counts.unresolvable + counts.orphaned; + if (acc !== counts.candidates) { + throw new Error(`INTEGRITY: conservation violated ${acc} != ${counts.candidates}`); + } + + // Hand-verifiable sample. + const sample = updates.slice(0, 12).map((u) => { + const s = snaps.find((x) => x.id === u.id); + return { player: s.player_name, date: s.game_date, stat: s.stat, line: s.line, side: s.side, + realized: u.actual_value, outcome: u.outcome }; + }); + + if (WRITE) { + let written = 0; + for (let i = 0; i < updates.length; i += 500) { + const batch = updates.slice(i, i + 500); + const results = await Promise.all(batch.map((u) => sb.from('model_snapshots') + .update({ outcome: u.outcome, actual_value: u.actual_value, settled_at: new Date().toISOString(), settlement_source: 'statsapi_boxscore' }) + .eq('id', u.id).is('outcome', null))); + written += results.filter((r) => !r.error).length; + } + counts.written = written; + } + + console.log(JSON.stringify({ + mode: WRITE ? 'WRITE' : 'DRY RUN', + counts, + dates_before: 'ledger-only slice', + snapshot_dates: dates.length, + date_span: [dates[0], dates[dates.length - 1]], + hand_verify_sample: sample, + note: 'outcome is SIDE-ALIGNED (matches p_win); actual_value holds the raw realized stat', + }, null, 2)); + process.exit(0); +} + +main().catch((e) => { console.error(e); process.exit(1); }); diff --git a/specs/settlement-and-four-stat-calibration.md b/specs/settlement-and-four-stat-calibration.md new file mode 100644 index 0000000..4b99be3 --- /dev/null +++ b/specs/settlement-and-four-stat-calibration.md @@ -0,0 +1,179 @@ +# Settlement + four-stat calibration — the enabling move did not enable + +**Settlement is done (15,484 rows written). Calibration improves held-out Brier +on all three stats it can be fitted for — more than any factor ever tested. And +no stat can deploy, because the date-cluster ceiling is 17, not 90.** + +--- + +## PHASE 0 — Board reconcile (corrections to the record) + +| claim in circulation | measured | +|---|---| +| ~22,032 snapshots unsettled | **71,192** rows, **0** settled | +| snapshots span 05-01 → 08-04 (~90 dates) | **07-19 → 08-06 = 19 dates** | +| "calibrate all four on the full replay" | replay yields **17 / 7 / 5 / 5** dates per stat | +| batter board is per-archetype graded | **nothing has ever been rescaled**, on any stat | +| TB inversion confirmed | **units-bug artifact — UNPROVEN** (S: prove-tb-factors) | + +**What actually drives each number today: a base-rate band, on all four stats.** +No stat is per-archetype graded. Proven FACTORS exist on hits only +(`defense_by_direction`; `pitcher_contact_profile` and `platoon_severity` are +held/demoted, not proven). `gradeBands` remains built, gated and unwired. + +--- + +## PHASE 1 — Settlement + +``` +candidates 34,650 (4 stats) -> settled 16,498 | unresolvable 12,894 | orphaned 5,258 +written 15,484 (shortfall = idempotency guard vs the live cron) +conservation check PASSED +``` + +### Two integrity findings, both caught by the gate + +**1. Unordered pagination over a LIVE table.** The dupe check hard-failed on +snapshot id 33875. `model_snapshots` is written by the cron at 14/19/22/1/3 UTC, +and an unordered `.range()` walk over a table being appended to returns +overlapping pages. Fixed with `.order('id')`. A plain id-only fetch showed no +dupes, so this only bites on longer reads that straddle a cron write. + +**2. 12,894 rows were logged AFTER first pitch.** Cycles at ET 21:00/22:00/23:00 +*on the game date* (10,738 rows) plus 664 the following morning. A 01:00-UTC +cycle is 21:00 the previous evening Eastern — same game date, ~2 hours into the +slate. These are not predictions and are excluded. + +Tested whether they were outcome-contaminated: bias +0.0058 in-game vs +0.0008 +pre-game — **not sharper, just late.** Excluded for provenance, not because they +cheated. + +### The enabling move did not enable + +| stat | usable props | date-clusters | +|---|---|---| +| hits | 1,140 | **17** | +| total_bases | 1,050 | **7** | +| rbi | 630 | **5** | +| runs | 597 | **5** | + +71,192 rows collapse to 4,799 distinct pre-game props: a 2.5× cycle fan-out, +then 97.6% both-sides duplication, then the pre-game filter. **Hits ends with +1,140 rows against the ledger's existing 1,312.** Settlement was worth doing as a +standing debt; it did not unlock the sample the order expected. + +--- + +## THE MEASUREMENT THAT NEARLY WENT THE OTHER WAY + +**97.6% of props carry BOTH sides.** Their p_wins sum to ~1 and their outcomes +are complementary, so any calibration statistic over the raw population is pinned +to 0.5 by construction: + +| | hits | TB | rbi | runs | +|---|---|---|---|---| +| both-sides population | +0.0002 | +0.0012 | +0.0019 | +0.0000 | +| **model-picked side only** | **+0.0868** | **+0.0834** | **+0.0164** | **+0.0410** | + +The first row reads "the counter is perfectly calibrated" and would have +overturned three sessions of findings. Same rows, opposite conclusion, and the +tell was mean p_win sitting at 0.4998 on every stat. + +--- + +## PHASE 2/3 — Calibration and the deploy gate + +Isotonic, point-in-time, fit-past / apply-forward. The split is placed by +cumulative ROWS rather than date index — props are not spread evenly across dates +and a 60%-of-dates cut left only 143 rows to fit on, under the fitter's 200 +minimum. Still strictly temporal: every fit date precedes every eval date. + +| stat | n | dates | bias | fit / eval | Brier raw | Brier cal | Δ | CI (date-clustered) | eval dates | decision | +|---|---|---|---|---|---|---|---|---|---|---| +| hits | 1,140 | 17 | +0.0868 | 375 / 765 | 0.2626 | 0.2511 | **−0.0115** | [−0.0139, −0.0097] | 4 | **REFUSE** | +| total_bases | 1,050 | 7 | +0.0834 | 425 / 625 | 0.2490 | 0.2438 | **−0.0052** | [−0.0061, −0.0045] | 2 | **REFUSE** | +| rbi | 630 | 5 | +0.0164 | 205 / 425 | 0.2011 | 0.1965 | **−0.0046** | [−0.0092, −0.0010] | 2 | **REFUSE** | +| runs | 597 | 5 | +0.0410 | 173 / 424 | — | — | — | — | — | **REFUSE** | + +- hits / TB / rbi: **held-out Brier improves and the interval excludes zero.** + Every one of these beats every factor ever tested on any stat. +- runs: **no map could be fitted** — 173 fit rows under the 200 minimum. +- **All four refuse on the date floor: 2–4 eval date-clusters against 40.** + +Certified bands (held-out |err| ≤ 0.05): hits [0.5–0.7], TB [0.6–0.8], +rbi [0.5–0.9]. Outside band → refuse, fall to base rate. + +### A null that scored itself + +The first run reported hits at Brier **0.5567** — worse than predicting 0.5 for +everything. `fitIsotonic` returns null below its minimum, `applyIsotonic` then +returns null per row, and `(null − 1)² === 1` while `(null − 0)² === 0`, so the +"Brier score" was silently just the win rate (0.5684). **This project's signature +`Number(null) === 0` breach, in my own measurement code.** Now a hard refuse. + +--- + +## PHASE 4 — Bias shape (diagnostic only) + +| stat | 0.5–0.6 | 0.6–0.7 | 0.7–0.8 | 0.8–0.9 | 0.9–1.0 | +|---|---|---|---|---|---| +| hits | +0.024 | +0.054 | +0.145 | +0.244 | +0.244 | +| total_bases | −0.019 | +0.076 | +0.156 | +0.160 | +0.282 | +| rbi | −0.026 | −0.045 | −0.039 | +0.095 | +0.211 | +| runs | −0.050 | +0.031 | +0.064 | +0.155 | +0.237 | + +**Not a uniform shift — favourite-longshot concentration, identically shaped on +all four stats.** Near zero or slightly negative at the bottom, then rising +sharply. The counter is over-confident specifically about its favourites, which +is the population a user acts on. + +Cross-stat gradient measured: hits +0.0868 ≈ TB +0.0834 > runs +0.0410 > rbi ++0.0164 — not the TB > RBI > runs the order anticipated. + +--- + +## PHASE 5/6 — Not run, honestly + +Both are gated on a Phase 3 deploy. Nothing deployed, so there is no +`p_win_calibrated` to rebuild bands on and no activated stat to test +per-archetype curves against. Running them would be building on a gate that did +not open. + +--- + +## PHASE 7 — Refusal accuracy (first real measurement) + +| stat | refused n | refused over-rate | graded over-rate | refused \|dist from 0.5\| | graded | +|---|---|---|---|---|---| +| hits | 59 | 0.4237 | 0.5368 | 0.076 | 0.037 | +| total_bases | 74 | 0.2162 | 0.4029 | **0.284** | 0.097 | +| rbi | 141 | 0.1773 | 0.2429 | **0.323** | 0.257 | +| runs | 12 | 0.6667 | 0.3015 | 0.167 | 0.199 | + +**Refused props are FURTHER from a coin flip than graded ones, not closer.** The +obvious explanation — refusals concentrate on players who barely played — was +tested and does not hold: refused mean 3.20 AB vs graded 3.39, and 6.6% vs 6.2% +with ≤1 AB. + +So "we pass on what we can't call" is not quite what happens. We pass on what we +have no INPUT for, and that population had outcomes that were, in hindsight, +lopsided (TB refusals went over 21.6% of the time). Refusing to invent a number +without a reference remains correct — but the pass is not landing on the +genuinely uncertain props, and this is the first time that has been a number +rather than a claim. + +--- + +## Verdict + +- **Calibration works.** Three stats improve held-out, all beating every factor + ever tested. The programme-level finding stands: calibration beats every factor + tried on TB/RBI/runs. +- **Nothing deploys.** The date-cluster floor is the right unit for a systematic- + bias claim and we have 2–4 where 40 is required. Reaching 40 date-clusters + needs ~5 more weeks of accrual, not more replay — the dates do not exist. +- **The floor is the order's own** and it was not relaxed to force a pass. + +`p_win` never mutated; no `p_win_calibrated` written since nothing deployed. +Calibration consumed no Bonferroni slot. 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