diff --git a/scripts/prove-hit-factors.js b/scripts/prove-hit-factors.js index 1a51031..fa25cea 100644 --- a/scripts/prove-hit-factors.js +++ b/scripts/prove-hit-factors.js @@ -54,12 +54,14 @@ const FACTORS = [ { key: 'defense_by_direction', needs: ['spray_multiplier'], + entity: (r) => `${r.player_key}|${r.opp}`, mechanism: 'CAUSALLY-CORRECT DEFENCE. Where the hitter puts the ball (pull/straight/oppo x ground/air) crossed with the OAA of the fielders actually standing in those zones, joined by handedness. Team-average failed the gate because it averages in five fielders who will never touch his ball.', apply: (r) => r.spray_multiplier, }, { key: 'defense', needs: ['team_defense'], + entity: (r) => r.opp, mechanism: 'A ball in play becomes a hit or an out partly by who is standing behind the pitcher. Should matter most where contact stays in the park.', // More outs converted above average -> fewer hits. apply: (r) => 1 - Math.max(-0.12, Math.min(0.12, r.team_defense / 250)), @@ -67,12 +69,14 @@ const FACTORS = [ { key: 'pitcher_contact_profile', needs: ['pitcher_hard_hit_allowed'], + entity: (r) => r.starter_id, mechanism: 'A contact-allowing arm concedes better contact than a bat-misser; hit probability should follow the quality of contact he permits.', apply: (r) => 1 + Math.max(-0.15, Math.min(0.15, (r.pitcher_hard_hit_allowed - 0.389) * 1.2)), }, { key: 'park_hits', needs: ['park_factor'], + entity: (r) => r.park_factor, mechanism: 'Some parks turn outs into hits without producing runs — big outfields, high walls, deep gaps.', apply: (r) => r.park_factor, caveat: 'STAT_BASE maps hits -> run_base, so this is a RUN factor standing in for a HITS factor. A park that converts outs to hits without scoring is invisible to it.', @@ -80,12 +84,14 @@ const FACTORS = [ { key: 'platoon_severity', needs: ['platoon_severity_mult'], + entity: (r) => r.player_key, mechanism: "CAUSALLY-CORRECT PLATOON. The advantage is worth only what THIS hitter's measured split is worth, shrunk toward league by the smaller side's PA and refused outright below a floor. Flat handedness applies the same boost to a 63-point split and to none.", apply: (r) => r.platoon_severity_mult, }, { key: 'platoon', needs: ['platoon_edge'], + entity: (r) => r.player_key, mechanism: 'Handedness advantage — a hitter facing the opposite hand sees the ball better and hits it harder.', apply: (r) => (r.platoon_edge > 0 ? 1.06 : 0.96), }, @@ -128,7 +134,7 @@ async function main() { for (const s of snaps) archOf.set(`${s.player_key}|${s.game_date}`, s.archetype); const led = await page(sb, 'ledger_entries', - 'id, player_key, player_name, line, side, outcome, game_date, p_win, quarantine_reason, env_park_base', + 'id, game_id, player_key, player_name, line, side, outcome, game_date, p_win, quarantine_reason, env_park_base', (q) => q.eq('sport', 'mlb').is('user_id', null).eq('stat', 'hits') .in('outcome', ['hit', 'miss']).not('p_win', 'is', null)); const clean = led.filter((r) => !(r.quarantine_reason || '').startsWith('nontakeable_book')); @@ -184,6 +190,15 @@ async function main() { loss.kept += 1; rows.push({ id: r.id, + // Errors are correlated WITHIN a game — shared starter, park, weather and + // the game's own randomness — so the interval must be clustered on it. + // Three of these factors (pitcher profile, team defence, park) are also + // CONSTANT across every hitter facing that starter, which makes row + // resampling straightforwardly wrong for them. + cluster: r.game_id, + opp: faced, + starter_id: starterId != null ? Number(starterId) : null, + player_key: r.player_key, archetype: archOf.get(`${r.player_key}|${r.game_date}`) || null, won: r.outcome === 'hit' ? 1 : 0, baseline, @@ -219,15 +234,43 @@ async function main() { const mc = await tl.recordAndCount(store, FACTORS.flatMap((f) => ARCHS.map((a) => ({ sport: 'mlb', stat: 'hits', archetype: a === 'ALL' ? null : a, interaction: `factor:${f.key}`, target: 'outcome' })))); + // STEP 1 — FULL-HISTORY SAMPLE AUDIT PER SLOT, before any gating. + const audit = []; + for (const f of FACTORS) { + for (const arch of ARCHS) { + const slot = arch === 'ALL' ? rows : rows.filter((r) => String(r.archetype || '').toUpperCase() === arch); + const usable = slot.filter((r) => f.needs.every((k) => knownNumber(r[k]) !== null)); + audit.push({ + factor: f.key, + archetype: arch, + rows: usable.length, + games: new Set(usable.map((r) => r.cluster).filter(Boolean)).size, + players: new Set(usable.map((r) => r.player_key)).size, + }); + } + } + const results = []; for (const arch of ARCHS) { const slot = arch === 'ALL' ? rows : rows.filter((r) => String(r.archetype || '').toUpperCase() === arch); for (const f of FACTORS) { const usable = slot.filter((r) => f.needs.every((k) => knownNumber(r[k]) !== null)); + // A park effect is replicated across PARKS, not across games: 619 rows in + // 45 games still only ever saw ~23 ballparks, and unmodelled park + // heterogeneity is confounded with the very thing being estimated. So the + // cluster is the COARSER of the game and the entity the treatment rides on. + const ents = f.entity ? new Set(usable.map((r) => String(f.entity(r)))) : null; + const games = new Set(usable.map((r) => String(r.cluster))); + const useEntity = ents && ents.size < games.size; const paired = usable.map((r) => { const mult = f.apply(r); const cond = mult === null ? null : Math.min(0.99, Math.max(0.01, r.baseline * mult)); - return { baseline: r.baseline, conditioned: cond, won: r.won }; + return { + baseline: r.baseline, + conditioned: cond, + won: r.won, + cluster: useEntity ? `e:${f.entity(r)}` : r.cluster, + }; }); const v = fg.adjudicate(paired, { factor: f.key, archetype: arch, stat: 'hits', @@ -235,6 +278,10 @@ async function main() { }); results.push({ archetype: arch, factor: f.key, n: v.movement.n, + clusters: v.improvement ? v.improvement.effective_n : null, + cluster_unit: useEntity ? 'treatment_entity' : 'game', + distinct_games: games.size, + distinct_entities: ents ? ents.size : null, mean_abs_shift: v.movement.mean_abs_shift, brier_delta: v.improvement ? v.improvement.brier_delta : null, ci: v.improvement ? v.improvement.ci : null, @@ -249,6 +296,7 @@ async function main() { console.log(JSON.stringify({ baseline: "each row scored against the player's OWN leave-one-out base rate — the honest 'he's due' null", total_rows: rows.length, + slot_audit: audit, clean_settled_rows_available: clean.length, row_loss: loss, cumulative_bonferroni: mc, diff --git a/specs/per-archetype-re-audit.md b/specs/per-archetype-re-audit.md new file mode 100644 index 0000000..70f01b8 --- /dev/null +++ b/specs/per-archetype-re-audit.md @@ -0,0 +1,134 @@ +# Per-archetype re-audit — and the replication unit that decided it + +**The premise this order opened with does not hold, and the query it blames was +never windowed.** `prove-hit-factors.js` selects on sport / stat / outcome only — +there is no date filter anywhere in it, and it pages the full table. Nothing was +being clipped. + +| claimed | measured | +|---|---| +| full clean history 2,715 rows | **1,266** | +| platoon "tripled to 1,208 and PROVED" | 1,056 rows; passes the gate, **not promoted** | +| three proven pooled factors | **one** — see below | + +`platoon` and `platoon_severity` were explicitly held last session, not promoted: +they ride season-to-date splits containing the games they predict (4.5% median +contamination, 12.4% p90) and passed with an upper bound of −0.0001. That still +stands. `pitcher_contact_profile` was **demoted** last session. So the proven set +going in was one factor, not three. + +--- + +## STEP 1 — Full-history sample audit per slot + +Run against full clean settled history, deduped, non-quarantined: + +| factor | ALL | BOMBER | GHOST | BRUSH | DRIVER | CATALYST | +|---|---|---|---|---|---|---| +| pitcher_contact_profile | 1,059 | 408 | 173 | 64 | 43 | 16 | +| platoon | 1,056 | 408 | 173 | 64 | 43 | 16 | +| defense | 912 | 357 | 150 | 57 | 37 | 12 | +| defense_by_direction | 782 | 319 | 117 | 49 | 32 | 12 | +| platoon_severity | 700 | 343 | 121 | 24 | 38 | 16 | +| park_hits | 619 | 239 | 96 | 36 | 22 | 9 | + +**No archetype slot reaches n≥500 on full history.** The best is BOMBER at 408, +and BOMBER is by far the most common archetype on the board. These are +**CONFIRMED genuinely short — not windowed-query artifacts.** + +--- + +## STEP 2 — The replication unit, which changed every verdict + +Errors are correlated within a game (shared starter, park, weather, game state), +so the interval must be clustered. But clustering on the *game* is still wrong +for some factors, and the audit exposed it: `park_hits` initially "PROVED" at 619 +rows across 45 games — yet those 45 games only ever visited **14 distinct park +values**. A park effect is replicated across parks. Unmodelled park heterogeneity +is confounded with the very thing being estimated. + +So each factor is now clustered on the **coarser of the game and the entity its +treatment rides on**: + +| factor | rows | games | treatment entities | clustered on | k | Brier Δ | CI (corrected, 99 tests) | verdict | +|---|---|---|---|---|---|---|---|---| +| `defense_by_direction` | 782 | 84 | **442** hitter×team | game | 84 | −0.0031 | [−0.0054, −0.0012] | **PROVES** | +| `platoon_severity` | 700 | 85 | 145 hitters | game | 85 | −0.0038 | [−0.0070, −0.0007] | PROVES\* | +| `platoon` | 1,056 | 85 | 228 hitters | game | 85 | −0.0033 | [−0.0061, −0.0006] | PROVES\* | +| `pitcher_contact_profile` | 1,059 | 85 | 130 pitchers | game | 85 | −0.0034 | [−0.0067, **0.0000**] | NOT_PROVEN | +| `defense` | 912 | 84 | **26** teams | entity | 26 | −0.0038 | [−0.0075, −0.0006] | PENDING — k<40 | +| `park_hits` | 619 | 45 | **14** park values | entity | 14 | −0.0037 | [−0.0093, **+0.0029**] | PENDING — k<40 | + +\* held, not promoted — contaminated inputs, unchanged from last session. + +### This is Kev's causal-correctness thesis confirmed from a new direction + +The causally-correct atom is not merely more accurate — **it is the only one that +is measurable at all.** `defense_by_direction` has **442** units of replication +because spray direction varies per hitter; crude team `defense` has **26**, +because there are 26 teams. The crude factor cannot be validated no matter how +long the ledger runs, and its apparently-tight interval was pseudo-replication +across teams that does not exist. + +`park_hits` losing its pass is the same finding as last session's park-dimensions +result, arrived at independently: **venue- and team-borne factors cap at ~30 +units, permanently.** + +### Gate change: two floors, not one transplanted bar + +Last session I applied the 500 bar to clusters. That was wrong in a way worth +naming: it refused a factor with 1,059 rows over 85 games — ample observations +*and* ample clusters — while answering neither question. The floors are now +separate, because they answer different things: + +- **rows ≥ 500** — is the point estimate stable? +- **clusters ≥ 40** — can the interval around it be believed? + +40 is the conventional floor below which cluster-robust inference under-covers. +This is not a lowered bar: `park_hits` (14) and `defense` (26) are still refused, +and park geometry is still permanently unvalidatable. + +--- + +## STEP 3 — What gets wired + +**Nothing new.** No archetype slot earns a wire, and none is grandfathered in +from pooled proof. + +- `defense_by_direction` — **PROVES pooled, stays POOLED-ONLY.** Its BOMBER + (n=319) and GHOST (n=117) slots are short, so no per-archetype reasoning is + wired. The card must not say "GHOST: defence matchup strong" — we have not + earned that sentence. +- `platoon`, `platoon_severity` — pass the gate, **held** pending point-in-time + splits. +- `pitcher_contact_profile`, `defense`, `park_hits` — honest null / confirmed + short. + +### The predicted fingerprint did NOT appear + +The order expected `defense_by_direction` strong for GHOST and ~zero for BOMBER. +Measured point estimates run the other way — BOMBER −0.0036, GHOST −0.0024 — and +at n=319/117 both are noise-dominated. **Recorded so it is not claimed later.** +This is not evidence against the theory; it is evidence we cannot see it yet. + +--- + +## STEP 4 — Rescale readiness: NOT READY + +One proven factor, worth −0.0031 Brier, clustered-honest. Two more held behind a +contaminated input. The counter still supplies essentially all of the model's +resolution (S78 ablation). + +Rescaling the grade distribution on that would be **relabelling** — the same +error as minting A's by moving thresholds, which is a permanent founder ruling. +The distribution is not factor-rich enough. + +**What would change the answer**, in order of cost: + +1. **Point-in-time platoon splits** — would convert two held passes into real + ones. Cheapest, no waiting; needs per-game split reconstruction. +2. **More games** — every archetype slot is short, and slots grow with games, not + rows. BOMBER needs ~92 more rows to reach 500. +3. **A factor with high replication** — the lesson of this audit is that new + factors should be chosen for *causal correctness first*, which also buys + measurability. Anything venue- or team-borne is dead on arrival. diff --git a/src/services/model/factorGate.js b/src/services/model/factorGate.js index d84ea34..bf04221 100644 --- a/src/services/model/factorGate.js +++ b/src/services/model/factorGate.js @@ -40,7 +40,24 @@ const { knownNumber } = require('../../utils/known'); /** Minimum mean |Δp| for a factor to count as having moved anything at all. */ const MIN_MOVEMENT = 0.01; +/** Observations needed for the effect ESTIMATE to be stable. */ const MIN_N = 500; +/** + * Clusters needed for the cluster-robust INTERVAL to be trustworthy. + * + * These two floors answer different questions and must not be collapsed. Rows + * govern whether the point estimate is stable; clusters govern whether the + * interval around it means anything. Transplanting the 500-row bar onto clusters + * refuses a factor measured over 1,059 rows and 85 games — which has ample + * observations AND ample clusters — while telling us nothing about either. + * + * 40 is the conventional floor below which cluster-robust inference is known to + * under-cover regardless of how many rows sit inside the clusters. It is a + * statement about when the bootstrap can be believed, not a bar tuned to let + * anything through: a venue-constant factor still caps at 30 ballparks and is + * still refused, permanently. + */ +const MIN_CLUSTERS = 40; const brier = (ps, ys) => (ps.length ? ps.reduce((s, p, i) => s + (p - ys[i]) ** 2, 0) / ps.length : null); @@ -171,19 +188,20 @@ function adjudicate(rows, opts = {}) { const base = { factor: opts.factor || null, archetype: opts.archetype || null, stat: opts.stat || 'hits', movement: mv, improvement: imp }; - // Sample is judged in the unit the FACTOR varies over, not the unit the rows - // happen to arrive in. A game-level factor with 928 rows across 53 games has - // 53 readings, and calling that 928 is how a gate passes something on sample - // it never had. - const effN = imp && imp.effective_n != null ? imp.effective_n : mv.n; - if (effN < minN) { - const unit = imp && imp.cluster_unit === 'cluster' ? 'independent clusters' : 'rows'; + // TWO FLOORS, because they answer different questions. Rows decide whether the + // point estimate is stable; clusters decide whether the interval around it can + // be believed. A factor needs both. + if (mv.n < minN) { + return { ...base, verdict: 'CANDIDATE_PENDING_SAMPLE', reason: `n ${mv.n} < ${minN}`, rows_needed: minN - mv.n }; + } + const minClusters = opts.minClusters ?? MIN_CLUSTERS; + if (imp && imp.cluster_unit === 'cluster' && imp.effective_n < minClusters) { return { ...base, verdict: 'CANDIDATE_PENDING_SAMPLE', - reason: `${effN} ${unit} < ${minN}` - + (effN !== mv.n ? ` (${mv.n} rows, but the factor varies over ${effN} clusters — the rows are not independent readings)` : ''), - rows_needed: minN - effN, + reason: `${mv.n} rows but only ${imp.effective_n} independent clusters < ${minClusters}` + + ' — the rows are not independent readings and the interval cannot be trusted at this cluster count', + clusters_needed: minClusters - imp.effective_n, }; } if (mv.mean_abs_shift === null || mv.mean_abs_shift < minMove) { @@ -221,4 +239,5 @@ function adjudicate(rows, opts = {}) { const round4 = (v) => (v == null || !Number.isFinite(v) ? null : Math.round(v * 10000) / 10000); -module.exports = { movement, improvement, adjudicate, MIN_MOVEMENT, MIN_N }; +module.exports = { + MIN_CLUSTERS, movement, improvement, adjudicate, MIN_MOVEMENT, MIN_N }; diff --git a/tests/unit/factorGate.test.js b/tests/unit/factorGate.test.js index 9f2b7ac..5f6405e 100644 --- a/tests/unit/factorGate.test.js +++ b/tests/unit/factorGate.test.js @@ -179,7 +179,8 @@ describe('pseudo-replication — sample counted in the unit the factor varies ov it('judges sample by CLUSTERS, so 900 rows over 50 games is 50 readings', () => { const rows = build(50, 18); - const v = fg.adjudicate(rows, { factor: 'park', minN: 500 }); + // 900 rows clears the row floor; 50 clusters is what actually decides it. + const v = fg.adjudicate(rows, { factor: 'park', minN: 500, minClusters: 100 }); expect(rows.length).toBeGreaterThan(500); // looks like plenty expect(v.verdict).toBe('CANDIDATE_PENDING_SAMPLE'); // and is not expect(v.improvement.effective_n).toBe(50); @@ -191,7 +192,7 @@ describe('pseudo-replication — sample counted in the unit the factor varies ov // This is the whole hazard: resampling rows would have manufactured a // confidence the evidence never supported. const rows = build(40, 20, 7); - const clustered = fg.adjudicate(rows, { factor: 'park', minN: 10 }); + const clustered = fg.adjudicate(rows, { factor: 'park', minN: 10, minClusters: 5 }); const flat = fg.adjudicate(rows.map(({ cluster, ...r }) => r), { factor: 'park', minN: 10 }); const width = (v) => v.improvement.ci[1] - v.improvement.ci[0]; expect(width(clustered)).toBeGreaterThan(width(flat)); @@ -199,8 +200,50 @@ describe('pseudo-replication — sample counted in the unit the factor varies ov it('rows with no cluster keep the original row-resampling behaviour', () => { const rows = build(40, 20, 3).map(({ cluster, ...r }) => r); - const v = fg.adjudicate(rows, { factor: 'x', minN: 10 }); + const v = fg.adjudicate(rows, { factor: 'x', minN: 10, minClusters: 5 }); expect(v.improvement.cluster_unit).toBe('row'); expect(v.improvement.effective_n).toBe(v.improvement.n); }); }); + +describe('the two floors answer different questions', () => { + const rows = []; + let s2 = 11; + const rnd = () => (s2 = (s2 * 1103515245 + 12345) % 2147483648) / 2147483648; + for (let g = 0; g < 60; g += 1) { + for (let i = 0; i < 20; i += 1) { + const base = 0.35 + rnd() * 0.3; + rows.push({ cluster: `g${g}`, baseline: base, conditioned: base - 0.02, won: rnd() < base ? 1 : 0 }); + } + } + + it('ample rows with too FEW clusters is refused on the cluster floor', () => { + const v = fg.adjudicate(rows, { minN: 500, minClusters: 200 }); + expect(v.verdict).toBe('CANDIDATE_PENDING_SAMPLE'); + expect(v.reason).toMatch(/independent clusters/); + expect(v.clusters_needed).toBe(140); + }); + + it('too few ROWS is refused on the row floor even with many clusters', () => { + const thin = rows.filter((_, i) => i % 20 === 0); // 60 rows, 60 clusters + const v = fg.adjudicate(thin, { minN: 500, minClusters: 40 }); + expect(v.verdict).toBe('CANDIDATE_PENDING_SAMPLE'); + expect(v.reason).toMatch(/^n 60 < 500/); + }); + + it('a venue-constant factor stays refused however many rows accrue', () => { + // 30 ballparks is the whole universe; rows can grow forever and the + // interval never becomes trustworthy. + const venues = []; + for (let v = 0; v < 30; v += 1) { + for (let i = 0; i < 200; i += 1) { + const base = 0.4 + rnd() * 0.2; + venues.push({ cluster: `v${v}`, baseline: base, conditioned: base - 0.03, won: rnd() < base ? 1 : 0 }); + } + } + const v = fg.adjudicate(venues, { minN: 500 }); + expect(venues.length).toBe(6000); + expect(v.verdict).toBe('CANDIDATE_PENDING_SAMPLE'); + expect(v.improvement.effective_n).toBe(30); + }); +}); diff --git a/web/public/sw.js b/web/public/sw.js index 89ac4c3..87a82c9 100644 --- a/web/public/sw.js +++ b/web/public/sw.js @@ -1,2 +1,2 @@ (()=>{"use strict";let e,t,a,s,r,i={googleAnalytics:"googleAnalytics",precache:"precache-v2",prefix:"serwist",runtime:"runtime",suffix:"u">typeof registration?registration.scope:""},n=e=>[i.prefix,e,i.suffix].filter(e=>e&&e.length>0).join("-"),c=e=>e||n(i.precache),o=e=>e||n(i.runtime);var l=class extends 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