vyndr/scripts/tb-solo-and-interactions.js

#!/usr/bin/env node
'use strict';

/**
 * tb-solo-and-interactions — PROVE BOTH, with the solo pass as the control.
 *
 * A feature can carry signal alone, only in combination, or both. Testing only
 * interactions misses solo-real features AND cannot tell whether an interaction
 * ADDS anything or merely re-encodes its own parts. So the solo result is the
 * baseline every interaction has to beat.
 *
 * ── HOW "ADDS OVER ITS PARTS" IS MEASURED ────────────────────────────────
 * Not by comparing two correlations by eye. The interaction's incremental
 * signal is the PARTIAL correlation of the interaction term with the counter's
 * residual, CONTROLLING FOR both component features:
 *
 *     resid_I = I  − OLS(I  ~ A, B)
 *     resid_Y = Y  − OLS(Y  ~ A, B)
 *     incremental r = corr(resid_I, resid_Y)
 *
 * If the interaction is just barrel-rate wearing a different hat, regressing out
 * barrel rate removes it and the incremental r collapses to ~0. That is exactly
 * the redundancy the order is guarding against, and it is the difference between
 * PASSES-AND-ADDS and PASSES-BUT-REDUNDANT.
 *
 * ── WHY THE COUNTER'S RESIDUAL IS THE TARGET ─────────────────────────────
 * Correlating with the raw outcome rewards a feature for knowing what the
 * counter already knows. Only the part the counter MISSES is new information,
 * and only new information can improve the product. Both are reported; the
 * residual one is the one that decides.
 *
 * ── THEORY FIRST ─────────────────────────────────────────────────────────
 * Every interaction below is declared with a MECHANISM before it is measured.
 * No blind pairwise search — with 8 features there are 28 pairs, and at α=.05
 * roughly one in twenty returns "significant" from noise alone.
 *
 *   SUPABASE_URL=... node scripts/tb-solo-and-interactions.js
 */

require('dotenv').config();
const { createClient } = require('@supabase/supabase-js');
const cv = require('../src/services/model/correlateValidator');
const sk = require('../src/services/model/skillProjection');
const reg = require('../src/services/model/featureRegistry');
const mlb = require('../src/services/adapters/mlbStatsAdapter');
const { knownRate, 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 PAGE = 1000;
const GAMES_SO_FAR = Number(process.env.STAGEA_GAMES_SO_FAR || 103);

const r4 = (v) => (v == null || !Number.isFinite(v) ? null : Math.round(v * 10000) / 10000);
const mean = (a) => (a.length ? a.reduce((x, y) => x + y, 0) / a.length : null);
const brier = (ps, ys) => (ps.length ? ps.reduce((s, p, i) => s + (p - ys[i]) ** 2, 0) / ps.length : null);

/** OLS residuals of y on the given predictor columns (with intercept). */
function olsResiduals(y, Xcols) {
  const n = y.length;
  const p = Xcols.length + 1;
  const X = [];
  for (let i = 0; i < n; i += 1) {
    const row = [1];
    for (const c of Xcols) row.push(c[i]);
    X.push(row);
  }
  // Normal equations (X'X) b = X'y, solved by Gauss-Jordan. p is 2-4 here.
  const XtX = Array.from({ length: p }, () => new Array(p).fill(0));
  const Xty = new Array(p).fill(0);
  for (let i = 0; i < n; i += 1) {
    for (let a = 0; a < p; a += 1) {
      Xty[a] += X[i][a] * y[i];
      for (let b = 0; b < p; b += 1) XtX[a][b] += X[i][a] * X[i][b];
    }
  }
  const M = XtX.map((row, i) => [...row, Xty[i]]);
  for (let col = 0; col < p; col += 1) {
    let piv = col;
    for (let r = col + 1; r < p; r += 1) if (Math.abs(M[r][col]) > Math.abs(M[piv][col])) piv = r;
    if (Math.abs(M[piv][col]) < 1e-12) return null;   // singular → cannot control honestly
    [M[col], M[piv]] = [M[piv], M[col]];
    const d = M[col][col];
    for (let k = col; k <= p; k += 1) M[col][k] /= d;
    for (let r = 0; r < p; r += 1) {
      if (r === col) continue;
      const f = M[r][col];
      for (let k = col; k <= p; k += 1) M[r][k] -= f * M[col][k];
    }
  }
  const beta = M.map((row) => row[p]);
  return y.map((v, i) => v - X[i].reduce((s, xv, j) => s + xv * beta[j], 0));
}

/**
 * Partial correlation of a with b, controlling for the columns in ctrl.
 *
 * COLLINEARITY IS CHECKED FIRST, and this is not pedantry — it caught a real
 * error in this very script. The archetype-power proxy was defined as
 * `barrel_pct / LEAGUE.barrel_pct`, an exact linear function of barrel_pct, so
 * "control for both components" was rank-deficient and the partial correlation
 * it produced (-0.132, the only one that looked like an incremental finding) was
 * an artifact of a singular design matrix. The Gauss-Jordan pivot test missed it
 * because the two columns differ by a scale factor, which keeps the pivot well
 * above an absolute epsilon. Scale-free pairwise correlation catches it.
 */
function partialCorr(a, b, ctrl) {
  for (let i = 0; i < ctrl.length; i += 1) {
    for (let j = i + 1; j < ctrl.length; j += 1) {
      const rr = cv.pearson(ctrl[i], ctrl[j]).r;
      if (rr !== null && Math.abs(rr) > 0.999) return null; // same variable twice
    }
  }
  const ra = olsResiduals(a, ctrl);
  const rb = olsResiduals(b, ctrl);
  if (!ra || !rb) return null;
  return cv.pearson(ra, rb).r;
}

/** Rows where every named key is known — the honest common sample. */
function completeRows(rows, keys) {
  return rows.filter((r) => keys.every((k) => knownNumber(r[k]) !== null));
}

function makeRnd(seed) {
  let s = seed >>> 0;
  return () => { s ^= s << 13; s >>>= 0; s ^= s >>> 17; s ^= s << 5; s >>>= 0; return s / 4294967296; };
}
function bootstrapDiff(rows, keyA, keyB, iters = 4000, seed = 20260804) {
  if (rows.length < 30) return null;
  const rnd = makeRnd(seed);
  const n = rows.length;
  const diffs = [];
  for (let it = 0; it < iters; it += 1) {
    const ys = []; const a = []; const b = [];
    for (let i = 0; i < n; i += 1) {
      const r = rows[Math.floor(rnd() * n)];
      ys.push(r.won); a.push(r[keyA]); b.push(r[keyB]);
    }
    const ca = cv.pearson(a, ys).r; const cb = cv.pearson(b, ys).r;
    if (ca == null || cb == null) continue;
    diffs.push(ca - cb);
  }
  if (diffs.length < 100) return null;
  diffs.sort((x, y) => x - y);
  const q = (pp) => r4(diffs[Math.floor(pp * (diffs.length - 1))]);
  const ci = [q(0.025), q(0.975)];
  return {
    point: r4(cv.pearson(rows.map((r) => r[keyA]), rows.map((r) => r.won)).r
      - cv.pearson(rows.map((r) => r[keyB]), rows.map((r) => r.won)).r),
    ci95: ci, ci_excludes_zero: ci[0] > 0 || ci[1] < 0,
  };
}

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)).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;
}

async function opposingStarters(dates) {
  const m = new Map();
  for (const d of dates) {
    let games = [];
    try { games = await mlb.getScheduleWithPitchers(d); } catch { games = []; }
    for (const g of games) {
      if (!g.home || !g.away) continue;
      if (g.home.probablePitcher) m.set(`${d}|OPP:${g.home.team}`, g.home.probablePitcher.id);
      if (g.away.probablePitcher) m.set(`${d}|OPP:${g.away.team}`, g.away.probablePitcher.id);
    }
  }
  return m;
}
async function opponentByPlayerDate(players) {
  const map = new Map();
  for (const [key, name] of players) {
    try {
      const found = await mlb.searchPlayer(name);
      if (!found || !found.id) continue;
      const log = await mlb.getPlayerGameLog(found.id);
      for (const g of log || []) {
        if (g && g.date && g.opponent) map.set(`${key}|${String(g.date).slice(0, 10)}`, g.opponent);
      }
    } catch { /* no log → no pitcher */ }
  }
  return map;
}

const SOLO = ['batter_barrel_pct', 'batter_hard_hit_pct', 'batter_exit_velo',
  'batter_launch_angle', 'batter_k_pct', 'batter_bb_pct',
  'pitcher_k_pct', 'pitcher_hard_hit_allowed'];

/** STEP 2 — theory first. Every interaction declares its mechanism. */
const INTERACTIONS = [
  {
    key: 'launch_x_exit_velo',
    components: ['batter_launch_angle', 'batter_exit_velo'],
    mechanism: 'Extra bases need BOTH conditions: hit hard AND hit in the air. A 105-mph ground ball is an out; a 25-degree popup is an out. Neither factor alone predicts bases, which is precisely why each may fail solo and the product may not.',
    build: (r) => r.batter_launch_angle * r.batter_exit_velo,
  },
  {
    key: 'exitvelo_x_pitcher_suppression',
    components: ['batter_exit_velo', 'pitcher_hard_hit_allowed'],
    mechanism: 'A hitter only realises his contact quality against a pitcher who permits contact quality. Elite suppression should attenuate a power bat; a contact-permitting arm should amplify it. The effect is conditional by construction.',
    build: (r) => r.batter_exit_velo * r.pitcher_hard_hit_allowed,
  },
  {
    key: 'barrel_x_power_archetype',
    components: ['batter_barrel_pct', 'archetype_power'],
    mechanism: 'ARCHETYPE-CONDITIONAL. Barrels convert to extra bases for hitters whose lane is power; for a speed/contact profile the same barrel rate is a rarer event on a swing built for something else. This is Discipline 2 stated as a testable interaction. NOTE: it is currently UNTESTABLE — statcast rows carry no archetype label, and the barrel-relative proxy is an exact linear function of barrel_pct, so controlling for both components is rank-deficient. It needs a real archetype classification joined in.',
    build: (r) => r.batter_barrel_pct * r.archetype_power,
  },
  {
    key: 'batterK_x_pitcherK',
    components: ['batter_k_pct', 'pitcher_k_pct'],
    mechanism: 'Strikeout risk compounds multiplicatively (log5 is exactly this shape). A high-K bat against a high-K arm loses plate appearances to strikeouts, and a PA lost is a base opportunity that never happens — so it suppresses total bases through OPPORTUNITY, not contact quality.',
    build: (r) => r.batter_k_pct * r.pitcher_k_pct,
  },
];

/** Latest settled game date in the pull — used to detect that the profile
 *  freeze now sits AFTER the data, i.e. no clean out-of-sample window exists. */
function clean0Max(rows) {
  return (rows || []).reduce((mx, r) => (String(r.game_date) > mx ? String(r.game_date) : mx), '');
}

async function main() {
  if (!SB_URL || !SB_KEY) throw new Error('SUPABASE_URL / service key required');
  const sb = createClient(SB_URL, SB_KEY, { auth: { persistSession: false } });

  const statcast = await page(sb, 'statcast_aggregates', '*', (q) => q.eq('sport', 'mlb'));
  const freezeDate = statcast.reduce((mx, r) => (String(r.updated_at) > mx ? String(r.updated_at) : mx), '').slice(0, 10);
  const batters = new Map(); const pitchersById = new Map();
  for (const r of statcast) {
    if (r.role === 'pitcher' && r.source_id != null) pitchersById.set(Number(r.source_id), sk.fromStatcastRow(r));
    if (r.player_key && r.role === 'batter') {
      const prev = batters.get(r.player_key);
      if (!prev || Number(r.sample_pa || 0) > Number(prev.rawPa || 0)) {
        batters.set(r.player_key, Object.assign(sk.fromStatcastRow(r), { rawPa: Number(r.sample_pa || 0) }));
      }
    }
  }

  // REAL ARCHETYPE LABELS. The barrel-relative proxy was a clipped monotone
  // transform of barrel_pct, so `barrel x proxy` measured NONLINEARITY IN BARREL,
  // not an archetype interaction — it could never have tested Discipline 2.
  // model_snapshots carries the actual classification per prop, so the
  // conditioning variable is now a genuine BOMBER indicator, which is
  // categorical and therefore not a transform of barrel at all.
  const snaps = await page(sb, 'model_snapshots', 'player_key, game_date, archetype',
    (q) => q.eq('sport', 'mlb').eq('stat', 'total_bases').not('archetype', 'is', null));
  const archetypeBy = new Map();
  for (const r of snaps) if (r.player_key && r.game_date) archetypeBy.set(`${r.player_key}|${r.game_date}`, r.archetype);

  const led = await page(sb, 'ledger_entries',
    'player_key, player_name, stat, line, side, outcome, game_date, p_win, quarantine_reason',
    (q) => q.eq('sport', 'mlb').is('user_id', null).eq('stat', 'total_bases')
      .in('outcome', ['hit', 'miss']).not('p_win', 'is', null));
  // POINT-IN-TIME IS NO LONGER AVAILABLE FROM THIS TABLE.
  //
  // `statcast_aggregates` is upserted in place and keeps one as-of date. The
  // first skill backtest was honest only by accident: the nightly refresh was
  // unreachable code, so the table sat frozen at 2026-07-21 — BEFORE the settled
  // window. Repairing that cron (correct for production) refreshed it to today,
  // and every prior version is gone.
  //
  // So scoring a 2026-07-25 game now uses a season aggregate that CONTAINS that
  // game. `statcast_history` (added this session) fixes it going forward; it has
  // one day of data, which is not yet a window. Until it fills, results here are
  // DIRECTIONAL AND CONTAMINATED, labelled as such, and are NOT gate verdicts.
  const contaminated = String(freezeDate) >= String(clean0Max(led));
  const clean = led.filter((r) => !(r.quarantine_reason || '').startsWith('nontakeable_book')
    && (contaminated ? true : String(r.game_date) > freezeDate));

  const dates = [...new Set(clean.map((r) => r.game_date))].sort();
  const starters = await opposingStarters(dates);
  const players = new Map();
  for (const r of clean) if (!players.has(r.player_key)) players.set(r.player_key, r.player_name);
  const oppByPlayerDate = await opponentByPlayerDate(players);

  if (process.env.TB_DEBUG === '1') {
    console.error(`[debug] statcast rows=${statcast.length} freeze=${freezeDate} batters=${batters.size} pitchers=${pitchersById.size}`);
    console.error(`[debug] ledger tb rows=${led.length} clean(after freeze)=${clean.length}`);
    const sampleKeys = clean.slice(0, 5).map((r) => r.player_key);
    console.error(`[debug] sample ledger player_keys=${JSON.stringify(sampleKeys)}`);
    console.error(`[debug] sample statcast keys=${JSON.stringify([...batters.keys()].slice(0, 5))}`);
    console.error(`[debug] matches in sample=${sampleKeys.filter((k) => batters.has(k)).length}/5`);
  }
  const allowed = reg.candidateFeaturesForStat('mlb', 'total_bases');
  const rows = [];
  for (const r of clean) {
    const bat = batters.get(r.player_key);
    if (!bat) continue;
    const faced = oppByPlayerDate.get(`${r.player_key}|${r.game_date}`) || null;
    const pit = faced ? pitchersById.get(Number(starters.get(`${r.game_date}|OPP:${faced}`))) || null : null;
    const paRate = bat.rawPa > 0 ? Math.min(5.2, Math.max(2.0, bat.rawPa / GAMES_SO_FAR)) : null;
    const under = String(r.side).toLowerCase() === 'under';
    const won = r.outcome === 'hit' ? 1 : 0;
    const champ = Number(r.p_win);

    const proj = sk.projectSkill({
      batter: bat, pitcher: pit, park: 1, archetype: null,
      statType: 'total_bases', line: Number(r.line), expectedPa: paRate, allowed,
    });

    // The REAL archetype for this prop — a 0/1 power indicator, categorical and
    // independent of barrel_pct by construction.
    const arch = archetypeBy.get(`${r.player_key}|${r.game_date}`) || null;
    const archetypePower = arch == null ? null : (String(arch).toUpperCase() === 'BOMBER' ? 1 : 0);

    rows.push({
      won, champ, residual: won - champ,
      skill: proj ? (under ? 1 - proj.p_over_line : proj.p_over_line) : null,
      had_pitcher: !!pit,
      batter_barrel_pct: knownRate(bat.barrel_pct),
      batter_hard_hit_pct: knownRate(bat.hard_hit_pct),
      batter_exit_velo: knownRate(bat.avg_exit_velo),
      batter_launch_angle: knownRate(bat.avg_launch_angle),
      batter_k_pct: knownRate(bat.k_pct),
      batter_bb_pct: knownRate(bat.bb_pct),
      pitcher_k_pct: pit ? knownRate(pit.k_pct) : null,
      pitcher_hard_hit_allowed: pit ? knownRate(pit.hard_hit_pct) : null,
      archetype_power: archetypePower,
      archetype: arch,
    });
  }

  // Bonferroni denominator = every test in this family (solo + interaction).
  // ── CUMULATIVE BONFERRONI ─────────────────────────────────────────────
  // The denominator is every DISTINCT hypothesis this programme has tested, not
  // this run's. A per-session count gives each new order a fresh, generous alpha
  // and lets the false-positive rate compound silently.
  const tl = require('../src/services/model/testLedger');
  const mcStore = tl.supabaseStore(sb);
  const mc = await tl.recordAndCount(mcStore, [
    ...SOLO.map((f) => ({ sport: 'mlb', stat: 'total_bases', archetype: null, interaction: `solo:${f}`, target: 'counter_residual' })),
    ...INTERACTIONS.map((x) => ({ sport: 'mlb', stat: 'total_bases', archetype: null, interaction: x.key, target: 'counter_residual' })),
  ]);
  const TESTS = mc.cumulative_tests;


  // ── STEP 1 — SOLO PASS (the control) ────────────────────────────────────
  const solo = {};
  for (const f of SOLO) {
    const rs = completeRows(rows, [f]);
    solo[f] = {
      n: rs.length,
      vs_outcome: cv.validateFactor(rs.map((r) => r[f]), rs.map((r) => r.won), TESTS),
      vs_counter_residual: cv.validateFactor(rs.map((r) => r[f]), rs.map((r) => r.residual), TESTS),
    };
  }

  // ── STEP 3 — INTERACTIONS, each against its own solo baseline ───────────
  const interactions = {};
  for (const ix of INTERACTIONS) {
    const keys = [...ix.components];
    const rs = completeRows(rows, keys);
    if (rs.length < 30) { interactions[ix.key] = { mechanism: ix.mechanism, n: rs.length, verdict: 'UNTESTABLE — no common sample' }; continue; }
    const I = rs.map(ix.build);
    const Y = rs.map((r) => r.residual);
    const ctrl = keys.map((k) => rs.map((r) => r[k]));

    const gate = cv.validateFactor(I, Y, TESTS);
    const incremental = partialCorr(I, Y, ctrl);
    // The best solo |r| among its own components, on the SAME rows.
    const componentSolo = keys.map((k) => ({
      feature: k, r: r4(cv.pearson(rs.map((r) => r[k]), Y).r),
    }));
    const bestComponent = Math.max(...componentSolo.map((c) => Math.abs(c.r ?? 0)));

    let verdict;
    if (incremental === null) verdict = 'UNTESTABLE — controls are collinear';
    else if (gate.validated && Math.abs(incremental) >= cv.VALIDATION_REQUIREMENTS.min_pearson_r) verdict = 'PASSES-AND-ADDS';
    else if (gate.validated) verdict = 'PASSES-BUT-REDUNDANT';
    else if (rs.length < cv.VALIDATION_REQUIREMENTS.min_historical_instances) verdict = 'UNDERPOWERED — n below the gate';
    else verdict = 'FAILS';

    interactions[ix.key] = {
      mechanism: ix.mechanism,
      components: keys,
      n: rs.length,
      raw_r_vs_residual: gate.pearson_r,
      gate: { validated: gate.validated, reason: gate.reason, p_value: gate.p_value, corrected_alpha: gate.corrected_alpha, underpowered: !!gate.underpowered },
      component_solo_r_same_rows: componentSolo,
      best_component_abs_r: r4(bestComponent),
      INCREMENTAL_partial_r: r4(incremental),
      adds_over_components: incremental !== null && Math.abs(incremental) > bestComponent,
      verdict,
    };
  }

  // ── STEP 4 — COMBINED vs COUNTER (valid at this n; the gate is not) ─────
  const h2h = rows.filter((r) => r.skill != null);
  const ys = h2h.map((r) => r.won);
  const bs = bootstrapDiff(h2h, 'skill', 'champ');

  console.log(JSON.stringify({
    stat: 'total_bases',
    VALIDITY: contaminated
      ? 'CONTAMINATED / DIRECTIONAL ONLY — statcast_aggregates now carries a single as-of date (' + freezeDate + ') that is AFTER the settled games, so season profiles contain the games being predicted. These are NOT gate verdicts. statcast_history (new) makes point-in-time possible from tomorrow.'
      : `CLEAN out-of-sample: profiles frozen ${freezeDate}; only game_date > ${freezeDate} scored`,
    contaminated,
    rows_scored: rows.length,
    gate_spec: cv.VALIDATION_REQUIREMENTS,
    bonferroni_tests: TESTS,
    multiple_comparisons: { ...mc, note: 'cumulative across the programme lifetime, not this session' },
    n_gap_note: `the gate needs ${cv.VALIDATION_REQUIREMENTS.min_historical_instances} rows; this run has ${rows.length}`,
    archetype_coverage: {
      labelled: rows.filter((r) => r.archetype).length,
      bomber: rows.filter((r) => r.archetype_power === 1).length,
      other: rows.filter((r) => r.archetype_power === 0).length,
    },
    step1_solo_baseline: solo,
    step3_interactions: interactions,
    step4_combined_vs_counter: {
      n: h2h.length,
      pitcher_coverage: r4(mean(h2h.map((r) => (r.had_pitcher ? 1 : 0)))),
      base_rate: r4(mean(ys)),
      resolution: { skill_tb: r4(cv.pearson(h2h.map((r) => r.skill), ys).r), counter: r4(cv.pearson(h2h.map((r) => r.champ), ys).r) },
      brier: { skill_tb: r4(brier(h2h.map((r) => r.skill), ys)), counter: r4(brier(h2h.map((r) => r.champ), ys)) },
      delta: bs,
      verdict: !bs ? 'N-BLOCKED'
        : (bs.ci_excludes_zero && bs.point > 0) ? 'SKILL TB BEATS THE COUNTER'
          : (bs.ci_excludes_zero && bs.point < 0) ? 'LOSES to the counter'
            : 'INCONCLUSIVE',
    },
  }, null, 2));
  process.exit(0);
}

main().catch((e) => { console.error(e); process.exit(1); });