diff --git a/scripts/prove-tb-factors.js b/scripts/prove-tb-factors.js
new file mode 100644
index 0000000..3120e45
--- /dev/null
+++ b/scripts/prove-tb-factors.js
@@ -0,0 +1,345 @@
+#!/usr/bin/env node
+'use strict';
+
+/**
+ * prove-tb-factors — TOTAL BASES IS A DIFFERENT EVENT FROM HITS.
+ *
+ * A hit asks whether the ball found a hole. Total bases asks how hard and how
+ * far it was struck. So the causally-correct factors differ, and the
+ * archetype differential is expected to INVERT: contact defence proved for hits,
+ * where a slap single is worth exactly one base regardless of who fielded it;
+ * for total bases the value should live with the power profiles.
+ *
+ * ── THE BASELINE IS THE CHAMPION, NOT "HE'S DUE" ─────────────────────────
+ * The hits gate used the player's own leave-one-out base rate as the null. That
+ * cannot be reproduced here: total-bases lines VARY (1.5 on 559 rows, 0.5 on
+ * 345, 2.5 on 45), and a player's rate of clearing 1.5 bases is a different
+ * quantity from his rate of clearing 0.5. With 988 rows over 341 players there
+ * are roughly two rows per player-line — far too thin to estimate a per-line
+ * personal base rate without inventing one.
+ *
+ * So the null here is the COUNTER'S OWN FORECAST (p_win), which already prices
+ * the line. That is a strictly HARDER null than a base rate, not an easier one:
+ * a factor must improve on the champion, not merely on "he's due". Stated
+ * plainly because it differs from the hits run and the difference matters when
+ * comparing the two.
+ *
+ *   SUPABASE_URL=... node scripts/prove-tb-factors.js
+ */
+
+require('dotenv').config();
+const { createClient } = require('@supabase/supabase-js');
+const fg = require('../src/services/model/factorGate');
+const sk = require('../src/services/model/skillProjection');
+const tl = require('../src/services/model/testLedger');
+const mlb = require('../src/services/adapters/mlbStatsAdapter');
+const { knownNumber, knownRate } = require('../src/utils/known');
+const { nameKey } = require('../src/utils/playerName');
+const sd = require('../src/services/model/sprayDefense');
+const pss = require('../src/services/model/platoonSeverity');
+const pw = require('../src/services/model/parkWeather');
+
+/** League-typical hit-type shares; the atom reshapes these and TB follows. */
+const BASE_SHARES = { single: 0.655, double: 0.195, triple: 0.017, home_run: 0.133 };
+const tbFrom = (s) => s.single + 2 * s.double + 3 * s.triple + 4 * s.home_run;
+
+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 ARCHS = (process.env.TB_ARCHETYPES || 'ALL,BOMBER,GHOST,BRUSH,DRIVER').split(',');
+
+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;
+}
+
+/**
+ * THE FACTORS. Each returns a MULTIPLIER on the base rate, or null when the
+ * input is absent — an absent factor must leave the baseline untouched rather
+ * than nudge it toward some default.
+ */
+const FACTORS = [
+  {
+    key: 'barrel_rate',
+    needs: ['barrel_pct'],
+    entity: (r) => r.player_key,
+    mechanism: 'THE EXTRA-BASE SKILL ITSELF. A barrel is the exit-velocity and launch-angle combination that produces extra bases; it is the most direct expression of what total bases measures, where for hits it is largely irrelevant to whether a grounder finds a hole.',
+    // UNITS: fromStatcastRow returns barrel_pct as a FRACTION (0.06), not the
+    // 0-100 the raw table stores. Writing this against the percentage scale
+    // clamped every row to the maximum negative shift, which then "improved"
+    // Brier only by leaning on the counter's known global over-prediction.
+    apply: (r) => 1 + Math.max(-0.20, Math.min(0.20, (r.barrel_pct - 0.078) * 1.8)),
+  },
+  {
+    key: 'exit_velo',
+    needs: ['avg_exit_velo'],
+    entity: (r) => r.player_key,
+    mechanism: 'How hard the ball leaves the bat. Separates a double in the gap from a fly out, which is exactly the margin total bases lives on.',
+    apply: (r) => 1 + Math.max(-0.15, Math.min(0.15, (r.avg_exit_velo - 88.9) * 0.020)),
+  },
+  {
+    key: 'hard_contact_allowed',
+    needs: ['pitcher_hard_hit_allowed'],
+    entity: (r) => r.starter_id,
+    mechanism: 'A pitcher who concedes hard contact concedes EXTRA BASES, not just hits. For total bases this should read stronger than it did for hits.',
+    apply: (r) => 1 + Math.max(-0.15, Math.min(0.15, (r.pitcher_hard_hit_allowed - 0.389) * 1.2)),
+  },
+  {
+    key: 'park_weather_hit_type',
+    needs: ['park_weather_ratio'],
+    entity: (r) => r.park_weather_ratio,
+    mechanism: 'Whether a struck ball becomes a double, clears the fence, or dies at the track. The atom reshapes HIT TYPE rather than P(hit), which is the only form that can express a total-bases effect.',
+    apply: (r) => r.park_weather_ratio,
+    caveat: 'venue-borne: replication caps at the number of distinct park readings, not the row count',
+  },
+  {
+    key: 'platoon_severity',
+    needs: ['platoon_severity_mult'],
+    entity: (r) => r.player_key,
+    mechanism: "The hitter's OWN measured split, shrunk by the smaller side's plate appearances and refused below a floor.",
+    apply: (r) => r.platoon_severity_mult,
+  },
+];
+
+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 batters = new Map(); const pitchersById = new Map();
+  for (const r of statcast) {
+    const prof = sk.fromStatcastRow(r);
+    if (r.role === 'pitcher' && r.source_id != null) pitchersById.set(Number(r.source_id), prof);
+    if (r.role === 'batter' && r.player_key) batters.set(r.player_key, prof);
+  }
+  const sprayRows = await page(sb, 'batter_spray', '*', (q) => q.eq('sport', 'mlb'));
+  const sprayByKey = new Map();
+  for (const r of sprayRows) {
+    if (!r.player_key) continue;
+    const prev = sprayByKey.get(r.player_key);
+    if (!prev || String(r.as_of_date) > String(prev.as_of_date)) sprayByKey.set(r.player_key, r);
+  }
+
+  const platRows = await page(sb, 'platoon_splits', '*', (q) => q.eq('sport', 'mlb'));
+  const platByKey = new Map();
+  for (const r of platRows) {
+    if (!r.player_key) continue;
+    const prev = platByKey.get(r.player_key);
+    if (!prev || String(r.as_of_date) > String(prev.as_of_date)) platByKey.set(r.player_key, r);
+  }
+
+  const parkRows = await page(sb, 'park_dimensions', '*', (q) => q.eq('sport', 'mlb'));
+  const parkByVenue = new Map();
+  for (const p of parkRows) if (!parkByVenue.has(p.venue_id)) parkByVenue.set(p.venue_id, p);
+  const parkLeague = pw.leagueGeometry([...parkByVenue.values()]);
+  const ctxRows = await page(sb, 'game_context', 'game_id, venue_id, wx_temp_f, wx_wind_speed_mph, wx_wind_direction_deg', (q) => q);
+  const ctxBy = new Map(ctxRows.map((c) => [c.game_id, c]));
+
+  const defRows = await page(sb, 'team_defense', '*', (q) => q.eq('sport', 'mlb'));
+  const defByTeam = new Map();
+  for (const d of defRows) defByTeam.set(d.team, d);
+
+  const snaps = await page(sb, 'model_snapshots', 'player_key, game_date, archetype, stat',
+    (q) => q.eq('sport', 'mlb').eq('stat', 'total_bases').not('archetype', 'is', null));
+  const archOf = new Map();
+  for (const s of snaps) archOf.set(`${s.player_key}|${s.game_date}`, s.archetype);
+
+  const led = await page(sb, 'ledger_entries',
+    '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', 'total_bases')
+      .in('outcome', ['hit', 'miss']).not('p_win', 'is', null));
+  const clean = led.filter((r) => !(r.quarantine_reason || '').startsWith('nontakeable_book'));
+
+  // Opponent faced, from each hitter's own game log.
+  const names = new Map();
+  for (const r of clean) if (!names.has(r.player_key)) names.set(r.player_key, r.player_name);
+  const oppBy = new Map(); const startersBy = new Map();
+  const dates = [...new Set(clean.map((r) => r.game_date))].sort();
+  for (const d of dates) {
+    try {
+      const games = await mlb.getScheduleWithPitchers(d);
+      for (const g of games) {
+        if (!g.home || !g.away) continue;
+        if (g.home.probablePitcher) startersBy.set(`${d}|OPP:${g.home.team}`, g.home.probablePitcher.id);
+        if (g.away.probablePitcher) startersBy.set(`${d}|OPP:${g.away.team}`, g.away.probablePitcher.id);
+      }
+    } catch { /* absent slate */ }
+  }
+  for (const [key, name] of names) {
+    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) oppBy.set(`${key}|${String(g.date).slice(0, 10)}`, g.opponent);
+    } catch { /* no log */ }
+  }
+
+  // Per-player base rate — the honest null: "he's due", no reading of tonight.
+  const byPlayer = new Map();
+  for (const r of clean) {
+    const cur = byPlayer.get(r.player_key) || { n: 0, w: 0 };
+    cur.n += 1; cur.w += r.outcome === 'hit' ? 1 : 0;
+    byPlayer.set(r.player_key, cur);
+  }
+
+  const loss = { no_batter_profile: 0, thin_base_rate: 0, no_opponent: 0, no_pitcher: 0, kept: 0 };
+  const rows = [];
+  for (const r of clean) {
+    const bat = batters.get(r.player_key);
+    const bp = byPlayer.get(r.player_key);
+    if (!bat) loss.no_batter_profile += 1;
+    if (!bp || bp.n < 3) { loss.thin_base_rate += 1; continue; }
+    // THE NULL IS THE CHAMPION. Total-bases lines vary, so a per-line personal
+    // base rate cannot be estimated from ~2 rows per player-line without
+    // inventing one. p_win already prices the line, and beating it is a harder
+    // bar than beating "he's due".
+    const baseline = knownNumber(r.p_win);
+    if (baseline === null) { loss.thin_base_rate += 1; continue; }
+    const faced = oppBy.get(`${r.player_key}|${r.game_date}`) || null;
+    const nick = faced ? String(faced).split(' ').pop() : null;
+    const def = faced ? (defByTeam.get(faced) || defByTeam.get(nick)) : null;
+    if (!faced) loss.no_opponent += 1;
+    const starterId = faced ? startersBy.get(`${r.game_date}|OPP:${faced}`) : null;
+    const pit = starterId != null ? pitchersById.get(Number(starterId)) : null;
+    if (faced && !pit) loss.no_pitcher += 1;
+    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,
+      team_defense: def ? knownNumber(def.oaa_sum) : null,
+      pitcher_hard_hit_allowed: pit ? knownRate(pit.hard_hit_pct) : null,
+      line: knownNumber(r.line),
+      barrel_pct: bat ? knownRate(bat.barrel_pct) : null,
+      avg_exit_velo: bat ? knownNumber(bat.avg_exit_velo) : null,
+      park_weather_ratio: (() => {
+        const c = ctxBy.get(r.game_id);
+        if (!c || c.venue_id == null) return null;
+        const dims = parkByVenue.get(c.venue_id);
+        if (!dims) return null;
+        const read = pw.parkWeatherRead({ dims, wx: c, league: parkLeague });
+        if (!read) return null;
+        const shaped = pw.applyToShares(BASE_SHARES, read);
+        return tbFrom(shaped) / tbFrom(BASE_SHARES);
+      })(),
+      platoon_severity_mult: (() => {
+        const sp = platByKey.get(r.player_key);
+        if (!sp || !bat || !bat.bats || !pit || !pit.throws) return null;
+        const out = pss.platoonRead({
+          splits: {
+            vl: { pa: sp.vl_pa, atBats: sp.vl_ab, hits: sp.vl_hits },
+            vr: { pa: sp.vr_pa, atBats: sp.vr_ab, hits: sp.vr_hits },
+          },
+          bats: bat.bats, throws: pit.throws,
+        });
+        return out && out.readable ? out.multiplier : null;
+      })(),
+      spray_multiplier: (() => {
+        const sp = sprayByKey.get(r.player_key);
+        const posOaa = def && def.position_oaa ? def.position_oaa : null;
+        if (!sp || !posOaa || !bat || !bat.bats) return null;
+        const out = sd.sprayDefenseMultiplier({ spray: sp, bats: bat.bats, positionOaa: posOaa });
+        return out ? out.multiplier : null;
+      })(),
+      platoon_edge: (bat && pit && bat.bats && pit.throws)
+        ? (String(bat.bats)[0] !== String(pit.throws)[0] ? 1 : -1) : null,
+    });
+  }
+
+  // Cumulative Bonferroni across the programme lifetime.
+  const store = tl.supabaseStore(sb);
+  const mc = await tl.recordAndCount(store, FACTORS.flatMap((f) =>
+    ARCHS.map((a) => ({ sport: 'mlb', stat: 'total_bases', archetype: a === 'ALL' ? null : a, interaction: `factor:${f.key}`, target: 'outcome' }))));
+
+  // STEP 1 — FULL-HISTORY SAMPLE AUDIT PER SLOT, before any gating.
+  const audit = [];
+  for (const f of FACTORS) {
+    for (const arch of ARCHS) {
+      const slot = arch === 'ALL' ? rows : rows.filter((r) => String(r.archetype || '').toUpperCase() === arch);
+      const usable = slot.filter((r) => f.needs.every((k) => knownNumber(r[k]) !== null));
+      audit.push({
+        factor: f.key,
+        archetype: arch,
+        rows: usable.length,
+        games: new Set(usable.map((r) => r.cluster).filter(Boolean)).size,
+        players: new Set(usable.map((r) => r.player_key)).size,
+      });
+    }
+  }
+
+  const results = [];
+  for (const arch of ARCHS) {
+    const slot = arch === 'ALL' ? rows : rows.filter((r) => String(r.archetype || '').toUpperCase() === arch);
+    for (const f of FACTORS) {
+      const usable = slot.filter((r) => f.needs.every((k) => knownNumber(r[k]) !== null));
+      // A park effect is replicated across PARKS, not across games: 619 rows in
+      // 45 games still only ever saw ~23 ballparks, and unmodelled park
+      // heterogeneity is confounded with the very thing being estimated. So the
+      // cluster is the COARSER of the game and the entity the treatment rides on.
+      const ents = f.entity ? new Set(usable.map((r) => String(f.entity(r)))) : null;
+      const games = new Set(usable.map((r) => String(r.cluster)));
+      const useEntity = ents && ents.size < games.size;
+      const paired = usable.map((r) => {
+        const mult = f.apply(r);
+        const cond = mult === null ? null : Math.min(0.99, Math.max(0.01, r.baseline * mult));
+        return {
+          baseline: r.baseline,
+          conditioned: cond,
+          won: r.won,
+          cluster: useEntity ? `e:${f.entity(r)}` : r.cluster,
+        };
+      });
+      const v = fg.adjudicate(paired, {
+        factor: f.key, archetype: arch, stat: 'total_bases',
+        cumulativeTests: mc.cumulative_tests,      // native cumulative correction
+      });
+      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,
+        ci_level: v.improvement ? v.improvement.ci_level : null,
+        verdict: v.verdict,
+        reason: v.reason,
+        ...(f.caveat ? { input_caveat: f.caveat } : {}),
+      });
+    }
+  }
+
+  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,
+    gate: 'a factor must MOVE the prediction AND improve out-of-sample Brier; movement alone is THEATER',
+    results,
+    proven: results.filter((r) => r.verdict === 'PROVES'),
+    theater: results.filter((r) => r.verdict === 'THEATER'),
+  }, null, 2));
+  process.exit(0);
+}
+
+main().catch((e) => { console.error(e); process.exit(1); });
diff --git a/specs/tb-per-archetype.md b/specs/tb-per-archetype.md
new file mode 100644
index 0000000..dcc0367
--- /dev/null
+++ b/specs/tb-per-archetype.md
@@ -0,0 +1,146 @@
+# total_bases — every power factor is THEATER, and the units bug nearly hid it
+
+**Nothing proved. The predicted inversion did not appear. And the one real
+finding is that the counter is badly miscalibrated on this stat, not that it is
+missing a factor.**
+
+---
+
+## Premise correction
+
+The order describes the per-archetype rescale method as "PROVEN and LIVE on
+hits". It is neither. `gradeBands` was built and gated two orders ago and
+explicitly **not wired**: no hits archetype slot reached sample, every band came
+back base-rate, and only `defense_by_direction` proved pooled. This is therefore
+applying an **unvalidated-at-archetype-level** method to a second stat, not
+rolling out a proven one.
+
+## STEP 1 — Full-history audit
+
+988 clean settled total_bases rows (101 quarantined, 948 carrying `p_win`), 341
+players, **9 distinct game dates**.
+
+| archetype | n | vs gate |
+|---|---|---|
+| UNLABELLED | 373 | short 127 |
+| **BOMBER** | **340** | short 160 |
+| GHOST | 147 | short 353 |
+| BRUSH | 55 | short 445 |
+| DRIVER | 40 | short 460 |
+
+**No slot reaches 500 — confirmed on full history, not a windowed artifact.**
+
+The 9-date figure matters more than the row count: with only ~49 games, any
+game-borne or venue-borne factor has almost no replication here.
+
+## The baseline had to change, and it is a HARDER null
+
+The hits gate used the player's leave-one-out base rate. That cannot be
+reproduced: TB lines vary (1.5 on 559 rows, 0.5 on 345, 2.5 on 45), and a
+player's rate of clearing 1.5 bases is a different quantity from 0.5. At ~2 rows
+per player-line, a per-line personal base rate would have to be invented.
+
+So the null here is the **counter's own forecast**, which already prices the
+line. A factor must beat the champion, not "he's due" — strictly harder.
+
+---
+
+## THE UNITS BUG (caught, and it had produced the best result in the programme)
+
+The first run reported `barrel_rate` at Brier **−0.0095**, the largest
+improvement ever measured here. It was an artifact.
+
+`fromStatcastRow` returns `barrel_pct` as a **FRACTION** (0.06); the raw table
+stores 0–100. The factor was written against the percentage scale, so
+`(0.06 − 7.8) × 0.018` clamped **every row** to the maximum negative shift. That
+uniform downward push "improved" Brier only by leaning on the counter's
+over-prediction bias — it contained no barrel information whatsoever.
+
+Same family as the S80 units trap, inverted: there the raw percentages were fed
+in unconverted; here the converted fractions were read as percentages.
+`exit_velo` was a second, simpler wiring bug — the column is `avg_exit_velo`, so
+the factor read null on every row and reported n=0. **A zero is a wiring bug
+until proven an honest absence.**
+
+---
+
+## STEP 2/3 — The gate, units fixed (138 cumulative tests)
+
+| factor | n | entities | clustered on | k | shift | Brier Δ | CI | verdict |
+|---|---|---|---|---|---|---|---|---|
+| `barrel_rate` | 707 | 179 | game | 49 | 0.0364 | **+0.0036** | [−0.0025, +0.0101] | **THEATER** |
+| `exit_velo` | 707 | 179 | game | 49 | 0.0229 | **+0.0022** | [−0.0030, +0.0072] | **THEATER** |
+| `hard_contact_allowed` | 707 | 93 | game | 49 | 0.0260 | **+0.0033** | [−0.0019, +0.0082] | **THEATER** |
+| `park_weather_hit_type` | 651 | **36** | treatment entity | 36 | 0.0070 | +0.0016 | [+0.0001, +0.0038] | PENDING — k<40 |
+| `platoon_severity` | 481 | 120 | game | 49 | 0.0243 | +0.0006 | [−0.0045, +0.0055] | PENDING — n<500 |
+
+All three contact-quality factors move the number and make it **worse**.
+
+### The predicted inversion did not appear — it went the other way
+
+The order expects barrel and park→hit-type to prove for POWER archetypes, since
+extra bases are where their value lives. Measured:
+
+| | BOMBER | GHOST |
+|---|---|---|
+| `barrel_rate` | **+0.0114** (most harmful cell in the table) | +0.0012 |
+| `exit_velo` | +0.0067 | +0.0011 |
+
+**BOMBER × barrel_rate is the single worst result**, exactly where the strongest
+proof was predicted. All slots are sample-blocked so none of this is a verdict —
+but the direction is recorded so it is not claimed later.
+
+### Why — and it is NOT double-counting
+
+The obvious explanation is that the counter already prices power, so adding
+barrel double-counts it. **Tested and refuted:** corr(barrel_pct, p_win) =
+**−0.061**. The counter is not pricing barrel at all.
+
+The actual answer is duller and more useful: corr(barrel_pct, counter
+**residual**) = **−0.012**. Barrel carries essentially no information about what
+the counter gets wrong. It is a real skill that does not help at this line.
+
+This also closes the S81 lead: `hard_hit_pct` marginal r = 0.153 at n=295, which
+drifted to 0.135 at n=383 (S82) and is now THEATER at n=707. An estimate
+regressing as noise averages out, followed to its conclusion.
+
+---
+
+## The real finding: TB is miscalibrated, not under-factored
+
+```
+mean p_win 0.5698   actual hit rate 0.5074   counter bias +0.0624
+```
+
+Held out on a strict time split (fit on dates < 2026-08-02, evaluated on 651 rows
+the fit never saw):
+
+| | Brier | Δ |
+|---|---|---|
+| raw counter | 0.25007 | — |
+| constant de-bias | 0.24740 | −0.00267 |
+| **isotonic** | **0.24621** | **−0.00386** |
+
+**The calibration fix is worth more than any factor tested, and it is the only
+intervention pointing the right way.** It is nonetheless refused at the corrected
+bar — 32 game clusters in the eval window against a 40 floor — so it is a
+CANDIDATE, not a result.
+
+That also explains the units bug's fake success precisely: a blanket downward
+shift is a crude de-bias, and it "worked" for that reason alone.
+
+## STEP 4 — No rescale
+
+Two-bar rule: nothing proved, nothing calibrated-and-certified for TB, no
+archetype slot at sample. Every band would be an honest base-rate band, which is
+what `gradeBands` already returns by construction. Running it would add nothing.
+
+## Next, in order of value
+
+1. **Calibrate total_bases** — largest measured effect, needs game-date accrual
+   to clear the cluster floor, not new inputs.
+2. **Stop adding contact-quality factors to TB.** Three tested, three THEATER,
+   and the residual correlation says there is nothing there to find.
+3. Archetype slots need ~160 more BOMBER rows before any per-archetype claim.
+
+Counter and frozen clusters byte-identical.
diff --git a/web/public/sw.js b/web/public/sw.js
index 238068e..5e3c85f 100644
--- a/web/public/sw.js
+++ b/web/public/sw.js
@@ -1,2 +1,2 @@
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