Model Train arc 1 (engine): de-vig + EV + takeable/value gates + hero v2 + triplet

Steps 1-6 — make "real opportunities at takeable prices" the engine, not a filter.

1. DE-VIG (src/utils/devig.js): two-way multiplicative de-vig strips the vig and
   returns fair prob + fair price per side + the overround. One side missing →
   fair UNAVAILABLE (null), never faked. Method noted in code + the `devig_method`
   field.
2. EV (devig.evPct): ev_pct = model prob × decimal − 1 at the graded side's
   ACTUAL price. This is the ranking signal now, replacing raw |model−consensus|.
3. TAKEABLE gate (src/config/valueEngine.js, TAKEABLE_ODDS_CEILING −160 .. +200,
   env-tunable): promoted surfaces only (hero/featured/alerts). The full board
   still shows everything; Parlay Lab exempt; JUICE_ODDS_FLOOR (−400) stays the
   absolute backstop underneath. Strict null-guard (Number(null)===0 would have
   made a missing price "takeable").
4. VALUE flag: passes BOTH gates (takeable AND ev_pct ≥ VALUE_EV_THRESHOLD).
   Grade = read quality; value = the price pays you. Shipped in payloads.
5. HERO v2 (heroPropService): highest ev_pct among takeable A/B reads — a huge
   gap on a −900 line is trivia, not an opportunity.
6. VALUE TRIPLET: book_odds · fair_odds · model_odds on every read (snapshot,
   hero, scan — they all spread the grade). Handoff documents the fields; the
   rendering is Session-2 Design's job.

All wired in analyzeViaEngine1's existing p_win/kelly block (real quantile
probability × real book odds, or nothing). 33 new tests; suite 276/3306 green.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

src/services/heroPropService.js

@@ -36,6 +36,14 @@ function toHero(g, sport, gap, isRecent) {
     gap: gap == null ? null : Math.round(gap * 100) / 100,
     team: g.team || null,
     reasoning: (g.reasoning && g.reasoning.summary) || null, // blurred paywall teaser
+    // Model Train (steps 2/4/6) — EV ranking, the value flag, and the value
+    // triplet (book price · fair de-vigged price · model price).
+    ev_pct: g.ev_pct ?? null,
+    value: g.value ?? null,
+    takeable: g.takeable ?? null,
+    book_odds: g.book_odds ?? (at.odds != null ? Number(at.odds) : null),
+    fair_odds: g.fair_odds ?? null,
+    model_odds: g.model_odds ?? null,
   };
 }
 
@@ -66,14 +74,21 @@ async function pickHeroProp(deps = {}) {
     }
   }
 
-  // The hero: largest |projection - line| among A/B candidates.
-  let hero = null, heroGap = -1;
+  // HERO RULE v2 (Model Train, step 5): the highest EV among reads that pass the
+  // TAKEABLE gate, A/B grades only. A huge model-vs-line gap on a -900 line is
+  // trivia; the hero is the best OPPORTUNITY at a price you'd actually take.
+  const { isTakeable } = require('../config/valueEngine');
+  let hero = null, heroEv = -Infinity;
   for (const { g, sport } of all) {
     if (!isAB(g.grade) || !candidate(g)) continue;
-    const gap = Math.abs(Number(g.projection) - Number(g.line));
-    if (gap > heroGap) { heroGap = gap; hero = { g, sport }; }
+    if (!Number.isFinite(Number(g.ev_pct))) continue;      // need a real EV to rank
+    if (!isTakeable(g.book_odds)) continue;                // promoted surface → takeable only
+    if (Number(g.ev_pct) > heroEv) { heroEv = Number(g.ev_pct); hero = { g, sport }; }
+  }
+  if (hero) {
+    const gap = candidate(hero.g) ? Math.abs(Number(hero.g.projection) - Number(hero.g.line)) : null;
+    return toHero(hero.g, hero.sport, gap, false);
   }
-  if (hero) return toHero(hero.g, hero.sport, heroGap, false);
 
   // Empty slate → the MOST RECENT real graded read (any grade), by timestamp.
   let recent = null, recentTs = '';