Product identity + widen books for DISPLAY, model input byte-identical

IDENTITY (CLAUDE.md top + MASTER-PLAN header). VYNDR is a PREDICTIVE MODEL:
it projects what a player will DO and picks accurately. Market edge is a
BYPRODUCT of a good prediction, never the success criterion. Success =
the forecast is honest about its own confidence AND still ranks --
calibration and resolution, both. No edge/CLV term belongs in a pass/fail
gate; they are diagnostics we report, not thresholds a model must clear.
A model tuned to beat a closing line has been fitted to the market instead
of to the game.

Per-sport doctrine (Phillips 2022, classify by what players DO not by
position): each sport is its own model -- own variables, archetypes,
conditions, calibration, honest ceiling. Shared across sports: ONLY the
Bayesian inference math.

Truth Law: no fabricated data; honest-absent over invented; label
limitations in-band; provisional stays provisional until re-run;
documented is not verified.

PHASE 2 -- AGGREGATOR WIDENING (live). normalizeProps now emits every
DISPLAY book instead of 5 of 18. Before this we discarded 13 books of our
own accord and 64.8% of the MLB slate was invisible to users. Every prop
carries book_role (both/takeable/reference/dfs/offshore) so the display
layer can say WHAT a price is -- a fixed-payout DFS number and a two-way
sportsbook price are not interchangeable objects. Unknown books are still
dropped.

PHASE 3 -- MODEL GATE (the model does not move). bookRoles splits
MODEL_BOOKS (the legacy allow-list, character for character) from
DISPLAY_BOOKS. Both model paths re-filter before they pick a line:
gradeSlateService.dedupeProps (before first-row-wins AND before the limit)
and intradayRefreshService.indexOddsProps (which RE-GRADES at the current
line -- without the gate, widening would have silently moved locked lines
onto books the model has never been calibrated against). A test asserts
the graded set is byte-identical through the widening.

CURRENT_RULER_VERSION stays v1_first_book. The gate lifts only when the
MLB calibration is re-run on the consensus ruler and v2 is promoted.

HONEST FRAMING, recorded in the plan: this is an AGGREGATOR win and it
does NOT fix the model. WNBA still abstains -- a model problem, not a
coverage problem; it is better covered than MLB. MLB isotonic still
provisional. The consensus is MARKET, not SHARP: pinnacle, matchbook and
polymarket are 0% on both sports, so no sharp anchor exists in our feed.

Two superseded tests updated to stronger properties rather than deleted:
roleOf now names the KIND of book, and the normalizer test asserts the
display set widens WHILE the model set does not.

Gates: 4,027 tests / 322 suites green; next build exit 0.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc

tests/unit/oddsNormalizer.test.js

@@ -66,27 +66,55 @@ describe('oddsNormalizer', () => {
       });
     });
 
-    it('filters out books not in the allowed set', () => {
+    // SUPERSEDED 2026-08-01 (Order Zero). This used to assert bovada was
+    // DROPPED. It is now emitted for DISPLAY — we were discarding 13 of the
+    // feed's 18 books, which made 64.8% of the MLB slate invisible. The
+    // property that replaces it is strictly stronger: the display set widens,
+    // every row is TAGGED with what kind of price it is, an unknown book is
+    // still dropped, and the MODEL set does not move.
+    it('emits every DISPLAY book, tagged with its role, and still drops unknown books', () => {
+      const { MODEL_BOOKS } = require('../../src/config/bookRoles');
       const event = makeEvent({
-        bookmakers: [
-          makeBookmaker('bovada', [
+        bookmakers: ['bovada', 'draftkings', 'prizepicks', 'novig', 'not_a_real_book'].map((k) =>
+          makeBookmaker(k, [
             makeMarket('player_points', [
               makeOutcome('Over', 'Jokic', -110, 26.5),
               makeOutcome('Under', 'Jokic', -110, 26.5),
             ]),
-          ]),
-          makeBookmaker('draftkings', [
-            makeMarket('player_points', [
-              makeOutcome('Over', 'Jokic', -110, 26.5),
-              makeOutcome('Under', 'Jokic', -110, 26.5),
-            ]),
-          ]),
-        ],
+          ])),
       });
 
       const result = normalizeProps([event]);
-      expect(result).toHaveLength(1);
-      expect(result[0].book).toBe('draftkings');
+      const byBook = Object.fromEntries(result.map((r) => [r.book, r.book_role]));
+
+      expect(byBook).toEqual({
+        draftkings: 'both',
+        bovada: 'reference',
+        novig: 'reference',
+        prizepicks: 'dfs',      // shown for breadth, tagged, never a market price
+      });
+      expect(byBook.not_a_real_book).toBeUndefined();   // unknown books still dropped
+      expect(ALLOWED_BOOKS).toBe(MODEL_BOOKS);          // the alias still means MODEL
+    });
+
+    it('MODEL INPUT IS BYTE-IDENTICAL despite the display widening', () => {
+      // The grader re-filters to MODEL_BOOKS before first-row-wins, so widening
+      // what the surfaces show cannot change a single graded prop. This is the
+      // gate that lifts only when the consensus ruler is promoted.
+      const { dedupeProps } = require('../../src/services/gradeSlateService').__internals;
+      const event = makeEvent({
+        bookmakers: ['prizepicks', 'novig', 'bovada', 'draftkings'].map((k) =>
+          makeBookmaker(k, [
+            makeMarket('player_points', [
+              makeOutcome('Over', 'Jokic', -110, 26.5),
+              makeOutcome('Under', 'Jokic', -110, 26.5),
+            ]),
+          ])),
+      });
+
+      const graded = dedupeProps(normalizeProps([event]), 25);
+      expect(graded).toHaveLength(1);
+      expect(graded[0].book).toBe('draftkings');   // exactly what it was before
     });
 
     it('maps every market key to its internal stat_type (NBA + soccer)', () => {