1a94ef5fcf
Folds re-sequenced steps 1+2 into one change (Kev's call): same bug
family — features wired to sources that return null.
THE PROBABILITY LAYER WAS DEAD IN PRODUCTION. p_win/ev_pct/kelly/
model_odds/value were absent on 0/8 live grades because
gameLogService.getGameLogs returns null for MLB by construction and
depends on the offline Python service for NBA/WNBA, so meta.gameLogs was
[] for every sport. This was the S46 bug in a second location — that fix
gave featureCache an MLB branch (why grades still worked) but never the
estimator. featureCache.getStatRows now supplies normalized rows
([{date,[statType]:v}], most-recent-first) for every sport, feeding the
estimator AND consistency AND game_count_in_7d from one fetch.
VERIFIED on real props: p_win 25/25 WNBA, 8/8 MLB (was 0).
GRADE RANGE, ON MERIT — never by rescaling (permanent founder ruling:
minting A's without new information is a relabelled B sold as an A and
corrupts an append-only ledger).
- refreshTeamStats wired into runSnapshot — it had ZERO production
callers, so opp_rank_stat was permanently null and a +/-1.0 factor
could never fire. Test-env no-op (opsNotify precedent).
- L20 made SYMMETRIC: both branches were delta +1.0, so the season
baseline could only ever ADD. No negative path was a structural reason
D was unreachable. New l20_contradicts_* carries -1.0.
- game_count_in_7d derived from real logged dates (heavy_workload_7d).
- NOT wired, deliberately, with reasons inline: teamId (no team_id
column; getFeatures reads it top-level; factor also needs a starter-id
list) and season_type (ESPN 2 = REGULAR season; threading it raw would
fire veteran_in_playoffs in July). Dead code dressed as a fix is the
thing we are removing, not adding.
CALIBRATION GUARD (found by verifying, not assuming): consistency CV is
NBA-tuned; for a Poisson-ish stat cv ~ 1/sqrt(mean), so any stat with
mean < 4 auto-classifies boom_bust. First verification run showed 8/8 MLB
props boom_bust — a blanket -1.0 that dropped the board to all-C. Floored
at CONSISTENCY_MIN_MEAN=4 -> 'unknown' below. Absent beats wrong. MLB
low-count stats therefore still get no consistency factor: honest, not
fixed. Scale-free index-of-dispersion classifier is the open follow-up.
CONFIDENCE IS NOT A PROBABILITY: payloads carry confidence_basis:
'grade_band'. Corrected mlb-grade-degradation.md — its "25/25
grade<->confidence agreement" is a TAUTOLOGY (confidence is derived FROM
the letter, so it would report 25/25 even if every grade were wrong), not
a validation. Removed dead mlbGrader.js (referenced only by its own test)
and the stale computeFeatures comment claiming a penalty that never ran.
VERIFICATION (scripts/verify-grade-range.js, real props/logs/engine):
WNBA 25 props B 68%->32%, C 32%->64%, D 0->1 (4%); 11-step spread went
from 2 steps to 5 (C/C+/B-/D). The D is earned: Angel Reese assists o2.5,
p_win 0.365. Nothing flooded — grades got HARDER. A did not emit locally
because opp_rank_stat needs the Redis cache only prod populates (local
ceiling +3.0 vs the +4.5 A needs); reachability is proven arithmetically
and locked in tests. Prod A-emission is the outstanding fingerprint.
MARKETING HOLD: "A-RATED" (AccuracyBadge, TopSignals) is unsupported
until that fingerprint. Confirmed honest fallbacks render today —
/api/ledger/accuracy has B and C buckets only, so the badge shows
"MODEL · 63% HIT" and TopSignals self-hides. Nothing fabricated ships.
Suite 276/3286 green, web build exit 0.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SmNjJAwEnqHPtXbvSZR8kA
85 lines
3.8 KiB
JavaScript
85 lines
3.8 KiB
JavaScript
// Verify computeFeaturesForProp routes soccer → soccerFeatureExtractor
|
|
// and NBA → existing path. The NBA path's full behavior is covered by
|
|
// computeFeatures.test.js (existing).
|
|
|
|
const mockExtractSoccerFeatures = jest.fn();
|
|
jest.mock('../../src/services/intelligence/soccerFeatureExtractor', () => ({
|
|
extractSoccerFeatures: (...args) => mockExtractSoccerFeatures(...args),
|
|
isSoccerSport: (s) => ['soccer', 'football'].includes(String(s || '').toLowerCase()),
|
|
}));
|
|
|
|
// Mock the rest of the upstream chain — none of it should be called on
|
|
// the soccer branch.
|
|
jest.mock('../../src/utils/supabase', () => ({
|
|
getSupabaseServiceClient: () => ({ from: jest.fn() }),
|
|
}));
|
|
jest.mock('axios');
|
|
jest.mock('../../src/services/intelligence/featureCache', () => ({
|
|
getFeatures: jest.fn(),
|
|
// Session 63 — computeFeatures now sources normalized per-game rows here
|
|
// (feeds consistency + the probability estimator + game_count_in_7d).
|
|
getStatRows: jest.fn(async () => []),
|
|
gameCountInWindow: jest.fn(() => null),
|
|
}));
|
|
jest.mock('../../src/services/intelligence/trapDetection', () => ({
|
|
getTrapScore: jest.fn(async () => ({ composite: 0.2, signals: {}, active_count: 1, recommendation: 'caution' })),
|
|
}));
|
|
jest.mock('../../src/services/intelligence/consistencyScore', () => ({
|
|
getConsistency: jest.fn(),
|
|
}));
|
|
jest.mock('../../src/services/intelligence/gameLogService', () => ({
|
|
getGameLogs: jest.fn(async () => []),
|
|
}));
|
|
|
|
const { computeFeaturesForProp } = require('../../src/services/intelligence/computeFeatures');
|
|
|
|
beforeEach(() => {
|
|
mockExtractSoccerFeatures.mockReset();
|
|
});
|
|
|
|
describe('computeFeaturesForProp — sport dispatch', () => {
|
|
test('sport=soccer routes to soccerFeatureExtractor (NBA path NOT invoked)', async () => {
|
|
mockExtractSoccerFeatures.mockResolvedValueOnce({
|
|
features: { goals_per_90: 0.4 },
|
|
trap: { composite: 0, signals: {}, active_count: 0, recommendation: 'proceed' },
|
|
consistency: { consistency: 'unknown', score: null, games: 0 },
|
|
prop: { line: 0.5, direction: 'over' },
|
|
meta: { player: 'Test', sport: 'soccer', statType: 'goals', errors: [] },
|
|
});
|
|
|
|
const result = await computeFeaturesForProp({
|
|
player: 'Test', stat_type: 'goals', line: 0.5, direction: 'over', sport: 'soccer',
|
|
});
|
|
|
|
expect(mockExtractSoccerFeatures).toHaveBeenCalledTimes(1);
|
|
expect(result.features.goals_per_90).toBe(0.4);
|
|
expect(result.meta.sport).toBe('soccer');
|
|
// The branch re-runs trap detection so the trap object is populated.
|
|
expect(result.trap.composite).toBeGreaterThanOrEqual(0);
|
|
});
|
|
|
|
test('sport=football is normalized into the soccer branch', async () => {
|
|
mockExtractSoccerFeatures.mockResolvedValueOnce({
|
|
features: {}, trap: {}, consistency: {}, prop: {}, meta: { sport: 'soccer', errors: [] },
|
|
});
|
|
await computeFeaturesForProp({ player: 'X', stat_type: 'goals', line: 0.5, sport: 'football' });
|
|
expect(mockExtractSoccerFeatures).toHaveBeenCalledTimes(1);
|
|
});
|
|
|
|
test('sport=nba does NOT invoke the soccer extractor', async () => {
|
|
const featureCache = require('../../src/services/intelligence/featureCache');
|
|
featureCache.getFeatures.mockResolvedValueOnce({ features: { l5_avg: 28 } });
|
|
await computeFeaturesForProp({
|
|
player: 'Jokic', stat_type: 'points', line: 26.5, direction: 'over', sport: 'nba',
|
|
});
|
|
expect(mockExtractSoccerFeatures).not.toHaveBeenCalled();
|
|
});
|
|
|
|
test('sport omitted defaults to NBA (legacy contract)', async () => {
|
|
const featureCache = require('../../src/services/intelligence/featureCache');
|
|
featureCache.getFeatures.mockResolvedValueOnce({ features: { l5_avg: 30 } });
|
|
await computeFeaturesForProp({ player: 'A', stat_type: 'points', line: 25, direction: 'over' });
|
|
expect(mockExtractSoccerFeatures).not.toHaveBeenCalled();
|
|
});
|
|
});
|