@buzzr/entertainment-engine

@buzzr/entertainment-engine

Transparent sports entertainment scoring, hybrid ML prediction, and personalized game recommendations for Buzzr.

The package is intentionally pure: it does not import React Native, Expo, Supabase, AsyncStorage, or app services — and it has zero runtime dependencies. Apps and jobs provide data through plain objects, then persist results however they choose.

Core API

import {
  resolveBuzzScores,
  enrichGameRowWithBuzzScores,
  predictGameWithDiagnostics,
} from '@buzzr/entertainment-engine';

const resolved = resolveBuzzScores({
  league: 'NBA',
  status: 'scheduled',
  startsAt: '2026-06-12T01:00:00Z',
  homeTeam: 'Boston Celtics',
  awayTeam: 'Los Angeles Lakers',
}, { upcomingLike: true });

const prediction = predictGameWithDiagnostics({
  league: 'NBA',
  status: 'scheduled',
  startsAt: '2026-06-12T01:00:00Z',
  homeTeam: 'Boston Celtics',
  awayTeam: 'Los Angeles Lakers',
}, {
  odds: { spread: -1.5, overUnder: 226.5 },
  teamPower: { home: 9.2, away: 8.7 },
});

New in v5

Injectable clock

Every time-sensitive entry point accepts an optional now (epoch millis or a Date). It defaults to Date.now(), so existing callers are unaffected, but jobs and tests can pin the clock for full determinism:

resolveBuzzScores(game, { upcomingLike: true, now: Date.UTC(2026, 4, 1) });
enrichGameRowWithBuzzScores(row, { now: Date.UTC(2026, 4, 1) });
predictGameWithDiagnostics(game, { now: Date.UTC(2026, 4, 1) });

Timezone-safe primetime detection

Primetime and weekend detection no longer depend on the host machine's timezone. Game inputs may carry an explicit venueUtcOffsetMinutes or localStartHour (also venue_utc_offset_minutes / local_start_hour on DB rows). When absent, the engine derives US Eastern local time with a proper DST calculation (second Sunday in March through first Sunday in November). easternUtcOffsetMinutes(utcMs) is exported for reuse.

Team-name normalization

All rivalry, marquee, and shared-city lookups run through one exported normalizeTeamName helper: lowercase, trimmed, whitespace-collapsed, and diacritic-insensitive ('Los Ángeles Lakers' matches 'los angeles lakers'). The NFL rivalry table now includes modern rivalries such as Bills–Bengals, Ravens–Steelers, Chiefs–Raiders, Bills–Chiefs, and Cowboys–49ers.

ML v5 (ml-v5)

The feature vector grew from 20 to 22 with two appended optional features:

• searchHeat — from context.searchHeat: { home?, away? }, each in [-1, 1], normalized to [0, 1] (absent → neutral 0.5).
• starPower — from context.starPower in [0, 1] (absent → 0.5).

Old 20-length v1 weight arrays remain fully supported: the appended features are skipped for them, so v1 predictions are bit-identical to prior releases.

trainSGD(examples, opts) upgrades (defaults preserve pre-v5 behavior):

• Feature standardization (on by default) — per-feature mean/std captured during training, stored as featureMeans/featureStds in the weights and applied automatically at prediction time.
• momentum — classical momentum coefficient (default 0, plain SGD).
• validationSplit — chronological tail held out for validation, with earlyStopping: true and patience stopping on validation MAE plateau.
• shuffle: true with seed — deterministic per-epoch shuffling via an embedded mulberry32 PRNG.
• Calibrated confidence — when a validation split exists, piecewise-linear calibration bins built from validation residuals are stored in confidenceCalibration and applied by predictGameWithDiagnostics (also exported directly as applyConfidenceCalibration).

Freshly trained weights carry modelVersion: 'ml-v5' (ML_MODEL_VERSION_V5), and buildModelRunReport carries the trained model's version through to the report.

Recommendations

import { rankGamesForUser, explainRecommendation } from '@buzzr/entertainment-engine';

const ranked = rankGamesForUser(
  [
    { id: 'game-1', game: celticsLakers, baseScore: 8.1 },
    { id: 'game-2', game: kingsJazz }, // base score estimated transparently
  ],
  {
    favoriteTeams: ['Boston Celtics'],
    favoriteLeagues: ['NBA'],
    teamAffinity: { 'utah jazz': -0.4 },
    leagueAffinity: { NHL: 0.6 },
    socialSignal: { 'game-2': 0.8 }, // fire-ratio in [-1, 1] keyed by game id
  },
  { now: Date.UTC(2026, 4, 1), limit: 20 },
);

const breakdown = explainRecommendation(ranked[0]);
// { baseScore, personalAdjustment, socialAdjustment, totalScore, factors }

Scoring is the base entertainment score (provided baseScore, else a transparent estimate) plus a bounded personal-affinity adjustment (±1.5, MAX_AFFINITY_ADJUSTMENT) and a bounded social adjustment (±0.75, MAX_SOCIAL_ADJUSTMENT). Ordering is deterministic: ties break by earliest start time, then id. explainRecommendation returns the factor list the app's BuzzBreakdownSheet renders — base score, personal adjustment, social adjustment, and individual signed factor deltas.

Design

• resolveBuzzScores is deterministic and explainable.
• predictGameWithDiagnostics layers trained weights over transparent features and returns factor impacts, input coverage, confidence, and model version metadata.
• Training helpers accept already-built examples; Supabase extraction belongs in the consuming app or job.
• ENGINE_PACKAGE_VERSION reports the package version (5.0.0).