// predict-engine.jsx — Pure scoring math for ASTRO predictions // ───────────────────────────────────────────────────────────────── // Five algorithm surfaces, all driven from the same feature vector: // // 01 SALES — projected territory/DSP/window growth (linear regression on velocity) // 02 PLAYLIST — likelihood-of-placement per editorial playlist (cosine match) // 03 SUCCESS — overall hit-potential score (weighted ensemble) // 04 MINING — catalog rank by upside (high score × low recent streams) // 05 SYNC — cinematic fit per sync category (rule-based + cosine) // // Math is real where it's tractable (linear projection, cosine, weighted means); // "deep learning" outputs are mocked deterministically from the feature vector // so they're stable across reloads and scrubable to a seed. // // EXPORT: window.PredictEngine = { ... } // ───────────────────────────────────────────────────────────────── (function () { if (typeof window === 'undefined') return; // ─── Deterministic RNG (sfc32) ──────────────────────────────── function pseed(seed) { let s = 0; for (let i = 0; i < seed.length; i++) s = (s * 31 + seed.charCodeAt(i)) | 0; let a = s ^ 0x9e3779b9, b = s ^ 0xdeadbeef, c = s ^ 0x41c6ce57, d = s ^ 0x6b79f5d3; return function () { a |= 0; b |= 0; c |= 0; d |= 0; const t = (((a + b) | 0) + d) | 0; d = (d + 1) | 0; a = b ^ (b >>> 9); b = (c + (c << 3)) | 0; c = (c << 21 | c >>> 11); c = (c + t) | 0; return (t >>> 0) / 4294967296; }; } // ─── Feature vector for any recording ───────────────────────── // Shape: { audio:{bpm,key,energy,valence,danceability,acousticness,instrumentalness}, // velocity:{d30,d90,d365,growth30,growth90}, catalog:{age,genre,writerScore}, // external:{tiktok,shazam,radio,press}, peer:[{recId,sim,success}] } function getFeatures(rec) { if (!rec) return null; const id = rec.id || rec.title || 'unk'; const rng = pseed('feat·' + id); // Audio: prefer real audio-intel data if cached, else synth. let audio = null; try { if (window.RecAudioIntelligence && window.__REC_AUDIO_CACHE && window.__REC_AUDIO_CACHE[id]) { audio = window.__REC_AUDIO_CACHE[id]; } } catch {} if (!audio) { audio = { bpm: Math.round(85 + rng() * 70), key: ['C','C♯','D','D♯','E','F','F♯','G','G♯','A','A♯','B'][Math.floor(rng()*12)], mode: rng() < 0.55 ? 'Major' : 'Minor', energy: 0.45 + rng() * 0.5, valence: 0.2 + rng() * 0.7, danceability: 0.4 + rng() * 0.55, acousticness: rng() * 0.6, instrumentalness: rng() * 0.35, }; } // Velocity: prefer real streaming history, else synth using existing patterns. const baseStreams = (rec.plays || rec.streams || 100000 + Math.floor(rng() * 4_000_000)); const d30 = Math.round(baseStreams * (0.04 + rng() * 0.06)); const d90 = Math.round(baseStreams * (0.14 + rng() * 0.10)); const d365 = Math.round(baseStreams * (0.55 + rng() * 0.30)); const growth30 = -0.30 + rng() * 0.85; // -30% .. +55% const growth90 = -0.20 + rng() * 0.55; const velocity = { d30, d90, d365, growth30, growth90, total: baseStreams }; // Catalog metadata const releaseYear = parseInt((rec.releaseDate || rec.year || '2022').toString().slice(0,4), 10); const age = Math.max(0, 2026 - releaseYear); const genrePool = ['Indie Pop','Hip-Hop','R&B','Electronic','Folk','Country','Rock','Latin','K-Pop','Lo-Fi']; const genre = rec.genre || genrePool[Math.floor(rng() * genrePool.length)]; const writerScore = 0.35 + rng() * 0.55; // composite writer track record const catalog = { age, genre, writerScore, releaseYear }; // External signals (mocked but coherent) const tiktok = rng() < 0.18 ? Math.round(rng() * 14_000_000) : Math.round(rng() * 80_000); const shazam = Math.round(velocity.d30 * (0.002 + rng() * 0.012)); const radio = rng() < 0.30 ? Math.round(40 + rng() * 320) : 0; const press = rng() < 0.22 ? Math.round(rng() * 18) : 0; const external = { tiktok, shazam, radio, press }; return { id, audio, velocity, catalog, external }; } // ─── Cosine similarity over audio features ──────────────────── function audioSim(a, b) { if (!a || !b) return 0; const va = [a.energy, a.valence, a.danceability, a.acousticness, a.instrumentalness, (a.bpm - 100) / 60]; const vb = [b.energy, b.valence, b.danceability, b.acousticness, b.instrumentalness, (b.bpm - 100) / 60]; let dot = 0, na = 0, nb = 0; for (let i = 0; i < va.length; i++) { dot += va[i]*vb[i]; na += va[i]*va[i]; nb += vb[i]*vb[i]; } return na && nb ? dot / Math.sqrt(na * nb) : 0; } // ─── Build comp-track set (nearest neighbors in audio space) ── function findComps(feat, allRecs, n) { n = n || 5; if (!feat) return []; const others = (allRecs || []).filter(r => r.id !== feat.id).slice(0, 240); const scored = others.map(r => { const f = getFeatures(r); return { rec: r, feat: f, sim: audioSim(feat.audio, f.audio) }; }).sort((x, y) => y.sim - x.sim).slice(0, n); return scored.map((s, i) => ({ ...s, // Inject a "success" metric for this comp (deterministic synth). success: Math.round(50 + (1 - i / n) * 35 + (s.feat.velocity.d30 / 200_000)), })); } // ─── 03 SONG SUCCESS — overall hit-potential score (0–100) ──── // Weighted ensemble. Returns score + per-component attribution. function songSuccess(feat, opts) { if (!feat) return null; opts = opts || {}; const w = Object.assign({ audio: 0.20, // is the song "ready" sonically (energy/danceability/valence balance) velocity: 0.30, // recent stream growth writer: 0.15, // writer track record external: 0.20, // tiktok/shazam/radio/press comp: 0.15, // similarity to recent successful tracks }, opts.weights || {}); // Component scores (all 0–100) const audioFit = Math.min(100, feat.audio.energy * 35 + feat.audio.danceability * 28 + feat.audio.valence * 22 + (1 - Math.abs((feat.audio.bpm - 120) / 60)) * 15 ); const velocityScore = Math.min(100, Math.max(0, 50 + feat.velocity.growth30 * 80) * 0.55 + Math.min(50, Math.log10(Math.max(1, feat.velocity.d30)) * 10) ); const writerScore = feat.catalog.writerScore * 100; // External signals — log-scaled const tk = Math.min(100, Math.log10(Math.max(1, feat.external.tiktok)) * 14); const sz = Math.min(100, Math.log10(Math.max(1, feat.external.shazam)) * 22); const rd = Math.min(100, feat.external.radio / 4); const pr = Math.min(100, feat.external.press * 6); const externalScore = tk * 0.45 + sz * 0.25 + rd * 0.18 + pr * 0.12; // Comp similarity (resolved later in scoreCatalog; default neutral here) const compScore = (opts.compScore != null ? opts.compScore : 55); const total = audioFit * w.audio + velocityScore * w.velocity + writerScore * w.writer + externalScore * w.external + compScore * w.comp; // Confidence: down-weighted by missing signals const conf = 0.55 + (feat.external.tiktok > 100_000 ? 0.10 : 0) + (feat.external.shazam > 1000 ? 0.10 : 0) + (feat.velocity.d30 > 50_000 ? 0.10 : 0) + (feat.external.radio > 0 ? 0.08 : 0) + (feat.external.press > 0 ? 0.05 : 0); return { score: Math.round(total), conf: Math.min(0.97, conf), bucket: total > 75 ? 'breakout' : total > 60 ? 'strong' : total > 45 ? 'developing' : total > 30 ? 'soft' : 'cold', attribution: [ { k: 'Audio fit', v: audioFit, w: w.audio }, { k: 'Stream velocity', v: velocityScore, w: w.velocity }, { k: 'Writer track', v: writerScore, w: w.writer }, { k: 'External heat', v: externalScore, w: w.external }, { k: 'Comp similarity', v: compScore, w: w.comp }, ], }; } // ─── 01 SALES OPTIMIZATION ──────────────────────────────────── // Output: per-territory and per-DSP projected next-90-day streams + recs const TERRITORIES = [ { code: 'US', name: 'United States', weight: 0.36 }, { code: 'GB', name: 'United Kingdom', weight: 0.09 }, { code: 'DE', name: 'Germany', weight: 0.07 }, { code: 'BR', name: 'Brazil', weight: 0.08 }, { code: 'MX', name: 'Mexico', weight: 0.06 }, { code: 'JP', name: 'Japan', weight: 0.10 }, { code: 'KR', name: 'South Korea', weight: 0.05 }, { code: 'IN', name: 'India', weight: 0.07 }, { code: 'AU', name: 'Australia', weight: 0.04 }, { code: 'NL', name: 'Netherlands', weight: 0.03 }, { code: 'ES', name: 'Spain', weight: 0.025 }, { code: 'FR', name: 'France', weight: 0.025 }, ]; const DSPS = ['Spotify','Apple Music','YouTube Music','Amazon Music','Tidal','Deezer','Pandora','SoundCloud']; function salesProjection(feat, opts) { if (!feat) return null; opts = opts || {}; const horizonDays = opts.horizonDays || 90; const rng = pseed('sales·' + feat.id); // Linear projection: next-90 = d90 × (1 + growth90). Clip to physical floor. const baseProjected = Math.max(feat.velocity.d30 * 3 * 0.9, feat.velocity.d90 * (1 + feat.velocity.growth90)); const projected = Math.round(baseProjected); // Distribute across territories, weighted by global music market + per-track fit const territories = TERRITORIES.map(t => { const trackFit = 0.6 + rng() * 0.8; const projShare = t.weight * trackFit; return { ...t, projShare }; }); const sumShare = territories.reduce((s, t) => s + t.projShare, 0); territories.forEach(t => t.projShare /= sumShare); const territoryProj = territories.map(t => { const proj = Math.round(projected * t.projShare); const current = Math.round(feat.velocity.d90 * t.projShare * (0.85 + rng() * 0.30)); const delta = proj - current; const deltaPct = current ? delta / current : 0; return { ...t, current, projected: proj, delta, deltaPct }; }).sort((a, b) => b.delta - a.delta); // DSP allocation const dspProj = DSPS.map(name => { const baseW = name === 'Spotify' ? 0.42 : name === 'Apple Music' ? 0.21 : name === 'YouTube Music' ? 0.18 : name === 'Amazon Music' ? 0.09 : 0.025; const trackFit = 0.7 + rng() * 0.6; const proj = Math.round(projected * baseW * trackFit); const current = Math.round(feat.velocity.d90 * baseW * (0.85 + rng() * 0.30)); return { name, current, projected: proj, delta: proj - current, deltaPct: current ? (proj - current) / current : 0 }; }).sort((a, b) => b.delta - a.delta); // Recommendations — rule-based on biggest gaps const recs = []; const topTerr = territoryProj[0]; if (topTerr.deltaPct > 0.2) { recs.push({ kind: 'territory-push', priority: 'high', title: `Push localization in ${topTerr.name}`, detail: `Projected +${Math.round(topTerr.deltaPct * 100)}% (+${(topTerr.delta/1000).toFixed(1)}k streams) in ${topTerr.code}. Add ${topTerr.code === 'BR' ? 'Portuguese' : topTerr.code === 'JP' ? 'Japanese' : topTerr.code === 'KR' ? 'Korean' : 'localized'} marketing assets and Spotify EQUAL-style placement.`, impact: topTerr.delta, }); } const topDsp = dspProj[0]; if (topDsp.deltaPct > 0.15) { recs.push({ kind: 'dsp-push', priority: 'high', title: `${topDsp.name} pitch window open`, detail: `Editorial response curve favors fresh tracks 14–28 days post-DSP push; pitch within next 10 days for projected +${(topDsp.delta/1000).toFixed(1)}k.`, impact: topDsp.delta, }); } if (feat.catalog.age >= 2 && feat.velocity.growth30 > 0) { recs.push({ kind: 'rerelease', priority: 'medium', title: 'Catalog re-trigger candidate', detail: `${feat.catalog.age}-year-old track showing positive 30-day growth (+${Math.round(feat.velocity.growth30*100)}%). Consider a remix, alt-version, or seasonal repush.`, impact: Math.round(projected * 0.15), }); } if (feat.audio.bpm < 100 && feat.audio.energy < 0.5) { recs.push({ kind: 'playlist-fit', priority: 'medium', title: 'Lo-fi/study playlist surface', detail: `BPM ${feat.audio.bpm} + energy ${feat.audio.energy.toFixed(2)} fits study/sleep/lofi pools. High-volume long-tail.`, impact: Math.round(projected * 0.08), }); } if (feat.external.tiktok > 1_000_000) { recs.push({ kind: 'tiktok-amplify', priority: 'high', title: 'TikTok velocity surge', detail: `${(feat.external.tiktok/1_000_000).toFixed(1)}M TikTok plays. Re-cut a 22s edit + sponsor 3 mid-tier creators in next 14 days.`, impact: Math.round(projected * 0.22), }); } if (recs.length < 3) { recs.push({ kind: 'pricing', priority: 'low', title: 'Pricing experiment', detail: `A/B test bundle vs single in 3 secondary markets (NL, AU, ES) to lift ARPU.`, impact: Math.round(projected * 0.04), }); } recs.sort((a, b) => b.impact - a.impact); return { horizonDays, currentTotal: feat.velocity.d90, projected, delta: projected - feat.velocity.d90, deltaPct: feat.velocity.d90 ? (projected - feat.velocity.d90) / feat.velocity.d90 : 0, territories: territoryProj, dsps: dspProj, recs: recs.slice(0, 4), conf: 0.62 + Math.min(0.30, Math.log10(Math.max(1, feat.velocity.d365)) * 0.04), }; } // ─── 02 PLAYLIST PLACEMENT ──────────────────────────────────── // Match to a curated set of editorial playlists with target feature ranges. const PLAYLISTS = [ // Spotify { dsp: 'Spotify', name: 'Today\'s Top Hits', followers: 35_200_000, target: { energy:[.55,.85], valence:[.45,.85], bpm:[95,135], dance:[.55,.90] } }, { dsp: 'Spotify', name: 'Pollen', followers: 1_200_000, target: { energy:[.40,.75], valence:[.30,.70], bpm:[90,125], dance:[.40,.75] } }, { dsp: 'Spotify', name: 'Lorem', followers: 980_000, target: { energy:[.30,.65], valence:[.20,.60], bpm:[70,115], dance:[.30,.65] } }, { dsp: 'Spotify', name: 'Lo-Fi Beats', followers: 5_400_000, target: { energy:[.20,.55], valence:[.30,.60], bpm:[65, 95], dance:[.40,.70], inst:[.30,1.0] } }, { dsp: 'Spotify', name: 'RapCaviar', followers:14_800_000, target: { energy:[.55,.85], valence:[.30,.75], bpm:[60,100], dance:[.65,.95] } }, { dsp: 'Spotify', name: 'Mint', followers: 6_600_000, target: { energy:[.65,.95], valence:[.40,.85], bpm:[115,135], dance:[.55,.85] } }, { dsp: 'Spotify', name: 'Chill Hits', followers: 7_100_000, target: { energy:[.30,.65], valence:[.35,.70], bpm:[80,115], dance:[.40,.70] } }, { dsp: 'Spotify', name: 'Anti Pop', followers: 920_000, target: { energy:[.45,.80], valence:[.20,.55], bpm:[85,130], dance:[.45,.80] } }, // Apple { dsp: 'Apple Music', name: 'A-List Pop', followers: 4_200_000, target: { energy:[.55,.85], valence:[.45,.80], bpm:[95,130], dance:[.55,.85] } }, { dsp: 'Apple Music', name: 'New In Indie', followers: 780_000, target: { energy:[.40,.75], valence:[.25,.65], bpm:[85,125], dance:[.35,.70] } }, { dsp: 'Apple Music', name: 'Pure Focus', followers: 1_300_000, target: { energy:[.20,.55], valence:[.30,.65], bpm:[60,100], dance:[.35,.65], inst:[.30,1.0] } }, // YouTube { dsp: 'YouTube Music', name: 'Hot Hits', followers: 2_400_000, target: { energy:[.55,.85], valence:[.40,.80], bpm:[90,135], dance:[.55,.85] } }, { dsp: 'YouTube Music', name: 'New Music Daily', followers: 1_100_000, target: { energy:[.45,.80], valence:[.30,.75], bpm:[85,130], dance:[.45,.80] } }, // Tidal { dsp: 'Tidal', name: 'Tidal Rising', followers: 210_000, target: { energy:[.40,.80], valence:[.20,.65], bpm:[80,130], dance:[.40,.80] } }, { dsp: 'Tidal', name: 'Master Quality Lounge',followers: 95_000, target: { energy:[.20,.55], valence:[.30,.65], bpm:[65,105], dance:[.30,.60], acoustic:[.30,1.0] } }, ]; function inRange(v, [lo, hi]) { if (v >= lo && v <= hi) return 1; const dist = v < lo ? lo - v : v - hi; return Math.max(0, 1 - dist * 2); } function playlistMatch(feat, pl) { const t = pl.target; let score = 0; let n = 0; if (t.energy) { score += inRange(feat.audio.energy, t.energy); n++; } if (t.valence) { score += inRange(feat.audio.valence, t.valence); n++; } if (t.bpm) { score += inRange(feat.audio.bpm, t.bpm); n++; } if (t.dance) { score += inRange(feat.audio.danceability, t.dance);n++; } if (t.inst) { score += inRange(feat.audio.instrumentalness, t.inst); n++; } if (t.acoustic) { score += inRange(feat.audio.acousticness, t.acoustic); n++; } return n ? score / n : 0; } function playlistPlacements(feat, opts) { if (!feat) return null; opts = opts || {}; const pool = (opts.dspFilter ? PLAYLISTS.filter(p => p.dsp === opts.dspFilter) : PLAYLISTS); const ranked = pool.map(pl => { const match = playlistMatch(feat, pl); // Likelihood of placement: match × (1 - log-followers penalty) × catalog-base const baseLikelihood = match * 0.78; const followerPenalty = Math.min(0.5, Math.log10(pl.followers / 100_000) * 0.08); const writerBoost = (feat.catalog.writerScore - 0.4) * 0.18; const lik = Math.max(0.02, Math.min(0.92, baseLikelihood - followerPenalty + writerBoost)); return { ...pl, match, likelihood: lik, verdict: lik > 0.55 ? 'strong' : lik > 0.32 ? 'plausible' : lik > 0.15 ? 'long-shot' : 'no-fit', actions: lik > 0.32 ? [ `Pitch ${pl.dsp} editorial 14–21d before release`, `Provide 2 cover-art variants (square + 16:9)`, `Co-pitch with label A&R using audio-feature similarity comp`, ] : null, }; }).sort((a, b) => b.likelihood - a.likelihood); return { ranked: ranked.slice(0, 12) }; } // ─── 04 CATALOG MINING ──────────────────────────────────────── // For each track, compute an upside score = success-score × (1 - normalized recent streams). // Sorted desc → top N "underperforming with potential". function catalogMining(allRecs, opts) { opts = opts || {}; const limit = opts.limit || 50; const scored = (allRecs || []).slice(0, 240).map(r => { const f = getFeatures(r); const success = songSuccess(f); const recentNorm = Math.min(1, Math.log10(Math.max(1, f.velocity.d30)) / 6); const upside = success.score * (0.30 + 0.70 * (1 - recentNorm)); return { rec: r, feat: f, success, upside: Math.round(upside) }; }); scored.sort((a, b) => b.upside - a.upside); return scored.slice(0, limit); } // ─── 05 SYNC PLACEMENT ──────────────────────────────────────── // Match audio profile to common sync archetypes. const SYNC_ARCHETYPES = [ { tag: 'Trailer-Action', target: { energy:[.75,1.0], valence:[.30,.65], bpm:[100,160], inst:[.30,1.0] }, brands: ['A24','Marvel','Netflix Originals','Sony Pictures'] }, { tag: 'Trailer-Drama', target: { energy:[.40,.70], valence:[.15,.45], bpm:[60, 95], acoustic:[.20,.85] }, brands: ['A24','HBO','FX','Apple TV+'] }, { tag: 'Comedy-Bouncy', target: { energy:[.55,.80], valence:[.65,.95], bpm:[110,140], dance:[.55,.85] }, brands: ['NBC','Paramount Comedy','Hulu'] }, { tag: 'Romance-Indie', target: { energy:[.30,.60], valence:[.40,.75], bpm:[75,115], acoustic:[.30,.80] }, brands: ['A24','Searchlight','Indie Wire'] }, { tag: 'Sport-Hype', target: { energy:[.80,1.0], valence:[.55,.90], bpm:[120,160], dance:[.55,.85] }, brands: ['Nike','Adidas','ESPN','Under Armour'] }, { tag: 'Tech-Ad', target: { energy:[.55,.80], valence:[.60,.90], bpm:[100,135], inst:[.10,.50] }, brands: ['Apple','Google','Samsung','Adobe'] }, { tag: 'Auto-Premium', target: { energy:[.50,.75], valence:[.40,.70], bpm:[90,125], inst:[.20,.70] }, brands: ['BMW','Mercedes','Audi','Lexus'] }, { tag: 'Fashion-Edit', target: { energy:[.45,.80], valence:[.30,.65], bpm:[95,130], dance:[.45,.80] }, brands: ['Vogue','Saint Laurent','Acne Studios'] }, { tag: 'Game-Cinematic', target: { energy:[.65,.95], valence:[.20,.55], bpm:[90,135], inst:[.40,1.0] }, brands: ['Riot Games','PlayStation','Xbox'] }, ]; function syncFit(feat) { if (!feat) return null; const ranked = SYNC_ARCHETYPES.map(arch => { const t = arch.target; let score = 0, n = 0; if (t.energy) { score += inRange(feat.audio.energy, t.energy); n++; } if (t.valence) { score += inRange(feat.audio.valence, t.valence); n++; } if (t.bpm) { score += inRange(feat.audio.bpm, t.bpm); n++; } if (t.dance) { score += inRange(feat.audio.danceability, t.dance);n++; } if (t.inst) { score += inRange(feat.audio.instrumentalness, t.inst); n++; } if (t.acoustic) { score += inRange(feat.audio.acousticness, t.acoustic); n++; } const fit = n ? score / n : 0; // Estimated dollar value (rule-of-thumb sync fee bands) const baseFee = arch.tag.startsWith('Trailer') ? 35_000 : arch.tag === 'Sport-Hype' ? 80_000 : arch.tag === 'Tech-Ad' ? 120_000 : arch.tag === 'Auto-Premium' ? 65_000 : 25_000; const feeRange = [Math.round(baseFee * 0.6), Math.round(baseFee * (fit > 0.7 ? 1.4 : 1.0))]; return { ...arch, fit, feeRange, viable: fit > 0.55 }; }).sort((a, b) => b.fit - a.fit); return { ranked: ranked.slice(0, 6) }; } // ─── Full per-recording scorecard ───────────────────────────── function scorecard(rec, opts) { opts = opts || {}; const feat = getFeatures(rec); if (!feat) return null; const allRecs = opts.allRecs || (window.RECORDINGS || []); const comps = findComps(feat, allRecs, 5); const compScore = comps.length ? Math.round(comps.reduce((s, c) => s + c.success * c.sim, 0) / comps.reduce((s, c) => s + c.sim, 0.001)) : 50; const success = songSuccess(feat, { weights: opts.weights, compScore }); const sales = salesProjection(feat, opts); const playlists = playlistPlacements(feat, opts); const sync = syncFit(feat); return { rec, feat, comps, success, sales, playlists, sync }; } window.PredictEngine = { getFeatures, audioSim, findComps, songSuccess, salesProjection, playlistMatch, playlistPlacements, catalogMining, syncFit, scorecard, PLAYLISTS, TERRITORIES, DSPS, SYNC_ARCHETYPES, }; console.log('[PredictEngine] loaded · 5 algorithms · ' + PLAYLISTS.length + ' playlists · ' + SYNC_ARCHETYPES.length + ' sync archetypes'); })();