builtbykev
411cb6f196
POST /api/scan/parlay — authenticated parlay analysis: - Supabase JWT auth middleware (auth.getUser verification) - 5 correlation types detected between legs (same_game, same_team, same_player_conflicting, positive_correlation, blowout_cascade) - Overall parlay grading (A/B/C/D) with correlation penalty adjustments - Free tier: 5 scans/month, atomic scan count increment - Scan 5: full analysis + personalized upgrade pitch - Scan 6+: 403 block with upgrade pitch - Pitch personalization from scan history (top stats, grades, tier rec) - DB writes: picks + scan_sessions per scan 30 new tests, 158 total (131 Node.js + 27 Python), all passing Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
64 lines
1.8 KiB
JavaScript
64 lines
1.8 KiB
JavaScript
function gradeParlayFromLegs(legResults, correlationFlags) {
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if (legResults.length === 0) {
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return { grade: 'D', confidence: 30, composite: 0 };
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}
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// Extract leg composites and grades
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const legComposites = legResults.map((l) => l._composite || 0);
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const legGrades = legResults.map((l) => l.grade);
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const legConfidences = legResults.map((l) => l.confidence);
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const dCount = legGrades.filter((g) => g === 'D').length;
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// Average of leg composites
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const legAvg = legComposites.reduce((a, b) => a + b, 0) / legComposites.length;
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// Correlation penalties
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let correlationPenalty = 0;
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let hasMajorNegative = false;
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for (const flag of correlationFlags) {
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if (flag.impact === 'minor_negative') correlationPenalty -= 0.3;
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if (flag.impact === 'major_negative') {
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correlationPenalty -= 1.0;
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hasMajorNegative = true;
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}
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}
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const parlayComposite = legAvg + correlationPenalty;
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// Grade assignment
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let grade;
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if (parlayComposite >= 2.5 && dCount === 0 && !hasMajorNegative) {
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grade = 'A';
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} else if (parlayComposite >= 1.5 && dCount <= 1) {
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grade = 'B';
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} else if (parlayComposite >= 0.5) {
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grade = 'C';
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} else {
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grade = 'D';
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}
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// 2+ D legs forces grade D
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if (dCount >= 2) grade = 'D';
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// Major negative caps at B
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if (hasMajorNegative && (grade === 'A')) {
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grade = 'B';
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}
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// Confidence: average of legs, adjusted for correlations
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let confidence = legConfidences.reduce((a, b) => a + b, 0) / legConfidences.length;
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for (const flag of correlationFlags) {
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if (flag.impact === 'minor_negative') confidence -= 5;
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if (flag.impact === 'major_negative') confidence -= 15;
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}
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confidence = Math.max(30, Math.min(95, Math.round(confidence)));
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return {
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grade,
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confidence,
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composite: Math.round(parlayComposite * 100) / 100,
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};
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}
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module.exports = { gradeParlayFromLegs };
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