Sessions 7e+7f: Grade adapter, normalize consolidation, computeFeatures, analyzeViaEngine1, scan/parlay migrated to engine1
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/**
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* analyzeViaEngine1 — the canonical single-prop analysis function.
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*
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* Composes the three pieces sessions 6c, 7e, and 7f built:
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* computeFeaturesForProp → engine1.gradeProp → toLegacyShape
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*
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* Output matches the legacy `analyzeProp()` shape byte-for-byte (DemoScan
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* + GradeCard read the same fields). The `reasoning.summary` here is built
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* from real feature values (l5_avg, opp_rank_stat, etc.) so users still
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* see concrete sentences, not abstract factor labels.
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*
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* Never throws. Every upstream failure mode is reflected as low-confidence
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* grade + an explanatory reasoning summary.
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*/
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const { computeFeaturesForProp } = require('./computeFeatures');
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const engine1 = require('./engine1');
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const { toLegacyShape } = require('../../utils/gradeAdapter');
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// Map an error code from computeFeaturesForProp.meta.errors into a human
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// sentence the user will see in reasoning.summary.
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const ERROR_EXPLANATIONS = Object.freeze({
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player_not_found_in_id_map: "We couldn't find this player in our roster index.",
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no_game_scheduled_today: "No game scheduled for this player tonight.",
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no_features_computed: "Statistical features unavailable for this player tonight.",
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});
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function explainErrors(errors) {
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if (!Array.isArray(errors) || errors.length === 0) return '';
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return errors.map((e) => ERROR_EXPLANATIONS[e] || `Data gap: ${e}.`).join(' ');
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}
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// Build a human-readable reasoning summary + steps from the actual
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// features (which carry real numbers) and engine1's grade.
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function buildConcreteReasoning(features = {}, engine1Result = {}, meta = {}, prop = {}) {
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const lines = [];
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// Recent form vs the line — L5 and L20 are the orchestrator's
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// canonical season-trend signals.
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if (Number.isFinite(features.l5_avg)) {
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lines.push(`${prop.player || 'Player'} is averaging ${features.l5_avg.toFixed(1)} ${prop.stat_type || ''} over his last 5 games.`);
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}
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if (Number.isFinite(features.l20_avg)) {
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lines.push(`Last 20 games average: ${features.l20_avg.toFixed(1)}.`);
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}
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// Trend direction relative to the line.
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if (Number.isFinite(features.l5_avg) && Number.isFinite(prop.line)) {
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const diff = features.l5_avg - prop.line;
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if (Math.abs(diff) >= 0.5) {
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const dir = diff > 0 ? 'above' : 'below';
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lines.push(`That's ${Math.abs(diff).toFixed(1)} ${dir} the line of ${prop.line}.`);
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}
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}
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// Home / away.
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if (features.home_away === 1.0) lines.push('Playing at home tonight.');
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else if (features.home_away === 0.0) lines.push('Playing on the road tonight.');
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// Opponent matchup. opp_rank_stat is 0..1 normalized
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// (0 = best D, 1 = worst D) — translate to friendlier language.
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if (Number.isFinite(features.opp_rank_stat) && meta.opponentAbbr) {
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if (features.opp_rank_stat >= 0.7) {
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lines.push(`${meta.opponentAbbr} is a bottom-tier defense vs this stat.`);
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} else if (features.opp_rank_stat <= 0.3) {
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lines.push(`${meta.opponentAbbr} is a top-tier defense vs this stat.`);
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} else {
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lines.push(`${meta.opponentAbbr} is a middling defense vs this stat.`);
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}
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}
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// Rest / fatigue context.
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if (features.rest_days === 0) lines.push('Back-to-back — fatigue concern.');
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else if (Number.isFinite(features.rest_days) && features.rest_days >= 2) {
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lines.push(`${features.rest_days} days of rest.`);
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}
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if (Number.isFinite(features.game_count_in_7d) && features.game_count_in_7d >= 4) {
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lines.push(`Heavy workload — ${features.game_count_in_7d} games in the last week.`);
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}
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// Injury context.
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if (Number.isFinite(features.injury_severity_score) && features.injury_severity_score > 0) {
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lines.push(`${features.injury_severity_score} opponent starter(s) on the injury report.`);
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}
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// Trap composite — surfaced when meaningful.
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// (Adapter handles the per-factor kill_conditions chips; this line
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// gives the user the overall warning.)
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if (engine1Result?.grade && engine1Result.grade.endsWith('-') === false
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&& Array.isArray(engine1Result.all_factors)
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&& engine1Result.all_factors.includes('trap_composite_high')) {
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lines.push('Multiple trap signals firing — proceed with caution.');
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}
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// Engine-1 verdict capper.
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const grade = engine1Result?.grade;
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if (grade) {
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const verb = grade.startsWith('A') ? 'favors the play'
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: grade.startsWith('B') ? 'leans toward the play'
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: grade.startsWith('C') ? 'is split'
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: 'leans against the play';
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lines.push(`Engine 1 graded ${grade} — ${verb}.`);
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}
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// Tack on any data-gap explanations.
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const gapNote = explainErrors(meta.errors);
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if (gapNote) lines.push(gapNote);
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const summary = lines.join(' ').trim()
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|| `Analysis complete. Grade: ${grade || 'C'}.`;
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// Legacy-shaped steps so backward-compat callers (integration tests,
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// anything pre-dating the engine swap) keep seeing the named
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// sub-blocks. Each is populated from engine1 features where the data
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// exists; missing sub-blocks contain null fields instead of being
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// absent so callers can dot-access without optional chaining.
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const seasonAvg = Number.isFinite(features.l20_avg) ? features.l20_avg : null;
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const recentAvg = Number.isFinite(features.l5_avg) ? features.l5_avg : null;
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const haContext = features.home_away === 1.0 ? 'home'
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: features.home_away === 0.0 ? 'away' : null;
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const restContext = features.rest_days === 0 ? 'b2b'
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: Number.isFinite(features.rest_days) && features.rest_days >= 2 ? 'rested' : null;
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return {
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summary,
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steps: {
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season_avg: {
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value: seasonAvg,
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vs_line: seasonAvg != null && Number.isFinite(prop.line)
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? Math.round((seasonAvg - prop.line) * 10) / 10 : null,
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signal: null,
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},
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recent_form: {
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value: recentAvg,
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vs_line: recentAvg != null && Number.isFinite(prop.line)
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? Math.round((recentAvg - prop.line) * 10) / 10 : null,
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signal: null,
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},
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situational: {
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home_away: { value: null, context: haContext, signal: null },
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rest_days: { value: features.rest_days ?? null, context: restContext, signal: null },
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vs_opponent: { value: null, games: null, signal: null },
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},
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line_comparison: {
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best_line: null,
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worst_line: null,
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edge_from_best: 0,
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signal: null,
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},
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kill_conditions: [],
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final_grade: grade || null,
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// Flat narrative bullets — kept under `steps` so legacy clients
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// can still find them but they don't collide with the named
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// sub-blocks above.
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narrative: lines.map((line, i) => ({ step: i + 1, detail: line })),
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},
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};
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}
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// edge_pct in the legacy shape compares the relevant average to the line.
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// We use l5_avg when present (matches legacy "recent form" weighting),
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// fall back to l20_avg, otherwise return 0 so the field is always present.
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function edgePctFor(features, prop) {
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const ref = Number.isFinite(features?.l5_avg) ? features.l5_avg
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: Number.isFinite(features?.l20_avg) ? features.l20_avg
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: null;
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if (ref == null || !Number.isFinite(prop?.line) || prop.line === 0) return 0;
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const signed = prop.direction === 'over' ? (ref - prop.line) : (prop.line - ref);
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return Math.round((signed / prop.line) * 1000) / 10;
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}
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// When computeFeatures fails so badly that even a partial feature vector
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// is empty, return a legacy-shaped low-confidence result rather than
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// asking engine1 to grade nothing.
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function fallbackLegacyResult(rawProp, errors) {
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return {
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player: rawProp.player ?? null,
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stat_type: rawProp.stat_type ?? null,
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line: rawProp.line ?? null,
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direction: rawProp.direction ?? null,
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book: rawProp.book || 'unknown',
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grade: 'C',
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confidence: 10,
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edge_pct: 0,
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kill_conditions_triggered: [],
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reasoning: {
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summary: `Unable to compute full analysis. ${explainErrors(errors) || ''} Grade is provisional.`.trim(),
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steps: [],
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},
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};
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}
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async function analyzeViaEngine1(rawProp = {}) {
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const featureResult = await computeFeaturesForProp(rawProp);
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const { features, trap, consistency, prop, meta } = featureResult;
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// Hard fallback only when computeFeatures couldn't produce anything
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// useful at all (no features AND no consistency input).
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if ((!features || Object.keys(features).length === 0)
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&& (!consistency || consistency.consistency === 'unknown')
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&& (!Array.isArray(meta?.gameLogs) || meta.gameLogs.length === 0)) {
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return fallbackLegacyResult(rawProp, meta?.errors);
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}
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// Engine 1: deterministic rule-based grade on the feature vector.
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const engine1Result = engine1.gradeProp({ features, trap, consistency, prop });
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// Translate engine1 output → legacy shape via the adapter from 7e.
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// The adapter handles kill_conditions_triggered + the 4-letter grade
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// collapse + the 0-100 confidence scale.
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const summaryOverride = buildConcreteReasoning(features, engine1Result, meta, {
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...rawProp,
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line: prop.line,
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}).summary;
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const legacy = toLegacyShape(engine1Result, {
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player: rawProp.player,
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stat_type: rawProp.stat_type,
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line: prop.line,
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direction: prop.direction,
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book: rawProp.book || 'unknown',
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sport: meta.sport,
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}, {
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summaryOverride,
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edgePct: edgePctFor(features, prop),
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});
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// The adapter's reasoning.steps was a single-element debug bag;
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// replace it with the line-by-line breakdown we built above so the
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// legacy UI's step list looks identical to before.
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legacy.reasoning = buildConcreteReasoning(features, engine1Result, meta, {
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...rawProp,
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line: prop.line,
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});
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return legacy;
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}
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module.exports = {
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analyzeViaEngine1,
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__internals: {
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buildConcreteReasoning,
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edgePctFor,
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fallbackLegacyResult,
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explainErrors,
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ERROR_EXPLANATIONS,
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},
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};
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