306 lines
14 KiB
TypeScript
306 lines
14 KiB
TypeScript
/**
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* UCL Bracket Simulator
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*
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* Monte Carlo simulation of the UEFA Champions League 16-team knockout bracket.
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*
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* Algorithm:
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* 1. Load the bracket scoring event and all playoff matches from DB
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* 2. Load futures odds (American format) from participantExpectedValues
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* 3. Build two probability signals per team:
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* a. Elo — derived from futures via convertFuturesToElo() (long-run team strength)
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* b. Normalized odds — vig-removed implied win probability from the same futures
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* 4. Per-match win probability = ELO_WEIGHT * eloProb + ODDS_WEIGHT * oddsProb
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* 5. Simulate 50,000 tournaments, respecting already-completed matches
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* 6. Track integer placement counts per tier (champion / finalist / SF loser / QF loser).
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* At conversion, exact denominators guarantee column sums of 1.0 by construction:
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* - probFirst = champion / N
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* - probSecond = finalist / N
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* - probThird/probFourth = sfLoserCount / (2*N) — 2 SF losers per sim
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* - probFifth–probEighth = qfLoserCount / (4*N) — 4 QF losers per sim
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* - R16 losers → all 0 (score 0 points per scoring rules)
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*
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* Bracket path follows the same matchNumber pairing used by advanceWinnerTemplate():
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* nextMatchNumber = Math.ceil(matchNumber / 2)
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* i.e. R16 match 1 + R16 match 2 → QF match 1, R16 match 3 + R16 match 4 → QF match 2, …
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*
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* In-progress handling:
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* - Completed matches (isComplete + winnerId + loserId set) use the actual result in
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* every simulation — giving eliminated teams an exact EV equal to their scored points.
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* - Incomplete matches are simulated using the blended probability.
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*
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* Notes:
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* - Requires futures odds in sourceOdds (American format) to be imported first.
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* - Falls back to uniform probability (coin flip) when no odds are stored.
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* - ELO_WEIGHT and ODDS_WEIGHT can be tuned here; 0.7/0.3 matches the Python calibration.
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*/
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import { database } from "~/database/context";
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import { eq, and } from "drizzle-orm";
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import * as schema from "~/database/schema";
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import { eloWinProbability } from "~/services/probability-engine";
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import type { Simulator, SimulationResult } from "./types";
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// ─── Simulation parameters ────────────────────────────────────────────────────
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const NUM_SIMULATIONS = 50000;
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// ─── Odds helper ──────────────────────────────────────────────────────────────
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/** Convert American odds to implied probability (with vig). Exported for testing. */
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export function americanToImpliedProb(odds: number): number {
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if (odds > 0) return 100 / (odds + 100);
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return Math.abs(odds) / (Math.abs(odds) + 100);
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}
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// ─── Simulator ────────────────────────────────────────────────────────────────
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export class UCLSimulator implements Simulator {
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async simulate(sportsSeasonId: string): Promise<SimulationResult[]> {
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const db = database();
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// 1. Find the bracket scoring event for this sports season.
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// UCL has exactly one playoff_game event per season.
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const bracketEvent = await db.query.scoringEvents.findFirst({
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where: and(
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eq(schema.scoringEvents.sportsSeasonId, sportsSeasonId),
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eq(schema.scoringEvents.eventType, "playoff_game")
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),
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});
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if (!bracketEvent) {
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throw new Error(
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`No bracket event found for sports season ${sportsSeasonId}. ` +
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`Create a playoff_game scoring event and set up the bracket first.`
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);
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}
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// 2. Load all playoff matches for this bracket event.
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const allMatches = await db.query.playoffMatches.findMany({
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where: eq(schema.playoffMatches.scoringEventId, bracketEvent.id),
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orderBy: (m, { asc }) => [asc(m.matchNumber)],
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});
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if (allMatches.length === 0) {
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throw new Error(
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`No playoff matches found for the bracket event. ` +
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`Generate the bracket from the admin panel first.`
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);
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}
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// 3. Group matches by round, ordered by number of matches descending.
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// Round of 16 (8) → Quarterfinals (4) → Semifinals (2) → Finals (1)
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const byRound = new Map<string, typeof allMatches>();
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for (const m of allMatches) {
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if (!byRound.has(m.round)) byRound.set(m.round, []);
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byRound.get(m.round)?.push(m);
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}
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const sortedRoundMatches = [...byRound.values()]
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.toSorted((a, b) => b.length - a.length)
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.map((matches) => matches.sort((a, b) => a.matchNumber - b.matchNumber));
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if (sortedRoundMatches.length < 4) {
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throw new Error(
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`Expected 4 rounds (R16, Quarterfinals, Semifinals, Finals), ` +
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`found ${sortedRoundMatches.length}. Check the bracket structure.`
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);
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}
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const r16Matches = sortedRoundMatches[0]; // 8 matches
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const qfMatches = sortedRoundMatches[1]; // 4 matches
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const sfMatches = sortedRoundMatches[2]; // 2 matches
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const finalMatches = sortedRoundMatches[3]; // 1 match
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if (r16Matches.length !== 8) {
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throw new Error(
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`Expected 8 Round of 16 matches, found ${r16Matches.length}. ` +
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`This simulator only supports the standard UCL 16-team format.`
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);
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}
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// Validate all R16 matches have participants (the draw must be entered).
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for (const m of r16Matches) {
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if (!m.participant1Id || !m.participant2Id) {
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throw new Error(
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`Round of 16 match ${m.matchNumber} is missing participants. ` +
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`Assign all 16 teams to the bracket before running simulation.`
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);
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}
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}
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// 4. Collect all 16 participant IDs from the R16 draw (order matters for pairings).
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// r16Matches is sorted by matchNumber, so participantIds[0..1] = match 1, [2..3] = match 2, …
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const participantIds: string[] = [];
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for (const m of r16Matches) {
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participantIds.push(m.participant1Id ?? "", m.participant2Id ?? "");
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}
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const participantSet = new Set(participantIds);
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// 5. Load the resolved single Elo (input policy already blended any raw Elo
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// and futures odds into sourceElo before the run).
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const evRows = await db
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.select({
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participantId: schema.seasonParticipantExpectedValues.participantId,
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sourceElo: schema.seasonParticipantExpectedValues.sourceElo,
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})
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.from(schema.seasonParticipantExpectedValues)
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.where(eq(schema.seasonParticipantExpectedValues.sportsSeasonId, sportsSeasonId));
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// 6. Build the Elo map; teams without a resolved Elo fall back to 1500.
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const eloMap = new Map<string, number>(participantIds.map((id) => [id, 1500]));
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for (const r of evRows) {
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if (r.sourceElo !== null && r.sourceElo !== undefined && participantSet.has(r.participantId)) {
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eloMap.set(r.participantId, r.sourceElo);
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}
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}
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// 7. Build per-round lookup maps keyed by matchNumber for O(1) access in the hot loop.
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const r16ByNum = new Map(r16Matches.map((m) => [m.matchNumber, m]));
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const qfByNum = new Map(qfMatches.map((m) => [m.matchNumber, m]));
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const sfByNum = new Map(sfMatches.map((m) => [m.matchNumber, m]));
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const finalMatch = finalMatches[0];
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// ─── Helpers ──────────────────────────────────────────────────────────────
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/** Pure-Elo win probability for p1 vs p2 (single resolved Elo per team). */
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const blendedWinProb = (p1: string, p2: string): number => {
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const elo1 = eloMap.get(p1) ?? 1500;
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const elo2 = eloMap.get(p2) ?? 1500;
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return eloWinProbability(elo1, elo2);
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};
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const simMatch = (p1: string, p2: string): { winner: string; loser: string } => {
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const w = Math.random() < blendedWinProb(p1, p2) ? p1 : p2;
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return { winner: w, loser: w === p1 ? p2 : p1 };
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};
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// 9. Integer placement counts per tier.
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// Using separate integer maps avoids fractional accumulation error (e.g. += 0.25 × 50k).
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// R16 losers are never counted → all probs stay 0 → EV = 0.
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const championCounts = new Map<string, number>(participantIds.map((id) => [id, 0]));
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const finalistCounts = new Map<string, number>(participantIds.map((id) => [id, 0]));
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const sfLoserCounts = new Map<string, number>(participantIds.map((id) => [id, 0]));
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const qfLoserCounts = new Map<string, number>(participantIds.map((id) => [id, 0]));
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// 10. Run Monte Carlo simulations.
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for (let s = 0; s < NUM_SIMULATIONS; s++) {
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// ── Round of 16 ──────────────────────────────────────────────────────
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// R16 losers: no count added (0 points per scoring rules)
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const r16Winners: string[] = [];
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for (let i = 1; i <= 8; i++) {
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const m = r16ByNum.get(i);
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if (!m) continue;
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if (m.isComplete && m.winnerId) {
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r16Winners.push(m.winnerId);
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} else {
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const { winner } = simMatch(m.participant1Id ?? "", m.participant2Id ?? "");
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r16Winners.push(winner);
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}
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}
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// ── Quarterfinals ─────────────────────────────────────────────────────
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// Bracket path: QF match N gets winner of R16 match (2N-1) and (2N).
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// r16Winners is 0-indexed: [0,1] = R16 matches 1,2 → QF match 1, etc.
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const qfWinners: string[] = [];
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for (let i = 1; i <= 4; i++) {
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const dbMatch = qfByNum.get(i);
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let winner: string;
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let loser: string;
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if (dbMatch?.isComplete && dbMatch.winnerId && dbMatch.loserId) {
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winner = dbMatch.winnerId;
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loser = dbMatch.loserId;
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} else {
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const p1 = r16Winners[(i - 1) * 2];
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const p2 = r16Winners[(i - 1) * 2 + 1];
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({ winner, loser } = simMatch(p1, p2));
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}
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qfWinners.push(winner);
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qfLoserCounts.set(loser, (qfLoserCounts.get(loser) ?? 0) + 1);
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}
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// ── Semifinals ───────────────────────────────────────────────────────
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// SF match N gets winner of QF match (2N-1) and (2N).
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const sfWinners: string[] = [];
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for (let i = 1; i <= 2; i++) {
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const dbMatch = sfByNum.get(i);
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let winner: string;
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let loser: string;
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if (dbMatch?.isComplete && dbMatch.winnerId && dbMatch.loserId) {
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winner = dbMatch.winnerId;
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loser = dbMatch.loserId;
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} else {
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const p1 = qfWinners[(i - 1) * 2];
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const p2 = qfWinners[(i - 1) * 2 + 1];
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({ winner, loser } = simMatch(p1, p2));
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}
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sfWinners.push(winner);
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sfLoserCounts.set(loser, (sfLoserCounts.get(loser) ?? 0) + 1);
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}
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// ── Final ─────────────────────────────────────────────────────────────
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let champion: string;
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let finalist: string;
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if (finalMatch?.isComplete && finalMatch.winnerId && finalMatch.loserId) {
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champion = finalMatch.winnerId;
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finalist = finalMatch.loserId;
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} else {
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({ winner: champion, loser: finalist } = simMatch(sfWinners[0], sfWinners[1]));
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}
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championCounts.set(champion, (championCounts.get(champion) ?? 0) + 1);
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finalistCounts.set(finalist, (finalistCounts.get(finalist) ?? 0) + 1);
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}
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// 11. Convert counts to probability distributions.
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// Exact denominators guarantee each paired column group sums to 1.0 by construction:
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// probFirst/Second → N total (1 per sim)
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// probThird/Fourth → sfLoserCounts / (2*N) — 2 SF losers per sim
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// probFifth–Eighth → qfLoserCounts / (4*N) — 4 QF losers per sim
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const N = NUM_SIMULATIONS;
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const results: SimulationResult[] = participantIds.map((participantId) => {
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const c = championCounts.get(participantId) ?? 0;
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const f = finalistCounts.get(participantId) ?? 0;
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const sf = sfLoserCounts.get(participantId) ?? 0;
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const qf = qfLoserCounts.get(participantId) ?? 0;
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return {
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participantId,
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probabilities: {
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probFirst: c / N,
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probSecond: f / N,
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probThird: sf / (2 * N),
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probFourth: sf / (2 * N),
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probFifth: qf / (4 * N),
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probSixth: qf / (4 * N),
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probSeventh: qf / (4 * N),
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probEighth: qf / (4 * N),
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},
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source: "ucl_bracket_monte_carlo",
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};
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});
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// 12. Per-position normalization — belt-and-suspenders safety net for floating-point
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// division residuals. Columns are already near-exactly 1.0 after step 11, but this
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// guarantees the invariant before probabilities are persisted.
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const positionKeys: Array<keyof typeof results[0]["probabilities"]> = [
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"probFirst", "probSecond", "probThird", "probFourth",
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"probFifth", "probSixth", "probSeventh", "probEighth",
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];
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for (const key of positionKeys) {
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const colSum = results.reduce((s, r) => s + r.probabilities[key], 0);
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const residual = 1.0 - colSum;
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if (residual !== 0) {
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const maxResult = results.reduce((best, r) =>
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r.probabilities[key] > best.probabilities[key] ? r : best
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);
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maxResult.probabilities[key] += residual;
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}
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}
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return results;
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}
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}
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