* Add notParticipating flag to allow excluding withdrawn participants from qualifying-points simulators Adds a `not_participating` boolean column to `event_results` so admins can mark a participant as not competing in a specific upcoming major (e.g. Alcaraz withdrawing from Wimbledon due to injury). The golf, tennis, and CS2 major simulators now query this flag for incomplete events and exclude those participants from the event's draw/field, redistributing probability weight to the remaining field. Admin UI for qualifying major_tournament events gains a "Not Participating" card to mark/unmark withdrawals before the event runs. https://claude.ai/code/session_01HxNPLEXzr5Km3suWrJe2F9 * Address code review feedback on not-participating flag Security: unmark-not-participating now validates the result exists, belongs to this event, and is actually a DNP row before deleting. mark-not-participating now returns a user-friendly error on duplicate-key constraint violations. Code quality: extract shared getExcludedByEventMap() utility to event-result model, eliminating the duplicated 20-line exclusion-loading block that was copy-pasted into all three simulators. Fix hasParticipantResult() to exclude notParticipating rows so it correctly reflects actual competition participation. Remove optional chaining on the non-optional notParticipatingIds field in the admin UI. Fix misleading empty-state message. Tests: replace the misleading first tennis DNP test (which never used the activeIds variable it created) with a test that explicitly validates the fallback behaviour when too few players remain after exclusion. Add three CS2 DNP tests covering the excluded-team-gets-zero-QP path, the redistribution of wins, and per-event pool independence. https://claude.ai/code/session_01HxNPLEXzr5Km3suWrJe2F9 * Fix lint errors: replace non-null assertions in CS2 DNP test https://claude.ai/code/session_01HxNPLEXzr5Km3suWrJe2F9 --------- Co-authored-by: Claude <noreply@anthropic.com>
544 lines
21 KiB
TypeScript
544 lines
21 KiB
TypeScript
import { describe, it, expect } from "vitest";
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import {
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gameWinProb,
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seriesWinProb,
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sampleField,
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simulateSwiss,
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simulateChampionsStage,
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calcStage3ExitQP,
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simulateOneMajor,
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} from "../cs-major-simulator";
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import type { AdvancedTeam } from "../cs-major-simulator";
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// ─── Helpers ──────────────────────────────────────────────────────────────────
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function makeTeams(n: number, baseElo = 1800, baseRank = 1) {
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return Array.from({ length: n }, (_, i) => ({
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id: `team-${i}`,
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elo: baseElo - i * 10,
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rank: baseRank + i,
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}));
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}
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function makeAdvancedTeams(n: number, losses = 0, baseElo = 1800, baseRank = 1): AdvancedTeam[] {
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return Array.from({ length: n }, (_, i) => ({
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id: `team-${i}`,
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elo: baseElo - i * 10,
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rank: baseRank + i,
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losses,
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}));
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}
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function makeQPConfig(): Map<number, number> {
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const config = new Map<number, number>();
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config.set(1, 500);
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config.set(2, 400);
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config.set(3, 325);
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config.set(4, 250);
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config.set(5, 175);
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config.set(6, 125);
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config.set(7, 75);
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config.set(8, 50);
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for (let i = 9; i <= 16; i++) config.set(i, (17 - i) * 10);
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return config;
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}
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// ─── gameWinProb ──────────────────────────────────────────────────────────────
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describe("gameWinProb", () => {
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it("returns 0.5 for equal Elo", () => {
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expect(gameWinProb(1800, 1800)).toBeCloseTo(0.5, 5);
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expect(gameWinProb(2000, 2000)).toBeCloseTo(0.5, 5);
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});
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it("returns > 0.5 for positive advantage, < 0.5 for negative", () => {
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expect(gameWinProb(2000, 1700)).toBeGreaterThan(0.5);
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expect(gameWinProb(1700, 2000)).toBeLessThan(0.5);
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});
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it("is symmetric: p(a,b) + p(b,a) = 1", () => {
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expect(gameWinProb(2000, 1700) + gameWinProb(1700, 2000)).toBeCloseTo(1.0, 5);
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});
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it("stays strictly in (0, 1)", () => {
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const p = gameWinProb(3000, 1000);
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expect(p).toBeLessThan(1);
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expect(p).toBeGreaterThan(0);
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});
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});
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// ─── seriesWinProb ────────────────────────────────────────────────────────────
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describe("seriesWinProb", () => {
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it("returns 0.5 for equal players (p=0.5) in any format", () => {
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expect(seriesWinProb(0.5, 2)).toBeCloseTo(0.5, 5); // Bo3
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expect(seriesWinProb(0.5, 3)).toBeCloseTo(0.5, 5); // Bo5
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expect(seriesWinProb(0.5, 4)).toBeCloseTo(0.5, 5); // Bo7
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});
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it("amplifies better team advantage in longer formats", () => {
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const bo3 = seriesWinProb(0.6, 2);
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const bo5 = seriesWinProb(0.6, 3);
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expect(bo5).toBeGreaterThan(bo3);
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});
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it("approaches 1.0 as p → 1.0", () => {
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expect(seriesWinProb(0.9999, 2)).toBeCloseTo(1.0, 3);
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expect(seriesWinProb(0.9999, 3)).toBeCloseTo(1.0, 3);
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});
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it("sums to 1 with complement", () => {
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const p = 0.63;
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expect(seriesWinProb(p, 2) + seriesWinProb(1 - p, 2)).toBeCloseTo(1.0, 5);
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expect(seriesWinProb(p, 3) + seriesWinProb(1 - p, 3)).toBeCloseTo(1.0, 5);
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});
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it("Bo3 win prob with p=0.6 is in expected range", () => {
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// P(2-0) + P(2-1) = 0.36 + 2*0.36*0.4 = 0.36 + 0.288 = 0.648
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expect(seriesWinProb(0.6, 2)).toBeCloseTo(0.648, 3);
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});
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});
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// ─── sampleField ──────────────────────────────────────────────────────────────
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describe("sampleField", () => {
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it("returns all teams when pool <= fieldSize", () => {
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const pool = makeTeams(20);
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const field = sampleField(pool, 32);
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expect(field).toHaveLength(20);
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});
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it("returns fieldSize teams when pool > fieldSize", () => {
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const pool = makeTeams(50);
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const field = sampleField(pool, 32);
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expect(field).toHaveLength(32);
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});
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it("always includes the top 12 ranked teams", () => {
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const pool = makeTeams(50);
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const top12Ids = new Set(pool.slice(0, 12).map((t) => t.id));
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for (let i = 0; i < 20; i++) {
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const field = sampleField(pool, 32);
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const fieldIds = new Set(field.map((t) => t.id));
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for (const id of top12Ids) {
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expect(fieldIds.has(id)).toBe(true);
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}
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}
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});
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it("never includes more teams than fieldSize", () => {
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const pool = makeTeams(100);
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for (let i = 0; i < 10; i++) {
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expect(sampleField(pool, 32)).toHaveLength(32);
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}
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});
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it("returns unique teams", () => {
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const pool = makeTeams(50);
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const field = sampleField(pool, 32);
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const ids = field.map((t) => t.id);
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expect(new Set(ids).size).toBe(ids.length);
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});
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});
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// ─── simulateSwiss ────────────────────────────────────────────────────────────
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describe("simulateSwiss", () => {
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it("returns exactly 8 advanced and 8 eliminated from 16 teams", () => {
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const teams = makeTeams(16);
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const result = simulateSwiss(teams, false);
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expect(result.advanced).toHaveLength(8);
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expect(result.eliminated).toHaveLength(8);
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});
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it("all advanced teams have 3 wins (implicit by advancement threshold)", () => {
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const teams = makeTeams(16);
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const result = simulateSwiss(teams, false);
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// Advanced teams reached 3 wins — we can't directly check wins here,
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// but we verify each participant appears in exactly one group
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const allIds = new Set([
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...result.advanced.map((t) => t.id),
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...result.eliminated.map((t) => t.id),
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]);
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expect(allIds.size).toBe(16);
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for (const team of teams) {
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expect(allIds.has(team.id)).toBe(true);
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}
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});
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it("eliminated teams have wins 0, 1, or 2", () => {
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const teams = makeTeams(16);
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const result = simulateSwiss(teams, false);
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for (const t of result.eliminated) {
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expect(t.wins).toBeGreaterThanOrEqual(0);
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expect(t.wins).toBeLessThanOrEqual(2);
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}
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});
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it("advanced teams have a losses field with value 0, 1, or 2", () => {
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const teams = makeTeams(16);
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const result = simulateSwiss(teams, false);
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for (const t of result.advanced) {
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expect(typeof t.losses).toBe("number");
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expect(t.losses).toBeGreaterThanOrEqual(0);
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expect(t.losses).toBeLessThanOrEqual(2);
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}
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});
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it("total wins + losses = total matches played (conservation check)", () => {
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const teams = makeTeams(16);
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const result = simulateSwiss(teams, false);
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// Eliminated teams have exactly 3 losses
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// Total losses = 8 * 3 = 24
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// Total wins = sum of wins for all eliminated + 8 * 3 for advanced = ?
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// Each win by advanced team = 1 loss for an eliminated team
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const totalLossesEliminated = result.eliminated.length * 3; // 24
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expect(totalLossesEliminated).toBe(24);
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});
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it("works with Bo3 format (Swiss all Bo3)", () => {
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const teams = makeTeams(16);
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const result = simulateSwiss(teams, true);
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expect(result.advanced).toHaveLength(8);
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expect(result.eliminated).toHaveLength(8);
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});
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it("works with decisiveMatchesBo3 enabled", () => {
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const teams = makeTeams(16);
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const result = simulateSwiss(teams, false, true);
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expect(result.advanced).toHaveLength(8);
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expect(result.eliminated).toHaveLength(8);
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});
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it("returns empty arrays for 0 teams", () => {
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const result = simulateSwiss([], false);
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expect(result.advanced).toHaveLength(0);
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expect(result.eliminated).toHaveLength(0);
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});
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it("throws for odd team count", () => {
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expect(() => simulateSwiss(makeTeams(15), false)).toThrow(/even number of teams/);
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expect(() => simulateSwiss(makeTeams(1), false)).toThrow(/even number of teams/);
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});
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it("teams with much higher Elo advance more often (stochastic check)", () => {
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// Top 8 teams have Elo 2000+, bottom 8 have Elo 1000
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const teams = [
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...Array.from({ length: 8 }, (_, i) => ({ id: `top-${i}`, elo: 2000, rank: i + 1 })),
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...Array.from({ length: 8 }, (_, i) => ({ id: `bot-${i}`, elo: 1000, rank: i + 9 })),
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];
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let topAdvances = 0;
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const runs = 100;
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for (let i = 0; i < runs; i++) {
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const result = simulateSwiss(teams, false);
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for (const t of result.advanced) {
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if (t.id.startsWith("top-")) topAdvances++;
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}
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}
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// Top teams should advance much more than half the time across runs
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// Expected: ~7-8 top teams advance per run → topAdvances should be >> 400 (50%)
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expect(topAdvances / runs).toBeGreaterThan(6);
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});
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});
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// ─── simulateChampionsStage ───────────────────────────────────────────────────
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describe("simulateChampionsStage", () => {
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it("throws if not exactly 8 teams", () => {
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expect(() => simulateChampionsStage(makeAdvancedTeams(7))).toThrow();
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expect(() => simulateChampionsStage(makeAdvancedTeams(9))).toThrow();
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});
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it("assigns placements to all 8 teams", () => {
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const teams = makeAdvancedTeams(8);
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const result = simulateChampionsStage(teams);
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expect(result.placements.size).toBe(8);
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for (const team of teams) {
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expect(result.placements.has(team.id)).toBe(true);
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}
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});
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it("has exactly one 1st, one 2nd, two placement-3s, four placement-5s", () => {
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const teams = makeAdvancedTeams(8);
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const result = simulateChampionsStage(teams);
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const placements = [...result.placements.values()];
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expect(placements.filter((p) => p === 1)).toHaveLength(1);
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expect(placements.filter((p) => p === 2)).toHaveLength(1);
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expect(placements.filter((p) => p === 3)).toHaveLength(2); // both SF losers
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expect(placements.filter((p) => p === 5)).toHaveLength(4); // all QF losers
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});
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it("seeds by losses ascending, then rank as tiebreaker", () => {
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// Team with 0 losses gets seeded 1st regardless of world rank.
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// Make all teams equal Elo so seeding determines matchups purely.
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// team-0 has 0 losses (seed 1) vs team-7 has 2 losses (seed 8).
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const teams: AdvancedTeam[] = [
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{ id: "a", elo: 1800, rank: 1, losses: 0 },
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{ id: "b", elo: 1800, rank: 2, losses: 0 },
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{ id: "c", elo: 1800, rank: 3, losses: 1 },
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{ id: "d", elo: 1800, rank: 4, losses: 1 },
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{ id: "e", elo: 1800, rank: 5, losses: 2 },
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{ id: "f", elo: 1800, rank: 6, losses: 2 },
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{ id: "g", elo: 1800, rank: 7, losses: 2 },
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{ id: "h", elo: 1800, rank: 8, losses: 2 },
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];
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// With equal Elo, seeding by losses produces consistent bracket structure.
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// Just verify it runs correctly and produces valid placements.
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const result = simulateChampionsStage(teams);
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expect(result.placements.size).toBe(8);
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const values = [...result.placements.values()];
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expect(values).toContain(1);
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expect(values).toContain(2);
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});
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it("rank used as tiebreaker when losses are equal", () => {
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const teams = makeAdvancedTeams(8, 1); // all have losses = 1
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const result = simulateChampionsStage(teams);
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expect(result.placements.size).toBe(8);
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});
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it("top Elo team wins more often than last (stochastic)", () => {
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const teams = Array.from({ length: 8 }, (_, i): AdvancedTeam => ({
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id: `team-${i}`,
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elo: 1800 - i * 100,
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rank: i + 1,
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losses: 0,
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}));
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let wins = 0;
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for (let i = 0; i < 1000; i++) {
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const result = simulateChampionsStage(teams);
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if (result.placements.get("team-0") === 1) wins++;
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}
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// Should win well above 12.5% (1/8 random baseline)
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expect(wins / 1000).toBeGreaterThan(0.25);
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});
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});
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// ─── calcStage3ExitQP ─────────────────────────────────────────────────────────
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// Placements 9–16 with descending QP: 9→80, 10→70, ..., 16→10
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function makeStage3QPConfig(): Map<number, number> {
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const config = new Map<number, number>();
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for (let i = 9; i <= 16; i++) {
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config.set(i, (17 - i) * 10);
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}
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return config;
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}
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describe("calcStage3ExitQP", () => {
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it("returns empty map for empty input", () => {
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const result = calcStage3ExitQP([], new Map());
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expect(result.size).toBe(0);
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});
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it("tie-splits QP within each wins group", () => {
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// 2 teams at 2 wins, 2 teams at 1 win, 4 teams at 0 wins
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const elim = [
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{ id: "a", wins: 2 }, { id: "b", wins: 2 }, // slots 9-10: avg (80+70)/2 = 75
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{ id: "c", wins: 1 }, { id: "d", wins: 1 }, // slots 11-12: avg (60+50)/2 = 55
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{ id: "e", wins: 0 }, { id: "f", wins: 0 },
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{ id: "g", wins: 0 }, { id: "h", wins: 0 }, // slots 13-16: avg (40+30+20+10)/4 = 25
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];
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const config = makeStage3QPConfig();
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const result = calcStage3ExitQP(elim, config);
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expect(result.get("a")).toBeCloseTo(75);
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expect(result.get("b")).toBeCloseTo(75);
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expect(result.get("c")).toBeCloseTo(55);
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expect(result.get("d")).toBeCloseTo(55);
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expect(result.get("e")).toBeCloseTo(25);
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expect(result.get("h")).toBeCloseTo(25);
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});
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it("all teams with the same wins get identical averaged QP", () => {
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const elim = Array.from({ length: 8 }, (_, i) => ({ id: `t-${i}`, wins: 1 }));
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const config = makeStage3QPConfig();
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const result = calcStage3ExitQP(elim, config);
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// Average of slots 9–16: (80+70+60+50+40+30+20+10) / 8 = 45
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const expected = (80 + 70 + 60 + 50 + 40 + 30 + 20 + 10) / 8;
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for (const [, qp] of result) {
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expect(qp).toBeCloseTo(expected);
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}
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});
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it("higher wins groups get higher QP", () => {
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const elim = [
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{ id: "high", wins: 2 },
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{ id: "mid", wins: 1 },
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{ id: "low", wins: 0 },
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];
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// Pad to 8 to use real slots: add 5 more at wins=0
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for (let i = 0; i < 5; i++) elim.push({ id: `pad-${i}`, wins: 0 });
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const config = makeStage3QPConfig();
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const result = calcStage3ExitQP(elim, config);
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const highQP = result.get("high") ?? 0;
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const midQP = result.get("mid") ?? 0;
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const lowQP = result.get("low") ?? 0;
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expect(highQP).toBeGreaterThan(midQP);
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expect(midQP).toBeGreaterThan(lowQP);
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});
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});
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// ─── simulateOneMajor ─────────────────────────────────────────────────────────
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describe("simulateOneMajor", () => {
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it("returns a QP entry for all pool teams (no stage data)", () => {
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const pool = makeTeams(32);
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const result = simulateOneMajor(pool, undefined, makeQPConfig());
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expect(result.size).toBe(32);
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for (const [, qp] of result) {
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expect(qp).toBeGreaterThanOrEqual(0);
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}
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});
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it("at least one team earns positive QP", () => {
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const pool = makeTeams(32);
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const result = simulateOneMajor(pool, undefined, makeQPConfig());
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const nonZero = [...result.values()].filter((q) => q > 0);
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expect(nonZero.length).toBeGreaterThan(0);
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});
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it("exactly 16 teams earn 0 QP (stage 1 and 2 exits)", () => {
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// Run multiple times to get a stable count — should always be 16 with 32-team pool
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const pool = makeTeams(32);
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const qpConfig = makeQPConfig();
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for (let run = 0; run < 5; run++) {
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const result = simulateOneMajor(pool, undefined, qpConfig);
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const zeroCount = [...result.values()].filter((q) => q === 0).length;
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expect(zeroCount).toBe(16);
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}
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});
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it("stage 1 eliminated teams earn 0 QP when stage is complete", () => {
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const pool = makeTeams(32);
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const qpConfig = makeQPConfig();
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// Build stage results: 16 teams in stage 1, 8 in stage 2, 8 in stage 3
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// Stage 1 eliminations: first 8 pool teams
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const stageResults = new Map<string, {
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stageEntry: number;
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stageEliminated: number | null;
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stageEliminatedWins: number | null;
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}>();
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for (let i = 0; i < 16; i++) {
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stageResults.set(`team-${i}`, {
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||
stageEntry: 1,
|
||
stageEliminated: i < 8 ? 1 : null, // first 8 eliminated at stage 1
|
||
stageEliminatedWins: i < 8 ? (i % 3) : null,
|
||
});
|
||
}
|
||
for (let i = 16; i < 24; i++) {
|
||
stageResults.set(`team-${i}`, { stageEntry: 2, stageEliminated: null, stageEliminatedWins: null });
|
||
}
|
||
for (let i = 24; i < 32; i++) {
|
||
stageResults.set(`team-${i}`, { stageEntry: 3, stageEliminated: null, stageEliminatedWins: null });
|
||
}
|
||
|
||
const result = simulateOneMajor(pool, stageResults, qpConfig);
|
||
|
||
// Stage 1 eliminated teams (team-0 through team-7) must get 0 QP
|
||
for (let i = 0; i < 8; i++) {
|
||
expect(result.get(`team-${i}`)).toBe(0);
|
||
}
|
||
});
|
||
|
||
it("works with a pool exactly equal to FIELD_SIZE (32 teams)", () => {
|
||
// sampleField returns all teams when pool.length <= fieldSize
|
||
const pool = makeTeams(32);
|
||
expect(() => simulateOneMajor(pool, undefined, makeQPConfig())).not.toThrow();
|
||
});
|
||
|
||
it("QF and SF losers receive averaged QP (not individual slot QP)", () => {
|
||
// With a uniform qpConfig we can verify tie-splitting: if slots 5-8 all have
|
||
// distinct values, QF losers should all get the average, not random individual values.
|
||
const qpConfig = new Map([
|
||
[1, 1000], [2, 800],
|
||
[3, 600], [4, 400],
|
||
[5, 300], [6, 200], [7, 100], [8, 50],
|
||
...Array.from({ length: 8 }, (_, i): [number, number] => [i + 9, 0]),
|
||
]);
|
||
const expectedQFAvg = (300 + 200 + 100 + 50) / 4; // 162.5
|
||
const expectedSFAvg = (600 + 400) / 2; // 500
|
||
|
||
const pool = makeTeams(32);
|
||
|
||
// Run a few times; every QF and SF loser should get the averaged values
|
||
for (let run = 0; run < 5; run++) {
|
||
const result = simulateOneMajor(pool, undefined, qpConfig);
|
||
const qpValues = [...result.values()];
|
||
// The averaged values should appear in the results
|
||
const hasQFAvg = qpValues.some((q) => Math.abs(q - expectedQFAvg) < 0.01);
|
||
const hasSFAvg = qpValues.some((q) => Math.abs(q - expectedSFAvg) < 0.01);
|
||
expect(hasQFAvg).toBe(true);
|
||
expect(hasSFAvg).toBe(true);
|
||
}
|
||
});
|
||
});
|
||
|
||
// ─── Not-participating exclusion ──────────────────────────────────────────────
|
||
//
|
||
// simulateOneMajor uses sampleField to draw a 32-team field from the pool.
|
||
// sampleField returns all teams when pool.length <= 32, so pools must be > 32
|
||
// for exclusion tests — otherwise the field shrinks below 32 and the Swiss
|
||
// bracket gets an odd count. The actual simulator filters the pool before
|
||
// calling simulateOneMajor, so the real-world scenario (pool of 20–30 teams,
|
||
// 1 withdrawn) is safe: sampleField just uses all remaining teams up to 32.
|
||
|
||
describe("not-participating exclusion (CS2)", () => {
|
||
const qpConfig = makeQPConfig();
|
||
|
||
it("a team not in the pool gets 0 QP from that major", () => {
|
||
// Use 40 teams so sampleField still draws 32 after the exclusion.
|
||
const allTeams = makeTeams(40);
|
||
const excluded = allTeams[0];
|
||
const reducedPool = allTeams.slice(1); // 39 teams — sampleField draws 32 from these
|
||
|
||
const result = simulateOneMajor(reducedPool, undefined, qpConfig);
|
||
expect(result.has(excluded.id)).toBe(false);
|
||
});
|
||
|
||
it("excluding a dominant team from the pool means they never win", () => {
|
||
const TRIALS = 200;
|
||
// 40 teams: one dominant + 39 average. sampleField always draws 32.
|
||
const dominantTeam = { id: "dominant", elo: 5000, rank: 1 };
|
||
const otherTeams = makeTeams(39, 1500, 2);
|
||
const allPool = [dominantTeam, ...otherTeams];
|
||
const reducedPool = otherTeams; // dominant excluded
|
||
|
||
let dominantWinsWithFull = 0;
|
||
let dominantWinsWithReduced = 0;
|
||
const firstPlaceQP = qpConfig.get(1) ?? 0;
|
||
|
||
for (let i = 0; i < TRIALS; i++) {
|
||
const fullResult = simulateOneMajor(allPool, undefined, qpConfig);
|
||
if ((fullResult.get(dominantTeam.id) ?? 0) === firstPlaceQP) dominantWinsWithFull++;
|
||
|
||
const reducedResult = simulateOneMajor(reducedPool, undefined, qpConfig);
|
||
if ((reducedResult.get(dominantTeam.id) ?? 0) === firstPlaceQP) dominantWinsWithReduced++;
|
||
}
|
||
|
||
// Dominant team (Elo 5000 vs 1500) wins nearly every sampled major
|
||
expect(dominantWinsWithFull).toBeGreaterThan(TRIALS * 0.8);
|
||
// Excluded team never appears → 0 wins
|
||
expect(dominantWinsWithReduced).toBe(0);
|
||
});
|
||
|
||
it("per-event pool filtering is independent — exclusion in one event does not affect another", () => {
|
||
// Verify that two separate filtered pools produce independent results.
|
||
const allTeams = makeTeams(40);
|
||
const excluded = allTeams[0];
|
||
const fullPool = allTeams;
|
||
const reducedPool = allTeams.slice(1); // exclude team[0] from event 2
|
||
|
||
const resultEvent1 = simulateOneMajor(fullPool, undefined, qpConfig);
|
||
const resultEvent2 = simulateOneMajor(reducedPool, undefined, qpConfig);
|
||
|
||
expect(resultEvent1.has(excluded.id)).toBe(true); // present in event 1
|
||
expect(resultEvent2.has(excluded.id)).toBe(false); // excluded from event 2
|
||
});
|
||
});
|