brackt/app/models/participant-expected-value.ts
Chris Parsons 79ec477a98 feat: implement Expected Value System with ICM probability calculator
Implements Phase 5.2 of the EV system with Harville-Malmuth Independent Chip Model
for calculating participant placement probabilities from futures odds.

## Key Features

### ICM Probability Calculator
- Implements Harville-Malmuth method for distributing probabilities
- Converts American odds to championship probabilities
- Generates P(1st) through P(8th) for all participants
- Column-normalized: each placement sums to 100% across all teams
- Works with any number of participants (not limited to 8)

### Admin UI - Futures Odds Entry
- Enter American odds (e.g., +550, -200) for championship futures
- Live preview of ICM-calculated probability distributions
- Displays all 8 placement probabilities
- Persists odds for editing on subsequent visits
- Automatic probability normalization (removes bookmaker vig)

### Database Schema Updates
- Renamed participant_expected_values.season_id → sports_season_id
- Updated foreign key to reference sports_seasons instead of seasons
- Added source_odds field to store original futures odds
- Migration 0025: Column rename and FK update
- Migration 0026: Add source_odds field

### Model Layer
- participant-expected-value: CRUD operations for probability distributions
- Supports multiple probability sources (manual, futures_odds, elo_simulation)
- Automatic EV calculation based on league scoring rules
- Probability validation and normalization

### Service Layer
- icm-calculator: Harville-Malmuth probability distribution
- probability-engine: Odds conversion and Elo utilities (for future use)
- bracket-simulator: Monte Carlo simulation (for future hybrid approach)
- ev-calculator: Expected value computation from probabilities

## Technical Details

- Uses exponential decay favoring top positions for strong teams
- Preserves championship probability ordering in final distributions
- Row sums vary (strong teams ~100%, weak teams lower)
- All probabilities between 0-1, mathematically valid
- Comprehensive test suite: 97 tests passing

## Future Enhancements

- Hybrid approach: ICM pre-playoffs, bracket simulation during playoffs
- Integration with league-specific scoring rules
- Historical probability tracking for accuracy analysis

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-17 22:19:46 -08:00

295 lines
8.1 KiB
TypeScript

/**
* Model for Participant Expected Values
*
* Manages probability distributions and calculated EVs for participants
* in sports seasons.
*/
import { database } from "~/database/context";
import { participantExpectedValues } from "~/database/schema";
import { eq, and } from "drizzle-orm";
import type { ProbabilityDistribution, ScoringRules } from "~/services/ev-calculator";
import { calculateEV, validateProbabilities, normalizeProbabilities } from "~/services/ev-calculator";
export type ProbabilitySource = "manual" | "futures_odds" | "elo_simulation" | "performance_model";
export interface ParticipantEV {
id: string;
participantId: string;
sportsSeasonId: string;
probFirst: string;
probSecond: string;
probThird: string;
probFourth: string;
probFifth: string;
probSixth: string;
probSeventh: string;
probEighth: string;
expectedValue: string;
source: ProbabilitySource | null;
sourceOdds: number | null;
calculatedAt: Date;
updatedAt: Date;
}
export interface CreateProbabilityInput {
participantId: string;
sportsSeasonId: string;
probabilities: ProbabilityDistribution;
scoringRules: ScoringRules;
source?: ProbabilitySource;
sourceOdds?: number; // American odds if source is futures_odds
}
export interface UpdateProbabilityInput {
probabilities: ProbabilityDistribution;
scoringRules: ScoringRules;
source?: ProbabilitySource;
}
/**
* Create or update participant probabilities and calculate EV
*
* @param input - Probabilities, scoring rules, and metadata
* @returns Created/updated participant EV record
* @throws Error if probabilities don't sum to 100% (within tolerance)
*/
export async function upsertParticipantEV(
input: CreateProbabilityInput
): Promise<ParticipantEV> {
const { participantId, sportsSeasonId, probabilities, scoringRules, source = "manual", sourceOdds } = input;
// Validate probabilities sum to 100%
if (!validateProbabilities(probabilities)) {
throw new Error(
`Probabilities must sum to 100% (±0.1%). Current sum: ${
Object.values(probabilities).reduce((a, b) => a + b, 0)
}%`
);
}
// Calculate EV
const expectedValue = calculateEV(probabilities, scoringRules);
const db = database();
// Check if record exists
const existing = await db
.select()
.from(participantExpectedValues)
.where(
and(
eq(participantExpectedValues.participantId, participantId),
eq(participantExpectedValues.sportsSeasonId, sportsSeasonId)
)
)
.limit(1);
const now = new Date();
if (existing.length > 0) {
// Update existing
const updated = await db
.update(participantExpectedValues)
.set({
probFirst: probabilities.probFirst.toString(),
probSecond: probabilities.probSecond.toString(),
probThird: probabilities.probThird.toString(),
probFourth: probabilities.probFourth.toString(),
probFifth: probabilities.probFifth.toString(),
probSixth: probabilities.probSixth.toString(),
probSeventh: probabilities.probSeventh.toString(),
probEighth: probabilities.probEighth.toString(),
expectedValue: expectedValue.toString(),
source,
sourceOdds: sourceOdds ?? null,
calculatedAt: now,
updatedAt: now,
})
.where(eq(participantExpectedValues.id, existing[0].id))
.returning();
return updated[0];
} else {
// Create new
const created = await db
.insert(participantExpectedValues)
.values({
participantId,
sportsSeasonId,
probFirst: probabilities.probFirst.toString(),
probSecond: probabilities.probSecond.toString(),
probThird: probabilities.probThird.toString(),
probFourth: probabilities.probFourth.toString(),
probFifth: probabilities.probFifth.toString(),
probSixth: probabilities.probSixth.toString(),
probSeventh: probabilities.probSeventh.toString(),
probEighth: probabilities.probEighth.toString(),
expectedValue: expectedValue.toString(),
source,
sourceOdds: sourceOdds ?? null,
calculatedAt: now,
updatedAt: now,
})
.returning();
return created[0];
}
}
/**
* Create or update with auto-normalization
* Automatically normalizes probabilities if they don't sum to 100%
*/
export async function upsertParticipantEVWithNormalization(
input: CreateProbabilityInput
): Promise<ParticipantEV> {
const normalized = normalizeProbabilities(input.probabilities);
return upsertParticipantEV({
...input,
probabilities: normalized,
});
}
/**
* Get participant EV for a specific sports season
*/
export async function getParticipantEV(
participantId: string,
sportsSeasonId: string
): Promise<ParticipantEV | null> {
const db = database();
const result = await db
.select()
.from(participantExpectedValues)
.where(
and(
eq(participantExpectedValues.participantId, participantId),
eq(participantExpectedValues.sportsSeasonId, sportsSeasonId)
)
)
.limit(1);
return result[0] || null;
}
/**
* Get all participant EVs for a sports season
*/
export async function getAllParticipantEVsForSeason(
sportsSeasonId: string
): Promise<ParticipantEV[]> {
const db = database();
return db
.select()
.from(participantExpectedValues)
.where(eq(participantExpectedValues.sportsSeasonId, sportsSeasonId));
}
/**
* Delete participant EV
*/
export async function deleteParticipantEV(
participantId: string,
sportsSeasonId: string
): Promise<void> {
const db = database();
await db
.delete(participantExpectedValues)
.where(
and(
eq(participantExpectedValues.participantId, participantId),
eq(participantExpectedValues.sportsSeasonId, sportsSeasonId)
)
);
}
/**
* Batch upsert multiple participant EVs
* Useful for updating all participants after generating probabilities
*/
export async function batchUpsertParticipantEVs(
inputs: CreateProbabilityInput[]
): Promise<ParticipantEV[]> {
const results: ParticipantEV[] = [];
// Process in batches to avoid overwhelming the database
const batchSize = 50;
for (let i = 0; i < inputs.length; i += batchSize) {
const batch = inputs.slice(i, i + batchSize);
const batchResults = await Promise.all(
batch.map((input) => upsertParticipantEV(input))
);
results.push(...batchResults);
}
return results;
}
/**
* Convert database record to ProbabilityDistribution
*/
export function toProbabilityDistribution(ev: ParticipantEV): ProbabilityDistribution {
return {
probFirst: parseFloat(ev.probFirst),
probSecond: parseFloat(ev.probSecond),
probThird: parseFloat(ev.probThird),
probFourth: parseFloat(ev.probFourth),
probFifth: parseFloat(ev.probFifth),
probSixth: parseFloat(ev.probSixth),
probSeventh: parseFloat(ev.probSeventh),
probEighth: parseFloat(ev.probEighth),
};
}
/**
* Recalculate EV for a participant with new scoring rules
* Keeps probabilities the same, only updates EV based on new scoring
*/
export async function recalculateEV(
participantId: string,
sportsSeasonId: string,
newScoringRules: ScoringRules
): Promise<ParticipantEV | null> {
const existing = await getParticipantEV(participantId, sportsSeasonId);
if (!existing) {
return null;
}
const probabilities = toProbabilityDistribution(existing);
const newEV = calculateEV(probabilities, newScoringRules);
const db = database();
const updated = await db
.update(participantExpectedValues)
.set({
expectedValue: newEV.toString(),
calculatedAt: new Date(),
updatedAt: new Date(),
})
.where(eq(participantExpectedValues.id, existing.id))
.returning();
return updated[0];
}
/**
* Recalculate EVs for all participants in a sports season
* Used when scoring rules change
*/
export async function recalculateAllEVsForSeason(
sportsSeasonId: string,
newScoringRules: ScoringRules
): Promise<number> {
const allEVs = await getAllParticipantEVsForSeason(sportsSeasonId);
await Promise.all(
allEVs.map((ev) =>
recalculateEV(ev.participantId, sportsSeasonId, newScoringRules)
)
);
return allEVs.length;
}