Simulation Engines

🎮 Simulation Types

⚡ Performance Optimization

Python Simulation Engine

# Fast season simulation with Numba
import numpy as np
from numba import jit, prange
from concurrent.futures import ProcessPoolExecutor

@jit(nopython=True, parallel=True)
def simulate_season_batch(team_ratings, schedule, n_sims):
    """Vectorized season simulation"""
    n_teams = len(team_ratings)
    n_games = len(schedule)
    
    # Pre-allocate results
    win_totals = np.zeros((n_sims, n_teams))
    
    for sim in prange(n_sims):
        # Seed for reproducibility
        np.random.seed(42 + sim)
        
        for game_idx in range(n_games):
            home, away = schedule[game_idx]
            
            # Win probability from ratings
            diff = team_ratings[home] - team_ratings[away] + 3  # Home advantage
            home_win_prob = 1 / (1 + 10 ** (-diff / 400))
            
            # Simulate game
            if np.random.random() < home_win_prob:
                win_totals[sim, home] += 1
            else:
                win_totals[sim, away] += 1
    
    return win_totals

# Run simulation
results = simulate_season_batch(ratings, schedule, 1000)

# Aggregate results
playoff_odds = (results >= 45).mean(axis=0)
championship_odds = (results.argmax(axis=1) == team_idx).mean()