elo.py

import math

def update_elo_ratings(ratings_dict, winner, loser):
    # Extract old ratings and games played
    winner_old_rating = ratings_dict.get(winner, {}).get('elo_rating', 1200)
    loser_old_rating = ratings_dict.get(loser, {}).get('elo_rating', 1200)
    winner_games_played = ratings_dict.get(winner, {}).get('games_played', 0)
    loser_games_played = ratings_dict.get(loser, {}).get('games_played', 0)

    # Function to determine the K-factor based on games played
    def determine_k_factor(games_played):
        # Define K-factor based on number of games played. Adjust these thresholds as needed.
        if games_played < 30:
            return 40
        elif games_played < 100:
            return 20
        else:
            return 10

    # Determine K-factors
    winner_k_factor = determine_k_factor(winner_games_played)
    loser_k_factor = determine_k_factor(loser_games_played)

    def elo(winner_rating, loser_rating, k_factor_winner=32, k_factor_loser=32):
        # Calculate the expected scores for each player
        winner_expected = 1 / (1 + 10 ** ((loser_rating - winner_rating) / 400))
        loser_expected = 1 / (1 + 10 ** ((winner_rating - loser_rating) / 400))

        # Calculate the new ratings for each player
        winner_new_rating = winner_rating + k_factor_winner * (1 - winner_expected)
        loser_new_rating = loser_rating + k_factor_loser * (0 - loser_expected)

        return winner_new_rating, loser_new_rating

    # Calculate new ratings
    winner_new_rating, loser_new_rating = elo(winner_old_rating, loser_old_rating, k_factor_winner=winner_k_factor, k_factor_loser=loser_k_factor)

    # Update ratings and games played in the dictionary
    ratings_dict[winner] = {'elo_rating': winner_new_rating, 'games_played': winner_games_played + 1}
    ratings_dict[loser] = {'elo_rating': loser_new_rating, 'games_played': loser_games_played + 1}

    return ratings_dict