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| 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 | |