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| import pandas as pd | |
| import math | |
| def update_elo_ratings(ratings_dict, winner, loser): | |
| # Check if the ratings_dict is empty | |
| if not ratings_dict: | |
| # Create a blank DataFrame with the required columns | |
| ratings_df = pd.DataFrame(columns=['bot_name', 'elo_rating', 'games_played']) | |
| else: | |
| # Convert the dictionary to a pandas DataFrame | |
| ratings_df = pd.DataFrame.from_dict(ratings_dict, orient='index') | |
| ratings_df.reset_index(inplace=True) | |
| ratings_df.columns = ['bot_name', 'elo_rating', 'games_played'] | |
| # Check and add new players if they don't exist in the dataset | |
| for player in [winner, loser]: | |
| if player not in ratings_df['bot_name'].values: | |
| new_player = {'bot_name': player, 'elo_rating': 1200, 'games_played': 0} | |
| ratings_df = pd.concat([ratings_df, pd.DataFrame([new_player])], ignore_index=True) | |
| # 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 | |
| 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 games played | |
| ratings_df.loc[ratings_df['bot_name'] == winner, 'games_played'] += 1 | |
| ratings_df.loc[ratings_df['bot_name'] == loser, 'games_played'] += 1 | |
| # Extract old ratings and games played | |
| winner_old_rating = ratings_df.loc[ratings_df['bot_name'] == winner, 'elo_rating'].iloc[0] | |
| loser_old_rating = ratings_df.loc[ratings_df['bot_name'] == loser, 'elo_rating'].iloc[0] | |
| winner_games_played = ratings_df.loc[ratings_df['bot_name'] == winner, 'games_played'].iloc[0] | |
| loser_games_played = ratings_df.loc[ratings_df['bot_name'] == loser, 'games_played'].iloc[0] | |
| # Determine K-factors | |
| winner_k_factor = determine_k_factor(winner_games_played) | |
| loser_k_factor = determine_k_factor(loser_games_played) | |
| # 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 the DataFrame | |
| ratings_df.loc[ratings_df['bot_name'] == winner, 'elo_rating'] = winner_new_rating | |
| ratings_df.loc[ratings_df['bot_name'] == loser, 'elo_rating'] = loser_new_rating | |
| # Convert the DataFrame to a dictionary | |
| updated_ratings_dict = ratings_df.set_index('bot_name').to_dict(orient='index') | |
| return updated_ratings_dict |