Simulate Bets
| def get_ml_bet_winnings(ml_odds, bet_amount): | |
| if ml_odds < 0: | |
| returns = (-100.0 / ml_odds) * bet_amount | |
| else: | |
| returns = (bet_amount / 100.0) * ml_odds | |
| return returns |
| # Function that simulates a betting strategy | |
| def simulate_bets(dataset_df=df_pin, bet_amount=100, win_prob_threshold=0.5): | |
| total_profit = 0 | |
| all_winnings = [] | |
| running_profits = [] | |
| df_generator = dataset_df.iterrows() | |
| for (i, row1), (j, row2) in zip(df_generator, df_generator): | |
| ### 1. Determine who to bet on for this game | |
| t1_ml = row1['ml_PIN'] | |
| t2_ml = row2['ml_PIN'] | |
| t1_score = row1['score'] | |
| t2_score = row2['score'] | |
| t1_win_prob = row1['win_prob_norm_PIN'] | |
| t2_win_prob = row2['win_prob_norm_PIN'] | |
| # If any odds are missing, just skip the game | |
| if pd.isnull(t1_ml) or pd.isnull(t2_ml): | |
| continue | |
| if t1_win_prob < win_prob_threshold: | |
| placed_bet_on = 1 | |
| bet_ml = t1_ml | |
| elif t2_win_prob <= win_prob_threshold: | |
| placed_bet_on = 2 | |
| bet_ml = t2_ml | |
| else: | |
| # Not a favorable bet. Skip to next game | |
| continue | |
| ### 2. Deterimine if we won or lost this bet | |
| if t1_score > t2_score: | |
| game_winner = 1 | |
| elif t2_score > t1_score: | |
| game_winner = 2 | |
| else: | |
| # Throw out any games missing scores (shouldn't be any) | |
| continue | |
| is_win = game_winner == placed_bet_on | |
| ### 3. Calculate winnings from this bet | |
| # If favorite lost, then deduct bet from net profits | |
| if is_win is False: | |
| winnings = -bet_amount | |
| # If favorite won, then add winnings to net profits | |
| if is_win is True: | |
| winnings = get_ml_bet_winnings(bet_ml, bet_amount) | |
| ### 4. Record winnings and update total profits | |
| total_profit += winnings | |
| all_winnings.append(winnings) | |
| running_profits.append(total_profit) | |
| return total_profit, all_winnings, running_profits |
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