Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
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
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment