NBA betting analysis

moneyline_to_win_probability.py

# Function that calculates win probability from moneyline
def ml_to_win_prob(ml):
    if ml < 0:
        prob = -ml / (-ml + 100)
    else:
        prob = 100 / (ml + 100)
    return prob

win_rates.py

# Data wrangling: add new columns for W/L outcomes and win probabilities
df_generator = df_nba_lines.iterrows()

for (i, row1), (j, row2) in zip(df_generator, df_generator):
    # Determine winner of each game and insert values in new column
    if row1['score'] < row2['score']:
        df_nba_lines.at[i, 'outcome'] = 'L'
        df_nba_lines.at[j, 'outcome'] = 'W'
    elif row1['score'] > row2['score']:
        df_nba_lines.at[i, 'outcome'] = 'W'
        df_nba_lines.at[j, 'outcome'] = 'L'

    # Calculate implied win probabilities for each team
    row1_win_prob = ml_to_win_prob(row1['ml_PIN'])
    row2_win_prob = ml_to_win_prob(row2['ml_PIN'])
    df_nba_lines.at[i, 'win_prob_PIN'] = row1_win_prob
    df_nba_lines.at[j, 'win_prob_PIN'] = row2_win_prob

    # Calculate NORMALIZED win probabilities for each team
    prob_sum = row1_win_prob + row2_win_prob
    df_nba_lines.at[i, 'win_prob_norm_PIN'] = row1_win_prob / prob_sum
    df_nba_lines.at[j, 'win_prob_norm_PIN'] = row2_win_prob / prob_sum