Update main.py

get_record.py

@@ -1,4 +1,4 @@
-from datetime import datetime
+from datetime import date, datetime
 import numpy as np
 import pandas as pd
 pd.set_option('chained_assignment',None)
@@ -47,17 +47,103 @@ predictions_df['over_under_correct'] = (predictions_df['predicted_over_under']==
 predictions_df['over_under_incorrect'] = ((predictions_df['predicted_over_under']!=predictions_df['over_under']) & (predictions_df['over_under']!='Push'))
 predictions_df['over_under_push'] = (predictions_df['over_under']=='Push')
 
-predictions_df['winner_return'] = [ao-1 if (pa and wc) else ho-1 if (ph and wc) else -1 for ao,ho,pa,ph,wc in predictions_df[['Away Odds Close','Home Odds Close','picked_away','picked_home','winner_correct']].values]
-predictions_df['over_under_return'] = [0.91 if ouc else -1 for ouc in predictions_df['over_under_correct']]
+predictions_df['winner_return'] = [0 if tie else ao-1 if (pa and wc) else ho-1 if (ph and wc) else -1 for ao,ho,pa,ph,wc,tie in predictions_df[['Away Odds Close','Home Odds Close','picked_away','picked_home','winner_correct','winner_tie']].values]
+predictions_df['over_under_return'] = [0 if push else 0.91 if ouc else -1 for ouc,push in predictions_df[['over_under_correct','over_under_push']].values]
+predictions_df = predictions_df.loc[predictions_df['game_date']>datetime(year=2023,month=9,day=19)]
 
+# Save
+predictions_df.to_csv('Source/Data/predictions.csv')
+bins = np.arange(0.5, 1.05, 0.05)
+bin_midpoints = [(bins[i] + bins[i+1]) / 2 for i in range(len(bins) - 1)]
+
+predictions_df['winner_probability_bin'] = pd.cut(predictions_df['predicted_winner_probability'], bins=bins, labels=bin_midpoints)
+predictions_df['over_under_probability_bin'] = pd.cut(predictions_df['predicted_over_under_probability'], bins=bins, labels=bin_midpoints)
+winner_binned = predictions_df.groupby('winner_probability_bin')['winner_correct'].mean().reset_index()
+over_under_binned = predictions_df.groupby('over_under_probability_bin')['over_under_correct'].mean().reset_index()
+
+## plot
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+def style_plot(ax, title):
+    ax.set_facecolor('black')
+    ax.set_title(title, color='white')
+    ax.set_xlabel('MARCI Predicted Probability', color='white')
+    ax.set_ylabel('Actual Probability', color='white')
+    ax.tick_params(axis='x', colors='white')
+    ax.tick_params(axis='y', colors='white')
+    ax.spines['bottom'].set_color('white')
+    ax.spines['top'].set_color('white')
+    ax.spines['left'].set_color('white')
+    ax.spines['right'].set_color('white')
+    #ax.grid(True, linestyle='--', linewidth=0.5, color='grey')
+    ax.set_ylim((0,1.1))
+
+def add_identity_line(ax, max_x):
+    x = np.linspace(0.5, max_x, 100)
+    ax.plot(x, x, linestyle='--', color='grey')
+
+def add_best_fit_line(ax, x_values, y_values):
+    x_values = x_values.astype('float64')
+    y_values = y_values.astype('float64')
+    mask = ~np.isnan(x_values) & ~np.isnan(y_values)
+    x_values = x_values[mask]
+    y_values = y_values[mask]
+    coef = np.polyfit(x_values, y_values, 1)
+    poly1d_fn = np.poly1d(coef)
+    ax.plot(x_values, poly1d_fn(x_values), color='green')
+    corr = np.corrcoef(x_values, y_values)[0,1]
+    max_x = np.max(x_values)
+    max_y = poly1d_fn(max_x)
+    #ax.text(max_x, max_y, f'Corr: {corr:.2f}', color='green')
+
+# Create the Winner scatter plot
+x_values_winner = winner_binned['winner_probability_bin']
+y_values_winner = winner_binned['winner_correct']
+fig1 = plt.figure(facecolor='black')
+ax1 = fig1.add_subplot(1, 1, 1)
+ax1.scatter(x_values_winner,
+            y_values_winner,
+            color=(0/255, 128/255, 0/255), s=100, marker='o')
+add_identity_line(ax1, predictions_df['predicted_winner_probability'].max())
+add_best_fit_line(ax1, predictions_df['predicted_winner_probability'], predictions_df['winner_correct'])
+line, = ax1.plot([], [], linestyle='--', color='grey') 
+marci_line, = ax1.plot([], [], color='green')
+ax1.legend([line, marci_line], ['Perfect Model', 'MARCI'], loc='upper left', facecolor='black', edgecolor='white', labelcolor='white')
+style_plot(ax1, 'Winner Predictions')
+plt.savefig('Static/Winner_Predictions_dark.png', facecolor='black')
+plt.close(fig1)
+
+# Create the Over/Under scatter plot
+x_values_over_under = over_under_binned['over_under_probability_bin']
+y_values_over_under = over_under_binned['over_under_correct'] 
+fig2 = plt.figure(facecolor='black')
+ax2 = fig2.add_subplot(1, 1, 1)
+ax2.scatter(x_values_over_under,
+            y_values_over_under,
+            color=(0/255, 128/255, 0/255), s=100, marker='o')
+add_identity_line(ax2, predictions_df['predicted_over_under_probability'].max())
+add_best_fit_line(ax2, predictions_df['predicted_over_under_probability'], predictions_df['over_under_correct'])
+line, = ax2.plot([], [], linestyle='--', color='grey') 
+marci_line, = ax2.plot([], [], color='green')
+ax2.legend([line, marci_line], ['Perfect Model', 'MARCI'], loc='upper left', facecolor='black', edgecolor='white', labelcolor='white')
+style_plot(ax2, 'Over/Under Predictions')
+plt.savefig('Static/Over_Under_Predictions_dark.png', facecolor='black')
+plt.close(fig2)
+
+
+## get record
 threshold = 0.6
 
 winners_correct = predictions_df.loc[predictions_df['predicted_winner_probability']>threshold, 'winner_correct'].sum()
+winners_accuracy = predictions_df.loc[predictions_df['predicted_winner_probability']>threshold, 'winner_correct'].mean()
 winners_incorrect = predictions_df.loc[predictions_df['predicted_winner_probability']>threshold,'winner_incorrect'].sum()
 winners_tie = predictions_df.loc[predictions_df['predicted_winner_probability']>threshold,'winner_tie'].sum()
 winners_return = predictions_df.loc[predictions_df['predicted_winner_probability']>threshold, 'winner_return'].sum()
 
 over_unders_correct = predictions_df.loc[predictions_df['predicted_over_under_probability']>threshold,'over_under_correct'].sum()
+over_unders_accuracy = predictions_df.loc[predictions_df['predicted_over_under_probability']>threshold,'over_under_correct'].mean()
 over_unders_incorrect = predictions_df.loc[predictions_df['predicted_over_under_probability']>threshold,'over_under_incorrect'].sum()
 over_unders_push = predictions_df.loc[predictions_df['predicted_over_under_probability']>threshold,'over_under_push'].sum()
 over_unders_return = predictions_df.loc[predictions_df['predicted_over_under_probability']>threshold,'over_under_return'].sum()
@@ -65,15 +151,25 @@ over_unders_return = predictions_df.loc[predictions_df['predicted_over_under_pro
 max_date = predictions_df['game_date'].max()
 latest_game = pd.Timestamp(max_date).strftime("%A, %m/%d")
 
+## get binom prob
+from scipy.stats import binom
+
+def compare_to_coinflip(c,n):
+    prob_fewer = binom.cdf(c, n, 0.5)
+    prob_more = 1 - prob_fewer
+    return f"{round(prob_more*100,1)}% chance of equal or better performance by flipping a coin."
+
 record = {"winners_correct":str(winners_correct),
         "winners_incorrect":str(winners_incorrect),
         "winners_tie":("-"+str(winners_tie) if winners_tie>0 else ''),
-        "winners_return":str(round(winners_return,1))+"x return",
+        "winners_return": str(round(winners_accuracy*100,1))+"% accuracy, " + str(round(winners_return,1))+"x return",
         "over_unders_correct":str(over_unders_correct),
         "over_unders_incorrect":str(over_unders_incorrect),
         "over_unders_push":("-"+str(over_unders_push) if over_unders_push>0 else ''),
-        "over_unders_return":str(round(over_unders_return,1))+"x return",
-        "latest_game":latest_game}
+        "over_unders_return": str(round(over_unders_accuracy*100,1))+"% accuracy, " + str(round(over_unders_return,1))+"x return",
+        "latest_game":latest_game,
+        "over_unders_binom":compare_to_coinflip(over_unders_correct, (over_unders_incorrect+over_unders_correct)),
+        "winners_binom":compare_to_coinflip(winners_correct, (winners_incorrect+winners_correct))}
 
 import json
 with open('Source/Data/record.json', 'w') as f:

main.py

@@ -23,7 +23,7 @@ app.secret_key = 'green-flounder'
 # get week, season
 current_week, season = predict.get_week()
 current_games = predict.get_games(current_week)[['Date','Away Team','Home Team']]
-available_weeks = list(range(current_week+1))[2:]
+available_weeks = list(range(current_week+1))[3:]
 available_weeks.reverse()
 
 # load current data by default