Enhance prop percentage calculations in app.py by incorporating 'over_adj' and 'under_adj' metrics into 'Over%' and 'Under%' formulas. This update improves the accuracy of player projections by factoring in adjustments based on the relationship between 'Mean_Outcome' and 'Prop', ensuring a more reliable analysis of prop outcomes.

app.py

@@ -288,15 +288,17 @@ with tab3:
                         players_only['Book'] = players_only['Player'].map(book_dict)
                         players_only['Trending Over'] = players_only['Player'].map(trending_over_dict)
                         players_only['Trending Under'] = players_only['Player'].map(trending_under_dict)
+                        players_only['over_adj'] = (players_only['Mean_Outcome'] / players_only['Prop']) - 1
+                        players_only['under_adj'] = (players_only['Prop'] / players_only['Mean_Outcome']) - 1
                         players_only['poisson_var'] = players_only.apply(calculate_poisson, axis=1)
                         players_only['10%'] = overall_file.quantile(0.1, axis=1)
                         players_only['90%'] = overall_file.quantile(0.9, axis=1)
                         players_only['Over'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], prop_check[prop_check > 0].count(axis=1)/float(total_sims))
                         players_only['Imp Over'] = players_only['Player'].map(over_dict)
-                        players_only['Over%'] = (players_only['Over'] * 0.4) + (players_only['Trending Over'] * 0.4) + (players_only['Imp Over'] * 0.2)
+                        players_only['Over%'] = (players_only['Over'] * 0.4) + (players_only['Trending Over'] * 0.4) + (players_only['Imp Over'] * 0.2) + players_only['over_adj']
                         players_only['Under'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], prop_check[prop_check < 0].count(axis=1)/float(total_sims))
                         players_only['Imp Under'] = players_only['Player'].map(under_dict)
-                        players_only['Under%'] = (players_only['Under'] * 0.4) + (players_only['Trending Under'] * 0.4) + (players_only['Imp Under'] * 0.2)
+                        players_only['Under%'] = (players_only['Under'] * 0.4) + (players_only['Trending Under'] * 0.4) + (players_only['Imp Under'] * 0.2) + players_only['under_adj']
                         players_only['Prop_avg'] = players_only['Prop'].mean() / 100
                         players_only['prop_threshold'] = .10
                         players_only = players_only[players_only['Mean_Outcome'] > 0]
@@ -416,15 +418,17 @@ with tab3:
                     players_only['Book'] = players_only['Player'].map(book_dict)
                     players_only['Trending Over'] = players_only['Player'].map(trending_over_dict)
                     players_only['Trending Under'] = players_only['Player'].map(trending_under_dict)
+                    players_only['over_adj'] = (players_only['Mean_Outcome'] / players_only['Prop']) - 1
+                    players_only['under_adj'] = (players_only['Prop'] / players_only['Mean_Outcome']) - 1
                     players_only['poisson_var'] = players_only.apply(calculate_poisson, axis=1)
                     players_only['10%'] = overall_file.quantile(0.1, axis=1)
                     players_only['90%'] = overall_file.quantile(0.9, axis=1)
                     players_only['Over'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], prop_check[prop_check > 0].count(axis=1)/float(total_sims))
                     players_only['Imp Over'] = players_only['Player'].map(over_dict)
-                    players_only['Over%'] = (players_only['Over'] * 0.4) + (players_only['Trending Over'] * 0.4) + (players_only['Imp Over'] * 0.2)
+                    players_only['Over%'] = (players_only['Over'] * 0.4) + (players_only['Trending Over'] * 0.4) + (players_only['Imp Over'] * 0.2) + players_only['over_adj']
                     players_only['Under'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], prop_check[prop_check < 0].count(axis=1)/float(total_sims))
                     players_only['Imp Under'] = players_only['Player'].map(under_dict)
-                    players_only['Under%'] = (players_only['Under'] * 0.4) + (players_only['Trending Under'] * 0.4) + (players_only['Imp Under'] * 0.2)
+                    players_only['Under%'] = (players_only['Under'] * 0.4) + (players_only['Trending Under'] * 0.4) + (players_only['Imp Under'] * 0.2) + players_only['under_adj']
                     players_only['Prop_avg'] = players_only['Prop'].mean() / 100
                     players_only['prop_threshold'] = .10
                     players_only = players_only[players_only['Mean_Outcome'] > 0]