Refactor app.py to improve 'over_adj' and 'under_adj' calculations by adjusting the logic to use conditional checks instead of division, enhancing the accuracy of player projections. Additionally, update 'Bet_check' calculations to incorporate these adjustments, ensuring a more reliable analysis of betting suggestions based on player performance metrics.

app.py

@@ -288,8 +288,8 @@ 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'] = np_where((players_only['Mean_Outcome'] / players_only['Prop']) - 1 > .10, .10, (players_only['Mean_Outcome'] / players_only['Prop']) - 1)
-                        players_only['under_adj'] = np_where((players_only['Prop'] / players_only['Mean_Outcome']) - 1 > .10, .10, (players_only['Prop'] / players_only['Mean_Outcome']) - 1)
+                        players_only['over_adj'] = np_where((players_only['Mean_Outcome'] - players_only['Prop']) > 0, 1, (players_only['Mean_Outcome'] / players_only['Prop']) - 1)
+                        players_only['under_adj'] = np_where((players_only['Prop'] - players_only['Mean_Outcome']) > 0, 1, (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)
@@ -304,7 +304,7 @@ with tab3:
                         players_only = players_only[players_only['Mean_Outcome'] > 0]
                         players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
                         players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
-                        players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
+                        players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] * players_only['over_adj'], players_only['Under_diff'] * players_only['under_adj'])
                         players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under")
                         players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet")
                         players_only['Edge'] = players_only['Bet_check']
@@ -418,23 +418,23 @@ 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'] = np_where((players_only['Mean_Outcome'] / players_only['Prop']) - 1 > .10, .10, (players_only['Mean_Outcome'] / players_only['Prop']) - 1)
-                    players_only['under_adj'] = np_where((players_only['Prop'] / players_only['Mean_Outcome']) - 1 > .10, .10, (players_only['Prop'] / players_only['Mean_Outcome']) - 1)
+                    players_only['over_adj'] = np_where((players_only['Mean_Outcome'] - players_only['Prop']) > 0, 1, (players_only['Mean_Outcome'] / players_only['Prop']) - 1)
+                    players_only['under_adj'] = np_where((players_only['Prop'] - players_only['Mean_Outcome']) > 0, 1, (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_adj']
+                    players_only['Over%'] = (players_only['Over'] * 0.4) + (players_only['Trending Over'] * 0.4) + (players_only['Imp Over'] * 0.2)
                     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_adj']
+                    players_only['Under%'] = (players_only['Under'] * 0.4) + (players_only['Trending Under'] * 0.4) + (players_only['Imp Under'] * 0.2)
                     players_only['Prop_avg'] = players_only['Prop'].mean() / 100
                     players_only['prop_threshold'] = .10
                     players_only = players_only[players_only['Mean_Outcome'] > 0]
                     players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
                     players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
-                    players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
+                    players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] * players_only['over_adj'], players_only['Under_diff'] * players_only['under_adj'])
                     players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under")
                     players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet")
                     players_only['Edge'] = players_only['Bet_check']