Refactor app.py to simplify 'over_adj' and 'under_adj' calculations by removing the subtraction of 1 in the conditional logic. This change enhances the accuracy of player projections by directly using the ratio of 'Mean_Outcome' to 'Prop', ensuring a more reliable analysis of prop outcomes.

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']) > 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['over_adj'] = np_where((players_only['Mean_Outcome'] - players_only['Prop']) > 0, 1, (players_only['Mean_Outcome'] / players_only['Prop']))
+                        players_only['under_adj'] = np_where((players_only['Prop'] - players_only['Mean_Outcome']) > 0, 1, (players_only['Prop'] / players_only['Mean_Outcome']))
                         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)
@@ -418,8 +418,8 @@ 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']) > 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['over_adj'] = np_where((players_only['Mean_Outcome'] - players_only['Prop']) > 0, 1, (players_only['Mean_Outcome'] / players_only['Prop']))
+                    players_only['under_adj'] = np_where((players_only['Prop'] - players_only['Mean_Outcome']) > 0, 1, (players_only['Prop'] / players_only['Mean_Outcome']))
                     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)