Refactor app.py to adjust 'over_adj' and 'under_adj' calculations by dividing by 100, enhancing the accuracy of player projections. This change ensures that the adjustments based on the relationship between 'Mean_Outcome' and 'Prop' are correctly scaled, improving the reliability of prop outcome analysis.

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'] = (players_only['Mean_Outcome'] / players_only['Prop']) - 1
-                        players_only['under_adj'] = (players_only['Prop'] / players_only['Mean_Outcome']) - 1
+                        players_only['over_adj'] = ((players_only['Mean_Outcome'] / players_only['Prop']) - 1) / 100
+                        players_only['under_adj'] = ((players_only['Prop'] / players_only['Mean_Outcome']) - 1) / 100
                         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'] = (players_only['Mean_Outcome'] / players_only['Prop']) - 1
-                    players_only['under_adj'] = (players_only['Prop'] / players_only['Mean_Outcome']) - 1
+                    players_only['over_adj'] = ((players_only['Mean_Outcome'] / players_only['Prop']) - 1) / 100
+                    players_only['under_adj'] = ((players_only['Prop'] / players_only['Mean_Outcome']) - 1) / 100
                     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)