Refactor app.py to update 'over_adj' and 'under_adj' calculations by replacing division by 100 with a conditional adjustment using np_where. This change ensures that adjustments are capped at 0.10, improving the accuracy and reliability of player projections based on the relationship between 'Mean_Outcome' and 'Prop'.

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