Refactor app.py to remove 'over_adj' and 'under_adj' from 'Over%' and 'Under%' calculations, streamlining the prop percentage formulas. This change enhances the clarity of player projections by focusing on the weighted averages of 'Over', 'Trending Over', 'Imp Over', 'Under', 'Trending Under', and 'Imp Under', ensuring a more accurate analysis of prop outcomes.

app.py

@@ -295,10 +295,10 @@ with tab3:
                         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]