Refactor app.py to enhance prop percentage calculations by updating 'Over%' and 'Under%' metrics to use a weighted average approach. This change improves the accuracy of player projections by incorporating 'Imp Over', 'Trending Over', 'Imp Under', and 'Trending Under' with specified weights, ensuring a more reliable analysis of prop outcomes.

app.py

@@ -60,7 +60,7 @@ prop_format = {'L5 Success': '{:.2%}', 'L10_Success': '{:.2%}', 'L20_success': '
                'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
 all_sim_vars = ['NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_BLOCKED', 'NHL_GAME_PLAYER_ASSISTS']
 pick6_sim_vars = ['Points', 'Shots on Goal', 'Assists', 'Blocks']
-sim_all_hold = pd.DataFrame(columns=['Player', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over', 'Trending Over', 'Over%', 'Imp Under', 'Under', 'Trending Under', 'Under%', 'Bet?', 'Edge'])
+sim_all_hold = pd.DataFrame(columns=['Player', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge'])
 
 @st.cache_resource(ttl=200)
 def pull_baselines():
@@ -293,10 +293,10 @@ 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", "Imp Over", "Trending Over"]].mean(axis=1)
+                        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", "Imp Under", "Trending Under"]].mean(axis=1)
+                        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]
@@ -311,7 +311,7 @@ with tab3:
                         players_only['Player'] = hold_file[['Player']]
                         players_only['Team'] = players_only['Player'].map(team_dict)
 
-                        leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over', 'Trending Over', 'Over%', 'Imp Under', 'Under', 'Trending Under', 'Under%', 'Bet?', 'Edge']]
+                        leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge']]
                         sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
                     
                         final_outcomes = sim_all_hold
@@ -421,10 +421,10 @@ 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", "Imp Over", "Trending Over"]].mean(axis=1)
+                    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", "Imp Under", "Trending Under"]].mean(axis=1)
+                    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]
@@ -439,7 +439,7 @@ with tab3:
                     players_only['Player'] = hold_file[['Player']]
                     players_only['Team'] = players_only['Player'].map(team_dict)
 
-                    leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop', 'Prop Type', 'Mean_Outcome', 'Imp Over', 'Over', 'Trending Over', 'Over%', 'Imp Under', 'Under', 'Trending Under', 'Under%', 'Bet?', 'Edge']]
+                    leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop', 'Prop Type', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge']]
                     sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
                     
                     final_outcomes = sim_all_hold