Update leg_outcomes DataFrame to include 'Trending Over' and 'Trending Under' columns for enhanced prop data analysis. This change improves the clarity and accuracy of player projections by aligning with recent updates in prop data handling.

app.py

@@ -59,7 +59,7 @@ 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_BLOCKED_SHOTS', '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%', 'Imp 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():
@@ -309,7 +309,7 @@ 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%', 'Imp 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
@@ -437,7 +437,7 @@ 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%', 'Imp 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