Refactor prop data handling to include 'Trending Over' and 'Trending Under' metrics in player prop simulations. Updated DataFrame structures and calculations to incorporate these new columns across various functions, enhancing the analysis of player performance trends.

app.py

@@ -68,7 +68,7 @@ prop_format = {'L5 Success': '{:.2%}', 'L10_Success': '{:.2%}', 'L20_success': '
 prop_table_options = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS']
 all_sim_vars = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS']
 pick6_sim_vars = ['Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds']
-sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
+sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge'])
 
 def calculate_poisson(row):
     mean_val = row['Mean_Outcome']
@@ -477,9 +477,9 @@ with tab6:
         else:
             book_selections = [book_select_var]
         if game_select_var == 'Aggregate':
-            prop_df = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+            prop_df = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
         elif game_select_var == 'Pick6':
-            prop_df = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+            prop_df = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
             book_selections = ['Pick6']
         st.download_button(
             label="Download Prop Source",
@@ -500,11 +500,11 @@ with tab6:
                     with df_hold_container.container():
                         if prop_type_var == 'All Props':
                             if game_select_var == 'Aggregate':
-                                prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                                 sim_vars = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS',
                                             'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_3_POINTERS_MADE']
                             elif game_select_var == 'Pick6':
-                                prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                                 sim_vars = ['Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds', '3-Pointers Made']
                             
                             player_df = player_stats.copy()
@@ -514,7 +514,7 @@ with tab6:
                                 for books in book_selections:
                                     prop_df = prop_df_raw[prop_df_raw['prop_type'] == prop]
                                     prop_df = prop_df[prop_df['book'] == books]
-                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                                     prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
                                     prop_df['Over'] = 1 / prop_df['over_line']
                                     prop_df['Under'] = 1 / prop_df['under_line']
@@ -524,10 +524,14 @@ with tab6:
                                     book_dict = dict(zip(prop_df.Player, prop_df.book))
                                     over_dict = dict(zip(prop_df.Player, prop_df.Over))
                                     under_dict = dict(zip(prop_df.Player, prop_df.Under))
+                                    trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over']))
+                                    trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending Under']))
 
                                     player_df['book'] = player_df['Player'].map(book_dict)
                                     player_df['Prop'] = player_df['Player'].map(prop_dict)
                                     player_df['prop_type'] = player_df['Player'].map(prop_type_dict)
+                                    player_df['Trending Over'] = player_df['Player'].map(trending_over_dict)
+                                    player_df['Trending Under'] = player_df['Player'].map(trending_under_dict)
 
                                     df = player_df.reset_index(drop=True)
 
@@ -586,15 +590,17 @@ with tab6:
                                     players_only['Mean_Outcome'] = overall_file.mean(axis=1)
                                     players_only['Book'] = players_only['Player'].map(book_dict)
                                     players_only['Prop'] = players_only['Player'].map(prop_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['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)
                                     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"]].mean(axis=1)
+                                    players_only['Over%'] = players_only[["Over", "Imp Over", "Trending Over"]].mean(axis=1)
                                     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"]].mean(axis=1)
+                                    players_only['Under%'] = players_only[["Under", "Imp Under", "Trending Under"]].mean(axis=1)
                                     players_only['Prop_avg'] = players_only['Prop'].mean() / 100
                                     players_only['prop_threshold'] = .10
                                     players_only = players_only[players_only['Mean_Outcome'] > 0]
@@ -609,7 +615,7 @@ with tab6:
                                     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
@@ -620,9 +626,9 @@ with tab6:
                             player_df = player_stats.copy()
                             
                             if game_select_var == 'Aggregate':
-                                prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                             elif game_select_var == 'Pick6':
-                                prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                                 
                             for books in book_selections:
                                 prop_df = prop_df_raw[prop_df_raw['book'] == books]
@@ -660,7 +666,7 @@ with tab6:
                                 elif prop_type_var == "Assists + Rebounds":
                                     prop_df = prop_df[prop_df['prop_type'] == 'Assists + Rebounds']
 
-                                prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                                 prop_df = prop_df.rename(columns={"over_prop": "Prop"})
                                 prop_df['Over'] = 1 / prop_df['over_line']
                                 prop_df['Under'] = 1 / prop_df['under_line']
@@ -670,10 +676,14 @@ with tab6:
                                 book_dict = dict(zip(prop_df.Player, prop_df.book))
                                 over_dict = dict(zip(prop_df.Player, prop_df.Over))
                                 under_dict = dict(zip(prop_df.Player, prop_df.Under))
+                                trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over']))
+                                trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending Under']))
 
                                 player_df['book'] = player_df['Player'].map(book_dict)
                                 player_df['Prop'] = player_df['Player'].map(prop_dict)
                                 player_df['prop_type'] = player_df['Player'].map(prop_type_dict)
+                                player_df['Trending Over'] = player_df['Player'].map(trending_over_dict)
+                                player_df['Trending Under'] = player_df['Player'].map(trending_under_dict)
 
                                 df = player_df.reset_index(drop=True)
 
@@ -732,15 +742,17 @@ with tab6:
                                 players_only['Mean_Outcome'] = overall_file.mean(axis=1)
                                 players_only['Book'] = players_only['Player'].map(book_dict)
                                 players_only['Prop'] = players_only['Player'].map(prop_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['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)
                                 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"]].mean(axis=1)
+                                players_only['Over%'] = players_only[["Over", "Imp Over", "Trending Over"]].mean(axis=1)
                                 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"]].mean(axis=1)
+                                players_only['Under%'] = players_only[["Under", "Imp Under", "Trending Under"]].mean(axis=1)
                                 players_only['Prop_avg'] = players_only['Prop'].mean() / 100
                                 players_only['prop_threshold'] = .10
                                 players_only = players_only[players_only['Mean_Outcome'] > 0]
@@ -755,7 +767,7 @@ with tab6:
                                 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