big update to incorporate pick6 and clean up loop on stat specific sim

app.py

@@ -87,10 +87,13 @@ non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
 team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
-prop_table_options = ['NFL_GAME_PLAYER_RUSHING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_YARDS', 'NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS', 'NFL_GAME_PLAYER_PASSING_ATTEMPTS']
+prop_table_options = ['NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RUSHING_YARDS', 'NFL_GAME_PLAYER_PASSING_ATTEMPTS', 'NFL_GAME_PLAYER_PASSING_TOUCHDOWNS', 'NFL_GAME_PLAYER_PASSING_COMPLETIONS', 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS',
+                      'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NFL_GAME_PLAYER_RECEIVING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS']
 prop_format = {'L3 Success': '{:.2%}', 'L6_Success': '{:.2%}', 'L10_success': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
                'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
-all_sim_vars = ['NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RUSHING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS', 'NFL_GAME_PLAYER_PASSING_ATTEMPTS']
+all_sim_vars = ['NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RUSHING_YARDS', 'NFL_GAME_PLAYER_PASSING_ATTEMPTS', 'NFL_GAME_PLAYER_PASSING_TOUCHDOWNS', 'NFL_GAME_PLAYER_PASSING_COMPLETIONS', 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS',
+                      'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NFL_GAME_PLAYER_RECEIVING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS']
+pick6_sim_vars = ['Rush + Rec Yards', 'Rush + Rec TDs', 'Interceptions Thrown', 'Passing Yards', 'Passing Attempts', 'Passing TDs', 'Completions', 'Rushing Yards', 'Receptions', 'Receiving Yards']
 sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
 
 tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Trends", "Player Prop Simulations", "Stat Specific Simulations"])
@@ -376,6 +379,11 @@ with tab6:
     
     with col1:
         game_select_var = st.selectbox('Select prop source', options = ['Aggregate', 'Pick6'])
+        book_select_var = st.selectbox('Select book', options = ['ALL', 'BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL'])
+        if book_select_var == 'ALL':
+            book_selections = ['BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL']
+        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']]
         elif game_select_var == 'Pick6':
@@ -384,18 +392,26 @@ with tab6:
         st.download_button(
             label="Download Prop Source",
             data=convert_df_to_csv(prop_df),
-            file_name='Nba_prop_source.csv',
+            file_name='NFL_prop_source.csv',
             mime='text/csv',
             key='prop_source',
         )
-        prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'pass_yards', 'rush_yards', 'rec_yards', 'receptions', 'rush_attempts', 'pass_attempts'])
+        if game_select_var == 'Aggregate':
+            prop_type_var = st.selectbox('Select prop category', options = ['All Props', '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'])
+        elif game_select_var == 'Pick6':
+            prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds'])
 
         if st.button('Simulate Prop Category'):
             with col2:
                    
                     with df_hold_container.container():
                         if prop_type_var == 'All Props':
-                            for prop in all_sim_vars:
+                            if game_select_var == 'Aggregate':
+                                sim_vars = ['NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RUSHING_YARDS', 'NFL_GAME_PLAYER_PASSING_ATTEMPTS', 'NFL_GAME_PLAYER_PASSING_TOUCHDOWNS', 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS',
+                                            'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NFL_GAME_PLAYER_RECEIVING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS']
+                            elif game_select_var == 'Pick6':
+                                sim_vars = ['Rush + Rec Yards', 'Rush + Rec TDs', 'Interceptions Thrown', 'Passing Yards', 'Passing Attempts', 'Passing TDs', 'Rushing Attempts', 'Rushing Yards', 'Receptions', 'Receiving Yards', 'Receiving TDs']
+                            for prop in sim_vars:
                                 
                                 if game_select_var == 'Aggregate':
                                     prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
@@ -409,44 +425,54 @@ with tab6:
                                     prop_df = prop_df[~((prop_df['over_prop'] < 15) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
                                     prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
                                     prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                                    prop_df = prop_df.drop_duplicates(subset=['Player'])
                                     prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                                    st.table(prop_df)
                                     prop_df['Over'] = 1 / prop_df['over_line']
                                     prop_df['Under'] = 1 / prop_df['under_line']
                                     df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                    
+                                    df = df.reset_index(drop=True)
+
                                     prop_dict = dict(zip(df.Player, df.Prop))
                                     book_dict = dict(zip(df.Player, df.book))
                                     over_dict = dict(zip(df.Player, df.Over))
+                                    team_dict = dict(zip(df.Player, df.Team))
                                     under_dict = dict(zip(df.Player, df.Under))
                                     
                                     total_sims = 1000
             
                                     df.replace("", 0, inplace=True)
                                     
-                                    if prop == "NFL_GAME_PLAYER_PASSING_YARDS":
+                                    if prop == "NFL_GAME_PLAYER_PASSING_YARDS" or prop == "Passing Yards":
                                         df['Median'] = df['pass_yards']
-                                    elif prop == "NFL_GAME_PLAYER_RUSHING_YARDS":
+                                    elif prop == "NFL_GAME_PLAYER_RUSHING_YARDS" or prop == "Rushing Yards":
                                         df['Median'] = df['rush_yards']
-                                    elif prop == "NFL_GAME_PLAYER_RECEIVING_YARDS":
-                                        df['Median'] = df['rec_yards']
-                                    elif prop == "NFL_GAME_PLAYER_RECEIVING_RECEPTIONS":
-                                        df['Median'] = df['rec']
-                                    elif prop == "NFL_GAME_PLAYER_RUSHING_ATTEMPTS":
-                                        df['Median'] = df['rush_att']
-                                    elif prop == "NFL_GAME_PLAYER_PASSING_ATTEMPTS":
+                                    elif prop == "NFL_GAME_PLAYER_PASSING_ATTEMPTS" or prop == "Passing Attempts":
                                         df['Median'] = df['pass_att']
+                                    elif prop == "NFL_GAME_PLAYER_PASSING_TOUCHDOWNS" or prop == "Passing TDs":
+                                        df['Median'] = df['pass_tds']
+                                    elif prop == "NFL_GAME_PLAYER_RUSHING_ATTEMPTS" or prop == "Rushing Attempts":
+                                        df['Median'] = df['rush_att']
+                                    elif prop == "NFL_GAME_PLAYER_RECEIVING_RECEPTIONS" or prop == "Receptions":
+                                        df['Median'] = df['rec']
+                                    elif prop == "NFL_GAME_PLAYER_RECEIVING_YARDS" or prop == "Receiving Yards":
+                                        df['Median'] = df['rec_yards']
+                                    elif prop == "NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS" or prop == "Receiving TDs":
+                                        df['Median'] = df['rec_tds']
+                                    elif prop == "Rush + Rec Yards":
+                                        df['Median'] = df['rush_yards'] + df['rec_yards']
+                                    elif prop == "Rush + Rec TDs":
+                                        df['Median'] = df['rush_tds'] + df['rec_tds']
                                         
-                                    flex_file = df
+                                    flex_file = df.copy()
                                     flex_file['Floor'] = flex_file['Median'] * .25
                                     flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1.75)
                                     flex_file['STD'] = flex_file['Median'] / 4
                                     flex_file['Prop'] = flex_file['Player'].map(prop_dict)
                                     flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
             
-                                    hold_file = flex_file
-                                    overall_file = flex_file
-                                    prop_file = flex_file
+                                    hold_file = flex_file.copy()
+                                    overall_file = flex_file.copy()
+                                    prop_file = flex_file.copy()
                                           
                                     overall_players = overall_file[['Player']]
             
@@ -495,115 +521,105 @@ with tab6:
                                     sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
                                     
                                     final_outcomes = sim_all_hold
+                                    st.write(f'finished {prop}')
                                 
                         elif 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']]
                             elif game_select_var == 'Pick6':
                                 prop_df_raw = pick_frame[['Full_name', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 prop_df_raw.rename(columns={"Full_name": "Player"}, inplace = True)
                                 
-                            for books in ['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS']:
+                            for books in book_selections:
                                 prop_df = prop_df_raw.loc[prop_df_raw['book'] == books]
-                                if prop_type_var == "pass_yards":
+                                
+                                if prop_type_var == "NFL_GAME_PLAYER_PASSING_YARDS":
                                     prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_YARDS']
-                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
-                                    prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                                    prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                                    st.table(prop_df)
-                                    prop_df['Over'] = 1 / prop_df['over_line']
-                                    prop_df['Under'] = 1 / prop_df['under_line']
-                                    df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                elif prop_type_var == "rush_yards":
+                                elif prop_type_var == "Passing Yards":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Passing Yards']
+                                elif prop_type_var == "NFL_GAME_PLAYER_RUSHING_YARDS":
                                     prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS']
-                                    prop_df = prop_df[~((prop_df['over_prop'] < 15) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
-                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
-                                    prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                                    prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                                    st.table(prop_df)
-                                    prop_df['Over'] = 1 / prop_df['over_line']
-                                    prop_df['Under'] = 1 / prop_df['under_line']
-                                    df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                elif prop_type_var == "rec_yards":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_YARDS']
-                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
-                                    prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                                    prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                                    st.table(prop_df)
-                                    prop_df['Over'] = 1 / prop_df['over_line']
-                                    prop_df['Under'] = 1 / prop_df['under_line']
-                                    df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                elif prop_type_var == "receptions":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS']
-                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
-                                    prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                                    prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                                    st.table(prop_df)
-                                    prop_df['Over'] = 1 / prop_df['over_line']
-                                    prop_df['Under'] = 1 / prop_df['under_line']
-                                    df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                elif prop_type_var == "rush_attempts":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS']
-                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
-                                    prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                                    prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                                    st.table(prop_df)
-                                    prop_df['Over'] = 1 / prop_df['over_line']
-                                    prop_df['Under'] = 1 / prop_df['under_line']
-                                    df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                elif prop_type_var == "pass_attempts":
+                                elif prop_type_var == "Rushing Yards":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Rushing Yards']
+                                elif prop_type_var == "NFL_GAME_PLAYER_PASSING_ATTEMPTS":
                                     prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_ATTEMPTS']
-                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
-                                    prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                                    prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                                    st.table(prop_df)
-                                    prop_df['Over'] = 1 / prop_df['over_line']
-                                    prop_df['Under'] = 1 / prop_df['under_line']
-                                    df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                elif prop_type_var == "pass_completions":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_COMPLETIONS']
-                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
-                                    prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                                    prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                                    st.table(prop_df)
-                                    prop_df['Over'] = 1 / prop_df['over_line']
-                                    prop_df['Under'] = 1 / prop_df['under_line']
-                                    df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                elif prop_type_var == "Passing Attempts":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Passing Attempts']
+                                elif prop_type_var == "NFL_GAME_PLAYER_PASSING_TOUCHDOWNS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_TOUCHDOWNS']
+                                elif prop_type_var == "Passing TDs":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Passing TDs']
+                                elif prop_type_var == "NFL_GAME_PLAYER_RUSHING_ATTEMPTS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS']
+                                elif prop_type_var == "Rushing Attempts":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Rushing Attempts']
+                                elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_RECEPTIONS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS']
+                                elif prop_type_var == "Receptions":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Receptions']
+                                elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_YARDS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_YARDS']
+                                elif prop_type_var == "Receiving Yards":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Receiving Yards']
+                                elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS']
+                                elif prop_type_var == "Receiving TDs":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Receiving TDs']
+                                elif prop_type_var == "Rush + Rec Yards":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Rush + Rec Yards']
+                                elif prop_type_var == "Rush + Rec TDs":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'Rush + Rec TDs']
+
+                                prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
+                                prop_df = prop_df.rename(columns={"over_prop": "Prop"})
+                                prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                                prop_df = prop_df.drop_duplicates(subset=['Player'])
+                                prop_df['Over'] = 1 / prop_df['over_line']
+                                prop_df['Under'] = 1 / prop_df['under_line']
+                                df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                df = df.reset_index(drop=True)
                                 
                                 prop_dict = dict(zip(df.Player, df.Prop))
                                 book_dict = dict(zip(df.Player, df.book))
                                 over_dict = dict(zip(df.Player, df.Over))
+                                team_dict = dict(zip(df.Player, df.Team))
                                 under_dict = dict(zip(df.Player, df.Under))
                                 
                                 total_sims = 1000
         
                                 df.replace("", 0, inplace=True)
                                 
-                                if prop_type_var == "pass_yards":
+                                if prop_type_var == "NFL_GAME_PLAYER_PASSING_YARDS" or prop_type_var == "Passing Yards":
                                     df['Median'] = df['pass_yards']
-                                elif prop_type_var == "rush_yards":
+                                elif prop_type_var == "NFL_GAME_PLAYER_RUSHING_YARDS" or prop_type_var == "Rushing Yards":
                                     df['Median'] = df['rush_yards']
-                                elif prop_type_var == "rec_yards":
-                                    df['Median'] = df['rec_yards']
-                                elif prop_type_var == "receptions":
-                                    df['Median'] = df['rec']
-                                elif prop_type_var == "rush_attempts":
-                                    df['Median'] = df['rush_att']
-                                elif prop_type_var == "pass_attempts":
+                                elif prop_type_var == "NFL_GAME_PLAYER_PASSING_ATTEMPTS" or prop_type_var == "Passing Attempts":
                                     df['Median'] = df['pass_att']
+                                elif prop_type_var == "NFL_GAME_PLAYER_PASSING_TOUCHDOWNS" or prop_type_var == "Passing TDs":
+                                    df['Median'] = df['pass_tds']
+                                elif prop_type_var == "NFL_GAME_PLAYER_RUSHING_ATTEMPTS" or prop_type_var == "Rushing Attempts":
+                                    df['Median'] = df['rush_att']
+                                elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_RECEPTIONS" or prop_type_var == "Receptions":
+                                    df['Median'] = df['rec']
+                                elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_YARDS" or prop_type_var == "Receiving Yards":
+                                    df['Median'] = df['rec_yards']
+                                elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS" or prop_type_var == "Receiving TDs":
+                                    df['Median'] = df['rec_tds']
+                                elif prop_type_var == "Rush + Rec Yards":
+                                    df['Median'] = df['rush_yards'] + df['rec_yards']
+                                elif prop_type_var == "Rush + Rec TDs":
+                                    df['Median'] = df['rush_tds'] + df['rec_tds']
         
-                                flex_file = df
+                                flex_file = df.copy()
                                 flex_file['Floor'] = flex_file['Median'] * .25
                                 flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1.75)
                                 flex_file['STD'] = flex_file['Median'] / 4
                                 flex_file['Prop'] = flex_file['Player'].map(prop_dict)
                                 flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
         
-                                hold_file = flex_file
-                                overall_file = flex_file
-                                prop_file = flex_file
+                                hold_file = flex_file.copy()
+                                overall_file = flex_file.copy()
+                                prop_file = flex_file.copy()
                                       
                                 overall_players = overall_file[['Player']]
         
@@ -652,8 +668,11 @@ with tab6:
                                 sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
                                 
                                 final_outcomes = sim_all_hold
+                                st.write(f'finished {prop_type_var}')
                         
                         final_outcomes = final_outcomes.dropna()
+                        if game_select_var == 'Pick6':
+                            final_outcomes = final_outcomes.drop_duplicates(subset=['Player', 'Prop Type'])
                         final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)
 
                         with df_hold_container: