Update app.py

app.py

@@ -73,8 +73,13 @@ qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
 non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
 timestamp = timestamp_table()
 prop_frame = player_prop_table()
+team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
+all_sim_vars = ['All Props', 'pass_yards', 'rush_yards', 'rec_yards', 'receptions', 'rush_attempts',
+                                                                'pass_attempts', 'pass_completions']
+sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
+
 tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Simulations", "Stat Specific Simulations"])
 
 def convert_df_to_csv(df):
@@ -330,127 +335,246 @@ with tab5:
         export_container = st.empty()
     
     with col1:
-        prop_type_var = st.selectbox('Select prop category', options = ['Pass Yards', 'Rush Yards', 'Receiving Yards', 'Receptions'])
+        prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'pass_yards', 'rush_yards', 'rec_yards', 'receptions', 'rush_attempts',
+                                                                        'pass_attempts', 'pass_completions'])
 
         if st.button('Simulate Prop Category'):
             with col2:
                    
                     with df_hold_container.container():
-
-                        if prop_type_var == "Pass Yards":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'pass_yards']
-                            prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
-                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "Rush Yards":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rush_yards']
-                            prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
-                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "Receiving Yards":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rec_yards']
-                            prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
-                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "Receptions":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'receptions']
-                            prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
-                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        
-                        prop_dict = dict(zip(df.Player, df.Prop))
-                        over_dict = dict(zip(df.Player, df.Over))
-                        under_dict = dict(zip(df.Player, df.Under))
-                        
-                        total_sims = 1000
-
-                        df.replace("", 0, inplace=True)
-
-                        if prop_type_var == "Pass Yards":
-                            df['Median'] = df['pass_yards']
-                        elif prop_type_var == "Rush Yards":
-                            df['Median'] = df['rush_yards']
-                        elif prop_type_var == "Receiving Yards":
-                            df['Median'] = df['rec_yards']
-                        elif prop_type_var == "Receptions":
-                            df['Median'] = df['rec']
-
-                        flex_file = df
-                        flex_file['Floor'] = flex_file['Median'] * .20
-                        flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .80)
-                        flex_file['STD'] = flex_file['Median'] / 4
-                        flex_file['Prop'] = flex_file['Player'].map(prop_dict)
-                        flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
-
-                        hold_file = flex_file
-                        overall_file = flex_file
-                        prop_file = flex_file
-                              
-                        overall_players = overall_file[['Player']]
-
-                        for x in range(0,total_sims):    
-                            prop_file[x] = prop_file['Prop']
-
-                        prop_file = prop_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-
-                        for x in range(0,total_sims):
-                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
-
-                        overall_file=overall_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-
-                        players_only = hold_file[['Player']]
-
-                        player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
-
-                        prop_check = (overall_file - prop_file)
-
-                        players_only['Mean_Outcome'] = overall_file.mean(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'] = 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['Under'] = 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['Prop'] = players_only['Player'].map(prop_dict)
-                        players_only['Prop_avg'] = players_only['Prop'].mean() / 100
-                        players_only['prop_threshold'] = .10
-                        players_only = players_only.loc[players_only['Mean_Outcome'] > 0]
-                        players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
-                        players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
-                        players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
-                        players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under")
-                        players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet")
-                        players_only['Edge'] = players_only['Bet_check']
-
-                        players_only['Player'] = hold_file[['Player']]
-
-                        final_outcomes = players_only[['Player', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                        if prop_type_var == 'All Props':
+                            for prop in all_sim_vars:
+                                
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == prop]
+                                prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                                prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                                df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                
+                                prop_dict = dict(zip(df.Player, df.Prop))
+                                over_dict = dict(zip(df.Player, df.Over))
+                                under_dict = dict(zip(df.Player, df.Under))
+                                
+                                total_sims = 5000
+        
+                                df.replace("", 0, inplace=True)
+        
+                                if prop_type_var == "pass_yards":
+                                    df['Median'] = df['pass_yards']
+                                elif prop_type_var == "rush_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['receptions']
+        
+                                flex_file = df
+                                flex_file['Floor'] = flex_file['Median'] * .20
+                                flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .80)
+                                flex_file['STD'] = flex_file['Median'] / 4
+                                flex_file['Prop'] = flex_file['Player'].map(prop_dict)
+                                flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
+        
+                                hold_file = flex_file
+                                overall_file = flex_file
+                                prop_file = flex_file
+                                      
+                                overall_players = overall_file[['Player']]
+        
+                                for x in range(0,total_sims):    
+                                    prop_file[x] = prop_file['Prop']
+        
+                                prop_file = prop_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+        
+                                for x in range(0,total_sims):
+                                    overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+        
+                                overall_file=overall_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+        
+                                players_only = hold_file[['Player']]
+        
+                                player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
+        
+                                prop_check = (overall_file - prop_file)
+        
+                                players_only['Mean_Outcome'] = overall_file.mean(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'] = 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['Under'] = 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['Prop'] = players_only['Player'].map(prop_dict)
+                                players_only['Prop_avg'] = players_only['Prop'].mean() / 100
+                                players_only['prop_threshold'] = .10
+                                players_only = players_only.loc[players_only['Mean_Outcome'] > 0]
+                                players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
+                                players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
+                                players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
+                                players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under")
+                                players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet")
+                                players_only['Edge'] = players_only['Bet_check']
+                                players_only['Prop type'] = prop
+        
+                                players_only['Player'] = hold_file[['Player']]
+                                players_only['Team'] = players_only['Player'].map(team_dict)
+        
+                                leg_outcomes = players_only[['Player', 'Team', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                                sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
+                                
+                                final_outcomes = sim_all_hold
+                                
+                        elif prop_type_var != 'All Props':
+
+                            if prop_type_var == "pass_yards":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'pass_yards']
+                                prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                                prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                                df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "rush_yards":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'rush_yards']
+                                prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                                prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                                df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "rec_yards":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'rec_yards']
+                                prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                                prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                                df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "receptions":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'receptions']
+                                prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                                prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                                df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "rush_attempts":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'rush_attempts']
+                                prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                                prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                                df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "pass_attempts":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'pass_attempts']
+                                prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                                prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                                df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "pass_completions":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'pass_completions']
+                                prop_df = prop_df[['Player', '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'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                                prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                                df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            
+                            prop_dict = dict(zip(df.Player, df.Prop))
+                            over_dict = dict(zip(df.Player, df.Over))
+                            under_dict = dict(zip(df.Player, df.Under))
+                            
+                            total_sims = 1000
+    
+                            df.replace("", 0, inplace=True)
+    
+                            if prop_type_var == "pass_yards":
+                                df['Median'] = df['pass_yards']
+                            elif prop_type_var == "rush_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['receptions']
+    
+                            flex_file = df
+                            flex_file['Floor'] = flex_file['Median'] * .20
+                            flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .80)
+                            flex_file['STD'] = flex_file['Median'] / 4
+                            flex_file['Prop'] = flex_file['Player'].map(prop_dict)
+                            flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
+    
+                            hold_file = flex_file
+                            overall_file = flex_file
+                            prop_file = flex_file
+                                  
+                            overall_players = overall_file[['Player']]
+    
+                            for x in range(0,total_sims):    
+                                prop_file[x] = prop_file['Prop']
+    
+                            prop_file = prop_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+    
+                            for x in range(0,total_sims):
+                                overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+    
+                            overall_file=overall_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+    
+                            players_only = hold_file[['Player']]
+    
+                            player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
+    
+                            prop_check = (overall_file - prop_file)
+    
+                            players_only['Mean_Outcome'] = overall_file.mean(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'] = 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['Under'] = 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['Prop'] = players_only['Player'].map(prop_dict)
+                            players_only['Prop_avg'] = players_only['Prop'].mean() / 100
+                            players_only['prop_threshold'] = .10
+                            players_only = players_only.loc[players_only['Mean_Outcome'] > 0]
+                            players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
+                            players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
+                            players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
+                            players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under")
+                            players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet")
+                            players_only['Edge'] = players_only['Bet_check']
+    
+                            players_only['Player'] = hold_file[['Player']]
+                            players_only['Team'] = players_only['Player'].map(team_dict)
+    
+                            final_outcomes = players_only[['Player', 'Team', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
                         
                         final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)
-                    
-                        final_outcomes = final_outcomes.set_index('Player')
 
                         with df_hold_container:
                             df_hold_container = st.empty()