Update app.py

app.py

@@ -43,6 +43,8 @@ game_format = {'Win%': '{:.2%}'}
 prop_format = {'L5 Success': '{:.2%}', 'L10_Success': '{:.2%}', 'L20_success': '{:.2%}', 'Matchup Boost': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
                'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
 prop_table_options = ['points', 'threes', 'rebounds', 'assists', 'blocks', 'steals', 'PRA', 'PR', 'PA', 'PR']
+all_sim_vars = ['points', 'rebounds', 'assists', 'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists']
+sim_all_hold = pd.DataFrame(columns=['Player', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
 
 @st.cache_resource(ttl = 300)
 def init_baselines():
@@ -309,158 +311,250 @@ with tab5:
         export_container = st.empty()
     
     with col1:
-        prop_type_var = st.selectbox('Select prop category', options = ['points', 'rebounds', 'assists', 'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists'])
+        prop_type_var = st.selectbox('Select prop category', options = ['points', 'rebounds', 'assists', 'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists',
+                                                                       'Sim all'])
 
         if st.button('Simulate Prop Category'):
             with col2:
                     with df_hold_container.container():
-
-                        if prop_type_var == "points":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'points']
-                            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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "rebounds":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds']
-                            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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "assists":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'assists']
-                            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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "PRA":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'PRA']
-                            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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "points+rebounds":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'points+rebounds']
-                            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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "points+assists":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'points+assists']
-                            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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "rebounds+assists":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds+assists']
-                            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(player_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 == 'points':
-                            df['Median'] = df['Points']
-                        elif prop_type_var == 'rebounds':
-                            df['Median'] = df['Rebounds']
-                        elif prop_type_var == 'assists':
-                            df['Median'] = df['Assists']
-                        elif prop_type_var == 'PRA':
-                            df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
-                        elif prop_type_var == 'points+rebounds':
-                            df['Median'] = df['Points'] + df['Rebounds']
-                        elif prop_type_var == 'points+assists':
-                            df['Median'] = df['Points'] + df['Assists']
-                        elif prop_type_var == 'rebounds+assists':
-                            df['Median'] = df['Assists'] + df['Rebounds']
-
-                        flex_file = df
-                        flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
-                        flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
-                        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 == 'Sim all':
+                            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(player_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 == 'points':
+                                    df['Median'] = df['Points']
+                                elif prop == 'rebounds':
+                                    df['Median'] = df['Rebounds']
+                                elif prop == 'assists':
+                                    df['Median'] = df['Assists']
+                                elif prop == 'PRA':
+                                    df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
+                                elif prop == 'points+rebounds':
+                                    df['Median'] = df['Points'] + df['Rebounds']
+                                elif prop == 'points+assists':
+                                    df['Median'] = df['Points'] + df['Assists']
+                                elif prop == 'rebounds+assists':
+                                    df['Median'] = df['Assists'] + df['Rebounds']
+        
+                                flex_file = df
+                                flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
+                                flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+                                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']]
+        
+                                leg_outcomes = players_only[['Player', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                                
+                                final_outcomes = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
+
+                        elif prop_type_var != 'Sim all':
+                            if prop_type_var == "points":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'points']
+                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "rebounds":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds']
+                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "assists":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'assists']
+                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "PRA":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'PRA']
+                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "points+rebounds":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'points+rebounds']
+                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "points+assists":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'points+assists']
+                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "rebounds+assists":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds+assists']
+                                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(player_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 == 'points':
+                                df['Median'] = df['Points']
+                            elif prop_type_var == 'rebounds':
+                                df['Median'] = df['Rebounds']
+                            elif prop_type_var == 'assists':
+                                df['Median'] = df['Assists']
+                            elif prop_type_var == 'PRA':
+                                df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
+                            elif prop_type_var == 'points+rebounds':
+                                df['Median'] = df['Points'] + df['Rebounds']
+                            elif prop_type_var == 'points+assists':
+                                df['Median'] = df['Points'] + df['Assists']
+                            elif prop_type_var == 'rebounds+assists':
+                                df['Median'] = df['Assists'] + df['Rebounds']
+    
+                            flex_file = df
+                            flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
+                            flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+                            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']]
                         
                         final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)