Update app.py

app.py

@@ -385,16 +385,179 @@ with tab6:
                                 elif game_select_var == 'Pick6':
                                     prop_df = pick_frame[['Full_name', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                     prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
-                                prop_df = prop_df.loc[prop_df['prop_type'] == prop]
-                                prop_df = prop_df[~((prop_df['over_prop'] < 10) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
-                                prop_df = prop_df[prop_df['book'].isin(['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS'])]
-                                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'])
+                                
+                                for books in ['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS']:
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == prop]
+                                    prop_df = prop_df[~((prop_df['over_prop'] < 10) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
+                                    prop_df = prop_df[prop_df['book'].isin(['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS'])]
+                                    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'])
+                                    
+                                    prop_dict = dict(zip(df.Player, df.Prop))
+                                    book_dict = dict(zip(df.Player, df.book))
+                                    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 == "pass_yards":
+                                        df['Median'] = df['NFL_GAME_PLAYER_PASSING_YARDS']
+                                    elif prop == "rush_yards":
+                                        df['Median'] = df['NFL_GAME_PLAYER_RUSHING_YARDS']
+                                    elif prop == "rec_yards":
+                                        df['Median'] = df['NFL_GAME_PLAYER_RECEIVING_YARDS']
+                                    elif prop == "receptions":
+                                        df['Median'] = df['NFL_GAME_PLAYER_RECEIVING_RECEPTIONS']
+                                    elif prop == "rush_attempts":
+                                        df['Median'] = df['NFL_GAME_PLAYER_RUSHING_ATTEMPTS']
+            
+                                    flex_file = df
+                                    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
+                                          
+                                    overall_players = overall_file[['Player']]
+            
+                                    for x in range(0,total_sims):    
+                                        prop_file[x] = prop_file['Prop']
+            
+                                    prop_file = prop_file.drop(['Player', 'book', '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['Book'] = players_only['Player'].map(book_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', 'Book', '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 game_select_var == 'Aggregate':
+                                prop_df = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                            elif game_select_var == 'Pick6':
+                                prop_df = pick_frame[['Full_name', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
+                                
+                            for books in ['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS']:
+                                if prop_type_var == "pass_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 = prop_df['book'] == books
+                                    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":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS']
+                                    prop_df = prop_df[~((prop_df['over_prop'] < 10) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
+                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
+                                    prop_df = prop_df['book'] == books
+                                    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 = prop_df['book'] == books
+                                    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 = prop_df['book'] == books
+                                    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 = prop_df['book'] == books
+                                    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":
+                                    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 = prop_df['book'] == books
+                                    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 = prop_df['book'] == books
+                                    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'])
                                 
                                 prop_dict = dict(zip(df.Player, df.Prop))
                                 book_dict = dict(zip(df.Player, df.book))
@@ -404,17 +567,17 @@ with tab6:
                                 total_sims = 5000
         
                                 df.replace("", 0, inplace=True)
-        
-                                if prop == "pass_yards":
-                                    df['Median'] = df['NFL_GAME_PLAYER_PASSING_YARDS']
-                                elif prop == "rush_yards":
-                                    df['Median'] = df['NFL_GAME_PLAYER_RUSHING_YARDS']
-                                elif prop == "rec_yards":
-                                    df['Median'] = df['NFL_GAME_PLAYER_RECEIVING_YARDS']
-                                elif prop == "receptions":
-                                    df['Median'] = df['NFL_GAME_PLAYER_RECEIVING_RECEPTIONS']
-                                elif prop == "rush_attempts":
-                                    df['Median'] = df['NFL_GAME_PLAYER_RUSHING_ATTEMPTS']
+                                
+                                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['rec']
+                                elif prop_type_var == "rush_attempts":
+                                    df['Median'] = df['rush_att']
         
                                 flex_file = df
                                 flex_file['Floor'] = flex_file['Median'] * .25
@@ -437,7 +600,7 @@ with tab6:
                                 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)
+                                overall_file=overall_file.drop(['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
         
                                 players_only = hold_file[['Player']]
         
@@ -454,8 +617,8 @@ with tab6:
                                 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['Book'] = players_only['Player'].map(book_dict)
+                                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]
@@ -465,170 +628,14 @@ with tab6:
                                 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', 'Book', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                                leg_outcomes = players_only[['Player', 'Team', 'Book', '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 game_select_var == 'Aggregate':
-                                prop_df = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            elif game_select_var == 'Pick6':
-                                prop_df = pick_frame[['Full_name', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                                prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
-                            
-                            if prop_type_var == "pass_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 = prop_df[prop_df['book'].isin(['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS'])]
-                                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":
-                                prop_df = prop_df.loc[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS']
-                                prop_df = prop_df[~((prop_df['over_prop'] < 10) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
-                                prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
-                                prop_df = prop_df[prop_df['book'].isin(['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS'])]
-                                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 = prop_df[prop_df['book'].isin(['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS'])]
-                                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 = prop_df[prop_df['book'].isin(['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS'])]
-                                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 = prop_df[prop_df['book'].isin(['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS'])]
-                                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":
-                                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 = prop_df[prop_df['book'].isin(['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS'])]
-                                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 = prop_df[prop_df['book'].isin(['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS'])]
-                                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'])
-                            
-                            prop_dict = dict(zip(df.Player, df.Prop))
-                            book_dict = dict(zip(df.Player, df.book))
-                            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['rec']
-                            elif prop_type_var == "rush_attempts":
-                                df['Median'] = df['rush_att']
-    
-                            flex_file = df
-                            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
-                                  
-                            overall_players = overall_file[['Player']]
-    
-                            for x in range(0,total_sims):    
-                                prop_file[x] = prop_file['Prop']
-    
-                            prop_file = prop_file.drop(['Player', 'book', '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', 'book', '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['Book'] = players_only['Player'].map(book_dict)
-                            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', 'Book', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
                         
                         final_outcomes = final_outcomes.dropna()
                         final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)