A lot of changes. Added loop for stat sim was the biggest change.

app.py

@@ -98,7 +98,7 @@ def init_baselines():
     worksheet = sh.worksheet('DK_Build_Up')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('', np.nan, inplace=True)
-    raw_display.rename(columns={"Name": "Player"}, inplace = True)
+    raw_display = raw_display.rename(columns={"Name": "Player"})
     
     raw_baselines = raw_display[['Player', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV']]
     raw_baselines = raw_baselines[raw_baselines['Minutes'] > 0]
@@ -119,14 +119,15 @@ def init_baselines():
     worksheet = sh.worksheet('Prop_Frame')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('', np.nan, inplace=True)
-    prop_frame = raw_display.dropna(subset='Name')
+    raw_display = raw_display.rename(columns={"Name": "Player", "OddsType": "book", "PropType": "prop_type"})
+    prop_frame = prop_frame.dropna(subset='Player')
     
     worksheet = sh.worksheet('Pick6_ingest')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('', np.nan, inplace=True)
     pick_frame = raw_display.dropna(subset='Player')
 
-    prop_frame['Name'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
+    prop_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
                                  ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.',
                                   'Trey Murphy III', 'Cam Thomas'], inplace=True)
     pick_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
@@ -203,13 +204,13 @@ with tab3:
         team_var5 = player_stats.Team.values.tolist()
     book_split5 = st.radio("Would you like to view all books or specific ones?", ('All', 'Specific Books'), key='book_split5')
     if book_split5 == 'Specific Books':
-        book_var5 = st.multiselect('Which books would you like to include in the tables?', options = prop_frame['OddsType'].unique(), key='book_var5')
+        book_var5 = st.multiselect('Which books would you like to include in the tables?', options = prop_frame['book'].unique(), key='book_var5')
     elif book_split5 == 'All':
-        book_var5 = prop_frame.OddsType.values.tolist()
+        book_var5 = prop_frame.book.values.tolist()
     prop_type_var2 = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options)
     prop_frame_disp = prop_frame[prop_frame['Team'].isin(team_var5)]
-    prop_frame_disp = prop_frame_disp[prop_frame_disp['OddsType'].isin(book_var5)]
-    prop_frame_disp = prop_frame_disp[prop_frame_disp['PropType'] == prop_type_var2]
+    prop_frame_disp = prop_frame_disp[prop_frame_disp['book'].isin(book_var5)]
+    prop_frame_disp = prop_frame_disp[prop_frame_disp['prop_type'] == prop_type_var2]
     prop_frame_disp = prop_frame_disp.sort_values(by='Trending Over', ascending=False)
     st.dataframe(prop_frame_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(prop_format, precision=2), use_container_width = True)
     st.download_button(
@@ -365,9 +366,8 @@ with tab4:
 
 with tab5:
     st.info(t_stamp)
-    st.info('The Over and Under percentages are a composite percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
-    st.warning('This model is currently down for maintence and will be back up soon! use the Player Prop Simulations if you wish to check on specific props.')
-    if st.button("Reset Data/Load Data", key='reset5'):
+    st.info('The Over and Under percentages are a compositve percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
+    if st.button("Reset Data/Load Data", key='reset6'):
               st.cache_data.clear()
               game_model, raw_baselines, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
@@ -380,11 +380,11 @@ with tab5:
         export_container = st.empty()
     
     with col1:
-        game_select_var = st.selectbox('Select prop source', options = ['Draftkings', 'Pick6'])
-        if game_select_var == 'Draftkings':
-            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+        game_select_var = st.selectbox('Select prop source', options = ['Aggregate', 'Pick6'])
+        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', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+            prop_df = pick_frame[['Full_name', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
             prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
         st.download_button(
             label="Download Prop Source",
@@ -393,62 +393,235 @@ with tab5:
             mime='text/csv',
             key='prop_source',
         )
-        prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'points', 'rebounds', 'assists', 'threes', 'PRA', 'points+rebounds',
-                                                                        'points+assists', 'rebounds+assists'])
-        if prop_type_var == 'All Props':
-            st.info('please note that the All Props run can take some time, you will see progress as tables show up in the sim area to the right')
+        prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_3_POINTERS_MADE', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS'])
 
         if st.button('Simulate Prop Category'):
             with col2:
+                   
                     with df_hold_container.container():
                         if prop_type_var == 'All Props':
                             for prop in all_sim_vars:
                                 
-                                if game_select_var == 'Draftkings':
-                                    prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                if game_select_var == 'Aggregate':
+                                    prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 elif game_select_var == 'Pick6':
-                                    prop_df = pick_frame[['Full_name', '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[['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_df_raw = pick_frame[['Full_name', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                    prop_df_raw.rename(columns={"Full_name": "Player"}, inplace = True)
+                                
+                                for books in ['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS']:
+                                    prop_df = prop_df_raw.loc[prop_df_raw['book'] == books]
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == prop]
+                                    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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                    
+                                    prop_dict = dict(zip(df.Player, df.Prop))
+                                    team_dict = dict(zip(df.Player, df.Team))
+                                    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 = 1000
+            
+                                    df.replace("", 0, inplace=True)
+                                    
+                                    if prop == "NBA_GAME_PLAYER_POINTS":
+                                        df['Median'] = df['Points']
+                                    elif prop == "NBA_GAME_PLAYER_REBOUNDS":
+                                        df['Median'] = df['Rebounds']
+                                    elif prop == "NBA_GAME_PLAYER_ASSISTS":
+                                        df['Median'] = df['Assists']
+                                    elif prop == "NBA_GAME_PLAYER_3_POINTERS_MADE":
+                                        df['Median'] = df['3P']
+                                    elif prop == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS":
+                                        df['Median'] = df['PRA']
+                                    elif prop == "NBA_GAME_PLAYER_POINTS_REBOUNDS":
+                                        df['Median'] = df['Points'] + df['Rebounds']
+                                    elif prop == "NBA_GAME_PLAYER_POINTS_ASSISTS":
+                                        df['Median'] = df['Points'] + df['Assists']
+                                    elif prop == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS":
+                                        df['Median'] = df['Rebounds'] + df['Assists']
+                                        
+                                    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['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_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                            elif game_select_var == 'Pick6':
+                                prop_df_raw = pick_frame[['Full_name', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df_raw.rename(columns={"Full_name": "Player"}, inplace = True)
+                                
+                            for books in ['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS']:
+                                prop_df = prop_df_raw.loc[prop_df_raw['book'] == books]
+                                if prop_type_var == "NBA_GAME_PLAYER_POINTS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS']
+                                    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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NBA_GAME_PLAYER_REBOUNDS']
+                                    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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                elif prop_type_var == "NBA_GAME_PLAYER_ASSISTS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NBA_GAME_PLAYER_ASSISTS']
+                                    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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                elif prop_type_var == "NBA_GAME_PLAYER_3_POINTERS_MADE":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NBA_GAME_PLAYER_3_POINTERS_MADE']
+                                    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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS']
+                                    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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_REBOUNDS']
+                                    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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_ASSISTS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_ASSISTS']
+                                    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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                                elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS']
+                                    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(player_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))
+                                team_dict = dict(zip(df.Player, df.Team))
                                 under_dict = dict(zip(df.Player, df.Under))
                                 
-                                total_sims = 5000
+                                total_sims = 1000
         
                                 df.replace("", 0, inplace=True)
-        
-                                if prop == 'points':
+                                
+                                if prop_type_var == "NBA_GAME_PLAYER_POINTS":
                                     df['Median'] = df['Points']
-                                elif prop == 'rebounds':
+                                elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS":
                                     df['Median'] = df['Rebounds']
-                                elif prop == 'assists':
+                                elif prop_type_var == "NBA_GAME_PLAYER_ASSISTS":
                                     df['Median'] = df['Assists']
-                                elif prop == 'threes':
+                                elif prop_type_var == "NBA_GAME_PLAYER_3_POINTERS_MADE":
                                     df['Median'] = df['3P']
-                                elif prop == 'PRA':
-                                    df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
-                                elif prop == 'points+rebounds':
+                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS":
+                                    df['Median'] = df['PRA']
+                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS":
                                     df['Median'] = df['Points'] + df['Rebounds']
-                                elif prop == 'points+assists':
+                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_ASSISTS":
                                     df['Median'] = df['Points'] + df['Assists']
-                                elif prop == 'rebounds+assists':
-                                    df['Median'] = df['Assists'] + df['Rebounds']
-        
+                                elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS":
+                                    df['Median'] = df['Rebounds'] + df['Assists']
+                                
                                 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['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', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
+                                flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
         
                                 hold_file = flex_file
                                 overall_file = flex_file
@@ -459,12 +632,12 @@ with tab5:
                                 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)
+                                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)
+                                overall_file=overall_file.drop(['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
         
                                 players_only = hold_file[['Player']]
         
@@ -481,6 +654,7 @@ with tab5:
                                 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
@@ -491,174 +665,17 @@ with tab5:
                                 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['Prop Type'] = prop_type_var
         
                                 players_only['Player'] = hold_file[['Player']]
+                                players_only['Team'] = players_only['Player'].map(team_dict)
         
-                                leg_outcomes = players_only[['Player', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
-                                
+                                leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', '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 == 'Draftkings':
-                                    prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            elif game_select_var == 'Pick6':
-                                    prop_df = pick_frame[['Full_name', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                                    prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
-                            if prop_type_var == "points":
-                                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_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_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 == "threes":
-                                prop_df = prop_df.loc[prop_df['prop_type'] == 'threes']
-                                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_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_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_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_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 == 'threes':
-                                df['Median'] = df['3P']
-                            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[final_outcomes['Prop'] > 0]
+                        final_outcomes = final_outcomes.dropna()
                         final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)
 
                         with df_hold_container:
@@ -669,34 +686,7 @@ with tab5:
                             st.download_button(
                                 label="Export Projections",
                                 data=convert_df_to_csv(final_outcomes),
-                                file_name='Nba_prop_proj.csv',
+                                file_name='NBA_prop_proj.csv',
                                 mime='text/csv',
                                 key='prop_proj',
-                            )
-with tab6:
-    st.info(t_stamp)
-    if st.button("Reset Data", key='reset6'):
-              st.cache_data.clear()
-              game_model, raw_baselines, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-    split_var6 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var6')
-    if split_var6 == 'Specific Teams':
-        team_var6 = st.multiselect('Which teams would you like to include in the tables?', options = player_stats['Team'].unique(), key='team_var6')
-    elif split_var6 == 'All':
-        team_var6 = player_stats.Team.values.tolist()
-    raw_stats_disp = raw_baselines[raw_baselines['Team'].isin(team_var6)]
-    st.header("Baselines to adjust")
-    editable_df = st.data_editor(
-        raw_stats_disp,
-        key="data",
-        hide_index=True,
-        use_container_width = True
-    )
-    st.header("Customized Projections")
-    st.dataframe(add_column(editable_df).style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-    st.download_button(
-        label="Export Customizable Model",
-        data=convert_df_to_csv(player_stats),
-        file_name='NBA_stats_export.csv',
-        mime='text/csv',
-    )
+                            )