Refactor app.py to improve UI layout and enhance prop simulation functionality. Introduced a settings container for better organization of selection options, including prop source, book, and prop category. Streamlined the simulation process for player props, ensuring accurate calculations and improved data handling. Updated download functionality for prop sources and projections, enhancing user experience and data accessibility.

app.py

@@ -165,17 +165,19 @@ with tab3:
               st.cache_data.clear()
               prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-    col1, col2 = st.columns([1, 5])
-    
-    with col2:
-        df_hold_container = st.empty()
-        info_hold_container = st.empty()
-        plot_hold_container = st.empty()
-        export_container = st.empty()
-    
-    with col1:
-        game_select_var = st.selectbox('Select prop source', options = ['Aggregate', 'Pick6'])
-        book_select_var = st.selectbox('Select book', options = ['ALL', 'BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL'])
+
+    settings_container = st.container()
+    df_hold_container = st.empty()
+    export_container = st.empty()
+
+    with settings_container.container():
+        col1, col2, col3, col4 = st.columns([3, 3, 3, 3])
+        with col1:
+            game_select_var = st.selectbox('Select prop source', options = ['Aggregate', 'Pick6'])
+        with col2:
+            book_select_var = st.selectbox('Select book', options = ['ALL', 'BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL'])
+        with col3:
+            prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_ASSISTS', 'NHL_GAME_PLAYER_BLOCKED_SHOTS'])
         if book_select_var == 'ALL':
             book_selections = ['BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL']
         else:
@@ -185,278 +187,279 @@ with tab3:
         elif game_select_var == 'Pick6':
             prop_df = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
             book_selections = ['Pick6']
-        st.download_button(
-            label="Download Prop Source",
-            data=convert_df_to_csv(prop_df),
-            file_name='NHL_prop_source.csv',
-            mime='text/csv',
-            key='prop_source',
-        )
+        
         if game_select_var == 'Aggregate':
             prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_ASSISTS', 'NHL_GAME_PLAYER_BLOCKED_SHOTS'])
         elif game_select_var == 'Pick6':
             prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'Points', 'Shots on Goal', 'Assists', 'Blocks'])
-
-        if st.button('Simulate Prop Category'):
-            with col2:
-                    with df_hold_container.container():
-                        if prop_type_var == 'All Props':
-
-                            if game_select_var == 'Aggregate':
-                                prop_df_raw = prop_trends[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
-                                sim_vars = ['NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_ASSISTS', 'NHL_GAME_PLAYER_BLOCKED_SHOTS']
-                            elif game_select_var == 'Pick6':
-                                prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
-                                sim_vars = ['Points', 'Shots on Goal', 'Assists', 'Blocks']
+        with col4:
+            st.download_button(
+                label="Download Prop Source",
+                data=convert_df_to_csv(prop_df),
+                file_name='NHL_prop_source.csv',
+                mime='text/csv',
+                key='prop_source',
+            )
+
+    if st.button('Simulate Prop Category'):
+        with df_hold_container.container():
+            if prop_type_var == 'All Props':
+
+                if game_select_var == 'Aggregate':
+                    prop_df_raw = prop_trends[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
+                    sim_vars = ['NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_ASSISTS', 'NHL_GAME_PLAYER_BLOCKED_SHOTS']
+                elif game_select_var == 'Pick6':
+                    prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
+                    sim_vars = ['Points', 'Shots on Goal', 'Assists', 'Blocks']
+                
+                player_df = prop_display.copy()
+
+                for prop in sim_vars:
+                    
+                    for books in book_selections:
+                        prop_df = prop_df_raw[prop_df_raw['prop_type'] == prop]
+                        prop_df = prop_df[prop_df['book'] == books]
+                        prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
+                        prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                        prop_df['Over'] = 1 / prop_df['over_line']
+                        prop_df['Under'] = 1 / prop_df['under_line']
+
+                        prop_dict = dict(zip(prop_df.Player, prop_df.Prop))
+                        prop_type_dict = dict(zip(prop_df.Player, prop_df.prop_type))
+                        book_dict = dict(zip(prop_df.Player, prop_df.book))
+                        over_dict = dict(zip(prop_df.Player, prop_df.Over))
+                        under_dict = dict(zip(prop_df.Player, prop_df.Under))
+                        trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over']))
+                        trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending Under']))
+
+                        player_df['book'] = player_df['Player'].map(book_dict)
+                        player_df['Prop'] = player_df['Player'].map(prop_dict)
+                        player_df['prop_type'] = player_df['Player'].map(prop_type_dict)
+                        player_df['Trending Over'] = player_df['Player'].map(trending_over_dict)
+                        player_df['Trending Under'] = player_df['Player'].map(trending_under_dict)
+
+                        df = player_df.reset_index(drop=True)
+
+                        team_dict = dict(zip(df.Player, df.Team))
+                        
+                        total_sims = 1000
+
+                        df.replace("", 0, inplace=True)
+
+                        if prop == 'NHL_GAME_PLAYER_POINTS' or prop == 'Points':
+                            df['Median'] = df['Player TP']
+                        elif prop == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL' or prop == 'Shots on Goal':
+                            df['Median'] = df['Player SOG']
+                        elif prop == 'NHL_GAME_PLAYER_ASSISTS' or prop == 'Assists':
+                            df['Median'] = df['Player Assists']
+                        elif prop == 'NHL_GAME_PLAYER_BLOCKED_SHOTS' or prop == 'Blocks':
+                            df['Median'] = df['Player Blocks']
+
+                        flex_file = df.copy()
+                        flex_file['Floor'] = (flex_file['Median'] * .15)
+                        flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1)
+                        flex_file['STD'] = (flex_file['Median']/3)
+                        flex_file['Prop'] = flex_file['Player'].map(prop_dict)
+                        flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
+
+                        hold_file = flex_file.copy()
+                        overall_file = flex_file.copy()
+                        prop_file = flex_file.copy()
                             
-                            player_df = prop_display.copy()
-
-                            for prop in sim_vars:
-                                
-                                for books in book_selections:
-                                    prop_df = prop_df_raw[prop_df_raw['prop_type'] == prop]
-                                    prop_df = prop_df[prop_df['book'] == books]
-                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
-                                    prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                                    prop_df['Over'] = 1 / prop_df['over_line']
-                                    prop_df['Under'] = 1 / prop_df['under_line']
-
-                                    prop_dict = dict(zip(prop_df.Player, prop_df.Prop))
-                                    prop_type_dict = dict(zip(prop_df.Player, prop_df.prop_type))
-                                    book_dict = dict(zip(prop_df.Player, prop_df.book))
-                                    over_dict = dict(zip(prop_df.Player, prop_df.Over))
-                                    under_dict = dict(zip(prop_df.Player, prop_df.Under))
-                                    trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over']))
-                                    trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending Under']))
-
-                                    player_df['book'] = player_df['Player'].map(book_dict)
-                                    player_df['Prop'] = player_df['Player'].map(prop_dict)
-                                    player_df['prop_type'] = player_df['Player'].map(prop_type_dict)
-                                    player_df['Trending Over'] = player_df['Player'].map(trending_over_dict)
-                                    player_df['Trending Under'] = player_df['Player'].map(trending_under_dict)
-
-                                    df = player_df.reset_index(drop=True)
-
-                                    team_dict = dict(zip(df.Player, df.Team))
-                                    
-                                    total_sims = 1000
-            
-                                    df.replace("", 0, inplace=True)
-            
-                                    if prop == 'NHL_GAME_PLAYER_POINTS' or prop == 'Points':
-                                        df['Median'] = df['Player TP']
-                                    elif prop == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL' or prop == 'Shots on Goal':
-                                        df['Median'] = df['Player SOG']
-                                    elif prop == 'NHL_GAME_PLAYER_ASSISTS' or prop == 'Assists':
-                                        df['Median'] = df['Player Assists']
-                                    elif prop == 'NHL_GAME_PLAYER_BLOCKED_SHOTS' or prop == 'Blocks':
-                                        df['Median'] = df['Player Blocks']
-            
-                                    flex_file = df.copy()
-                                    flex_file['Floor'] = (flex_file['Median'] * .15)
-                                    flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1)
-                                    flex_file['STD'] = (flex_file['Median']/3)
-                                    flex_file['Prop'] = flex_file['Player'].map(prop_dict)
-                                    flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
-            
-                                    hold_file = flex_file.copy()
-                                    overall_file = flex_file.copy()
-                                    prop_file = flex_file.copy()
-                                        
-                                    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['Prop'] = players_only['Player'].map(prop_dict)
-                                    players_only['Book'] = players_only['Player'].map(book_dict)
-                                    players_only['Trending Over'] = players_only['Player'].map(trending_over_dict)
-                                    players_only['Trending Under'] = players_only['Player'].map(trending_under_dict)
-                                    players_only['poisson_var'] = players_only.apply(calculate_poisson, 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'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], 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", "Trending Over"]].mean(axis=1)
-                                    players_only['Under'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], 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", "Trending Under"]].mean(axis=1)
-                                    players_only['Prop_avg'] = players_only['Prop'].mean() / 100
-                                    players_only['prop_threshold'] = .10
-                                    players_only = players_only[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
+                        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['Prop'] = players_only['Player'].map(prop_dict)
+                        players_only['Book'] = players_only['Player'].map(book_dict)
+                        players_only['Trending Over'] = players_only['Player'].map(trending_over_dict)
+                        players_only['Trending Under'] = players_only['Player'].map(trending_under_dict)
+                        players_only['poisson_var'] = players_only.apply(calculate_poisson, 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'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], 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", "Trending Over"]].mean(axis=1)
+                        players_only['Under'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], 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", "Trending Under"]].mean(axis=1)
+                        players_only['Prop_avg'] = players_only['Prop'].mean() / 100
+                        players_only['prop_threshold'] = .10
+                        players_only = players_only[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', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge']]
+                        sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
+                    
+                        final_outcomes = sim_all_hold
+                        st.write(f'finished {prop} for {books}')
+
+            elif prop_type_var != 'All Props':
+
+                player_df = prop_display.copy()
+
+                if game_select_var == 'Aggregate':
+                    prop_df_raw = prop_trends[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
+                elif game_select_var == 'Pick6':
+                    prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
             
-                                    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', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge']]
-                                    sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
-                                
-                                    final_outcomes = sim_all_hold
-                                    st.write(f'finished {prop} for {books}')
-
-                        elif prop_type_var != 'All Props':
-
-                            player_df = prop_display.copy()
-
-                            if game_select_var == 'Aggregate':
-                                prop_df_raw = prop_trends[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
-                            elif game_select_var == 'Pick6':
-                                prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
+                for books in book_selections:
+                    prop_df = prop_df_raw[prop_df_raw['book'] == books]
+
+                    if prop_type_var == "NHL_GAME_PLAYER_SHOTS_ON_GOAL":
+                        prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL']
+                    elif prop_type_var == 'Shots on Goal':
+                        prop_df = prop_df[prop_df['prop_type'] == 'Player SOG']
+                    elif prop_type_var == "NHL_GAME_PLAYER_POINTS":
+                        prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_POINTS']
+                    elif prop_type_var == "Points":
+                        prop_df = prop_df[prop_df['prop_type'] == 'Player TP']
+                    elif prop_type_var == "NHL_GAME_PLAYER_ASSISTS":
+                        prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_ASSISTS']
+                    elif prop_type_var == "Assists":
+                        prop_df = prop_df[prop_df['prop_type'] == 'Player Assists']
+                    elif prop_type_var == "NHL_GAME_PLAYER_BLOCKED_SHOTS":
+                        prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_BLOCKED_SHOTS']
+                    elif prop_type_var == "Blocks":
+                        prop_df = prop_df[prop_df['prop_type'] == 'Player Blocks']
+
+                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
+                    prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                    prop_df['Over'] = 1 / prop_df['over_line']
+                    prop_df['Under'] = 1 / prop_df['under_line']
+
+                    prop_dict = dict(zip(prop_df.Player, prop_df.Prop))
+                    prop_type_dict = dict(zip(prop_df.Player, prop_df.prop_type))
+                    book_dict = dict(zip(prop_df.Player, prop_df.book))
+                    over_dict = dict(zip(prop_df.Player, prop_df.Over))
+                    under_dict = dict(zip(prop_df.Player, prop_df.Under))
+                    trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over']))
+                    trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending Under']))
+
+                    player_df['book'] = player_df['Player'].map(book_dict)
+                    player_df['Prop'] = player_df['Player'].map(prop_dict)
+                    player_df['prop_type'] = player_df['Player'].map(prop_type_dict)
+                    player_df['Trending Over'] = player_df['Player'].map(trending_over_dict)
+                    player_df['Trending Under'] = player_df['Player'].map(trending_under_dict)
+
+                    df = player_df.reset_index(drop=True)
+
+                    team_dict = dict(zip(df.Player, df.Team))
+                    
+                    total_sims = 1000
+
+                    df.replace("", 0, inplace=True)
+
+                    if prop_type_var == 'NHL_GAME_PLAYER_POINTS' or prop_type_var == 'Points':
+                        df['Median'] = df['Player TP']
+                    elif prop_type_var == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL' or prop_type_var == 'Shots on Goal':
+                        df['Median'] = df['Player SOG']
+                    elif prop_type_var == 'NHL_GAME_PLAYER_ASSISTS' or prop_type_var == 'Assists':
+                        df['Median'] = df['Player Assists']
+                    elif prop_type_var == 'NHL_GAME_PLAYER_BLOCKED_SHOTS' or prop_type_var == 'Blocks':
+                        df['Median'] = df['Player Blocks']
+
+                    flex_file = df.copy()
+                    flex_file['Floor'] = (flex_file['Median'] * .15)
+                    flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1)
+                    flex_file['STD'] = (flex_file['Median']/3)
+                    flex_file['Prop'] = flex_file['Player'].map(prop_dict)
+                    flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
+
+                    hold_file = flex_file.copy()
+                    overall_file = flex_file.copy()
+                    prop_file = flex_file.copy()
                         
-                            for books in book_selections:
-                                prop_df = prop_df_raw[prop_df_raw['book'] == books]
-
-                                if prop_type_var == "NHL_GAME_PLAYER_SHOTS_ON_GOAL":
-                                    prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL']
-                                elif prop_type_var == 'Shots on Goal':
-                                    prop_df = prop_df[prop_df['prop_type'] == 'Player SOG']
-                                elif prop_type_var == "NHL_GAME_PLAYER_POINTS":
-                                    prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_POINTS']
-                                elif prop_type_var == "Points":
-                                    prop_df = prop_df[prop_df['prop_type'] == 'Player TP']
-                                elif prop_type_var == "NHL_GAME_PLAYER_ASSISTS":
-                                    prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_ASSISTS']
-                                elif prop_type_var == "Assists":
-                                    prop_df = prop_df[prop_df['prop_type'] == 'Player Assists']
-                                elif prop_type_var == "NHL_GAME_PLAYER_BLOCKED_SHOTS":
-                                    prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_BLOCKED_SHOTS']
-                                elif prop_type_var == "Blocks":
-                                    prop_df = prop_df[prop_df['prop_type'] == 'Player Blocks']
-
-                                prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
-                                prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                                prop_df['Over'] = 1 / prop_df['over_line']
-                                prop_df['Under'] = 1 / prop_df['under_line']
-
-                                prop_dict = dict(zip(prop_df.Player, prop_df.Prop))
-                                prop_type_dict = dict(zip(prop_df.Player, prop_df.prop_type))
-                                book_dict = dict(zip(prop_df.Player, prop_df.book))
-                                over_dict = dict(zip(prop_df.Player, prop_df.Over))
-                                under_dict = dict(zip(prop_df.Player, prop_df.Under))
-                                trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over']))
-                                trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending Under']))
-
-                                player_df['book'] = player_df['Player'].map(book_dict)
-                                player_df['Prop'] = player_df['Player'].map(prop_dict)
-                                player_df['prop_type'] = player_df['Player'].map(prop_type_dict)
-                                player_df['Trending Over'] = player_df['Player'].map(trending_over_dict)
-                                player_df['Trending Under'] = player_df['Player'].map(trending_under_dict)
-
-                                df = player_df.reset_index(drop=True)
-
-                                team_dict = dict(zip(df.Player, df.Team))
-                                
-                                total_sims = 1000
-        
-                                df.replace("", 0, inplace=True)
-        
-                                if prop_type_var == 'NHL_GAME_PLAYER_POINTS' or prop_type_var == 'Points':
-                                    df['Median'] = df['Player TP']
-                                elif prop_type_var == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL' or prop_type_var == 'Shots on Goal':
-                                    df['Median'] = df['Player SOG']
-                                elif prop_type_var == 'NHL_GAME_PLAYER_ASSISTS' or prop_type_var == 'Assists':
-                                    df['Median'] = df['Player Assists']
-                                elif prop_type_var == 'NHL_GAME_PLAYER_BLOCKED_SHOTS' or prop_type_var == 'Blocks':
-                                    df['Median'] = df['Player Blocks']
-        
-                                flex_file = df.copy()
-                                flex_file['Floor'] = (flex_file['Median'] * .15)
-                                flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1)
-                                flex_file['STD'] = (flex_file['Median']/3)
-                                flex_file['Prop'] = flex_file['Player'].map(prop_dict)
-                                flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
-        
-                                hold_file = flex_file.copy()
-                                overall_file = flex_file.copy()
-                                prop_file = flex_file.copy()
-                                    
-                                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['Prop'] = players_only['Player'].map(prop_dict)
-                                players_only['Book'] = players_only['Player'].map(book_dict)
-                                players_only['Trending Over'] = players_only['Player'].map(trending_over_dict)
-                                players_only['Trending Under'] = players_only['Player'].map(trending_under_dict)
-                                players_only['poisson_var'] = players_only.apply(calculate_poisson, 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'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], 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", "Trending Over"]].mean(axis=1)
-                                players_only['Under'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], 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", "Trending Under"]].mean(axis=1)
-                                players_only['Prop_avg'] = players_only['Prop'].mean() / 100
-                                players_only['prop_threshold'] = .10
-                                players_only = players_only[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_type_var
-        
-                                players_only['Player'] = hold_file[['Player']]
-                                players_only['Team'] = players_only['Player'].map(team_dict)
-        
-                                leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop', 'Prop Type', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge']]
-                                sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
-                                
-                                final_outcomes = sim_all_hold
-                                st.write(f'finished {prop_type_var} for {books}')
-                            
-                        final_outcomes = final_outcomes[final_outcomes['Prop'] > 0]
-                        if game_select_var == 'Pick6':
-                            final_outcomes = final_outcomes.drop_duplicates(subset=['Player', 'Prop Type'])
-                        final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)
-
-                        with df_hold_container:
-                            df_hold_container = st.empty()
-                            st.dataframe(final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-                        with export_container:
-                            export_container = st.empty()
-                            st.download_button(
-                                label="Export Projections",
-                                data=convert_df_to_csv(final_outcomes),
-                                file_name='NHL_prop_proj.csv',
-                                mime='text/csv',
-                                key='prop_proj',
-                            )
+                    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['Prop'] = players_only['Player'].map(prop_dict)
+                    players_only['Book'] = players_only['Player'].map(book_dict)
+                    players_only['Trending Over'] = players_only['Player'].map(trending_over_dict)
+                    players_only['Trending Under'] = players_only['Player'].map(trending_under_dict)
+                    players_only['poisson_var'] = players_only.apply(calculate_poisson, 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'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], 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", "Trending Over"]].mean(axis=1)
+                    players_only['Under'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], 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", "Trending Under"]].mean(axis=1)
+                    players_only['Prop_avg'] = players_only['Prop'].mean() / 100
+                    players_only['prop_threshold'] = .10
+                    players_only = players_only[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_type_var
+
+                    players_only['Player'] = hold_file[['Player']]
+                    players_only['Team'] = players_only['Player'].map(team_dict)
+
+                    leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop', 'Prop Type', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge']]
+                    sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
+                    
+                    final_outcomes = sim_all_hold
+                    st.write(f'finished {prop_type_var} for {books}')
+                
+            final_outcomes = final_outcomes[final_outcomes['Prop'] > 0]
+            if game_select_var == 'Pick6':
+                final_outcomes = final_outcomes.drop_duplicates(subset=['Player', 'Prop Type'])
+            final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)
+
+            with df_hold_container:
+                df_hold_container = st.empty()
+                st.dataframe(final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+            with export_container:
+                export_container = st.empty()
+                st.download_button(
+                    label="Export Projections",
+                    data=convert_df_to_csv(final_outcomes),
+                    file_name='NHL_prop_proj.csv',
+                    mime='text/csv',
+                    key='prop_proj',
+                )