Lots of changes to stat specifc loop, added pick6 functionality

app.py

@@ -52,10 +52,11 @@ def init_conn():
     
 gcservice_account, gcservice_account2, NHL_Data = init_conn()
 
-prop_table_options = ['SOG', 'points', 'blocked_shots', 'assists']
+all_sim_vars = ['NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_BLOCKED_SHOTS', 'NHL_GAME_PLAYER_ASSISTS']
 prop_format = {'L5 Success': '{:.2%}', 'L10_Success': '{:.2%}', 'L20_success': '{:.2%}', 'Matchup Boost': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
                'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
 all_sim_vars = ['NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_BLOCKED_SHOTS', 'NHL_GAME_PLAYER_ASSISTS']
+pick6_sim_vars = ['Points', 'Shots on Goal', 'Assists', 'Blocks']
 sim_all_hold = pd.DataFrame(columns=['Player', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
 
 @st.cache_resource(ttl=300)
@@ -63,7 +64,7 @@ def pull_baselines():
     sh = gcservice_account.open_by_url(NHL_Data)
     worksheet = sh.worksheet('Prop_Betting_Table')
     raw_display = pd.DataFrame(worksheet.get_all_records())
-    prop_display = raw_display.loc[raw_display['Player'] != ""]
+    prop_display = raw_display[raw_display['Player'] != ""]
     prop_display['Player Blocks'].replace("", np.nan, inplace=True)
     prop_display['SOG Edge'].replace("", np.nan, inplace=True)
     prop_display['Assist Edge'].replace("", np.nan, inplace=True)
@@ -159,12 +160,16 @@ with tab3:
         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_trends[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+        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'])
+        if book_select_var == 'ALL':
+            book_selections = ['BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL']
+        else:
+            book_selections = [book_select_var]
+        if game_select_var == 'Aggregate':
+            prop_df = prop_trends[['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 = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
         st.download_button(
             label="Download Prop Source",
             data=convert_df_to_csv(prop_df),
@@ -172,61 +177,72 @@ with tab3:
             mime='text/csv',
             key='prop_source',
         )
-        prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'SOG', 'points', 'blocked_shots', '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')
+        if game_select_var == 'Aggregate':
+            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_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS'])
+        elif game_select_var == 'Pick6':
+            prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds'])
 
         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 == 'Aggregate':
+                                prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                sim_vars = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS']
+                            elif game_select_var == 'Pick6':
+                                prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                sim_vars = ['Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds']
+                            
+                            player_df = prop_display.copy()
+
+                            for prop in sim_vars:
+                                prop_df = prop_df_raw[prop_df_raw['prop_type'] == prop]
                                 
-                                if game_select_var == 'Draftkings':
-                                    prop_df_raw = prop_trends[['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]
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == prop]
-                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
+                                for books in book_selections:
+                                    prop_df = prop_df[prop_df['book'] == books]
+                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                     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(prop_display, 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))
+
+                                    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))
+
+                                    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)
+
+                                    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':
+                                    if prop == 'NHL_GAME_PLAYER_POINTS' or prop == 'Points':
                                         df['Median'] = df['Player TP']
-                                    elif prop == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL':
+                                    elif prop == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL' or prop == 'Shots on Goal':
                                         df['Median'] = df['Player SOG']
-                                    elif prop == 'NHL_GAME_PLAYER_ASSISTS':
+                                    elif prop == 'NHL_GAME_PLAYER_ASSISTS' or prop == 'Assists':
                                         df['Median'] = df['Player Assists']
-                                    elif prop == 'NHL_GAME_PLAYER_BLOCKED_SHOTS':
+                                    elif prop == 'NHL_GAME_PLAYER_BLOCKED_SHOTS' or prop == 'Blocks':
                                         df['Median'] = df['Player Blocks']
             
-                                    flex_file = df
+                                    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
-                                    overall_file = flex_file
-                                    prop_file = flex_file
+                                    hold_file = flex_file.copy()
+                                    overall_file = flex_file.copy()
+                                    prop_file = flex_file.copy()
                                         
                                     overall_players = overall_file[['Player']]
             
@@ -259,7 +275,7 @@ with tab3:
                                     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 = 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'])
@@ -275,87 +291,79 @@ with tab3:
                                     sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
                                 
                                     final_outcomes = sim_all_hold
+                                    st.write(f'finished {prop}')
 
                         elif prop_type_var != 'All Props':
 
-                            if game_select_var == 'Draftkings':
+                            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']]
                             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)
+                                prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                         
-                            for books in ['FANDUEL', 'DRAFTKINGS', 'BET365', 'CONSENSUS']:
-                                prop_df = prop_df_raw.loc[prop_df_raw['book'] == books]
-                                if prop_type_var == "SOG":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL']
-                                    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']
-                                    prop = 'SOG'
-                                    df = pd.merge(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                elif prop_type_var == "points":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NHL_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']
-                                    prop = 'points'
-                                    df = pd.merge(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                elif prop_type_var == "assists":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NHL_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']
-                                    prop = 'assists'
-                                    df = pd.merge(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                elif prop_type_var == "blocked_shots":
-                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NHL_GAME_PLAYER_BLOCKED_SHOTS']
-                                    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']
-                                    prop = 'blocked_shots'
-                                    df = pd.merge(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                
-                                st.table(df)
-                                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))
+                            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))
+
+                                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)
+
+                                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 == 'points':
+                                if prop_type_var == 'NHL_GAME_PLAYER_POINTS' or prop_type_var == 'Points':
                                     df['Median'] = df['Player TP']
-                                elif prop == 'SOG':
+                                elif prop_type_var == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL' or prop_type_var == 'Shots on Goal':
                                     df['Median'] = df['Player SOG']
-                                elif prop == 'assists':
+                                elif prop_type_var == 'NHL_GAME_PLAYER_ASSISTS' or prop_type_var == 'Assists':
                                     df['Median'] = df['Player Assists']
-                                elif prop == 'blocked_shots':
+                                elif prop_type_var == 'NHL_GAME_PLAYER_BLOCKED_SHOTS' or prop_type_var == 'Blocks':
                                     df['Median'] = df['Player Blocks']
         
-                                flex_file = df
+                                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
-                                overall_file = flex_file
-                                prop_file = flex_file
+                                hold_file = flex_file.copy()
+                                overall_file = flex_file.copy()
+                                prop_file = flex_file.copy()
                                     
                                 overall_players = overall_file[['Player']]
         
@@ -388,7 +396,7 @@ with tab3:
                                 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 = 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'])
@@ -403,8 +411,11 @@ with tab3:
                                 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}')
                             
                         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: