Full update to prop sim to fix trends and object orientations

app.py

@@ -55,7 +55,7 @@ gcservice_account, gcservice_account2, NHL_Data = init_conn()
 prop_table_options = ['SOG', 'points', 'blocked_shots', '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 = ['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']
 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)
@@ -183,40 +183,42 @@ with tab3:
                             for prop in all_sim_vars:
                                 
                                 if game_select_var == 'Draftkings':
-                                    prop_df = prop_trends[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                    prop_df_raw = 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 = 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(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                                if len(prop_df) > 0:
-                                
+                                    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', '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(prop_display, 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
+                                    total_sims = 1000
             
                                     df.replace("", 0, inplace=True)
             
-                                    if prop == 'points':
+                                    if prop == 'NHL_GAME_PLAYER_POINTS':
                                         df['Median'] = df['Player TP']
-                                    elif prop == 'SOG':
+                                    elif prop == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL':
                                         df['Median'] = df['Player SOG']
-                                    elif prop == 'assists':
+                                    elif prop == 'NHL_GAME_PLAYER_ASSISTS':
                                         df['Median'] = df['Player Assists']
-                                    elif prop == 'blocked_shots':
+                                    elif prop == 'NHL_GAME_PLAYER_BLOCKED_SHOTS':
                                         df['Median'] = df['Player Blocks']
             
                                     flex_file = df
                                     flex_file['Floor'] = (flex_file['Median'] * .15)
-                                    flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .85)
+                                    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', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
@@ -224,7 +226,7 @@ with tab3:
                                     hold_file = flex_file
                                     overall_file = flex_file
                                     prop_file = flex_file
-                                          
+                                        
                                     overall_players = overall_file[['Player']]
             
                                     for x in range(0,total_sims):    
@@ -268,130 +270,136 @@ with tab3:
                                     players_only['Team'] = players_only['Player'].map(team_dict)
             
                                     leg_outcomes = players_only[['Player', 'Team', '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
+                                    final_outcomes = sim_all_hold
 
                         elif prop_type_var != 'All Props':
+
                             if game_select_var == 'Draftkings':
-                                prop_df = prop_trends[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                prop_df_raw = 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)
-                            if prop_type_var == "SOG":
-                                prop_df = prop_df.loc[prop_df['prop_type'] == 'SOG']
-                                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))
-                                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'] == '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))
-                                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'] == '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))
-                                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'] == 'blocked_shots']
-                                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))
-                                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))
-                            over_dict = dict(zip(df.Player, df.Over))
-                            under_dict = dict(zip(df.Player, df.Under))
-                            
-                            total_sims = 5000
-    
-                            df.replace("", 0, inplace=True)
-    
-                            if prop == 'points':
-                                df['Median'] = df['Player TP']
-                            elif prop == 'SOG':
-                                df['Median'] = df['Player SOG']
-                            elif prop == 'assists':
-                                df['Median'] = df['Player Assists']
-                            elif prop == 'blocked_shots':
-                                df['Median'] = df['Player Blocks']
-    
-                            flex_file = df
-                            flex_file['Floor'] = (flex_file['Median'] * .15)
-                            flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .85)
-                            flex_file['STD'] = (flex_file['Median']/3)
-                            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']]
-                            players_only['Team'] = players_only['Player'].map(team_dict)
-    
-                            final_outcomes = players_only[['Player', 'Team', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                                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 == "SOG":
+                                    prop_df = prop_df.loc[prop_df['prop_type'] == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL']
+                                    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'] = 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', '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', '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', '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))
+                                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 == 'NHL_GAME_PLAYER_POINTS':
+                                    df['Median'] = df['Player TP']
+                                elif prop == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL':
+                                    df['Median'] = df['Player SOG']
+                                elif prop == 'NHL_GAME_PLAYER_ASSISTS':
+                                    df['Median'] = df['Player Assists']
+                                elif prop == 'NHL_GAME_PLAYER_BLOCKED_SHOTS':
+                                    df['Median'] = df['Player Blocks']
+        
+                                flex_file = df
+                                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', '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']]
+                                players_only['Team'] = players_only['Player'].map(team_dict)
+        
+                                leg_outcomes = players_only[['Player', 'Team', '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
+                            
                         final_outcomes = final_outcomes[final_outcomes['Prop'] > 0]
                         final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)