Update app.py

app.py

@@ -34,6 +34,11 @@ def init_conn():
 gspreadcon = init_conn()
 
 master_hold = 'https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit#gid=195454038'
+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']
+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)
 def pull_baselines():
@@ -48,24 +53,29 @@ def pull_baselines():
     prop_table = prop_display[['Player', 'Position', 'Team', 'Opp', 'Team_Total', 'Player SOG', 'Player Goals', 'Player Assists',
                                'Player TP', 'Player Blocks', 'Player Saves']]
     
+    worksheet = sh.worksheet('prop_trends')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace('', np.nan, inplace=True)
+    prop_trends = raw_display.dropna(subset='Player')
+    
     worksheet = sh.worksheet('Timestamp')
     timestamp = worksheet.acell('A1').value
 
-    return prop_table, timestamp
+    return prop_table, prop_trends, timestamp
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-prop_display, timestamp = pull_baselines()
+prop_display, prop_trends, timestamp = pull_baselines()
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
-tab1, tab2 = st.tabs(["Player Stat Table", 'Game Betting Model'])
+tab1, tab2, tab3 = st.tabs(["Player Stat Table", 'Prop Trend Table', 'Stat Specific Simulations'])
 
 with tab1:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              prop_display, timestamp = pull_baselines()
+              prop_display, prop_trends, timestamp = pull_baselines()
     prop_frame = prop_display
     st.dataframe(prop_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
     
@@ -78,4 +88,264 @@ with tab1:
     )
 
 with tab2:
-    st.info('Coming soon!')
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset3'):
+              st.cache_data.clear()
+              prop_display, prop_trends, timestamp = pull_baselines()
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+    split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5')
+    if split_var5 == 'Specific Teams':
+        team_var5 = st.multiselect('Which teams would you like to include in the tables?', options = prop_trends['Team'].unique(), key='team_var5')
+    elif split_var5 == 'All':
+        team_var5 = prop_trends.Team.values.tolist()
+    prop_type_var2 = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options)
+    prop_frame_disp = prop_trends[prop_trends['Team'].isin(team_var5)]
+    prop_frame_disp = prop_frame_disp[prop_frame_disp['prop_type'] == prop_type_var2]
+    prop_frame_disp = prop_frame_disp.set_index('Player')
+    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(
+        label="Export Prop Trends Model",
+        data=convert_df_to_csv(prop_frame),
+        file_name='NHL_prop_trends_export.csv',
+        mime='text/csv',
+    )
+
+with tab3:
+    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.')
+    if st.button("Reset Data/Load Data", key='reset5'):
+              st.cache_data.clear()
+              prop_display, prop_trends, timestamp = 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:
+        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 st.button('Simulate Prop Category'):
+            with col2:
+                    with df_hold_container.container():
+                        if prop_type_var == 'All Props':
+                            for prop in all_sim_vars:
+                                
+                                prop_df = prop_trends[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                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'])
+                                
+                                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['Prop type'] = prop
+        
+                                players_only['Player'] = hold_file[['Player']]
+        
+                                leg_outcomes = players_only[['Player', '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 prop_type_var == "SOG":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                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))
+                                df = pd.merge(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "points":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                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(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "assists":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                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(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "blocked_shots":
+                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                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))
+                                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
+    
+                            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']]
+    
+                            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.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='Nba_prop_proj.csv',
+                                mime='text/csv',
+                                key='prop_proj',
+                            )