Update app.py

app.py

@@ -42,411 +42,37 @@ master_hold = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqr
 @st.cache_resource(ttl = 300)
 def init_baselines():
     sh = gcservice_account.open_by_url(master_hold)
-    worksheet = sh.worksheet('Betting Model Clean')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.replace('#DIV/0!', np.nan, inplace=True)
-    game_model = raw_display.dropna()
-
-    worksheet = sh.worksheet('DK_Build_Up')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.replace('', np.nan, inplace=True)
-    raw_display.rename(columns={"Name": "Player"}, inplace = True)
-    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Minutes', '3P', 'Points', 'Rebounds', 'Assists', 'Steals', 'Blocks', 'Turnovers', 'Fantasy']]
-    player_stats = raw_display[raw_display['Minutes'] > 0]
-    
-    worksheet = sh.worksheet('Timestamp')
-    timestamp = worksheet.acell('A1').value
 
-    worksheet = sh.worksheet('Prop_Frame')
+    worksheet = sh.worksheet('Arturo Props')
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('', np.nan, inplace=True)
-    prop_frame = raw_display.dropna()
+    raw_display = raw_display[['Player', 'Pos', 'Team', 'Opponent', 'Min', 'mpgL3', 'Diff', 'Status', 'Pts', 'Rbs', 'Asst', 'TOs', '3PM',
+                              'Steals', 'Blk', 'FD', 'DK']]
+    player_stats = raw_display[raw_display['Min'] > 0]
 
-    return game_model, player_stats, prop_frame, timestamp
+    return player_stats
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-game_model, player_stats, prop_frame, timestamp = init_baselines()
-t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-
-tab1, tab2, tab3, tab4 = st.tabs(["Game Betting Model", "Player Projections", "Player Prop Simulations", "Stat Specific Simulations"])
-
-with tab1:
-    st.info(t_stamp)
-    if st.button("Reset Data", key='reset1'):
-              st.cache_data.clear()
-              game_model, player_stats, prop_frame, timestamp = init_baselines()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-    line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
-    team_frame = game_model
-    if line_var1 == 'Percentage':
-        team_frame = team_frame[['Team', 'Opp', 'Team Points', 'Opp Points', 'Proj Total', 'Proj Spread', 'Proj Winner', 'Win%']]
-        team_frame = team_frame.set_index('Team')
-        st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-    if line_var1 == 'American':
-        team_frame = team_frame[['Team', 'Opp', 'Team Points', 'Opp Points', 'Proj Total', 'Proj Spread', 'Proj Winner', 'Odds Line']]
-        team_frame = team_frame.set_index('Team')
-        st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-    
-    st.download_button(
-        label="Export Team Model",
-        data=convert_df_to_csv(team_frame),
-        file_name='NBA_team_betting_export.csv',
-        mime='text/csv',
-        key='team_export',
-    )
-
-with tab2:
-    st.info(t_stamp)
-    if st.button("Reset Data", key='reset2'):
-              st.cache_data.clear()
-              game_model, player_stats, prop_frame, timestamp = init_baselines()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-    split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
-    if split_var1 == 'Specific Teams':
-        team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = player_stats['Team'].unique(), key='team_var1')
-    elif split_var1 == 'All':
-        team_var1 = player_stats.Team.values.tolist()
-    player_stats = player_stats[player_stats['Team'].isin(team_var1)]
-    player_stats_disp = player_stats.set_index('Player')
-    player_stats_disp = player_stats_disp.sort_values(by='Fantasy', ascending=False)
-    st.dataframe(player_stats_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-    st.download_button(
-        label="Export Prop Model",
-        data=convert_df_to_csv(player_stats),
-        file_name='NBA_stats_export.csv',
-        mime='text/csv',
-    )
-    
-with tab3:
-    st.info(t_stamp)
-    if st.button("Reset Data", key='reset3'):
-              st.cache_data.clear()
-              game_model, player_stats, prop_frame, timestamp = init_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()
-    
-    with col1:
-        player_check = st.selectbox('Select player to simulate props', options = player_stats['Player'].unique())
-        prop_type_var = st.selectbox('Select type of prop to simulate', options = ['points', 'threes', 'rebounds', 'assists', 'blocks', 'steals',
-                                                                                   'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists'])
-
-        ou_var = st.selectbox('Select wether it is an over or under', options = ['Over', 'Under'])
-        if prop_type_var == 'points':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 15.5, step = .5)
-        elif prop_type_var == 'threes':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
-        elif prop_type_var == 'rebounds':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 25.5, value = 5.5, step = .5)
-        elif prop_type_var == 'assists':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 25.5, value = 5.5, step = .5)
-        elif prop_type_var == 'blocks':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
-        elif prop_type_var == 'steals':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
-        elif prop_type_var == 'PRA':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 65.5, value = 20.5, step = .5)
-        elif prop_type_var == 'points+rebounds':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
-        elif prop_type_var == 'points+assists':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
-        elif prop_type_var == 'rebounds+assists':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
-        line_var = st.number_input('Type in the line on the prop (i.e. -120)', min_value = -1500, max_value = 1500, value = -150, step = 1)
-        line_var = line_var + 1
-
-        if st.button('Simulate Prop'):
-            with col2:
-                   
-                    with df_hold_container.container():
-
-                        df = player_stats
-
-                        total_sims = 5000
-
-                        df.replace("", 0, inplace=True)
-
-                        player_var = df.loc[df['Player'] == player_check]
-                        player_var = player_var.reset_index()
-
-                        if prop_type_var == 'points':
-                            df['Median'] = df['Points']
-                        elif prop_type_var == 'threes':
-                            df['Median'] = df['3P']
-                        elif prop_type_var == 'rebounds':
-                            df['Median'] = df['Rebounds']
-                        elif prop_type_var == 'assists':
-                            df['Median'] = df['Assists']
-                        elif prop_type_var == 'blocks':
-                            df['Median'] = df['Blocks']
-                        elif prop_type_var == 'steals':
-                            df['Median'] = df['Steals']
-                        elif prop_type_var == 'PRA':
-                            df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
-                        elif prop_type_var == 'points+rebounds':
-                            df['Median'] = df['Points'] + df['Rebounds']
-                        elif prop_type_var == 'points+assists':
-                            df['Median'] = df['Points'] + df['Assists']
-                        elif prop_type_var == 'rebounds+assists':
-                            df['Median'] = df['Assists'] + df['Rebounds']
-
-                        flex_file = df
-                        flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
-                        flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
-                        flex_file['STD'] = (flex_file['Median']/4)
-                        flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]
-
-                        hold_file = flex_file
-                        overall_file = flex_file
-                        salary_file = flex_file
-
-                        overall_players = overall_file[['Player']]
-
-                        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', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                        overall_file.astype('int').dtypes
-
-                        players_only = hold_file[['Player']]
-
-                        player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
-
-                        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)
-                        if ou_var == 'Over':
-                            players_only['beat_prop'] = overall_file[overall_file > prop_var].count(axis=1)/float(total_sims)
-                        elif ou_var == 'Under':
-                            players_only['beat_prop'] = (overall_file[overall_file < prop_var].count(axis=1)/float(total_sims))
-
-                        players_only['implied_odds'] = np.where(line_var <= 0, (-(line_var)/((-(line_var))+100)), 100/(line_var+100))
-
-                        players_only['Player'] = hold_file[['Player']]
-
-                        final_outcomes = players_only[['Player', '10%', 'Mean_Outcome', '90%', 'implied_odds', 'beat_prop']]
-                        final_outcomes['Bet?'] = np.where(final_outcomes['beat_prop'] - final_outcomes['implied_odds'] >= .10, "Bet", "No Bet")
-                        final_outcomes = final_outcomes.loc[final_outcomes['Player'] == player_check]
-                        player_outcomes = player_outcomes.loc[player_outcomes['Player'] == player_check]
-                        player_outcomes = player_outcomes.drop(columns=['Player']).transpose()
-                        player_outcomes = player_outcomes.reset_index()
-                        player_outcomes.columns = ['Instance', 'Outcome']
-
-                        x1 = player_outcomes.Outcome.to_numpy()
-
-                        print(x1)
-
-                        hist_data = [x1]
-
-                        group_labels = ['player outcomes']
-
-                        fig = px.histogram(
-                                player_outcomes, x='Outcome')
-                        fig.add_vline(x=prop_var, line_dash="dash", line_color="green")
-
-                        with df_hold_container:
-                            df_hold_container = st.empty()
-                            format_dict = {'10%': '{:.2f}', 'Mean_Outcome': '{:.2f}','90%': '{:.2f}', 'beat_prop': '{:.2%}','implied_odds': '{:.2%}'}
-                            st.dataframe(final_outcomes.style.format(format_dict), use_container_width = True)
-
-                        with info_hold_container:
-                            st.info('The Y-axis is the percent of times in simulations that the player reaches certain thresholds, while the X-axis is the threshold to be met. The Green dotted line is the prop you entered. You can hover over any spot and see the percent to reach that mark.')
-
-                        with plot_hold_container:
-                            st.dataframe(player_outcomes, use_container_width = True)
-                            plot_hold_container = st.empty()
-                            st.plotly_chart(fig, use_container_width=True)
-
-with tab4:
-    st.info(t_stamp)
-    st.info('The Over and Under percentages are a compositve percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
-    if st.button("Reset Data/Load Data", key='reset5'):
-              st.cache_data.clear()
-              game_model, player_stats, prop_frame, timestamp = init_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 = ['points', 'rebounds', 'assists', 'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists'])
-
-        if st.button('Simulate Prop Category'):
-            with col2:
-                   
-                    with df_hold_container.container():
-
-                        if 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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "rebounds":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds']
-                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
-                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                            st.table(prop_df)
-                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
-                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "assists":
-                            prop_df = prop_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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "PRA":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'PRA']
-                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
-                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                            st.table(prop_df)
-                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
-                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "points+rebounds":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'points+rebounds']
-                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
-                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                            st.table(prop_df)
-                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
-                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "points+assists":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'points+assists']
-                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
-                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                            st.table(prop_df)
-                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
-                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        elif prop_type_var == "rebounds+assists":
-                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds+assists']
-                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
-                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
-                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
-                            st.table(prop_df)
-                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
-                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
-                            df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                        
-                        prop_dict = dict(zip(df.Player, df.Prop))
-                        over_dict = dict(zip(df.Player, df.Over))
-                        under_dict = dict(zip(df.Player, df.Under))
-                        
-                        total_sims = 5000
-
-                        df.replace("", 0, inplace=True)
-
-                        if prop_type_var == 'points':
-                            df['Median'] = df['Points']
-                        elif prop_type_var == 'rebounds':
-                            df['Median'] = df['Rebounds']
-                        elif prop_type_var == 'assists':
-                            df['Median'] = df['Assists']
-                        elif prop_type_var == 'PRA':
-                            df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
-                        elif prop_type_var == 'points+rebounds':
-                            df['Median'] = df['Points'] + df['Rebounds']
-                        elif prop_type_var == 'points+assists':
-                            df['Median'] = df['Points'] + df['Assists']
-                        elif prop_type_var == 'rebounds+assists':
-                            df['Median'] = df['Assists'] + df['Rebounds']
-
-                        flex_file = df
-                        flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
-                        flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
-                        flex_file['STD'] = (flex_file['Median']/4)
-                        flex_file['Prop'] = flex_file['Player'].map(prop_dict)
-                        flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
-
-                        hold_file = flex_file
-                        overall_file = flex_file
-                        prop_file = flex_file
-                              
-                        overall_players = overall_file[['Player']]
-
-                        for x in range(0,total_sims):    
-                            prop_file[x] = prop_file['Prop']
-
-                        prop_file = prop_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-
-                        for x in range(0,total_sims):
-                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
-
-                        overall_file=overall_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-
-                        players_only = hold_file[['Player']]
-
-                        player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
-
-                        prop_check = (overall_file - prop_file)
-
-                        players_only['Mean_Outcome'] = overall_file.mean(axis=1)
-                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
-                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
-                        players_only['Over'] = prop_check[prop_check > 0].count(axis=1)/float(total_sims)
-                        players_only['Imp Over'] = players_only['Player'].map(over_dict)
-                        players_only['Over%'] = players_only[["Over", "Imp Over"]].mean(axis=1)
-                        players_only['Under'] = prop_check[prop_check < 0].count(axis=1)/float(total_sims)
-                        players_only['Imp Under'] = players_only['Player'].map(under_dict)
-                        players_only['Under%'] = players_only[["Under", "Imp Under"]].mean(axis=1)
-                        players_only['Prop'] = players_only['Player'].map(prop_dict)
-                        players_only['Prop_avg'] = players_only['Prop'].mean() / 100
-                        players_only['prop_threshold'] = .10
-                        players_only = players_only.loc[players_only['Mean_Outcome'] > 0]
-                        players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
-                        players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
-                        players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
-                        players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under")
-                        players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet")
-                        players_only['Edge'] = players_only['Bet_check']
-
-                        players_only['Player'] = hold_file[['Player']]
-
-                        final_outcomes = players_only[['Player', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
-                        
-                        final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)
-                    
-                        final_outcomes = final_outcomes.set_index('Player')
-
-                        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',
-                            )
-                            
+player_stats = init_baselines()
+
+st.info(t_stamp)
+if st.button("Reset Data", key='reset2'):
+          st.cache_data.clear()
+          player_stats = init_baselines()
+split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
+if split_var1 == 'Specific Teams':
+    team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = player_stats['Team'].unique(), key='team_var1')
+elif split_var1 == 'All':
+    team_var1 = player_stats.Team.values.tolist()
+player_stats = player_stats[player_stats['Team'].isin(team_var1)]
+player_stats_disp = player_stats.set_index('Player')
+player_stats_disp = player_stats_disp.sort_values(by='Team', ascending=False)
+st.dataframe(player_stats_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+st.download_button(
+    label="Export Prop Model",
+    data=convert_df_to_csv(player_stats),
+    file_name='AmericanNumbers_stats_export.csv',
+    mime='text/csv',
+)