Create app.py

app.py

@@ -0,0 +1,621 @@
+import streamlit as st
+st.set_page_config(layout="wide")
+
+for name in dir():
+    if not name.startswith('_'):
+        del globals()[name]
+
+import numpy as np
+import pandas as pd
+import streamlit as st
+import gspread
+import plotly.express as px
+import random
+import gc
+
+@st.cache_resource
+def init_conn():
+        scope = ['https://www.googleapis.com/auth/spreadsheets',
+        "https://www.googleapis.com/auth/drive"]
+
+        credentials = {
+          "type": "service_account",
+          "project_id": "model-sheets-connect",
+          "private_key_id": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
+          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
+          "client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
+          "client_id": "100369174533302798535",
+          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+          "token_uri": "https://oauth2.googleapis.com/token",
+          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
+        }
+
+        gc_con = gspread.service_account_from_dict(credentials)
+      
+        return gc_con
+
+gcservice_account = init_conn()
+
+master_hold = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=853878325'
+
+game_format = {'Win%': '{:.2%}'}
+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%}'}
+prop_table_options = ['points', 'threes', 'rebounds', 'assists', 'blocks', 'steals', 'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists']
+all_sim_vars = ['points', 'rebounds', 'assists', 'threes', 'PRA', 'points+rebounds', 'points+assists', 'rebounds+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 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)
+    raw_display['Win%'] = raw_display['Win%'].replace({'%': ''}, regex=True).astype(float) / 100
+    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]
+
+    player_stats['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
+                                 ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.',
+                                  'Trey Murphy III', 'Cam Thomas'], inplace=True)
+    
+    worksheet = sh.worksheet('Timestamp')
+    timestamp = worksheet.acell('A1').value
+
+    worksheet = sh.worksheet('Prop_Frame')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace('', np.nan, inplace=True)
+    prop_frame = raw_display.dropna(subset='Player')
+    
+    worksheet = sh.worksheet('Pick6_ingest')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace('', np.nan, inplace=True)
+    pick_frame = raw_display.dropna(subset='Player')
+
+    prop_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
+                                 ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.',
+                                  'Trey Murphy III', 'Cam Thomas'], inplace=True)
+    pick_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
+                                 ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.',
+                                  'Trey Murphy III', 'Cam Thomas'], inplace=True)
+    return game_model, player_stats, prop_frame, pick_frame, timestamp
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
+t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+
+tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "Player Projections", "Prop Trend Table", "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, pick_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(game_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, pick_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, pick_frame, timestamp = init_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 = player_stats['Team'].unique(), key='team_var5')
+    elif split_var5 == 'All':
+        team_var5 = player_stats.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_frame[prop_frame['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='NBA_prop_trends_export.csv',
+        mime='text/csv',
+    )
+    
+with tab4:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset4'):
+              st.cache_data.clear()
+              game_model, player_stats, prop_frame, pick_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 tab5:
+    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()
+              game_model, player_stats, prop_frame, pick_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:
+        game_select_var = st.selectbox('Select prop source', options = ['Draftkings', 'Pick6'])
+        if game_select_var == 'Draftkings':
+            prop_df = prop_frame[['Player', '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)
+        st.download_button(
+            label="Download Prop Source",
+            data=convert_df_to_csv(prop_df),
+            file_name='Nba_prop_source.csv',
+            mime='text/csv',
+            key='prop_source',
+        )
+        prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'points', 'rebounds', 'assists', 'threes', 'PRA', 'points+rebounds',
+                                                                        'points+assists', 'rebounds+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:
+                                
+                                if game_select_var == 'Draftkings':
+                                    prop_df = prop_frame[['Player', '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(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 == 'points':
+                                    df['Median'] = df['Points']
+                                elif prop == 'rebounds':
+                                    df['Median'] = df['Rebounds']
+                                elif prop == 'assists':
+                                    df['Median'] = df['Assists']
+                                elif prop == 'threes':
+                                    df['Median'] = df['3P']
+                                elif prop == 'PRA':
+                                    df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
+                                elif prop == 'points+rebounds':
+                                    df['Median'] = df['Points'] + df['Rebounds']
+                                elif prop == 'points+assists':
+                                    df['Median'] = df['Points'] + df['Assists']
+                                elif prop == '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['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 game_select_var == 'Draftkings':
+                                    prop_df = prop_frame[['Player', '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 == "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))
+                                df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                            elif prop_type_var == "rebounds":
+                                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_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 == "threes":
+                                prop_df = prop_df.loc[prop_df['prop_type'] == 'threes']
+                                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_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_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_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_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 == 'threes':
+                                df['Median'] = df['3P']
+                            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[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',
+                            )