app.py

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
from numpy import where as np_where
import pandas as pd
import streamlit as st
import gspread
import plotly.express as px
import pymongo
import random
import gc
import scipy.stats as stats
from datetime import datetime

@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": st.secrets['model_sheets_connect_pk'],
          "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"
        }

        credentials2 = {
          "type": "service_account",
          "project_id": "sheets-api-connect-378620",
          "private_key_id": st.secrets['sheets_api_connect_pk'],
          "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
          "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
          "client_id": "106625872877651920064",
          "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%40sheets-api-connect-378620.iam.gserviceaccount.com"
        }

        uri = st.secrets['mongo_uri']
        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000)
        db = client["NBA_DFS"]
        prop_db = client["Props_DB"]

        NBA_Data = st.secrets['NBA_Data']

        gc_con = gspread.service_account_from_dict(credentials)
        gc_con2 = gspread.service_account_from_dict(credentials2)
      
        return gc_con, gc_con2, db, prop_db, NBA_Data

gcservice_account, gcservice_account2, db, prop_db, NBA_Data = init_conn()

game_format = {'Paydirt Win%': '{:.2%}', 'Vegas 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 = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS']
all_sim_vars = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS']
pick6_sim_vars = ['Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds']
sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])

def calculate_poisson(row):
    mean_val = row['Mean_Outcome']
    threshold = row['Prop']
    cdf_value = stats.poisson.cdf(threshold, mean_val)
    probability = 1 - cdf_value
    return probability

def add_column(df):
    return_df = df
    return_df['2P'] = return_df["Minutes"] * return_df["FG2M"]
    return_df['3P'] = return_df["Minutes"] * return_df["Threes"]
    return_df['FT'] = return_df["Minutes"] * return_df["FTM"]
    return_df['Points'] = (return_df["2P"] * 2) + (return_df["3P"] * 3) + return_df['FT']
    return_df['Rebounds'] = return_df["Minutes"] * return_df["TRB"]
    return_df['Assists'] = return_df["Minutes"] * return_df["AST"]
    return_df['PRA'] = return_df['Points'] + return_df['Rebounds'] + return_df['Assists']
    return_df['PR'] = return_df['Points'] + return_df['Rebounds']
    return_df['PA'] = return_df['Points'] + return_df['Assists']
    return_df['RA'] = return_df['Rebounds'] + return_df['Assists']
    return_df['Steals'] = return_df["Minutes"] * return_df["STL"]
    return_df['Blocks'] = return_df["Minutes"] * return_df["BLK"]
    return_df['Turnovers'] = return_df["Minutes"] * return_df["TOV"]
    return_df['Fantasy'] = (return_df["2P"] * 3) + (return_df["3P"] * 3.5) + return_df['FT'] + (return_df["Rebounds"] * 1.25) + (return_df["Assists"] * 1.5) + (return_df["Steals"] * 2) + (return_df["Blocks"] * 2) + (return_df["Turnovers"] * -.5)
    
    export_df = return_df[['Player', 'Position', 'Team', 'Opp', 'Minutes', '2P', '3P', 'FT', 'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy']]
    
    return export_df

@st.cache_resource(ttl = 300)
def init_baselines():
    collection = db["Game_Betting_Model"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Team', 'Opp', 'PD Team Points', 'PD Opp Points', 'VEG Team Points', 'VEG Opp Points', 'PD Proj Total', 'VEG Proj Total', 'PD Over%', 'PD Over Odds', 'PD Under%', 'PD Under Odds',
                               'PD Proj Winner', 'PD Proj Spread', 'PD W Spread', 'VEG W Spread', 'PD Win%', 'PD Odds']]
    raw_display.replace('#DIV/0!', np.nan, inplace=True)
    game_model = raw_display.dropna()

    collection = db["Player_Stats"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display.replace('', np.nan, inplace=True)
    raw_display = raw_display.rename(columns={"Name": "Player"})
    raw_baselines = raw_display[['Player', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PRA', 'PR', 'PA', 'RA']]
    raw_baselines = raw_baselines[raw_baselines['Minutes'] > 0]
    raw_baselines['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)
    
    player_stats = raw_display[['Player', 'Position', 'Team', 'Opp', 'Minutes', '3P', 'Points', 'Rebounds', 'Assists', 'Steals', 'Blocks', 'Turnovers', 'Fantasy']]
    player_stats = player_stats[player_stats['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)
    

    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

    collection = db["Prop_Trends"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display.replace('', np.nan, inplace=True)
    raw_display = raw_display[['Name', 'over_prop', 'over_line', 'under_prop', 'under_line', 'OddsType', 'PropType', 'No Vig', 'Team', 'L5 Success', 'L10_Success', 'L20_success', 'L10 Avg', 'Projection',
                               'Proj Diff', 'Matchup Boost', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']]
    raw_display = raw_display.rename(columns={"Name": "Player", "OddsType": "book", "PropType": "prop_type"})
    prop_frame = raw_display.dropna(subset='Player')
    
    collection = db["Pick6_Trends"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L5 Success', 'L10_Success', 'L20_success', 'L10 Avg', 'Projection',
                               'Proj Diff', 'Matchup Boost', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']]
    pick_frame = raw_display.drop_duplicates(subset=['Player', 'prop_type'], keep='first')
    pick_frame = pick_frame.reset_index(drop=True)

    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)
    
    collection = prop_db["NBA_Props"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    market_props = raw_display[['Name', 'Position', 'Projection', 'PropType', 'OddsType', 'over_pay', 'under_pay']]
    market_props['over_prop'] = market_props['Projection']
    market_props['over_line'] = market_props['over_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1))
    market_props['under_prop'] = market_props['Projection']
    market_props['under_line'] = market_props['under_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1))
    
    return game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp

def calculate_no_vig(row):
    def implied_probability(american_odds):
        if american_odds < 0:
            return (-american_odds) / ((-american_odds) + 100)
        else:
            return 100 / (american_odds + 100)

    over_line = row['over_line']
    under_line = row['under_line']
    over_prop = row['over_prop']
    
    over_prob = implied_probability(over_line)
    under_prob = implied_probability(under_line)
    
    total_prob = over_prob + under_prob
    no_vig_prob = (over_prob / total_prob + 0.5) * over_prop
    
    return no_vig_prob

def convert_df_to_csv(df):
    return df.to_csv().encode('utf-8')

game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
t_stamp = f"Last Update: " + str(timestamp) + f" CST"

tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Game Betting Model", 'Prop Market', "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, raw_baselines, player_stats, prop_frame, pick_frame, market_props, 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', 'PD Team Points', 'PD Opp Points', 'VEG Team Points', 'VEG Opp Points', 'PD Proj Total', 'VEG Proj Total', 'PD Over%', 'PD Under%', 'PD Proj Winner', 'PD Proj Spread', 'PD W Spread', 'VEG W Spread', 'PD 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', 'PD Team Points', 'PD Opp Points', 'VEG Team Points', 'VEG Opp Points', 'PD Proj Total', 'VEG Proj Total', 'PD Over Odds', 'PD Under Odds', 'PD Proj Winner', 'PD Proj Spread', 'PD W Spread', 'VEG W Spread', 'PD Odds']]
        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)
    
    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, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    market_type = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options, key = 'market_type_key')
    disp_market = market_props.copy()
    disp_market = disp_market[disp_market['PropType'] == market_type]
    disp_market['No_Vig_Prop'] = disp_market.apply(calculate_no_vig, axis=1)
    fanduel_frame = disp_market[disp_market['OddsType'] == 'FANDUEL']
    fanduel_dict = dict(zip(fanduel_frame['Name'], fanduel_frame['No_Vig_Prop']))
    draftkings_frame = disp_market[disp_market['OddsType'] == 'DRAFTKINGS']
    draftkings_dict = dict(zip(draftkings_frame['Name'], draftkings_frame['No_Vig_Prop']))
    mgm_frame = disp_market[disp_market['OddsType'] == 'MGM']
    mgm_dict = dict(zip(mgm_frame['Name'], mgm_frame['No_Vig_Prop']))
    bet365_frame = disp_market[disp_market['OddsType'] == 'BET_365']
    bet365_dict = dict(zip(bet365_frame['Name'], bet365_frame['No_Vig_Prop']))

    disp_market['FANDUEL'] = disp_market['Name'].map(fanduel_dict)
    disp_market['DRAFTKINGS'] = disp_market['Name'].map(draftkings_dict)
    disp_market['MGM'] = disp_market['Name'].map(mgm_dict)
    disp_market['BET365'] = disp_market['Name'].map(bet365_dict)

    disp_market = disp_market[['Name', 'Position','FANDUEL', 'DRAFTKINGS', 'MGM', 'BET365']]
    disp_market = disp_market.drop_duplicates(subset=['Name'], keep='first', ignore_index=True)

    st.dataframe(disp_market.style.background_gradient(axis=1, subset=['FANDUEL', 'DRAFTKINGS', 'MGM', 'BET365'], cmap='RdYlGn').format(prop_format, precision=2), height = 1000, use_container_width = True)
    st.download_button(
        label="Export Market Props",
        data=convert_df_to_csv(disp_market),
        file_name='NFL_market_props_export.csv',
        mime='text/csv',
    )

with tab3:
    st.info(t_stamp)
    if st.button("Reset Data", key='reset3'):
              st.cache_data.clear()
              game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, 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 tab4:
    st.info(t_stamp)
    if st.button("Reset Data", key='reset4'):
              st.cache_data.clear()
              game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, 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()
    book_split5 = st.radio("Would you like to view all books or specific ones?", ('All', 'Specific Books'), key='book_split5')
    if book_split5 == 'Specific Books':
        book_var5 = st.multiselect('Which books would you like to include in the tables?', options = ['BET_365', 'DRAFTKINGS', 'CONSENSUS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL'], key='book_var5')
    elif book_split5 == 'All':
        book_var5 = ['BET_365', 'DRAFTKINGS', 'CONSENSUS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL']
    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['book'].isin(book_var5)]
    prop_frame_disp = prop_frame_disp[prop_frame_disp['prop_type'] == prop_type_var2]
    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 tab5:
    st.info(t_stamp)
    if st.button("Reset Data", key='reset5'):
              st.cache_data.clear()
              game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, 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
                        st.write("sim started")

                        total_sims = 1000

                        df.replace("", 0, inplace=True)

                        player_var = df[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)

                        players_only = hold_file[['Player']]

                        player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
                        st.write("sim finished, calculating outcomes")

                        players_only['Mean_Outcome'] = overall_file.mean(axis=1)
                        players_only['Prop'] = prop_var
                        players_only['poisson_var'] = players_only.apply(calculate_poisson, 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'] = np.where(players_only['Prop'] <= 3, players_only['poisson_var'], overall_file[overall_file > prop_var].count(axis=1)/float(total_sims))
                        elif ou_var == 'Under':
                            players_only['beat_prop'] = np.where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], (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[final_outcomes['Player'] == player_check]
                        player_outcomes = player_outcomes[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 tab6:
    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='reset6'):
              st.cache_data.clear()
              game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, 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 = ['Aggregate', 'Pick6'])
        book_select_var = st.selectbox('Select book', options = ['ALL', 'BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL'])
        if book_select_var == 'ALL':
            book_selections = ['BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL']
        else:
            book_selections = [book_select_var]
        if game_select_var == 'Aggregate':
            prop_df = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
        elif game_select_var == 'Pick6':
            prop_df = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
            book_selections = ['Pick6']
        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',
        )
        if game_select_var == 'Aggregate':
            prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS',
                                                                            'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_3_POINTERS_MADE'])
        elif game_select_var == 'Pick6':
            prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds', '3-Pointers Made'])

        if st.button('Simulate Prop Category'):
            with col2:
                   
                    with df_hold_container.container():
                        if prop_type_var == 'All Props':
                            if game_select_var == 'Aggregate':
                                prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                sim_vars = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS',
                                            'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_3_POINTERS_MADE']
                            elif game_select_var == 'Pick6':
                                prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                sim_vars = ['Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds', '3-Pointers Made']
                            
                            player_df = player_stats.copy()
                                
                            for prop in sim_vars:
                                
                                for books in book_selections:
                                    prop_df = prop_df_raw[prop_df_raw['prop_type'] == prop]
                                    prop_df = prop_df[prop_df['book'] == books]
                                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                    prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
                                    prop_df['Over'] = 1 / prop_df['over_line']
                                    prop_df['Under'] = 1 / prop_df['under_line']

                                    prop_dict = dict(zip(prop_df.Player, prop_df.Prop))
                                    prop_type_dict = dict(zip(prop_df.Player, prop_df.prop_type))
                                    book_dict = dict(zip(prop_df.Player, prop_df.book))
                                    over_dict = dict(zip(prop_df.Player, prop_df.Over))
                                    under_dict = dict(zip(prop_df.Player, prop_df.Under))

                                    player_df['book'] = player_df['Player'].map(book_dict)
                                    player_df['Prop'] = player_df['Player'].map(prop_dict)
                                    player_df['prop_type'] = player_df['Player'].map(prop_type_dict)

                                    df = player_df.reset_index(drop=True)

                                    team_dict = dict(zip(df.Player, df.Team))
                                    
                                    total_sims = 1000
            
                                    df.replace("", 0, inplace=True)
                                    
                                    if prop == "NBA_GAME_PLAYER_POINTS" or prop == "Points":
                                        df['Median'] = df['Points']
                                    elif prop == "NBA_GAME_PLAYER_REBOUNDS" or prop == "Rebounds":
                                        df['Median'] = df['Rebounds']
                                    elif prop == "NBA_GAME_PLAYER_ASSISTS" or prop == "Assists":
                                        df['Median'] = df['Assists']
                                    elif prop == "NBA_GAME_PLAYER_3_POINTERS_MADE" or prop == "3-Pointers Made":
                                        df['Median'] = df['3P']
                                    elif prop == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS" or prop == "Points + Assists + Rebounds":
                                        df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
                                    elif prop == "NBA_GAME_PLAYER_POINTS_REBOUNDS" or prop == "Points + Rebounds":
                                        df['Median'] = df['Points'] + df['Rebounds']
                                    elif prop == "NBA_GAME_PLAYER_POINTS_ASSISTS" or prop == "Points + Assists":
                                        df['Median'] = df['Points'] + df['Assists']
                                    elif prop == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS" or prop == "Assists + Rebounds":
                                        df['Median'] = df['Rebounds'] + df['Assists']
                                        
                                    flex_file = df.copy()
                                    flex_file['Floor'] = flex_file['Median'] * .25
                                    flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1.75)
                                    flex_file['STD'] = flex_file['Median'] / 4
                                    flex_file['Prop'] = flex_file['Player'].map(prop_dict)
                                    flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
            
                                    hold_file = flex_file.copy()
                                    overall_file = flex_file.copy()
                                    prop_file = flex_file.copy()
                                          
                                    overall_players = overall_file[['Player']]
            
                                    for x in range(0,total_sims):    
                                        prop_file[x] = prop_file['Prop']
            
                                    prop_file = prop_file.drop(['Player', 'book', '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', 'book', '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['Book'] = players_only['Player'].map(book_dict)
                                    players_only['Prop'] = players_only['Player'].map(prop_dict)
                                    players_only['poisson_var'] = players_only.apply(calculate_poisson, 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'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], 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'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], 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_avg'] = players_only['Prop'].mean() / 100
                                    players_only['prop_threshold'] = .10
                                    players_only = players_only[players_only['Mean_Outcome'] > 0]
                                    players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
                                    players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
                                    players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
                                    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']]
                                    players_only['Team'] = players_only['Player'].map(team_dict)
            
                                    leg_outcomes = players_only[['Player', 'Team', 'Book', '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
                                    st.write(f'finished {prop} for {books}')
                                
                        elif prop_type_var != 'All Props':
                            
                            player_df = player_stats.copy()
                            
                            if game_select_var == 'Aggregate':
                                prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                            elif game_select_var == 'Pick6':
                                prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                
                            for books in book_selections:
                                prop_df = prop_df_raw[prop_df_raw['book'] == books]

                                if prop_type_var == "NBA_GAME_PLAYER_POINTS":
                                    prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS']
                                elif prop_type_var == "Points":
                                    prop_df = prop_df[prop_df['prop_type'] == 'Points']
                                elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS":
                                    prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_REBOUNDS']
                                elif prop_type_var == "Rebounds":
                                    prop_df = prop_df[prop_df['prop_type'] == 'Rebounds']
                                elif prop_type_var == "NBA_GAME_PLAYER_ASSISTS":
                                    prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_ASSISTS']
                                elif prop_type_var == "Assists":
                                    prop_df = prop_df[prop_df['prop_type'] == 'Assists']
                                elif prop_type_var == "NBA_GAME_PLAYER_3_POINTERS_MADE":
                                    prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_3_POINTERS_MADE']
                                elif prop_type_var == "3-Pointers Made":
                                    prop_df = prop_df[prop_df['prop_type'] == '3-Pointers Made']
                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS":
                                    prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS']
                                elif prop_type_var == "Points + Assists + Rebounds":
                                    prop_df = prop_df[prop_df['prop_type'] == 'Points + Assists + Rebounds']
                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS":
                                    prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_REBOUNDS']
                                elif prop_type_var == "Points + Rebounds":
                                    prop_df = prop_df[prop_df['prop_type'] == 'Points + Rebounds']
                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_ASSISTS":
                                    prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_ASSISTS']
                                elif prop_type_var == "Points + Assists":
                                    prop_df = prop_df[prop_df['prop_type'] == 'Points + Assists']
                                elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS":
                                    prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS']
                                elif prop_type_var == "Assists + Rebounds":
                                    prop_df = prop_df[prop_df['prop_type'] == 'Assists + Rebounds']

                                prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                prop_df = prop_df.rename(columns={"over_prop": "Prop"})
                                prop_df['Over'] = 1 / prop_df['over_line']
                                prop_df['Under'] = 1 / prop_df['under_line']

                                prop_dict = dict(zip(prop_df.Player, prop_df.Prop))
                                prop_type_dict = dict(zip(prop_df.Player, prop_df.prop_type))
                                book_dict = dict(zip(prop_df.Player, prop_df.book))
                                over_dict = dict(zip(prop_df.Player, prop_df.Over))
                                under_dict = dict(zip(prop_df.Player, prop_df.Under))

                                player_df['book'] = player_df['Player'].map(book_dict)
                                player_df['Prop'] = player_df['Player'].map(prop_dict)
                                player_df['prop_type'] = player_df['Player'].map(prop_type_dict)

                                df = player_df.reset_index(drop=True)

                                team_dict = dict(zip(df.Player, df.Team))
                                
                                total_sims = 1000
        
                                df.replace("", 0, inplace=True)
                                
                                if prop_type_var == "NBA_GAME_PLAYER_POINTS" or prop_type_var == "Points":
                                    df['Median'] = df['Points']
                                elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS" or prop_type_var == "Rebounds":
                                    df['Median'] = df['Rebounds']
                                elif prop_type_var == "NBA_GAME_PLAYER_ASSISTS" or prop_type_var == "Assists":
                                    df['Median'] = df['Assists']
                                elif prop_type_var == "NBA_GAME_PLAYER_3_POINTERS_MADE" or prop_type_var == "3-Pointers Made":
                                    df['Median'] = df['3P']
                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS" or prop_type_var == "Points + Assists + Rebounds":
                                    df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS" or prop_type_var == "Points + Rebounds":
                                    df['Median'] = df['Points'] + df['Rebounds']
                                elif prop_type_var == "NBA_GAME_PLAYER_POINTS_ASSISTS" or prop_type_var == "Points + Assists":
                                    df['Median'] = df['Points'] + df['Assists']
                                elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS" or prop_type_var == "Assists + Rebounds":
                                    df['Median'] = df['Rebounds'] + df['Assists']
                                
                                flex_file = df.copy()
                                flex_file['Floor'] = flex_file['Median'] * .25
                                flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1.75)
                                flex_file['STD'] = flex_file['Median'] / 4
                                flex_file['Prop'] = flex_file['Player'].map(prop_dict)
                                flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
        
                                hold_file = flex_file.copy()
                                overall_file = flex_file.copy()
                                prop_file = flex_file.copy()
                                      
                                overall_players = overall_file[['Player']]
        
                                for x in range(0,total_sims):    
                                    prop_file[x] = prop_file['Prop']
        
                                prop_file = prop_file.drop(['Player', 'book', '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', 'book', '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['Book'] = players_only['Player'].map(book_dict)
                                players_only['Prop'] = players_only['Player'].map(prop_dict)
                                players_only['poisson_var'] = players_only.apply(calculate_poisson, 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'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], 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'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], 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_avg'] = players_only['Prop'].mean() / 100
                                players_only['prop_threshold'] = .10
                                players_only = players_only[players_only['Mean_Outcome'] > 0]
                                players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
                                players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
                                players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
                                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_type_var
        
                                players_only['Player'] = hold_file[['Player']]
                                players_only['Team'] = players_only['Player'].map(team_dict)
        
                                leg_outcomes = players_only[['Player', 'Team', 'Book', '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
                                st.write(f'finished {prop_type_var} for {books}')
                        
                        final_outcomes = final_outcomes.dropna()
                        if game_select_var == 'Pick6':
                            final_outcomes = final_outcomes.drop_duplicates(subset=['Player', 'Prop Type'])
                        final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)

                        with df_hold_container:
                            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',
                            )