Add Streamlit NBA prop betting analysis app with MongoDB integration

app.py

@@ -0,0 +1,762 @@
+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 pymongo
+import random
+import gc
+import scipy.stats as stats
+from datetime import datetime
+
+@st.cache_resource
+def init_conn():
+
+        uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000)
+        db = client["NBA_DFS"]
+        prop_db = client["Props_DB"]
+      
+        return db, prop_db
+
+db, prop_db = 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%}'}
+sim_format = {'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}', 'Imp Over': '{:.2%}', 'Imp Under': '{:.2%}', 'Over%': '{:.2%}', 'Under%': '{:.2%}', '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', 'Trending Over', 'Over%', 'Imp Under', 'Trending 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'] = pd.to_numeric(df['Points'], errors='coerce')
+                        elif prop_type_var == 'threes':
+                            df['Median'] = pd.to_numeric(df['3P'], errors='coerce')
+                        elif prop_type_var == 'rebounds':
+                            df['Median'] = pd.to_numeric(df['Rebounds'], errors='coerce')
+                        elif prop_type_var == 'assists':
+                            df['Median'] = pd.to_numeric(df['Assists'], errors='coerce')
+                        elif prop_type_var == 'blocks':
+                            df['Median'] = pd.to_numeric(df['Blocks'], errors='coerce')
+                        elif prop_type_var == 'steals':
+                            df['Median'] = pd.to_numeric(df['Steals'], errors='coerce')
+                        elif prop_type_var == 'PRA':
+                            df['Median'] = pd.to_numeric(df['Points'], errors='coerce') + pd.to_numeric(df['Rebounds'], errors='coerce') + pd.to_numeric(df['Assists'], errors='coerce')
+                        elif prop_type_var == 'points+rebounds':
+                            df['Median'] = pd.to_numeric(df['Points'], errors='coerce') + pd.to_numeric(df['Rebounds'], errors='coerce')
+                        elif prop_type_var == 'points+assists':
+                            df['Median'] = pd.to_numeric(df['Points'], errors='coerce') + pd.to_numeric(df['Assists'], errors='coerce')
+                        elif prop_type_var == 'rebounds+assists':
+                            df['Median'] = pd.to_numeric(df['Assists'], errors='coerce') + pd.to_numeric(df['Rebounds'], errors='coerce')
+
+                        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"
+
+    settings_container = st.empty()
+    df_hold_container = st.empty()
+    export_container = st.empty()
+    
+    with settings_container.container():
+        col1, col2, col3, col4 = st.columns([3, 3, 3, 3])
+        with col1:
+            game_select_var = st.selectbox('Select prop source', options = ['Aggregate', 'Pick6'])
+        with col2:
+            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', 'Trending Over', 'Trending Under']]
+            elif game_select_var == 'Pick6':
+                prop_df = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
+                book_selections = ['Pick6']
+        with col3:
+            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'])
+        with col4:
+            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 st.button('Simulate Prop Category'):
+                   
+        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', 'Trending Over', 'Trending Under']]
+                    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', 'Trending Over', 'Trending Under']]
+                    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', 'Trending Over', 'Trending Under']]
+                        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))
+                        trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over']))
+                        trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending 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)
+                        player_df['Trending Over'] = player_df['Player'].map(trending_over_dict)
+                        player_df['Trending Under'] = player_df['Player'].map(trending_under_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'] = pd.to_numeric(df['Points'], errors='coerce')
+                        elif prop == "NBA_GAME_PLAYER_REBOUNDS" or prop == "Rebounds":
+                            df['Median'] = pd.to_numeric(df['Rebounds'], errors='coerce')
+                        elif prop == "NBA_GAME_PLAYER_ASSISTS" or prop == "Assists":
+                            df['Median'] = pd.to_numeric(df['Assists'], errors='coerce')
+                        elif prop == "NBA_GAME_PLAYER_3_POINTERS_MADE" or prop == "3-Pointers Made":
+                            df['Median'] = pd.to_numeric(df['3P'], errors='coerce')
+                        elif prop == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS" or prop == "Points + Assists + Rebounds":
+                            df['Median'] = pd.to_numeric(df['Points'], errors='coerce') + pd.to_numeric(df['Rebounds'], errors='coerce') + pd.to_numeric(df['Assists'], errors='coerce')
+                        elif prop == "NBA_GAME_PLAYER_POINTS_REBOUNDS" or prop == "Points + Rebounds":
+                            df['Median'] = pd.to_numeric(df['Points'], errors='coerce') + pd.to_numeric(df['Rebounds'], errors='coerce')
+                        elif prop == "NBA_GAME_PLAYER_POINTS_ASSISTS" or prop == "Points + Assists":
+                            df['Median'] = pd.to_numeric(df['Points'], errors='coerce') + pd.to_numeric(df['Assists'], errors='coerce')
+                        elif prop == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS" or prop == "Assists + Rebounds":
+                            df['Median'] = pd.to_numeric(df['Rebounds'], errors='coerce') + pd.to_numeric(df['Assists'], errors='coerce')
+                            
+                        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['Trending Over'] = players_only['Player'].map(trending_over_dict)
+                        players_only['Trending Under'] = players_only['Player'].map(trending_under_dict)
+                        players_only['over_adj'] = np_where((players_only['Mean_Outcome'] - players_only['Prop']) > 0, 1, (players_only['Mean_Outcome'] / players_only['Prop']))
+                        players_only['under_adj'] = np_where((players_only['Prop'] - players_only['Mean_Outcome']) > 0, 1, (players_only['Prop'] / players_only['Mean_Outcome']))
+                        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", "Trending 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", "Trending 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['over_adj'], players_only['Under_diff'] * players_only['under_adj'])
+                        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', 'Trending Over', 'Over%', 'Imp Under', 'Trending 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', 'Trending Over', 'Trending Under']]
+                elif game_select_var == 'Pick6':
+                    prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
+                    
+                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', 'Trending Over', 'Trending Under']]
+                    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))
+                    trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over']))
+                    trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending 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)
+                    player_df['Trending Over'] = player_df['Player'].map(trending_over_dict)
+                    player_df['Trending Under'] = player_df['Player'].map(trending_under_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'] = pd.to_numeric(df['Points'], errors='coerce')
+                    elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS" or prop_type_var == "Rebounds":
+                        df['Median'] = pd.to_numeric(df['Rebounds'], errors='coerce')
+                    elif prop_type_var == "NBA_GAME_PLAYER_ASSISTS" or prop_type_var == "Assists":
+                        df['Median'] = pd.to_numeric(df['Assists'], errors='coerce')
+                    elif prop_type_var == "NBA_GAME_PLAYER_3_POINTERS_MADE" or prop_type_var == "3-Pointers Made":
+                        df['Median'] = pd.to_numeric(df['3P'], errors='coerce')
+                    elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS" or prop_type_var == "Points + Assists + Rebounds":
+                        df['Median'] = pd.to_numeric(df['Points'], errors='coerce') + pd.to_numeric(df['Rebounds'], errors='coerce') + pd.to_numeric(df['Assists'], errors='coerce')
+                    elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS" or prop_type_var == "Points + Rebounds":
+                        df['Median'] = pd.to_numeric(df['Points'], errors='coerce') + pd.to_numeric(df['Rebounds'], errors='coerce')
+                    elif prop_type_var == "NBA_GAME_PLAYER_POINTS_ASSISTS" or prop_type_var == "Points + Assists":
+                        df['Median'] = pd.to_numeric(df['Points'], errors='coerce') + pd.to_numeric(df['Assists'], errors='coerce')
+                    elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS" or prop_type_var == "Assists + Rebounds":
+                        df['Median'] = pd.to_numeric(df['Rebounds'], errors='coerce') + pd.to_numeric(df['Assists'], errors='coerce')
+                    
+                    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['Trending Over'] = players_only['Player'].map(trending_over_dict)
+                    players_only['Trending Under'] = players_only['Player'].map(trending_under_dict)
+                    players_only['over_adj'] = np_where((players_only['Mean_Outcome'] - players_only['Prop']) > 0, 1, (players_only['Mean_Outcome'] / players_only['Prop']))
+                    players_only['under_adj'] = np_where((players_only['Prop'] - players_only['Mean_Outcome']) > 0, 1, (players_only['Prop'] / players_only['Mean_Outcome']))
+                    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", "Trending 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", "Trending 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['over_adj'], players_only['Under_diff'] * players_only['under_adj'])
+                    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', 'Trending Over', 'Over%', 'Imp Under', 'Trending 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(sim_format, precision=2), height=500, 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',
+                )

app.yaml

@@ -0,0 +1,10 @@
+runtime: python
+env: flex
+
+runtime_config:
+  python_version: 3
+
+entrypoint: streamlit run streamlit-app.py --server.port $PORT
+
+automatic_scaling:
+  max_num_instances: 200

requirements.txt

@@ -0,0 +1,9 @@
+streamlit
+gspread
+openpyxl
+matplotlib
+pymongo
+pulp
+docker
+plotly
+scipy