Add comprehensive NHL prop betting Streamlit application with data simulation and analysis

app.py

@@ -0,0 +1,433 @@
+import streamlit as st
+st.set_page_config(layout="wide")
+
+for name in dir():
+    if not name.startswith('_'):
+        del globals()[name]
+
+import pulp
+import numpy as np
+from numpy import where as np_where
+import pandas as pd
+import streamlit as st
+import pymongo
+import scipy.stats as stats
+
+@st.cache_resource
+def init_conn():
+        uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        db = client["NHL_Database"]
+
+        return db
+    
+db = init_conn()
+
+prop_table_options = ['NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_BLOCKED', 'NHL_GAME_PLAYER_ASSISTS']
+prop_format = {'L5 Success': '{:.2%}', 'L10_Success': '{:.2%}', 'L20_success': '{:.2%}', 'Matchup Boost': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
+               'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
+all_sim_vars = ['NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_BLOCKED', 'NHL_GAME_PLAYER_ASSISTS']
+pick6_sim_vars = ['Points', 'Shots on Goal', 'Assists', 'Blocks']
+sim_all_hold = pd.DataFrame(columns=['Player', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge'])
+
+@st.cache_resource(ttl=200)
+def pull_baselines():
+    collection = db["Player_Level_ROO"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(cursor)
+    prop_display = raw_display[raw_display['Player'] != ""]
+    prop_display['Player Blocks'].replace("", np.nan, inplace=True)
+    prop_display['SOG Edge'].replace("", np.nan, inplace=True)
+    prop_display['Assist Edge'].replace("", np.nan, inplace=True)
+    prop_display['TP Edge'].replace("", np.nan, inplace=True)
+    prop_table = prop_display[['Player', 'Position', 'Team', 'Opp', 'Team_Total', 'Player SOG', 'Player Goals', 'Player Assists',
+                               'Player TP', 'Player Blocks', 'Player Saves']]
+    prop_table['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'],
+                                  ['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True)
+    prop_table['Player'] = prop_table['Player'].str.strip()
+
+    stat_columns = ['Team_Total', 'Player SOG', 'Player Goals', 'Player Assists', 'Player TP', 'Player Blocks', 'Player Saves']
+    for stat in stat_columns:
+        prop_table[stat] = prop_table[stat].astype(float)
+    
+    collection = db["prop_trends"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(cursor)
+    raw_display.replace('', np.nan, inplace=True)
+    prop_trends = raw_display.dropna(subset='Player')
+    prop_trends['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'],
+                                  ['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True)
+    
+    collection = db["Pick6_ingest"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(cursor)
+    raw_display.replace('', np.nan, inplace=True)
+    pick_frame = raw_display.dropna(subset='Player')
+    pick_frame['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'],
+                                  ['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True)
+    
+    team_dict = dict(zip(prop_table['Player'], prop_table['Team']))
+
+    return prop_table, prop_trends, pick_frame, team_dict
+
+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 convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+prop_display, prop_trends, pick_frame, team_dict = pull_baselines()
+
+tab1, tab2, tab3 = st.tabs(["Player Stat Table", 'Prop Trend Table', 'Stat Specific Simulations'])
+
+with tab1:
+    if st.button("Reset Data", key='reset1'):
+              st.cache_data.clear()
+              prop_display, prop_trends, pick_frame, team_dict = pull_baselines()
+    prop_frame = prop_display
+    st.dataframe(prop_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    
+    st.download_button(
+        label="Export Table",
+        data=convert_df_to_csv(prop_frame),
+        file_name='NHL_prop_stat_export.csv',
+        mime='text/csv',
+        key='prop_export',
+    )
+
+with tab2:
+    if st.button("Reset Data", key='reset3'):
+              st.cache_data.clear()
+              prop_display, prop_trends, pick_frame, team_dict = pull_baselines()
+    split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5')
+    if split_var5 == 'Specific Teams':
+        team_var5 = st.multiselect('Which teams would you like to include in the tables?', options = prop_trends['Team'].unique(), key='team_var5')
+    elif split_var5 == 'All':
+        team_var5 = prop_trends.Team.values.tolist()
+    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_trends[prop_trends['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_disp),
+        file_name='NHL_prop_trends_export.csv',
+        mime='text/csv',
+    )
+
+with tab3:
+    st.info('The Over and Under percentages are a composite percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
+    if st.button("Reset Data/Load Data", key='reset5'):
+              st.cache_data.clear()
+              prop_display, prop_trends, pick_frame, team_dict = pull_baselines()
+
+    settings_container = st.container()
+    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_trends[['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', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_ASSISTS', 'NHL_GAME_PLAYER_SHOTS_BLOCKED'])
+            elif game_select_var == 'Pick6':
+                prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'Points', 'Shots on Goal', 'Assists', 'Blocks'])
+        with col4:
+            st.download_button(
+                label="Download Prop Source",
+                data=convert_df_to_csv(prop_df),
+                file_name='NHL_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_trends[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
+                    sim_vars = ['NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_ASSISTS', 'NHL_GAME_PLAYER_SHOTS_BLOCKED']
+                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', 'Shots on Goal', 'Assists', 'Blocks']
+                
+                player_df = prop_display.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 == 'NHL_GAME_PLAYER_POINTS' or prop == 'Points':
+                            df['Median'] = df['Player TP']
+                        elif prop == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL' or prop == 'Shots on Goal':
+                            df['Median'] = df['Player SOG']
+                        elif prop == 'NHL_GAME_PLAYER_ASSISTS' or prop == 'Assists':
+                            df['Median'] = df['Player Assists']
+                        elif prop == 'NHL_GAME_PLAYER_SHOTS_BLOCKED' or prop == 'Blocks':
+                            df['Median'] = df['Player Blocks']
+
+                        flex_file = df.copy()
+                        flex_file['Floor'] = (flex_file['Median'] * .15)
+                        flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1)
+                        flex_file['STD'] = (flex_file['Median']/3)
+                        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['Prop'] = players_only['Player'].map(prop_dict)
+                        players_only['Book'] = players_only['Player'].map(book_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'] * 0.4) + (players_only['Trending Over'] * 0.4) + (players_only['Imp Over'] * 0.2)
+                        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'] * 0.4) + (players_only['Trending Under'] * 0.4) + (players_only['Imp Under'] * 0.2)
+                        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 = prop_display.copy()
+
+                if game_select_var == 'Aggregate':
+                    prop_df_raw = prop_trends[['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 == "NHL_GAME_PLAYER_SHOTS_ON_GOAL":
+                        prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL']
+                    elif prop_type_var == 'Shots on Goal':
+                        prop_df = prop_df[prop_df['prop_type'] == 'Player SOG']
+                    elif prop_type_var == "NHL_GAME_PLAYER_POINTS":
+                        prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_POINTS']
+                    elif prop_type_var == "Points":
+                        prop_df = prop_df[prop_df['prop_type'] == 'Player TP']
+                    elif prop_type_var == "NHL_GAME_PLAYER_ASSISTS":
+                        prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_ASSISTS']
+                    elif prop_type_var == "Assists":
+                        prop_df = prop_df[prop_df['prop_type'] == 'Player Assists']
+                    elif prop_type_var == "NHL_GAME_PLAYER_SHOTS_BLOCKED":
+                        prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_SHOTS_BLOCKED']
+                    elif prop_type_var == "Blocks":
+                        prop_df = prop_df[prop_df['prop_type'] == 'Player Blocks']
+
+                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']]
+                    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_type_var == 'NHL_GAME_PLAYER_POINTS' or prop_type_var == 'Points':
+                        df['Median'] = df['Player TP']
+                    elif prop_type_var == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL' or prop_type_var == 'Shots on Goal':
+                        df['Median'] = df['Player SOG']
+                    elif prop_type_var == 'NHL_GAME_PLAYER_ASSISTS' or prop_type_var == 'Assists':
+                        df['Median'] = df['Player Assists']
+                    elif prop_type_var == 'NHL_GAME_PLAYER_SHOTS_BLOCKED' or prop_type_var == 'Blocks':
+                        df['Median'] = df['Player Blocks']
+
+                    flex_file = df.copy()
+                    flex_file['Floor'] = (flex_file['Median'] * .15)
+                    flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1)
+                    flex_file['STD'] = (flex_file['Median']/3)
+                    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['Prop'] = players_only['Player'].map(prop_dict)
+                    players_only['Book'] = players_only['Player'].map(book_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'] * 0.4) + (players_only['Trending Over'] * 0.4) + (players_only['Imp Over'] * 0.2)
+                    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'] * 0.4) + (players_only['Trending Under'] * 0.4) + (players_only['Imp Under'] * 0.2)
+                    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', 'Prop Type', '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[final_outcomes['Prop'] > 0]
+            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='NHL_prop_proj.csv',
+                    mime='text/csv',
+                    key='prop_proj',
+                )