Create app.py

app.py

@@ -0,0 +1,549 @@
+import pulp
+import numpy as np
+import pandas as pd
+import random
+import sys
+import openpyxl
+import re
+import time
+import streamlit as st
+import matplotlib
+from  matplotlib.colors import LinearSegmentedColormap
+from st_aggrid import GridOptionsBuilder, AgGrid, GridUpdateMode, DataReturnMode
+import json
+import requests
+import gspread
+import plotly.figure_factory as ff
+
+scope = ['https://www.googleapis.com/auth/spreadsheets',
+          "https://www.googleapis.com/auth/drive"]
+
+credentials = {
+  "type": "service_account",
+  "project_id": "sheets-api-connect-378620",
+  "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
+  "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"
+}
+
+gc = gspread.service_account_from_dict(credentials)
+
+st.set_page_config(layout="wide")
+
+game_format = {'Win Percentage': '{:.2%}','Cover Spread Percentage': '{:.2%}', 'First Inning Lead Percentage': '{:.2%}',
+              'Fifth Inning Lead Percentage': '{:.2%}'}
+american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.2%}'}
+
+master_hold = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=340831852'
+
+@st.cache_data
+def load_pitcher_props():
+          sh = gc.open_by_url(master_hold)
+          worksheet = sh.worksheet('Pitcher_Stats')
+          props_frame_hold = pd.DataFrame(worksheet.get_all_records())
+          props_frame_hold.rename(columns={"Names": "Player"}, inplace = True)
+          props_frame_hold = props_frame_hold[['Player', 'Team', 'BB', 'Hits', 'HRs', 'ERs', 'Ks', 'Outs', 'Fantasy', 'FD_Fantasy', 'PrizePicks']]
+          props_frame_hold = props_frame_hold.drop_duplicates(subset='Player')
+          
+          return props_frame_hold
+
+@st.cache_data
+def load_time():
+          sh = gc.open_by_url(master_hold)
+          worksheet = sh.worksheet('Timestamp')
+          raw_stamp = worksheet.acell('a1').value
+          
+          t_stamp = f"Last update was at {raw_stamp}"
+          
+          return t_stamp
+
+@st.cache_data
+def load_hitter_props():
+          sh = gc.open_by_url(master_hold)
+          worksheet = sh.worksheet('Hitter_Stats')
+          props_frame_hold = pd.DataFrame(worksheet.get_all_records())
+          props_frame_hold.rename(columns={"Names": "Player"}, inplace = True)
+          props_frame_hold = props_frame_hold[['Player', 'Team', 'Walks', 'Steals', 'Hits', 'Singles', 'Doubles', 'HRs', 'RBIs', 'Runs', 'Fantasy', 'FD_Fantasy', 'PrizePicks']]
+          props_frame_hold['Total Bases'] = props_frame_hold['Singles'] + (props_frame_hold['Doubles'] * 2) + (props_frame_hold['HRs'] * 4)
+          props_frame_hold['Hits + Runs + RBIs'] = props_frame_hold['Hits'] + props_frame_hold['Runs'] + props_frame_hold['RBIs']
+          props_frame_hold = props_frame_hold.drop_duplicates(subset='Player')
+          
+          return props_frame_hold
+
+@st.cache_data
+def load_team_table():
+          sh = gc.open_by_url(master_hold)
+          worksheet = sh.worksheet('Game_Betting_Model')
+          team_frame = pd.DataFrame(worksheet.get_all_records())
+          team_frame = team_frame.drop_duplicates(subset='Names')
+          team_frame['Win Percentage'] = team_frame['Win Percentage'].str.replace('%', '').astype('float')/100
+          team_frame['Cover Spread Percentage'] = team_frame['Cover Spread Percentage'].str.replace('%', '').astype('float')/100
+          
+          return team_frame
+
+@st.cache_data
+def load_strikeout_props():
+          sh = gc.open_by_url(master_hold)
+          worksheet = sh.worksheet('Strikeout_Props')
+          prop_type_frame = pd.DataFrame(worksheet.get_all_records())
+          prop_type_frame = prop_type_frame.drop_duplicates(subset='Player')
+          
+          return prop_type_frame
+
+@st.cache_data
+def load_total_outs_props():
+          sh = gc.open_by_url(master_hold)
+          worksheet = sh.worksheet('Total_Outs_Props')
+          prop_type_frame = pd.DataFrame(worksheet.get_all_records())
+          prop_type_frame = prop_type_frame.drop_duplicates(subset='Player')
+          
+          return prop_type_frame
+
+@st.cache_data
+def load_total_bases_props():
+          sh = gc.open_by_url(master_hold)
+          worksheet = sh.worksheet('Total_Base_Props')
+          prop_type_frame = pd.DataFrame(worksheet.get_all_records())
+          prop_type_frame = prop_type_frame.drop_duplicates(subset='Player')
+          
+          return prop_type_frame
+
+@st.cache_data
+def load_stolen_bases_props():
+          sh = gc.open_by_url(master_hold)
+          worksheet = sh.worksheet('SB_Props')
+          prop_type_frame = pd.DataFrame(worksheet.get_all_records())
+          prop_type_frame = prop_type_frame.drop_duplicates(subset='Player')
+          
+          return prop_type_frame
+
+pitcher_frame_hold = load_pitcher_props()
+hitter_frame_hold = load_hitter_props()
+team_frame_hold = load_team_table()
+t_stamp = load_time()
+
+tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "Pitcher Prop Projections", "Hitter Prop Projections", "Player Prop Simulations", "Stat Specific Simulations"])
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+with tab1:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset1'):
+              st.cache_data.clear()
+              pitcher_frame_hold = load_pitcher_props()
+              hitter_frame_hold = load_hitter_props()
+              team_frame_hold = load_team_table()
+              t_stamp = load_time()
+    line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
+    team_frame = team_frame_hold
+    if line_var1 == 'Percentage':
+        team_frame = team_frame[['Names', 'Game', 'Win Percentage', 'Spread', 'Cover Spread Percentage', 'Avg Score', 'Game Total', 'Avg Fifth Inning', 'Fifth Inning Lead Percentage']]
+        team_frame = team_frame.set_index('Names')
+        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[['Names', 'Game', 'American ML', 'Spread', 'American Cover', 'Avg Score', 'Game Total', 'Avg Fifth Inning', 'Fifth Inning Lead Percentage']]
+        team_frame.rename(columns={"American ML": "Win Percentage", "American Cover": "Cover Spread Percentage"}, inplace = True)
+        team_frame = team_frame.set_index('Names')
+        st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(american_format, precision=2), use_container_width = True)
+    
+    st.download_button(
+        label="Export Team Model",
+        data=convert_df_to_csv(team_frame),
+        file_name='MLB_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()
+              pitcher_frame_hold = load_pitcher_props()
+              hitter_frame_hold = load_hitter_props()
+              team_frame_hold = load_team_table()
+              t_stamp = load_time()
+    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 = pitcher_frame_hold['Team'].unique(), key='team_var1')
+    elif split_var1 == 'All':
+        team_var1 = pitcher_frame_hold.Team.values.tolist()
+    pitcher_frame_hold = pitcher_frame_hold[pitcher_frame_hold['Team'].isin(team_var1)]
+    pitcher_frame = pitcher_frame_hold.set_index('Player')
+    pitcher_frame = pitcher_frame.sort_values(by='Ks', ascending=False)
+    st.dataframe(pitcher_frame.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(pitcher_frame),
+        file_name='MLB_pitcher_prop_export.csv',
+        mime='text/csv',
+        key='pitcher_prop_export',
+    )
+
+with tab3:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset3'):
+              st.cache_data.clear()
+              pitcher_frame_hold = load_pitcher_props()
+              hitter_frame_hold = load_hitter_props()
+              team_frame_hold = load_team_table()
+              t_stamp = load_time()
+    split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
+    if split_var2 == 'Specific Teams':
+        team_var2 = st.multiselect('Which teams would you like to include in the tables?', options = hitter_frame_hold['Team'].unique(), key='team_var2')
+    elif split_var2 == 'All':
+        team_var2 = hitter_frame_hold.Team.values.tolist()
+    hitter_frame_hold = hitter_frame_hold[hitter_frame_hold['Team'].isin(team_var2)]
+    hitter_frame = hitter_frame_hold.set_index('Player')
+    hitter_frame = hitter_frame.sort_values(by='Hits + Runs + RBIs', ascending=False)
+    st.dataframe(hitter_frame.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(hitter_frame),
+        file_name='MLB_hitter_prop_export.csv',
+        mime='text/csv',
+        key='hitter_prop_export',
+    )
+
+with tab4:
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset4'):
+              st.cache_data.clear()
+              pitcher_frame_hold = load_pitcher_props()
+              hitter_frame_hold = load_hitter_props()
+              team_frame_hold = load_team_table()
+              t_stamp = load_time()
+    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:
+        prop_group_var = st.selectbox('What kind of props are you simulating?', options = ['Pitchers', 'Hitters'])
+        if prop_group_var == 'Pitchers':
+            player_check = st.selectbox('Select player to simulate props', options = pitcher_frame_hold['Player'].unique())
+            prop_type_var = st.selectbox('Select type of prop to simulate', options = ['Strikeouts', 'Walks', 'Hits', 'Homeruns', 'Earned Runs', 'Outs', 'Fantasy', 'FD_Fantasy', 'PrizePicks'])
+        elif prop_group_var == 'Hitters':
+            player_check = st.selectbox('Select player to simulate props', options = hitter_frame_hold['Player'].unique())
+            prop_type_var = st.selectbox('Select type of prop to simulate', options = ['Total Bases', 'Walks', 'Steals', 'Hits', 'Singles', 'Doubles', 'Homeruns', 'RBIs', 'Runs', 'Hits + Runs + RBIs', 'Fantasy', 'FD_Fantasy', 'PrizePicks'])
+        
+        ou_var = st.selectbox('Select wether it is an over or under', options = ['Over', 'Under'])
+        prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 5.5, step = .5)
+        line_var = st.number_input('Type in the line on the prop (i.e. -120)', min_value = -1000, max_value = 1000, value = -150, step = 1)
+        line_var = line_var + 1
+
+        if st.button('Simulate Prop'):
+            with col2:
+                   
+                    with df_hold_container.container():
+
+                        if prop_group_var == 'Pitchers':
+                            df = pitcher_frame_hold
+                        elif prop_group_var == 'Hitters':
+                            df = hitter_frame_hold
+
+                        total_sims = 1000
+
+                        df.replace("", 0, inplace=True)
+
+                        player_var = df.loc[df['Player'] == player_check]
+                        player_var = player_var.reset_index()
+
+                        if prop_group_var == 'Pitchers':
+                            if prop_type_var == "Walks":
+                                df['Median'] = df['BB']
+                            elif prop_type_var == "Hits":
+                                df['Median'] = df['Hits']
+                            elif prop_type_var == "Homeruns":
+                                df['Median'] = df['HRs']
+                            elif prop_type_var == "Earned Runs":
+                                df['Median'] = df['ERs']
+                            elif prop_type_var == "Strikeouts":
+                                df['Median'] = df['Ks']
+                            elif prop_type_var == "Outs":
+                                df['Median'] = df['Outs']
+                            elif prop_type_var == "Fantasy":
+                                df['Median'] = df['Fantasy']
+                            elif prop_type_var == "FD_Fantasy":
+                                df['Median'] = df['FD_Fantasy']
+                            elif prop_type_var == "PrizePicks":
+                                df['Median'] = df['PrizePicks']
+                        elif prop_group_var == 'Hitters':
+                            if prop_type_var == "Walks":
+                                df['Median'] = df['Walks']
+                            elif prop_type_var == "Total Bases":
+                                df['Median'] = df['Total Bases']
+                            elif prop_type_var == "Hits + Runs + RBIs":
+                                df['Median'] = df['Hits + Runs + RBIs']
+                            elif prop_type_var == "Steals":
+                                df['Median'] = df['Steals']
+                            elif prop_type_var == "Hits":
+                                df['Median'] = df['Hits']
+                            elif prop_type_var == "Singles":
+                                df['Median'] = df['Singles']
+                            elif prop_type_var == "Doubles":
+                                df['Median'] = df['Doubles']
+                            elif prop_type_var == "Homeruns":
+                                df['Median'] = df['HRs']
+                            elif prop_type_var == "RBIs":
+                                df['Median'] = df['RBIs']
+                            elif prop_type_var == "Runs":
+                                df['Median'] = df['Runs']
+                            elif prop_type_var == "Fantasy":
+                                df['Median'] = df['Fantasy']
+                            elif prop_type_var == "FD_Fantasy":
+                                df['Median'] = df['FD_Fantasy']
+                            elif prop_type_var == "PrizePicks":
+                                df['Median'] = df['PrizePicks']
+
+                        flex_file = df
+                        if prop_group_var == 'Pitchers':
+                            flex_file['Floor'] = flex_file['Median'] * .20
+                            flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .80)
+                            flex_file['STD'] = flex_file['Median'] / 4
+                            flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]
+
+                        elif prop_group_var == 'Hitters':
+                            flex_file['Floor'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] * .20, 0)
+                            flex_file['Ceiling'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] + (flex_file['Median'] * .80), flex_file['Median'] * 4)
+                            flex_file['STD'] = flex_file['Median'] / 1.5
+                            flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]
+
+                        hold_file = flex_file
+                        overall_file = flex_file
+                        salary_file = flex_file
+
+                        overall_players = overall_file[['Player']]
+
+                        for x in range(0,total_sims):
+                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                        overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                        overall_file.astype('int').dtypes
+
+                        players_only = hold_file[['Player']]
+
+                        player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
+
+                        players_only['Mean_Outcome'] = overall_file.mean(axis=1)
+                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
+                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
+                        if ou_var == 'Over':
+                            players_only['beat_prop'] = overall_file[overall_file > prop_var].count(axis=1)/float(total_sims)
+                        elif ou_var == 'Under':
+                            players_only['beat_prop'] = (overall_file[overall_file < prop_var].count(axis=1)/float(total_sims))
+
+                        players_only['implied_odds'] = np.where(line_var <= 0, (-(line_var)/((-(line_var))+100)), 100/(line_var+100))
+
+                        players_only['Player'] = hold_file[['Player']]
+
+                        final_outcomes = players_only[['Player', '10%', 'Mean_Outcome', '90%', 'implied_odds', 'beat_prop']]
+                        final_outcomes['Bet?'] = np.where(final_outcomes['beat_prop'] - final_outcomes['implied_odds'] >= .10, "Bet", "No Bet")
+                        final_outcomes = final_outcomes.loc[final_outcomes['Player'] == player_check]
+                        player_outcomes = player_outcomes.loc[player_outcomes['Player'] == player_check]
+                        player_outcomes = player_outcomes.drop(columns=['Player']).transpose()
+                        player_outcomes = player_outcomes.reset_index()
+                        player_outcomes.columns = ['Instance', 'Outcome']
+
+                        x1 = player_outcomes.Outcome.to_numpy()
+
+                        print(x1)
+
+                        hist_data = [x1]
+
+                        group_labels = ['player outcomes']
+
+                        fig = ff.create_distplot(
+                                hist_data, group_labels, bin_size=[.05])
+                        fig.add_vline(x=prop_var, line_dash="dash", line_color="green")
+
+                        with df_hold_container:
+                            df_hold_container = st.empty()
+                            format_dict = {'10%': '{:.2f}', 'Mean_Outcome': '{:.2f}','90%': '{:.2f}', 'beat_prop': '{:.2%}','implied_odds': '{:.2%}'}
+                            st.dataframe(final_outcomes.style.format(format_dict), use_container_width = True)
+
+                        with info_hold_container:
+                            st.info('The Y-axis is the percent of times in simulations that the player reaches certain thresholds, while the X-axis is the threshold to be met. The Green dotted line is the prop you entered. You can hover over any spot and see the percent to reach that mark.')
+
+                        with plot_hold_container:
+                            st.dataframe(player_outcomes, use_container_width = True)
+                            plot_hold_container = st.empty()
+                            st.plotly_chart(fig, use_container_width=True)
+
+with tab5:
+    st.info(t_stamp)
+    st.info('The Over and Under percentages are a compositve percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
+    if st.button("Reset Data/Load Data", key='reset5'):
+              # Clear values from *all* all in-memory and on-disk data caches:
+              # i.e. clear values from both square and cube
+              st.cache_data.clear()
+              t_stamp = load_time()
+    col1, col2 = st.columns([1, 5])
+    
+    with col2:
+        df_hold_container = st.empty()
+        info_hold_container = st.empty()
+        plot_hold_container = st.empty()
+        export_container = st.empty()
+    
+    with col1:
+        prop_type_var = st.selectbox('Select prop category', options = ['Strikeouts (Pitchers)', 'Total Outs (Pitchers)'])
+
+        if st.button('Simulate Prop Category'):
+            with col2:
+                   
+                    with df_hold_container.container():
+
+                        if prop_type_var == "Strikeouts (Pitchers)":
+                            player_df = pitcher_frame_hold
+                            prop_df = load_strikeout_props()
+                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
+                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
+                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
+                            df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        elif prop_type_var == "Total Outs (Pitchers)":
+                            player_df = pitcher_frame_hold
+                            prop_df = load_total_outs_props()
+                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
+                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)   
+                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
+                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
+                            df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        elif prop_type_var == "Total Bases (Hitters)":
+                            player_df = hitter_frame_hold
+                            prop_df = load_total_bases_props()
+                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
+                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
+                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
+                            df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        elif prop_type_var == "Stolen Bases (Hitters)":
+                            player_df = hitter_frame_hold
+                            prop_df = load_stolen_base_props()
+                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
+                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+100)), 100/(prop_df['over_line']+100))
+                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+100)), 100/(prop_df['under_line']+100))
+                            df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        
+                        prop_dict = dict(zip(df.Player, df.Prop))
+                        over_dict = dict(zip(df.Player, df.Over))
+                        under_dict = dict(zip(df.Player, df.Under))
+                        
+                        total_sims = 1000
+
+                        df.replace("", 0, inplace=True)
+
+                        if prop_type_var == "Strikeouts (Pitchers)":
+                            df['Median'] = df['Ks']
+                        elif prop_type_var == "Total Outs (Pitchers)":
+                            df['Median'] = df['Outs']
+                        elif prop_type_var == "Total Bases (Hitters)":
+                            df['Median'] = df['Total Bases']
+                        elif prop_type_var == "Stolen Bases (Hitters)":
+                            df['Median'] = df['Stolen Bases (Hitters)']
+
+                        flex_file = df
+                        if prop_type_var == 'Strikeouts (Pitchers)':
+                            flex_file['Floor'] = flex_file['Median'] * .20
+                            flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .80)
+                            flex_file['STD'] = flex_file['Median'] / 4
+                            flex_file['Prop'] = flex_file['Player'].map(prop_dict)
+                            flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
+
+                        elif prop_type_var == 'Total Outs (Pitchers)':
+                            flex_file['Floor'] = flex_file['Median'] * .20
+                            flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .80)
+                            flex_file['STD'] = flex_file['Median'] / 4
+                            flex_file['Prop'] = flex_file['Player'].map(prop_dict)
+                            flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
+
+                        elif prop_type_var == 'Total Bases (Hitters)':
+                            flex_file['Floor'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] * .20, 0)
+                            flex_file['Ceiling'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] + (flex_file['Median'] * .80), flex_file['Median'] * 4)
+                            flex_file['STD'] = flex_file['Median'] / 1.5
+                            flex_file['Prop'] = flex_file['Player'].map(prop_dict)
+                            flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
+
+                        elif prop_type_var == 'Stolen Bases (Hitters)':
+                            flex_file['Floor'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] * .20, 0)
+                            flex_file['Ceiling'] = np.where((prop_type_var == "Fantasy") | (prop_type_var == "FD_Fantasy") | (prop_type_var == "PrizePicks"), flex_file['Median'] + (flex_file['Median'] * .80), flex_file['Median'] * 4)
+                            flex_file['STD'] = flex_file['Median'] / 1.5
+                            flex_file['Prop'] = flex_file['Player'].map(prop_dict)
+                            flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]
+
+                        hold_file = flex_file
+                        overall_file = flex_file
+                        prop_file = flex_file
+                              
+                        overall_players = overall_file[['Player']]
+
+                        for x in range(0,total_sims):    
+                            prop_file[x] = prop_file['Prop']
+
+                        prop_file = prop_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+                        for x in range(0,total_sims):
+                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                        overall_file=overall_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+                        players_only = hold_file[['Player']]
+
+                        player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
+
+                        prop_check = (overall_file - prop_file)
+
+                        players_only['Mean_Outcome'] = overall_file.mean(axis=1)
+                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
+                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
+                        players_only['Over'] = prop_check[prop_check > 0].count(axis=1)/float(total_sims)
+                        players_only['Imp Over'] = players_only['Player'].map(over_dict)
+                        players_only['Over%'] = players_only[["Over", "Imp Over"]].mean(axis=1)
+                        players_only['Under'] = prop_check[prop_check < 0].count(axis=1)/float(total_sims)
+                        players_only['Imp Under'] = players_only['Player'].map(under_dict)
+                        players_only['Under%'] = players_only[["Under", "Imp Under"]].mean(axis=1)
+                        players_only['Prop'] = players_only['Player'].map(prop_dict)
+                        players_only['Prop_avg'] = players_only['Prop'].mean() / 100
+                        players_only['prop_threshold'] = .10
+                        players_only = players_only.loc[players_only['Mean_Outcome'] > 0]
+                        players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
+                        players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
+                        players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
+                        players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under")
+                        players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet")
+                        players_only['Edge'] = players_only['Bet_check']
+
+                        players_only['Player'] = hold_file[['Player']]
+
+                        final_outcomes = players_only[['Player', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
+                        
+                        final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)
+                    
+                        final_outcomes = final_outcomes.set_index('Player')
+
+                        with df_hold_container:
+                            df_hold_container = st.empty()
+                            st.dataframe(final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+                        with export_container:
+                            export_container = st.empty()
+                            st.download_button(
+                                label="Export Projections",
+                                data=convert_df_to_csv(final_outcomes),
+                                file_name='MLB_DFS_prop_proj.csv',
+                                mime='text/csv',
+                                key='prop_proj',
+                            )
+