Update app.py

app.py

@@ -1,19 +1,8 @@
-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
+import plotly.express as px
 
 scope = ['https://www.googleapis.com/auth/spreadsheets',
           "https://www.googleapis.com/auth/drive"]
@@ -35,115 +24,303 @@ gc = gspread.service_account_from_dict(credentials)
 
 st.set_page_config(layout="wide")
 
-roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}',
-              '120+%': '{:.2%}','10x%': '{:.2%}','11x%': '{:.2%}','12x%': '{:.2%}','Own': '{:.2%}','LevX': '{:.2%}'}
-stat_format = {'Win%': '{:.2%}'}
+game_format = {'Win%': '{:.2%}', 'Vegas': '{:.2%}', 'Win% Diff': '{:.2%}'}
+american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.2%}'}
 
-game_betting_model = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260'
-props_overall = 'DK_NBA_Props'
-player_overall = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260'
-points_overall = 'DK_Points_Props'
-assists_overall = 'DK_Assists_Props'
-rebounds_overall = 'DK_Rebounds_Props'
-pa_overall = 'DK_PA_Props'
-pr_overall = 'DK_PR_Props'
-pra_overall = 'DK_PRA_Props'
+master_hold = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=694077504'
 
 @st.cache_data
-def create_player_props(URL):
-    sh = gc.open_by_url(URL)
-    worksheet = sh.get_worksheet(8)
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    overall_data = load_display[['Name', 'Position', 'Team', '3P', 'Points', 'Rebounds', 'Assists', 'Steals', 'Blocks']]
-    overall_data.rename(columns={"Name": "player"}, inplace = True)
-    overall_data['Points + Rebounds'] = overall_data['Points'] + overall_data['Rebounds']
-    overall_data['Points + Assists'] = overall_data['Points'] + overall_data['Assists']
-    overall_data['Points + Rebounds + Assists'] = overall_data['Points'] + overall_data['Rebounds'] + overall_data['Assists']
-          
-    return overall_data
-
-@st.cache_data
-def load_game_betting(URL):
-    sh = gc.open_by_url(URL)
-    worksheet = sh.get_worksheet(1)
+def game_betting_model():
+    sh = gc.open_by_url(master_hold)
+    worksheet = sh.worksheet('Game_Betting')
     raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace('#DIV/0!', np.nan, inplace=True)
+    raw_display = raw_display.dropna()
 
     return raw_display
 
 @st.cache_data
-def load_props(URL):
-    sh = gc.open(URL)
-    worksheet = sh.get_worksheet(0)
+def player_stat_table():
+    sh = gc.open_by_url(master_hold)
+    worksheet = sh.worksheet('Prop_Table')
     raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.rename(columns={"player": "Player"}, inplace = True)
+    raw_display.replace('', np.nan, inplace=True)
+    raw_display = raw_display.dropna()
 
     return raw_display
 
 @st.cache_data
-def load_player_baselines(URL):
-    sh = gc.open(URL)
-    worksheet = sh.get_worksheet(0)
-    raw_display = pd.DataFrame(worksheet.get_all_records())
+def timestamp_table():
+    sh = gc.open_by_url(master_hold)
+    worksheet = sh.worksheet('DK_ROO')
+    raw_display = worksheet.acell('U2').value
 
     return raw_display
 
 @st.cache_data
-def load_stat_specific(URL):
-    sh = gc.open(URL)
-    worksheet = sh.get_worksheet(0)
+def player_prop_table():
+    sh = gc.open_by_url(master_hold)
+    worksheet = sh.worksheet('prop_frame')
     raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.rename(columns={"player": "Player"}, inplace = True)
-    raw_display = raw_display.drop(columns=['Model Probability', 'short%', 'mid%', 'long%', 's_weighted%', 'm_weighted%', 'l_weighted%', 'weighted prob%'])
+    raw_display.replace('', np.nan, inplace=True)
+    raw_display = raw_display.dropna()
 
     return raw_display
 
-team_frame = load_game_betting(game_betting_model)
-props_frame = create_player_props(player_overall)
+game_model = game_betting_model()
+overall_stats = player_stat_table()
+qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
+non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+timestamp = timestamp_table()
+prop_frame = player_prop_table()
+t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
-tab1, tab2, tab3, tab4 = st.tabs(["Game Betting Model", "Player Prop Baselines", "Stat Specific Props Projections", "Player Prop Simulations"])
+tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Simulations", "Stat Specific Simulations"])
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
 with tab1:
-    if st.button("Reset Data/Load Data", key='reset1'):
-              # Clear values from *all* all in-memory and on-disk data caches:
-              # i.e. clear values from both square and cube
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-    st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+              game_model = game_betting_model()
+              overall_stats = player_stat_table()
+              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              prop_frame = player_prop_table()
+              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + 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', 'Win%', 'Vegas', 'Win% Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
+        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', 'Win Line', 'Vegas Line', 'Line Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
+        team_frame = team_frame.set_index('team')
+        st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    
     st.download_button(
-            label="Export Projections",
-            data=convert_df_to_csv(team_frame),
-            file_name='NBA_DFS_team_frame.csv',
-            mime='text/csv',
-            key='team_frame',
+        label="Export Team Model",
+        data=convert_df_to_csv(team_frame),
+        file_name='NFL_team_betting_export.csv',
+        mime='text/csv',
+        key='team_export',
     )
 
 with tab2:
-    if st.button("Reset Data/Load Data", key='reset2'):
-              # Clear values from *all* all in-memory and on-disk data caches:
-              # i.e. clear values from both square and cube
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset2'):
               st.cache_data.clear()
-    team_var1 = st.multiselect('View specific team?', options = props_frame['Team'].unique(), key = 'prop_teamvar')
-    if team_var1:
-          props_frame = props_frame[props_frame['Team'].isin(team_var1)]
-    props_frame = props_frame.set_index('player')
-    st.dataframe(props_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+              game_model = game_betting_model()
+              overall_stats = player_stat_table()
+              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              prop_frame = player_prop_table()
+              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + 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 = qb_stats['Team'].unique(), key='team_var1')
+    elif split_var1 == 'All':
+        team_var1 = qb_stats.Team.values.tolist()
+    qb_stats = qb_stats[qb_stats['Team'].isin(team_var1)]
+    qb_stats_disp = qb_stats.set_index('Player')
+    qb_stats_disp = qb_stats_disp.sort_values(by='PPR', ascending=False)
+    st.dataframe(qb_stats_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
     st.download_button(
-            label="Export Projections",
-            data=convert_df_to_csv(props_frame),
-            file_name='NBA_DFS_props_frame.csv',
-            mime='text/csv',
-            key='props_frame',
+        label="Export Prop Model",
+        data=convert_df_to_csv(qb_stats_disp),
+        file_name='NFL_qb_stats_export.csv',
+        mime='text/csv',
+        key='pitcher_prop_export',
     )
 
 with tab3:
-    st.write("The Stat specific models are currently not accurate due to an API issue. Apoligies!")
+    st.info(t_stamp)
+    if st.button("Reset Data", key='reset3'):
+              st.cache_data.clear()
+              game_model = game_betting_model()
+              overall_stats = player_stat_table()
+              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              prop_frame = player_prop_table()
+              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
+    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 = non_qb_stats['Team'].unique(), key='team_var2')
+    elif split_var2 == 'All':
+        team_var2 = non_qb_stats.Team.values.tolist()
+    non_qb_stats = non_qb_stats[non_qb_stats['Team'].isin(team_var2)]
+    non_qb_stats_disp = non_qb_stats.set_index('Player')
+    non_qb_stats_disp = non_qb_stats_disp.sort_values(by='PPR', ascending=False)
+    st.dataframe(non_qb_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(non_qb_stats_disp),
+        file_name='NFL_nonqb_stats_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()
+              game_model = game_betting_model()
+              overall_stats = player_stat_table()
+              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              prop_frame = player_prop_table()
+              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + 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 = overall_stats['Player'].unique())
+        prop_type_var = st.selectbox('Select type of prop to simulate', options = ['Pass Yards', 'Pass TDs', 'Rush Yards', 'Rush TDs', 'Receptions', 'Rec Yards', 'Rec TDs', 'Fantasy', 'FD Fantasy', 'PrizePicks'])
+
+        ou_var = st.selectbox('Select wether it is an over or under', options = ['Over', 'Under'])
+        if prop_type_var == 'Pass Yards':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 100.0, max_value = 400.5, value = 250.5, step = .5)
+        elif prop_type_var == 'Pass TDs':
+            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 == 'Rush Yards':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 155.5, value = 25.5, step = .5)
+        elif prop_type_var == 'Rush TDs':
+            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 == 'Receptions':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 15.5, value = 5.5, step = .5)
+        elif prop_type_var == 'Rec Yards':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 155.5, value = 25.5, step = .5)
+        elif prop_type_var == 'Rec TDs':
+            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 == 'Fantasy':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 10.5, step = .5)
+        elif prop_type_var == 'FD Fantasy':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 10.5, step = .5)
+        elif prop_type_var == 'PrizePicks':
+            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 10.5, 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():
+
+                        df = overall_stats
+
+                        total_sims = 5000
+
+                        df.replace("", 0, inplace=True)
+
+                        player_var = df.loc[df['Player'] == player_check]
+                        player_var = player_var.reset_index()
+
+                        if prop_type_var == 'Pass Yards':
+                            df['Median'] = df['pass_yards']
+                        elif prop_type_var == 'Pass TDs':
+                            df['Median'] = df['pass_tds']
+                        elif prop_type_var == 'Rush Yards':
+                            df['Median'] = df['rush_yards']
+                        elif prop_type_var == 'Rush TDs':
+                            df['Median'] = df['rush_tds']
+                        elif prop_type_var == 'Receptions':
+                            df['Median'] = df['rec']
+                        elif prop_type_var == 'Rec Yards':
+                            df['Median'] = df['rec_yards']
+                        elif prop_type_var == 'Rec TDs':
+                            df['Median'] = df['rec_tds']
+                        elif prop_type_var == 'Fantasy':
+                            df['Median'] = df['PPR']
+                        elif prop_type_var == 'FD Fantasy':
+                            df['Median'] = df['Half_PPF']
+                        elif prop_type_var == 'PrizePicks':
+                            df['Median'] = df['Half_PPF']
+
+                        flex_file = df
+                        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']]
+
+                        hold_file = flex_file
+                        overall_file = flex_file
+                        salary_file = flex_file
+
+                        overall_players = overall_file[['Player']]
+
+                        for x in range(0,total_sims):
+                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                        overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                        overall_file.astype('int').dtypes
+
+                        players_only = hold_file[['Player']]
+
+                        player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)
+
+                        players_only['Mean_Outcome'] = overall_file.mean(axis=1)
+                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
+                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
+                        if ou_var == 'Over':
+                            players_only['beat_prop'] = overall_file[overall_file > prop_var].count(axis=1)/float(total_sims)
+                        elif ou_var == 'Under':
+                            players_only['beat_prop'] = (overall_file[overall_file < prop_var].count(axis=1)/float(total_sims))
+
+                        players_only['implied_odds'] = np.where(line_var <= 0, (-(line_var)/((-(line_var))+100)), 100/(line_var+100))
+
+                        players_only['Player'] = hold_file[['Player']]
+
+                        final_outcomes = players_only[['Player', '10%', 'Mean_Outcome', '90%', 'implied_odds', 'beat_prop']]
+                        final_outcomes['Bet?'] = np.where(final_outcomes['beat_prop'] - final_outcomes['implied_odds'] >= .10, "Bet", "No Bet")
+                        final_outcomes = final_outcomes.loc[final_outcomes['Player'] == player_check]
+                        player_outcomes = player_outcomes.loc[player_outcomes['Player'] == player_check]
+                        player_outcomes = player_outcomes.drop(columns=['Player']).transpose()
+                        player_outcomes = player_outcomes.reset_index()
+                        player_outcomes.columns = ['Instance', 'Outcome']
+
+                        x1 = player_outcomes.Outcome.to_numpy()
+
+                        print(x1)
+
+                        hist_data = [x1]
+
+                        group_labels = ['player outcomes']
+
+                        fig = px.histogram(
+                                player_outcomes, x='Outcome')
+                        fig.add_vline(x=prop_var, line_dash="dash", line_color="green")
+
+                        with df_hold_container:
+                            df_hold_container = st.empty()
+                            format_dict = {'10%': '{:.2f}', 'Mean_Outcome': '{:.2f}','90%': '{:.2f}', 'beat_prop': '{:.2%}','implied_odds': '{:.2%}'}
+                            st.dataframe(final_outcomes.style.format(format_dict), use_container_width = True)
+
+                        with info_hold_container:
+                            st.info('The Y-axis is the percent of times in simulations that the player reaches certain thresholds, while the X-axis is the threshold to be met. The Green dotted line is the prop you entered. You can hover over any spot and see the percent to reach that mark.')
+
+                        with plot_hold_container:
+                            st.dataframe(player_outcomes, use_container_width = True)
+                            plot_hold_container = st.empty()
+                            st.plotly_chart(fig, use_container_width=True)
+
+with tab5:
+    st.info(t_stamp)
     st.info('The Over and Under percentages are a 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='reset3'):
-              # Clear values from *all* all in-memory and on-disk data caches:
-              # i.e. clear values from both square and cube
+    if st.button("Reset Data/Load Data", key='reset5'):
               st.cache_data.clear()
+              game_model = game_betting_model()
+              overall_stats = player_stat_table()
+              qb_stats = overall_stats.loc[overall_stats['Position'] == 'QB']
+              non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
+              prop_frame = player_prop_table()
+              t_stamp = f"Last Update: " + str(prop_frame['timestamp'][0]) + f" CST"
     col1, col2 = st.columns([1, 5])
     
     with col2:
@@ -153,124 +330,81 @@ with tab3:
         export_container = st.empty()
     
     with col1:
-        prop_type_var = st.selectbox('Select prop category', options = ['Points', 'Assists', 'Rebounds', 'Points + Assists', 'Points + Rebounds', 'Points + Rebounds + Assists'])
+        prop_type_var = st.selectbox('Select prop category', options = ['Pass Yards', 'Rush Yards', 'Receiving Yards'])
 
         if st.button('Simulate Prop Category'):
             with col2:
-                
-                with st.spinner('Wait for it...'):
                    
                     with df_hold_container.container():
 
-                        if prop_type_var == "Points":
-                            player_df = load_stat_specific(points_overall)
-                            prop_df = load_props(props_overall)
-                            prop_df = prop_df[['Player', 'points', 'over_points_line', 'under_points_line']]
-                            prop_df = prop_df.loc[prop_df['points'] > 0]
-                            prop_df['Over'] = np.where(prop_df['over_points_line'] < 0, (-(prop_df['over_points_line'])/((-(prop_df['over_points_line']))+100)), 100/(prop_df['over_points_line']+100))
-                            prop_df['Under'] = np.where(prop_df['under_points_line'] < 0, (-(prop_df['under_points_line'])/((-(prop_df['under_points_line']))+100)), 100/(prop_df['under_points_line']+100))
-                            prop_df.rename(columns={"points": "Prop"}, inplace = True)
-                            df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            df.rename(columns={"weighted%": "weighted"}, inplace = True)
-                        elif prop_type_var == "Assists":
-                            player_df = load_stat_specific(assists_overall)
-                            prop_df = load_props(props_overall)
-                            prop_df = prop_df[['Player', 'assists', 'over_assists_line', 'under_assists_line']]
-                            prop_df = prop_df.loc[prop_df['assists'] > 0]
-                            prop_df['Over'] = np.where(prop_df['over_assists_line'] < 0, (-(prop_df['over_assists_line'])/((-(prop_df['over_assists_line']))+100)), 100/(prop_df['over_assists_line']+100))
-                            prop_df['Under'] = np.where(prop_df['under_assists_line'] < 0, (-(prop_df['under_assists_line'])/((-(prop_df['under_assists_line']))+100)), 100/(prop_df['under_assists_line']+100))
-                            prop_df.rename(columns={"assists": "Prop"}, inplace = True)
-                            df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            df.rename(columns={"weighted%": "weighted"}, inplace = True)
-                        elif prop_type_var == "Rebounds":
-                            player_df = load_stat_specific(rebounds_overall)
-                            prop_df = load_props(props_overall)
-                            prop_df = prop_df[['Player', 'rebounds', 'over_rebounds_line', 'under_rebounds_line']]
-                            prop_df = prop_df.loc[prop_df['rebounds'] > 0]
-                            prop_df['Over'] = np.where(prop_df['over_rebounds_line'] < 0, (-(prop_df['over_rebounds_line'])/((-(prop_df['over_rebounds_line']))+100)), 100/(prop_df['over_rebounds_line']+100))
-                            prop_df['Under'] = np.where(prop_df['under_rebounds_line'] < 0, (-(prop_df['under_rebounds_line'])/((-(prop_df['under_rebounds_line']))+100)), 100/(prop_df['under_rebounds_line']+100))
-                            prop_df.rename(columns={"rebounds": "Prop"}, inplace = True)
-                            df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            df.rename(columns={"weighted%": "weighted"}, inplace = True)
-                        elif prop_type_var == "Points + Assists":
-                            player_df = load_stat_specific(pa_overall)
-                            prop_df = load_props(props_overall)
-                            prop_df = prop_df[['Player', 'points_assists', 'over_points_assists_line', 'under_points_assists_line']]
-                            prop_df = prop_df.loc[prop_df['points_assists'] > 0]
-                            prop_df['Over'] = np.where(prop_df['over_points_assists_line'] < 0, (-(prop_df['over_points_assists_line'])/((-(prop_df['over_points_assists_line']))+100)), 100/(prop_df['over_points_assists_line']+100))
-                            prop_df['Under'] = np.where(prop_df['under_points_assists_line'] < 0, (-(prop_df['under_points_assists_line'])/((-(prop_df['under_points_assists_line']))+100)), 100/(prop_df['under_points_assists_line']+100))
-                            prop_df.rename(columns={"points_assists": "Prop"}, inplace = True)
-                            df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            df.rename(columns={"weighted%": "weighted"}, inplace = True)
-                        elif prop_type_var == "Points + Rebounds":
-                            player_df = load_stat_specific(pr_overall)
-                            prop_df = load_props(props_overall)
-                            prop_df = prop_df[['Player', 'points_rebounds', 'over_points_rebounds_line', 'under_points_rebounds_line']]
-                            prop_df = prop_df.loc[prop_df['points_rebounds'] > 0]
-                            prop_df['Over'] = np.where(prop_df['over_points_rebounds_line'] < 0, (-(prop_df['over_points_rebounds_line'])/((-(prop_df['over_points_rebounds_line']))+100)), 100/(prop_df['over_points_rebounds_line']+100))
-                            prop_df['Under'] = np.where(prop_df['under_points_rebounds_line'] < 0, (-(prop_df['under_points_rebounds_line'])/((-(prop_df['under_points_rebounds_line']))+100)), 100/(prop_df['under_points_rebounds_line']+100))
-                            prop_df.rename(columns={"points_rebounds": "Prop"}, inplace = True)
-                            prop_df = prop_df[['Player', 'Prop', 'Over', 'Under']]
-                            df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            df.rename(columns={"weighted%": "weighted"}, inplace = True)
-                        elif prop_type_var == "Points + Rebounds + Assists":
-                            player_df = load_stat_specific(pra_overall)
-                            prop_df = load_props(props_overall)
-                            prop_df = prop_df[['Player', 'points_rebounds_assists', 'over_points_rebounds_assists_line', 'under_points_rebounds_assists_line']]
-                            prop_df = prop_df.loc[prop_df['points_rebounds_assists'] > 0]
-                            prop_df['Over'] = np.where(prop_df['over_points_rebounds_assists_line'] < 0, (-(prop_df['over_points_rebounds_assists_line'])/((-(prop_df['over_points_rebounds_assists_line']))+100)), 100/(prop_df['over_points_rebounds_assists_line']+100))
-                            prop_df['Under'] = np.where(prop_df['under_points_rebounds_assists_line'] < 0, (-(prop_df['under_points_rebounds_assists_line'])/((-(prop_df['under_points_rebounds_assists_line']))+100)), 100/(prop_df['under_points_rebounds_assists_line']+100))
-                            prop_df.rename(columns={"points_rebounds_assists": "Prop"}, inplace = True)
-                            prop_df = prop_df[['Player', 'Prop', 'Over', 'Under']]
-                            df = pd.merge(player_df, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            df.rename(columns={"weighted%": "weighted"}, inplace = True)
+                        if prop_type_var == "Pass Yards":
+                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                            prop_df = prop_df.loc[prop_df['prop_type'] == 'pass_yards']
+                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
+                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                            st.table(prop_df)
+                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                            df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        elif prop_type_var == "Rush Yards":
+                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rush_yards']
+                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
+                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                            st.table(prop_df)
+                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                            df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
+                        elif prop_type_var == "Receiving Yards":
+                            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                            prop_df = prop_df.loc[prop_df['prop_type'] == 'rec_yards']
+                            prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
+                            prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
+                            prop_df = prop_df.loc[prop_df['Prop'] != 0]
+                            st.table(prop_df)
+                            prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101))
+                            prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101))
+                            df = pd.merge(overall_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
                         
                         prop_dict = dict(zip(df.Player, df.Prop))
                         over_dict = dict(zip(df.Player, df.Over))
                         under_dict = dict(zip(df.Player, df.Under))
-                        weighted_dict = dict(zip(df.Player, df.weighted))
                         
                         total_sims = 1000
 
                         df.replace("", 0, inplace=True)
 
-                        if prop_type_var == "Points":
-                            df['Median'] = df['Points']
-                        elif prop_type_var == "Assists":
-                            df['Median'] = df['Assists']
-                        elif prop_type_var == "Rebounds":
-                            df['Median'] = df['Rebounds']
-                        elif prop_type_var == "Points + Assists":
-                            df['Median'] = df['Points + Assists']
-                        elif prop_type_var == "Points + Rebounds":
-                            df['Median'] = df['Points + Rebounds']
-                        elif prop_type_var == "Points + Rebounds + Assists":
-                            df['Median'] = df['Points + Rebounds + Assists']
+                        if prop_type_var == "Pass Yards":
+                            df['Median'] = df['pass_yards']
+                        elif prop_type_var == "Rush Yards":
+                            df['Median'] = df['rush_yards']
+                        elif prop_type_var == "Receiving Yards":
+                            df['Median'] = df['rec_yards']
 
                         flex_file = df
                         flex_file['Floor'] = flex_file['Median'] * .20
-                        flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .20)
-                        flex_file['STD'] = (flex_file['Median'] / 4)
+                        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']]
 
                         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', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                        prop_file.astype('int').dtypes
+                        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)
-                        overall_file.astype('int').dtypes
 
                         players_only = hold_file[['Player']]
 
@@ -279,19 +413,17 @@ with tab3:
                         prop_check = (overall_file - prop_file)
 
                         players_only['Mean_Outcome'] = overall_file.mean(axis=1)
-                        players_only['Weighted_over'] = players_only['Player'].map(weighted_dict)
-                        players_only['Weighted_under'] = 1 - players_only['Player'].map(weighted_dict)
                         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 >= 1].count(axis=1)/float(total_sims)
+                        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", "Weighted_over", "Imp Over"]].mean(axis=1)
-                        players_only['Under'] = prop_check[prop_check < 1].count(axis=1)/float(total_sims)
+                        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", "Weighted_under", "Imp Under"]].mean(axis=1)
+                        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'] = np.where(.25 - players_only['Prop_avg'] < .10, .10, .25 - players_only['Prop_avg'])
+                        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']
@@ -316,9 +448,8 @@ with tab3:
                             st.download_button(
                                 label="Export Projections",
                                 data=convert_df_to_csv(final_outcomes),
-                                file_name='NBA_DFS_prop_proj.csv',
+                                file_name='NFL_prop_proj.csv',
                                 mime='text/csv',
                                 key='prop_proj',
                             )
-with tab4:
-    st.info('Coming soon!')
+