Update app.py

app.py

@@ -37,215 +37,101 @@ def init_conn():
 
 gcservice_account = init_conn()
 
-master_hold = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=853878325'
+master_hold = 'https://docs.google.com/spreadsheets/d/1D526UlXmrz-8qxVcUKrA-u7f6FftUiBufxDnzQv980k/edit#gid=791804525'
 
-game_format = {'Win%': '{:.2%}'}
-prop_format = {'L5 Success': '{:.2%}', 'L10_Success': '{:.2%}', 'L20_success': '{:.2%}', 'Matchup Boost': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
-               'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
-prop_table_options = ['points', 'threes', 'rebounds', 'assists', 'blocks', 'steals', 'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists']
-all_sim_vars = ['points', 'rebounds', 'assists', 'threes', 'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists']
-sim_all_hold = pd.DataFrame(columns=['Player', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
 
-@st.cache_resource(ttl = 300)
+@st.cache_resource(ttl = 600)
 def init_baselines():
     sh = gcservice_account.open_by_url(master_hold)
-    worksheet = sh.worksheet('Betting Model Clean')
+    worksheet = sh.worksheet('Pitcher_Proj')
     raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.replace('#DIV/0!', np.nan, inplace=True)
-    raw_display['Win%'] = raw_display['Win%'].replace({'%': ''}, regex=True).astype(float) / 100
-    game_model = raw_display.dropna()
+    pitcher_proj = raw_display.dropna()
 
-    worksheet = sh.worksheet('DK_Build_Up')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.replace('', np.nan, inplace=True)
-    raw_display.rename(columns={"Name": "Player"}, inplace = True)
-    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Minutes', '3P', 'Points', 'Rebounds', 'Assists', 'Steals', 'Blocks', 'Turnovers', 'Fantasy']]
-    player_stats = raw_display[raw_display['Minutes'] > 0]
-
-    player_stats['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
-                                 ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.',
-                                  'Trey Murphy III', 'Cam Thomas'], inplace=True)
-    
-    worksheet = sh.worksheet('Timestamp')
-    timestamp = worksheet.acell('A1').value
-
-    worksheet = sh.worksheet('Prop_Frame')
+    sh = gcservice_account.open_by_url(master_hold)
+    worksheet = sh.worksheet('Hitter_Proj')
     raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.replace('', np.nan, inplace=True)
-    prop_frame = raw_display.dropna(subset='Player')
+    hitter_proj = raw_display.dropna()
     
-    worksheet = sh.worksheet('Pick6_ingest')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.replace('', np.nan, inplace=True)
-    pick_frame = raw_display.dropna(subset='Player')
-
-    prop_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
-                                 ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.',
-                                  'Trey Murphy III', 'Cam Thomas'], inplace=True)
-    pick_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'],
-                                 ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.',
-                                  'Trey Murphy III', 'Cam Thomas'], inplace=True)
-    return game_model, player_stats, prop_frame, pick_frame, timestamp
+    return pitcher_proj, hitter_proj
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
-t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+pitcher_proj, hitter_proj = init_baselines()
 
-tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "Player Projections", "Prop Trend Table", "Player Prop Simulations", "Stat Specific Simulations"])
+tab1, tab2, tab3, tab4 = st.tabs(["Pitcher Projections", "Hitter Projections", "Pitcher Simulations", "Hitter Simulations"])
 
 with tab1:
-    st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-    line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
-    team_frame = game_model
-    if line_var1 == 'Percentage':
-        team_frame = team_frame[['Team', 'Opp', 'Team Points', 'Opp Points', 'Proj Total', 'Proj Spread', 'Proj Winner', 'Win%']]
-        team_frame = team_frame.set_index('Team')
-        st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(game_format, precision=2), use_container_width = True)
-    if line_var1 == 'American':
-        team_frame = team_frame[['Team', 'Opp', 'Team Points', 'Opp Points', 'Proj Total', 'Proj Spread', 'Proj Winner', 'Odds Line']]
-        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)
+              pitcher_proj, hitter_proj = init_baselines()
+    raw_frame = pitcher_proj
+    export_frame = raw_frame[['Name', 'Team', 'TBF', 'Ceiling_var', 'True_AVG', 'Hits', 'Singles%', 'Singles', 'Doubles%', 'Doubles', 'xHR%', 'Homeruns', 'Strikeout%', 'Strikeouts',
+                              'Walk%', 'Walks', 'Runs%', 'Runs', 'ERA', 'Wins', 'Quality_starts', 'ADP', 'UD_fpts']]
+    disp_frame = raw_frame[['Name', 'Team', 'TBF', 'True_AVG', 'Hits', 'Singles', 'Doubles', 'Homeruns', 'Strikeouts',
+                            'Walks', 'Runs', 'ERA', 'Wins', 'Quality_starts', 'ADP', 'UD_fpts']]
+    st.dataframe(disp_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
     
     st.download_button(
-        label="Export Team Model",
-        data=convert_df_to_csv(team_frame),
-        file_name='NBA_team_betting_export.csv',
+        label="Export Pitcher Projections",
+        data=convert_df_to_csv(export_frame),
+        file_name='MLB_pitcher_proj_export.csv',
         mime='text/csv',
-        key='team_export',
+        key='pitcher_proj_export',
     )
 
 with tab2:
-    st.info(t_stamp)
     if st.button("Reset Data", key='reset2'):
               st.cache_data.clear()
-              game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-    split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
-    if split_var1 == 'Specific Teams':
-        team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = player_stats['Team'].unique(), key='team_var1')
-    elif split_var1 == 'All':
-        team_var1 = player_stats.Team.values.tolist()
-    player_stats = player_stats[player_stats['Team'].isin(team_var1)]
-    player_stats_disp = player_stats.set_index('Player')
-    player_stats_disp = player_stats_disp.sort_values(by='Fantasy', ascending=False)
-    st.dataframe(player_stats_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+              pitcher_proj, hitter_proj = init_baselines()
+    raw_frame = hitter_proj
+    export_frame = raw_frame[['Name', 'Team', 'PA', 'Ceiling_var', 'Walk%', 'Walks', 'xHits', 'Singles%', 'Singles', 'Doubles%', 'Doubles',
+                              'xHR%', 'Homeruns', 'Runs%', 'Runs', 'RBI%', 'RBI', 'Steal%', 'Stolen_bases', 'UD_fpts', 'ADP']]
+    disp_frame = raw_frame[['Name', 'Team', 'PA', 'Walks', 'xHits', 'Singles', 'Doubles',
+                            'Homeruns', 'Runs', 'RBI', 'Stolen_bases', 'UD_fpts', 'ADP']]
+    st.dataframe(disp_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(player_stats),
-        file_name='NBA_stats_export.csv',
+        label="Export Hitter Projections",
+        data=convert_df_to_csv(export_frame),
+        file_name='MLB_hitter_proj_export.csv',
         mime='text/csv',
+        key='hitter_proj_export',
     )
     
 with tab3:
-    st.info(t_stamp)
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
-              game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-    split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5')
-    if split_var5 == 'Specific Teams':
-        team_var5 = st.multiselect('Which teams would you like to include in the tables?', options = player_stats['Team'].unique(), key='team_var5')
-    elif split_var5 == 'All':
-        team_var5 = player_stats.Team.values.tolist()
-    prop_type_var2 = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options)
-    prop_frame_disp = prop_frame[prop_frame['Team'].isin(team_var5)]
-    prop_frame_disp = prop_frame_disp[prop_frame_disp['prop_type'] == prop_type_var2]
-    prop_frame_disp = prop_frame_disp.set_index('Player')
-    prop_frame_disp = prop_frame_disp.sort_values(by='Trending Over', ascending=False)
-    st.dataframe(prop_frame_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(prop_format, precision=2), use_container_width = True)
-    st.download_button(
-        label="Export Prop Trends Model",
-        data=convert_df_to_csv(prop_frame),
-        file_name='NBA_prop_trends_export.csv',
-        mime='text/csv',
-    )
-    
-with tab4:
-    st.info(t_stamp)
-    if st.button("Reset Data", key='reset4'):
-              st.cache_data.clear()
-              game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+              pitcher_proj, hitter_proj = init_baselines()
     col1, col2 = st.columns([1, 5])
     
     with col2:
         df_hold_container = st.empty()
-        info_hold_container = st.empty()
-        plot_hold_container = st.empty()
     
     with col1:
-        player_check = st.selectbox('Select player to simulate props', options = player_stats['Player'].unique())
-        prop_type_var = st.selectbox('Select type of prop to simulate', options = ['points', 'threes', 'rebounds', 'assists', 'blocks', 'steals',
-                                                                                   'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists'])
-
-        ou_var = st.selectbox('Select wether it is an over or under', options = ['Over', 'Under'])
-        if prop_type_var == 'points':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 15.5, step = .5)
-        elif prop_type_var == 'threes':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
-        elif prop_type_var == 'rebounds':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 25.5, value = 5.5, step = .5)
-        elif prop_type_var == 'assists':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 25.5, value = 5.5, step = .5)
-        elif prop_type_var == 'blocks':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
-        elif prop_type_var == 'steals':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
-        elif prop_type_var == 'PRA':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 65.5, value = 20.5, step = .5)
-        elif prop_type_var == 'points+rebounds':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
-        elif prop_type_var == 'points+assists':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
-        elif prop_type_var == 'rebounds+assists':
-            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5)
-        line_var = st.number_input('Type in the line on the prop (i.e. -120)', min_value = -1500, max_value = 1500, value = -150, step = 1)
-        line_var = line_var + 1
-
-        if st.button('Simulate Prop'):
+        prop_type_var = st.selectbox('Select type of prop to simulate', options = ['Strikeouts', 'Wins', 'Quality_starts'])
+
+        if st.button('Simulate Stat'):
             with col2:
                    
                     with df_hold_container.container():
 
-                        df = player_stats
+                        df = pitcher_proj
 
                         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 == 'points':
-                            df['Median'] = df['Points']
-                        elif prop_type_var == 'threes':
-                            df['Median'] = df['3P']
-                        elif prop_type_var == 'rebounds':
-                            df['Median'] = df['Rebounds']
-                        elif prop_type_var == 'assists':
-                            df['Median'] = df['Assists']
-                        elif prop_type_var == 'blocks':
-                            df['Median'] = df['Blocks']
-                        elif prop_type_var == 'steals':
-                            df['Median'] = df['Steals']
-                        elif prop_type_var == 'PRA':
-                            df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
-                        elif prop_type_var == 'points+rebounds':
-                            df['Median'] = df['Points'] + df['Rebounds']
-                        elif prop_type_var == 'points+assists':
-                            df['Median'] = df['Points'] + df['Assists']
-                        elif prop_type_var == 'rebounds+assists':
-                            df['Median'] = df['Assists'] + df['Rebounds']
+                        if prop_type_var == 'Strikeouts':
+                            df['Median'] = df['Strikeouts']
+                        elif prop_type_var == 'Wins':
+                            df['Median'] = df['Wins']
+                        elif prop_type_var == 'Quality_starts':
+                            df['Median'] = df['Quality_starts']
 
                         flex_file = df
-                        flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
-                        flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+                        flex_file['Floor'] = (flex_file['Median'] * .25)
+                        flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * (1.25 * flex_file['Ceiling_var']))
                         flex_file['STD'] = (flex_file['Median']/4)
                         flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]
 
@@ -268,354 +154,83 @@ with tab4:
                         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['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                        players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                        players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                        
                         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)
+                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', 'Median', '90%']]
 
-                        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 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'):
+                        final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                        final_Proj = final_Proj[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', 'Median', '90%']]
+                        
+                        st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    
+with tab4:
+    if st.button("Reset Data", key='reset4'):
               st.cache_data.clear()
-              game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+              pitcher_proj, hitter_proj = init_baselines()
     col1, col2 = st.columns([1, 5])
     
     with col2:
         df_hold_container = st.empty()
-        info_hold_container = st.empty()
-        plot_hold_container = st.empty()
-        export_container = st.empty()
     
     with col1:
-        game_select_var = st.selectbox('Select prop source', options = ['Draftkings', 'Pick6'])
-        if game_select_var == 'Draftkings':
-            prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-        elif game_select_var == 'Pick6':
-            prop_df = pick_frame[['Full_name', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-            prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
-        st.download_button(
-            label="Download Prop Source",
-            data=convert_df_to_csv(prop_df),
-            file_name='Nba_prop_source.csv',
-            mime='text/csv',
-            key='prop_source',
-        )
-        prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'points', 'rebounds', 'assists', 'threes', 'PRA', 'points+rebounds',
-                                                                        'points+assists', 'rebounds+assists'])
-        if prop_type_var == 'All Props':
-            st.info('please note that the All Props run can take some time, you will see progress as tables show up in the sim area to the right')
-
-        if st.button('Simulate Prop Category'):
+        prop_type_var = st.selectbox('Select type of prop to simulate', options = ['Strikeouts', 'Wins', 'Quality_starts'])
+
+        if st.button('Simulate Stat'):
             with col2:
+                   
                     with df_hold_container.container():
-                        if prop_type_var == 'All Props':
-                            for prop in all_sim_vars:
-                                
-                                if game_select_var == 'Draftkings':
-                                    prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                                elif game_select_var == 'Pick6':
-                                    prop_df = pick_frame[['Full_name', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                                    prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
-                                prop_df = prop_df.loc[prop_df['prop_type'] == prop]
-                                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(player_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))
-                                
-                                total_sims = 5000
-        
-                                df.replace("", 0, inplace=True)
-        
-                                if prop == 'points':
-                                    df['Median'] = df['Points']
-                                elif prop == 'rebounds':
-                                    df['Median'] = df['Rebounds']
-                                elif prop == 'assists':
-                                    df['Median'] = df['Assists']
-                                elif prop == 'threes':
-                                    df['Median'] = df['3P']
-                                elif prop == 'PRA':
-                                    df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
-                                elif prop == 'points+rebounds':
-                                    df['Median'] = df['Points'] + df['Rebounds']
-                                elif prop == 'points+assists':
-                                    df['Median'] = df['Points'] + df['Assists']
-                                elif prop == 'rebounds+assists':
-                                    df['Median'] = df['Assists'] + df['Rebounds']
-        
-                                flex_file = df
-                                flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
-                                flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
-                                flex_file['STD'] = (flex_file['Median']/4)
-                                flex_file['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['Prop type'] = prop
-        
-                                players_only['Player'] = hold_file[['Player']]
-        
-                                leg_outcomes = players_only[['Player', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']]
-                                
-                                sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
-                                
-                                final_outcomes = sim_all_hold
-
-                        elif prop_type_var != 'All Props':
-                            if game_select_var == 'Draftkings':
-                                    prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                            elif game_select_var == 'Pick6':
-                                    prop_df = pick_frame[['Full_name', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                                    prop_df.rename(columns={"Full_name": "Player"}, inplace = True)
-                            if prop_type_var == "points":
-                                prop_df = prop_df.loc[prop_df['prop_type'] == 'points']
-                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            elif prop_type_var == "rebounds":
-                                prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds']
-                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            elif prop_type_var == "assists":
-                                prop_df = prop_df.loc[prop_df['prop_type'] == 'assists']
-                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            elif prop_type_var == "threes":
-                                prop_df = prop_df.loc[prop_df['prop_type'] == 'threes']
-                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            elif prop_type_var == "PRA":
-                                prop_df = prop_df.loc[prop_df['prop_type'] == 'PRA']
-                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            elif prop_type_var == "points+rebounds":
-                                prop_df = prop_df.loc[prop_df['prop_type'] == 'points+rebounds']
-                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            elif prop_type_var == "points+assists":
-                                prop_df = prop_df.loc[prop_df['prop_type'] == 'points+assists']
-                                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(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player'])
-                            elif prop_type_var == "rebounds+assists":
-                                prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds+assists']
-                                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(player_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))
-                            
-                            total_sims = 5000
-    
-                            df.replace("", 0, inplace=True)
-    
-                            if prop_type_var == 'points':
-                                df['Median'] = df['Points']
-                            elif prop_type_var == 'rebounds':
-                                df['Median'] = df['Rebounds']
-                            elif prop_type_var == 'assists':
-                                df['Median'] = df['Assists']
-                            elif prop_type_var == 'threes':
-                                df['Median'] = df['3P']
-                            elif prop_type_var == 'PRA':
-                                df['Median'] = df['Points'] + df['Rebounds'] + df['Assists']
-                            elif prop_type_var == 'points+rebounds':
-                                df['Median'] = df['Points'] + df['Rebounds']
-                            elif prop_type_var == 'points+assists':
-                                df['Median'] = df['Points'] + df['Assists']
-                            elif prop_type_var == 'rebounds+assists':
-                                df['Median'] = df['Assists'] + df['Rebounds']
-    
-                            flex_file = df
-                            flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
-                            flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
-                            flex_file['STD'] = (flex_file['Median']/4)
-                            flex_file['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']]
+
+                        df = pitcher_proj
+
+                        total_sims = 5000
+
+                        df.replace("", 0, inplace=True)
+
+                        if prop_type_var == 'Strikeouts':
+                            df['Median'] = df['Strikeouts']
+                        elif prop_type_var == 'Wins':
+                            df['Median'] = df['Wins']
+                        elif prop_type_var == 'Quality_starts':
+                            df['Median'] = df['Quality_starts']
+
+                        flex_file = df
+                        flex_file['Floor'] = (flex_file['Median'] * .25)
+                        flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * (1.25 * flex_file['Ceiling_var']))
+                        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)
+                        players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                        players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                        players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                        
+                        players_only['Player'] = hold_file[['Player']]
+
+                        final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', 'Median', '90%']]
+
+                        final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                        final_Proj = final_Proj[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '10%', 'Median', '90%']]
                         
-                        final_outcomes = final_outcomes[final_outcomes['Prop'] > 0]
-                        final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)
-
-                        with df_hold_container:
-                            df_hold_container = st.empty()
-                            st.dataframe(final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-                        with export_container:
-                            export_container = st.empty()
-                            st.download_button(
-                                label="Export Projections",
-                                data=convert_df_to_csv(final_outcomes),
-                                file_name='Nba_prop_proj.csv',
-                                mime='text/csv',
-                                key='prop_proj',
-                            )
+                        st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)