Create app.py

app.py

@@ -0,0 +1,372 @@
+import pulp
+import numpy as np
+import pandas as pd
+import streamlit as st
+import gspread
+from itertools import combinations
+
+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%}','First Inning Lead Percentage': '{:.2%}',
+              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
+
+player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
+                   '4x%': '{:.2%}','GPP%': '{:.2%}'}
+
+wrong_acro = ['WSH', 'AZ']
+right_acro = ['WAS', 'ARI']
+
+dk_player_projections = 'https://docs.google.com/spreadsheets/d/1MdzPFqIT0MFid2IhegWf39VNR8IXUyo_Fb5dolOSt3o/edit#gid=340831852'
+fd_player_projections = 'https://docs.google.com/spreadsheets/d/1MdzPFqIT0MFid2IhegWf39VNR8IXUyo_Fb5dolOSt3o/edit#gid=340831852'
+
+secondary_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1lP4t8N7UhjR94MEwPn6powRyLl_cQBDUMSCs6cbL9ms/edit#gid=340831852'
+secondary_fd_player_projections = 'https://docs.google.com/spreadsheets/d/1lP4t8N7UhjR94MEwPn6powRyLl_cQBDUMSCs6cbL9ms/edit#gid=340831852'
+
+all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=500994479'
+all_fd_player_projections = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=500994479'
+final_Proj = 0
+
+@st.cache_data
+def load_time():
+    sh = gc.open_by_url(dk_player_projections)
+    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 set_slate_teams():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('Site_Info')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+
+    for checkVar in range(len(wrong_acro)):
+                    raw_display['FD Main'] = raw_display['FD Main'].replace(wrong_acro, right_acro)
+                    
+    for checkVar in range(len(wrong_acro)):
+                    raw_display['FD Secondary'] = raw_display['FD Secondary'].replace(wrong_acro, right_acro)
+                    
+    for checkVar in range(len(wrong_acro)):
+                    raw_display['FD Overall'] = raw_display['FD Overall'].replace(wrong_acro, right_acro)
+
+    return raw_display
+
+@st.cache_data
+def load_dk_player_projections(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('DK_Projections')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    load_display = load_display.drop_duplicates(subset='Player')
+    raw_display = load_display.dropna(subset=['Median'])
+
+    return raw_display
+
+@st.cache_data
+def load_fd_player_projections(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('FD_Projections')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    load_display = load_display.drop_duplicates(subset='Player')
+    raw_display = load_display.dropna(subset=['Median'])
+
+    return raw_display
+
+@st.cache_data
+def load_dk_player_roo(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('Player_ROO')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = load_display
+
+    return raw_display
+
+@st.cache_data
+def load_fd_player_roo(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('FD_Player_ROO')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = load_display
+
+    return raw_display
+
+@st.cache_data
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+t_stamp = load_time()
+site_slates = set_slate_teams()
+col1, col2 = st.columns([1, 5])
+
+with col1:
+    #st.info(t_stamp)
+    if st.button("Load/Reset Data", key='reset3'):
+          t_stamp = load_time()
+          st.cache_data.clear()
+    slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'All Games'), key='slate_var1')
+    site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1')
+    custom_var1 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var1')
+    if custom_var1 == 'No':
+              if slate_var1 == 'Main Slate':
+                    if site_var1 == 'Draftkings':
+                        slate_teams = site_slates['DK Main'].values.tolist()
+                        raw_baselines = load_dk_player_projections(all_dk_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+                    elif site_var1 == 'Fanduel':
+                        slate_teams = site_slates['FD Main'].values.tolist()
+                        raw_baselines = load_fd_player_projections(all_fd_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+              elif slate_var1 == 'Secondary Slate':
+                    if site_var1 == 'Draftkings':
+                        slate_teams = site_slates['DK Secondary'].values.tolist()
+                        raw_baselines = load_dk_player_projections(all_dk_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+                    elif site_var1 == 'Fanduel':
+                        slate_teams = site_slates['FD Secondary'].values.tolist()
+                        raw_baselines = load_fd_player_projections(all_fd_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+              elif slate_var1 == 'All Games':
+                    if site_var1 == 'Draftkings':
+                        slate_teams = site_slates['DK Overall'].values.tolist()
+                        raw_baselines = load_dk_player_projections(all_dk_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+                    elif site_var1 == 'Fanduel':
+                        slate_teams = site_slates['FD Overall'].values.tolist()
+                        raw_baselines = load_fd_player_projections(all_fd_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+              split_var1 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
+              if split_var1 == 'Specific Games':
+                  team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1')
+              elif split_var1 == 'Full Slate Run':
+                  team_var1 = raw_baselines.Team.values.tolist()
+              pos_split1 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')
+              if pos_split1 == 'Specific Positions':
+                  pos_var1 = st.multiselect('What Positions would you like to view?', options = ['SP', 'P', 'C', '1B', '2B', '3B', 'SS', 'OF'])
+              elif pos_split1 == 'All Positions':
+                  pos_var1 = 'All'
+    if custom_var1 == 'Yes':
+              contest_var1 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var1')
+              if slate_var1 == 'Main Slate':
+                    if site_var1 == 'Draftkings':
+                        slate_teams = site_slates['DK Main'].values.tolist()
+                        raw_baselines = load_dk_player_projections(all_dk_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+                    elif site_var1 == 'Fanduel':
+                        slate_teams = site_slates['FD Main'].values.tolist()
+                        raw_baselines = load_fd_player_projections(all_fd_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+              elif slate_var1 == 'Secondary Slate':
+                    if site_var1 == 'Draftkings':
+                        slate_teams = site_slates['DK Secondary'].values.tolist()
+                        raw_baselines = load_dk_player_projections(all_dk_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+                    elif site_var1 == 'Fanduel':
+                        slate_teams = site_slates['FD Secondary'].values.tolist()
+                        raw_baselines = load_fd_player_projections(all_fd_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+              elif slate_var1 == 'All Games':
+                    if site_var1 == 'Draftkings':
+                        slate_teams = site_slates['DK Overall'].values.tolist()
+                        raw_baselines = load_dk_player_projections(all_dk_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+                    elif site_var1 == 'Fanduel':
+                        slate_teams = site_slates['FD Overall'].values.tolist()
+                        raw_baselines = load_fd_player_projections(all_fd_player_projections)
+                        raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)]
+              split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
+              if split_var1 == 'Specific Games':
+                  team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1')
+              elif split_var1 == 'Full Slate Run':
+                  team_var1 = raw_baselines.Team.values.tolist()
+              pos_split1 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')
+              if pos_split1 == 'Specific Positions':
+                  pos_var1 = st.multiselect('What Positions would you like to view?', options = ['SP', 'P', 'C', '1B', '2B', '3B', 'SS', 'OF'])
+              elif pos_split1 == 'All Positions':
+                  pos_var1 = 'All'
+
+
+with col2:
+    if custom_var1 == 'No':
+          if slate_var1 == 'Main Slate':
+                 if site_var1 == 'Draftkings':
+                      final_Proj = load_dk_player_roo(dk_player_projections)
+                 elif site_var1 == 'Fanduel':
+                      final_Proj = load_fd_player_roo(fd_player_projections)
+          elif slate_var1 == 'Secondary Slate':
+                 if site_var1 == 'Draftkings':
+                      final_Proj = load_dk_player_roo(secondary_dk_player_projections)
+                 elif site_var1 == 'Fanduel':
+                      final_Proj = load_fd_player_roo(secondary_fd_player_projections)
+          elif slate_var1 == 'All Games':
+                 if site_var1 == 'Draftkings':
+                      final_Proj = load_dk_player_roo(all_dk_player_projections)
+                 elif site_var1 == 'Fanduel':
+                      final_Proj = load_fd_player_roo(all_fd_player_projections)
+          final_Proj = final_Proj[final_Proj['Team'].isin(team_var1)]
+          if pos_var1 != 'All':
+                 final_Proj = final_Proj[final_Proj['Position'].str.contains('|'.join(pos_var1))]
+          st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+          st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(final_Proj),
+                file_name='Custom_MLB_export.csv',
+                mime='text/csv',
+          )
+    elif custom_var1 == 'Yes':
+              hold_container = st.empty()
+              if st.button('Create Range of Outcomes for Slate'):
+                  with hold_container:
+           #           if slate_var1 == 'Main Slate':
+           #               if site_var1 == 'Draftkings':
+           #                   raw_baselines = load_dk_player_projections(dk_player_projections)
+           #               elif site_var1 == 'Fanduel':
+           #                   raw_baselines = load_fd_player_projections(fd_player_projections)
+           #           elif slate_var1 == 'Secondary Slate':
+           #               if site_var1 == 'Draftkings':
+           #                   raw_baselines = load_dk_player_projections(secondary_dk_player_projections)
+           #               elif site_var1 == 'Fanduel':
+           #                   raw_baselines = load_fd_player_projections(secondary_fd_player_projections)
+           #            elif slate_var1 == 'All Games':
+           #               if site_var1 == 'Draftkings':
+           #                   raw_baselines = load_dk_player_projections(all_dk_player_projections)
+           #               elif site_var1 == 'Fanduel':
+           #                   raw_baselines = load_fd_player_projections(all_fd_player_projections)
+                      working_roo = raw_baselines
+                      working_roo = working_roo[working_roo['Team'].isin(team_var1)]
+                      own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                      team_dict = dict(zip(working_roo.Player, working_roo.Team))
+                      total_sims = 1000
+
+                      flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Ceiling_Var']]
+                      flex_file['Floor'] = flex_file['Median']*.25
+                      flex_file['Ceiling'] = np.where(flex_file['Position'] == 'SP', (flex_file['Median'] + (flex_file['Floor'])) + ((flex_file['Ceiling_Var'] * 10) * 3), (flex_file['Median'] + (flex_file['Floor'])) + ((flex_file['Ceiling_Var'] * 10)))
+                      flex_file['STD'] = (flex_file['Median']/4)
+                      flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                      if pos_split1 == 'All Positions':
+                          flex_file = flex_file
+                      elif pos_split1 != 'All Positions':
+                          if pos_var1 == 'Pitchers':
+                              flex_file = flex_file[flex_file['Position'] == 'SP']
+                          elif pos_var1 == 'Hitters':
+                              flex_file = flex_file[flex_file['Position'] != 'SP']
+                          elif pos_var1 not in ['Pitchers', 'Hitters']: 
+                              flex_file = flex_file[flex_file['Position'].str.contains('|'.join(pos_var1))]
+                      hold_file = flex_file
+                      overall_file = flex_file
+                      salary_file = flex_file
+
+                      overall_players = overall_file[['Player']]
+
+                      for x in range(0,total_sims):    
+                          salary_file[x] = salary_file['Salary']
+
+                      salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                      salary_file.astype('int').dtypes
+
+                      salary_file = salary_file.div(1000)
+
+                      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', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                      overall_file.astype('int').dtypes
+
+                      players_only = hold_file[['Player']]
+                      raw_lineups_file = players_only
+
+                      for x in range(0,total_sims):
+                          maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                          raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                          players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                      players_only=players_only.drop(['Player'], axis=1)
+                      players_only.astype('int').dtypes
+
+                      salary_2x_check = (overall_file - (salary_file*2))
+                      salary_3x_check = (overall_file - (salary_file*3))
+                      salary_4x_check = (overall_file - (salary_file*4))
+                      gpp_check = (overall_file - ((salary_file*2)+10))
+
+                      players_only['Average_Rank'] = players_only.mean(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['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                      players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                      players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                      players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+                      players_only['GPP%'] = gpp_check[gpp_check >= 1].count(axis=1)/float(total_sims)
+
+                      players_only['Player'] = hold_file[['Player']]
+
+                      final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%']]
+
+                      final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                      final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%']]
+                      final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                      final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                      final_Proj['Own'] = final_Proj['Own'].astype('float')
+                      if contest_var1 == 'Small Field GPP':
+                            if site_var1 == 'Draftkings':
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own'])
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (10 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%'])
+                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
+                            elif site_var1 == 'Fanduel':
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own'])
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (10 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%'])
+                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
+                      elif contest_var1 == 'Large Field GPP':
+                            if site_var1 == 'Draftkings':
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (2.5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own'])
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%'])
+                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
+                            elif site_var1 == 'Fanduel':
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (2.5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own'])
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (5 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%'])
+                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
+                      elif contest_var1 == 'Cash':
+                            if site_var1 == 'Draftkings':
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (6 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'SP', 'Own'].mean(), final_Proj['Own'])
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'SP') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean() >= 0), final_Proj['Own'] * (11 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] != 'SP', 'Own'].mean(), final_Proj['Own%'])
+                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])      
+                            elif site_var1 == 'Fanduel':
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] == 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (6 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean())/100) + final_Proj.loc[final_Proj['Position'] == 'P', 'Own'].mean(), final_Proj['Own'])
+                                final_Proj['Own%'] = np.where((final_Proj['Position'] != 'P') & (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean() >= 0), final_Proj['Own'] * (11 * (final_Proj['Own'] - final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean())/150) + final_Proj.loc[final_Proj['Position'] != 'P', 'Own'].mean(), final_Proj['Own%'])
+                                final_Proj['Own%'] = np.where(final_Proj['Own%'] > 75, 75, final_Proj['Own%'])
+
+                      final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'GPP%', 'Own%']]
+                      final_Proj = final_Proj.set_index('Player')
+                      final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+
+                  with hold_container:
+                      hold_container = st.empty()
+                      final_Proj = final_Proj
+                      st.dataframe(final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+
+                  st.download_button(
+                          label="Export Tables",
+                          data=convert_df_to_csv(final_Proj),
+                          file_name='Custom_MLB_export.csv',
+                          mime='text/csv',
+                  )