Create app.py

app.py

@@ -0,0 +1,792 @@
+import pulp
+import numpy as np
+import pandas as pd
+import streamlit as st
+import gspread
+from itertools import combinations
+
+st.set_page_config(layout="wide")
+
+@st.cache_resource
+def init_conn():
+          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)
+          return gc
+
+gc = init_conn()
+
+wrong_acro = ['WSH', 'AZ']
+right_acro = ['WAS', 'ARI']
+
+game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
+              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
+
+team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}',
+                   '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'}
+
+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_resource(ttl=600)
+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_resource(ttl=600)
+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_resource(ttl=600)
+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_resource(ttl=600)
+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_resource(ttl=600)
+def load_scoring_percentages(URL):
+    sh = gc.open_by_url(URL)
+    worksheet = sh.worksheet('Scoring_Percentages')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display['8+ runs'] = raw_display['8+ runs'].str.replace('%', '').astype(float)/100
+    raw_display['Win Percentage'] = raw_display['Win Percentage'].str.replace('%', '').astype(float)/100
+    raw_display['DK LevX'] = raw_display['DK LevX'].str.replace('%', '').astype(float)/100
+    raw_display['FD LevX'] = raw_display['FD LevX'].str.replace('%', '').astype(float)/100
+
+    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='reset5'):
+          st.cache_data.clear()
+          raw_baselines = load_dk_player_projections(all_dk_player_projections)
+          
+          team_baselines = load_scoring_percentages(all_dk_player_projections)
+          
+    slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'All Games'), key='slate_var1')
+    site_var5 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var5')
+    if slate_var1 == 'Main Slate':
+        if site_var5 == '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)]
+            team_baselines = load_scoring_percentages(all_dk_player_projections)
+            team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)]
+            Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names'])
+            SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP'])
+            team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)]
+            Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names'])
+            raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
+        elif site_var5 == '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)]
+            team_baselines = load_scoring_percentages(all_fd_player_projections)
+            team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)]
+            Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names'])
+            SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP'])
+            team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)]
+            Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names'])
+            raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
+    elif slate_var1 == 'Secondary Slate':
+        if site_var5 == '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)]
+            team_baselines = load_scoring_percentages(all_dk_player_projections)
+            team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)]
+            Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names'])
+            SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP'])
+            team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)]
+            Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names'])
+            raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
+        elif site_var5 == '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)]
+            team_baselines = load_scoring_percentages(all_fd_player_projections)
+            team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)]
+            Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names'])
+            SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP'])
+            team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)]
+            Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names'])
+            raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
+    elif slate_var1 == 'All Games':
+        if site_var5 == '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)]
+            team_baselines = load_scoring_percentages(all_dk_player_projections)
+            team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)]
+            Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names'])
+            SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP'])
+            team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)]
+            Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names'])
+            raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
+        elif site_var5 == '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)]
+            team_baselines = load_scoring_percentages(all_fd_player_projections)
+            team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)]
+            Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names'])
+            SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP'])
+            team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)]
+            Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names'])
+            raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
+    contest_var5 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var5')
+    if contest_var5 == 'Small Field GPP':
+        opto_var5 = st.selectbox("Pivot optimal or Manual?", ('Pivot Optimal', 'Manual'), key='opto_var5')
+        if opto_var5 == "Manual":
+            stack_var5 = st.selectbox('Which teams are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var5')
+        elif opto_var5 == "Pivot Optimal":
+            stack_var5 = Max_Rank[0]
+    elif contest_var5 == 'Large Field GPP':
+        opto_var5 = st.selectbox("Pivot optimal or Manual?", ('Pivot Optimal', 'Manual'), key='opto_var5')
+        if opto_var5 == "Manual":
+            stack_var5 = st.selectbox('Which team are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var5')
+            ministack_var5 = st.selectbox('Which team is your secondary stack?', options = raw_baselines['Team'].unique(), key='ministack_var5')
+        elif opto_var5 == "Pivot Optimal":
+            stack_var5 = Max_Upside[0]
+            ministack_var5 = Max_Rank[0]
+    split_var5 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var5')
+    if split_var5 == 'Specific Games':
+        team_var5 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var5')
+    elif split_var5 == 'Full Slate Run':
+        team_var5 = raw_baselines.Team.values.tolist()
+    lock_var5 = st.multiselect("Are there any players you want to use in all lineups (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var5')
+    avoid_var5 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = raw_baselines['Player'].unique(), key='avoid_var5')
+    linenum_var5 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 20, step = 1, key='linenum_var5')
+    if site_var5 == 'Draftkings':
+        min_sal5 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal5')
+        max_sal5 = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100, key='max_sal5')
+    elif site_var5 == 'Fanduel':
+        min_sal5 = st.number_input('Min Salary', min_value = 25000, max_value = 34900, value = 34000, step = 100, key='min_sal5')
+        max_sal5 = st.number_input('Max Salary', min_value = 25000, max_value = 35000, value = 35000, step = 100, key='max_sal5')
+
+
+with col2:
+    raw_baselines = raw_baselines[raw_baselines['Team'].isin(team_var5)]
+    raw_baselines = raw_baselines[~raw_baselines['Player'].isin(avoid_var5)]
+    if contest_var5 == 'Small Field GPP':
+        if site_var5 == 'Draftkings':
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean(), raw_baselines['Own'])
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (10 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean(), raw_baselines['Own%'])
+            raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%'])
+        elif site_var5 == 'Fanduel':
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean())/50) + raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean(), raw_baselines['Own'])
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (10 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean())/150) + raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean(), raw_baselines['Own%'])
+            raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%'])
+    elif contest_var5 == 'Large Field GPP':
+        if site_var5 == 'Draftkings':
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (2.5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean(), raw_baselines['Own'])
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean(), raw_baselines['Own%'])
+            raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%'])
+        elif site_var5 == 'Fanduel':
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (2.5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean())/50) + raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean(), raw_baselines['Own'])
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean())/150) + raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean(), raw_baselines['Own%'])
+            raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%'])
+    elif contest_var5 == 'Cash':
+        if site_var5 == 'Draftkings':
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (6 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean(), raw_baselines['Own'])
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (11 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean(), raw_baselines['Own%'])
+            raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%'])
+        elif site_var5 == 'Fanduel':
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (6 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean())/50) + raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean(), raw_baselines['Own'])
+            raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (11 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean())/150) + raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean(), raw_baselines['Own%'])
+            raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%'])
+    raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own%']]
+    raw_baselines.rename(columns={"Own%": "Own"}, inplace = True)
+    raw_baselines = raw_baselines.sort_values(by='Median', ascending=False)
+    raw_baselines['lock'] = np.where(raw_baselines['Player'].isin(lock_var5), 1, 0)
+    st.dataframe(raw_baselines.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(raw_baselines),
+            file_name='MLB_proj_export.csv',
+            mime='text/csv',
+        )
+    if st.button('Optimize'):
+        max_proj = 1000
+        max_own = 1000
+        total_proj = 0
+        total_own = 0
+        optimize_container = st.empty()
+        lineup_display = []
+        check_list = []
+        lineups = []
+        portfolio = pd.DataFrame()
+        x = 1
+
+        with st.spinner('Wait for it...'):
+            with optimize_container:
+
+                    while x <= linenum_var5:
+                        sorted_lineup = []
+                        p_used = []
+                        cvar = 0
+                        firvar = 0
+                        secvar = 0
+                        thirvar = 0
+
+                        raw_proj_file = raw_baselines
+                        raw_flex_file = raw_proj_file.dropna(how='all')
+                        raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0]
+                        flex_file = raw_flex_file
+                        flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True)
+                        flex_file['name_var'] = flex_file['Player']
+                        flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var5), 1, 0)
+                        player_ids = flex_file.index
+
+                        overall_players = flex_file[['Player']]
+                        overall_players['player_var_add'] = flex_file.index
+                        overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str)
+
+                        player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger)
+                        total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize)
+                        player_match = dict(zip(overall_players['player_var'], overall_players['Player']))
+                        player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add']))
+
+                        player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%']))
+                        player_team = dict(zip(flex_file['Player'], flex_file['Team']))
+                        player_pos = dict(zip(flex_file['Player'], flex_file['Position']))
+                        player_sal = dict(zip(flex_file['Player'], flex_file['Salary']))
+                        player_proj = dict(zip(flex_file['Player'], flex_file['Median']))
+
+                        # obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
+                        # total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
+
+                        # obj_points_max = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
+                        # obj_own_max = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
+
+                        obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index}
+                        total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal5
+                        total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal5
+
+                        if site_var5 == 'Draftkings':
+                            
+                            if contest_var5 == 'Cash':
+                                for flex in flex_file['Team'].unique():
+                                    sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'SP')].index
+                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5
+                            elif contest_var5 == 'Small Field GPP':
+                                for flex in flex_file['Team'].unique():
+                                    sub_idx = flex_file[(flex_file['Team'] == stack_var5) & (flex_file['Position'] != 'SP')].index
+                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5
+                            elif contest_var5 == 'Large Field GPP':
+                                for flex in flex_file['Team'].unique():
+                                    sub_idx = flex_file[(flex_file['Team'] == stack_var5) & (flex_file['Position'] != 'SP')].index
+                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5
+                                for flex in flex_file['Team'].unique():
+                                    sub_idx = flex_file[(flex_file['Team'] == ministack_var5) & (flex_file['Position'] != 'SP')].index
+                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 3
+                            
+                            for flex in flex_file['lock'].unique():
+                                sub_idx = flex_file[flex_file['lock'] == 1].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var5)
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] != "Var"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 10
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("SP")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 2
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] == "C"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] == "1B"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] == "2B"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] == "3B"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] == "SS"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] == "OF"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("C")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("1B")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("2B")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("3B")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("SS")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("OF")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "3B/SS")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+                            
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "2B")| (flex_file['Position'] == "2B/SS")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+                            
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "2B") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "2B/3B")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+                               
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "1B") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "1B/3B")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+                    
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "1B") | (flex_file['Position'] == "C")| (flex_file['Position'] == "1B/C")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "OF")| (flex_file['Position'] == "SS/OF")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4  
+
+                        elif site_var5 == 'Fanduel':
+
+                            if contest_var5 == 'Cash':
+                                for flex in flex_file['Team'].unique():
+                                    sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'P')].index
+                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4
+                            elif contest_var5 == 'Small Field GPP':
+                                for flex in flex_file['Team'].unique():
+                                    sub_idx = flex_file[(flex_file['Team'] == stack_var5) & (flex_file['Position'] != 'P')].index
+                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 4
+                            elif contest_var5 == 'Large Field GPP':
+                                for flex in flex_file['Team'].unique():
+                                    sub_idx = flex_file[(flex_file['Team'] == stack_var5)].index
+                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 4
+                                for flex in flex_file['Team'].unique():
+                                    sub_idx = flex_file[(flex_file['Team'] == ministack_var5)].index
+                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 4
+                            
+                            for flex in flex_file['lock'].unique():
+                                sub_idx = flex_file[flex_file['lock'] == 1].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var5)
+                              
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] != "Var"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("P")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "C") | (flex_file['Position'] == "1B")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+                              
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "C") | (flex_file['Position'] == "1B") | (flex_file['Position'] == "OF")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] == "2B"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] == "3B"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] == "SS"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] == "OF"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4
+                            
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "OF") | (flex_file['Position'] == "C")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5
+                            
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "OF") | (flex_file['Position'] == "1B")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5
+                            
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'].str.contains("C")) | (flex_file['Position'].str.contains("1B"))].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+                                 
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'].str.contains("2B")) | (flex_file['Position'].str.contains("SS"))].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2
+                                   
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "C") | (flex_file['Position'] == "SS")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3
+                              
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "2B") | (flex_file['Position'] == "OF") | (flex_file['Position'] == "2B/SS/OF")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5
+                            
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("2B")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("3B")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("SS")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("OF")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3
+                              
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'].str.contains("OF")].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4
+
+                        player_count = []
+                        player_trim = []
+                        lineup_list = []
+
+                        if contest_var5 == 'Cash':
+                                obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
+                                total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
+                                total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_own - .001
+                        elif contest_var5 != 'Cash':
+                                obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
+                                total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
+                                total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_proj - .01
+
+                        total_score.solve()
+                        for v in total_score.variables():
+                            if v.varValue > 0:
+                                lineup_list.append(v.name)
+                        df = pd.DataFrame(lineup_list)
+                        df['Names'] = df[0].map(player_match)
+                        df['Cost'] = df['Names'].map(player_sal)
+                        df['Proj'] = df['Names'].map(player_proj)
+                        df['Own'] = df['Names'].map(player_own)
+                        total_cost = sum(df['Cost'])
+                        total_own = sum(df['Own'])
+                        total_proj = sum(df['Proj'])
+                        lineup_raw = pd.DataFrame(lineup_list)
+                        lineup_raw['Names'] = lineup_raw[0].map(player_match)
+                        lineup_raw['value'] = lineup_raw[0].map(player_index_match)
+                        lineup_final = lineup_raw.sort_values(by=['value'])
+                        del lineup_final[lineup_final.columns[0]]
+                        del lineup_final[lineup_final.columns[1]]
+                        lineup_final = lineup_final.reset_index(drop=True)
+                        
+                        if site_var5 == 'Draftkings':
+                            line_hold = lineup_final[['Names']]
+                            line_hold['pos'] = line_hold['Names'].map(player_pos)
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == 'SP':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                            p_used.extend(sorted_lineup)        
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == 'C':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                                        cvar = 1
+                            p_used.extend(sorted_lineup)  
+
+                            if cvar != 1:
+                                for pname in range(0,len(line_hold)):
+                                    if 'C' in line_hold.iat[pname,1]:
+                                        if line_hold.iat[pname,0] not in p_used:
+                                            sorted_lineup.append(line_hold.iat[pname,0])
+                                p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == '1B':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                                        firvar = 1
+                            p_used.extend(sorted_lineup)  
+
+                            if firvar != 1:
+                                for pname in range(0,len(line_hold)):
+                                    if '1B' in line_hold.iat[pname,1]:
+                                        if line_hold.iat[pname,0] not in p_used:
+                                            sorted_lineup.append(line_hold.iat[pname,0])
+                                p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == '2B':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                                        secvar = 1
+                            p_used.extend(sorted_lineup)  
+
+                            if secvar != 1:
+                                for pname in range(0,len(line_hold)):
+                                    if '2B' in line_hold.iat[pname,1]:
+                                        if line_hold.iat[pname,0] not in p_used:
+                                            sorted_lineup.append(line_hold.iat[pname,0])
+                                p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == '3B':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                                        thirvar = 1
+                            p_used.extend(sorted_lineup)  
+
+                            if thirvar != 1:
+                                for pname in range(0,len(line_hold)):
+                                    if '3B' in line_hold.iat[pname,1]:
+                                        if line_hold.iat[pname,0] not in p_used:
+                                            sorted_lineup.append(line_hold.iat[pname,0])
+                                p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == 'SS':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                            p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if 'SS' in line_hold.iat[pname,1]:
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                            p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == 'OF':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                            p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if 'OF' in line_hold.iat[pname,1]:
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                            p_used.extend(sorted_lineup)
+                            
+                            lineup_final['sorted'] = sorted_lineup
+                            lineup_final = lineup_final.drop(columns=['Names'])
+                            lineup_final.rename(columns={"sorted": "Names"}, inplace = True)
+                              
+                        elif site_var5 == 'Fanduel':
+                            line_hold = lineup_final[['Names']]
+                            line_hold['pos'] = line_hold['Names'].map(player_pos)
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == 'P':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                            p_used.extend(sorted_lineup)        
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == 'C' or line_hold.iat[pname,1] == '1B':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                                        cvar = 1
+                            p_used.extend(sorted_lineup)  
+
+                            if cvar != 1:
+                                for pname in range(0,len(line_hold)):
+                                    if  line_hold.iat[pname,1] in ['C', '1B']:
+                                        if line_hold.iat[pname,0] not in p_used:
+                                            sorted_lineup.append(line_hold.iat[pname,0])
+                                p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == '2B':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                                        secvar = 1
+                            p_used.extend(sorted_lineup)  
+
+                            if secvar != 1:
+                                for pname in range(0,len(line_hold)):
+                                    if '2B' in line_hold.iat[pname,1]:
+                                        if line_hold.iat[pname,0] not in p_used:
+                                            sorted_lineup.append(line_hold.iat[pname,0])
+                                p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == '3B':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                                        thirvar = 1
+                            p_used.extend(sorted_lineup)  
+
+                            if thirvar != 1:
+                                for pname in range(0,len(line_hold)):
+                                    if '3B' in line_hold.iat[pname,1]:
+                                        if line_hold.iat[pname,0] not in p_used:
+                                            sorted_lineup.append(line_hold.iat[pname,0])
+                                p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == 'SS':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                            p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if 'SS' in line_hold.iat[pname,1]:
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                            p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,1] == 'OF':
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                            p_used.extend(sorted_lineup)  
+
+                            for pname in range(0,len(line_hold)):
+                                if 'OF' in line_hold.iat[pname,1]:
+                                    if line_hold.iat[pname,0] not in p_used:
+                                        sorted_lineup.append(line_hold.iat[pname,0])
+                            p_used.extend(sorted_lineup)
+                            
+                            for pname in range(0,len(line_hold)):
+                                if line_hold.iat[pname,0] not in p_used:
+                                    sorted_lineup.append(line_hold.iat[pname,0])
+                            p_used.extend(sorted_lineup)
+
+                            lineup_final['sorted'] = sorted_lineup
+                            lineup_final = lineup_final.drop(columns=['Names'])
+                            lineup_final.rename(columns={"sorted": "Names"}, inplace = True)
+                              
+                        lineup_test = lineup_final
+                        lineup_final = lineup_final.T
+                        lineup_final['Cost'] = total_cost
+                        lineup_final['Proj'] = total_proj
+                        lineup_final['Own'] = total_own
+                    
+                        lineup_test['Team'] = lineup_test['Names'].map(player_team)
+                        lineup_test['Position'] = lineup_test['Names'].map(player_pos)
+                        lineup_test['Salary'] = lineup_test['Names'].map(player_sal)
+                        lineup_test['Proj'] = lineup_test['Names'].map(player_proj)
+                        lineup_test['Own'] = lineup_test['Names'].map(player_own)
+                        lineup_test = lineup_test.set_index('Names')
+                        lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0)
+
+                        lineup_display.append(lineup_test)
+
+                        with col2:
+                            with st.container():
+                                st.table(lineup_test)
+
+                        max_proj = total_proj
+                        max_own = total_own
+
+                        check_list.append(total_proj)
+
+                        portfolio = pd.concat([portfolio, lineup_final], ignore_index = True)
+
+                        x += 1
+
+                    if site_var5 == 'Draftkings':
+                        portfolio.rename(columns={0: "SP1", 1: "SP2", 2: "C", 3: "1B", 4: "2B", 5: "3B", 6: "SS", 7: "OF1", 8: "OF2", 9: "OF3"}, inplace = True)
+                    elif site_var5 == 'Fanduel':
+                        portfolio.rename(columns={0: "SP1", 1: "C/1B", 2: "2B", 3: "3B", 4: "SS", 5: "OF1", 6: "OF2", 7: "OF3", 8: "UTIL"}, inplace = True)
+                    portfolio = portfolio.dropna()
+                    portfolio = portfolio.reset_index()
+                    portfolio['Lineup_num'] = portfolio['index'] + 1
+                    portfolio.rename(columns={'Lineup_num': "Lineup"}, inplace = True)
+                    portfolio = portfolio.set_index('Lineup')
+                    portfolio = portfolio.drop(columns=['index'])
+
+                    final_outcomes = portfolio
+
+            with optimize_container:
+                optimize_container = st.empty()
+                st.dataframe(portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+
+            st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(final_outcomes),
+                file_name='MLB_optimals_export.csv',
+                mime='text/csv',
+            )