Refactor app.py to replace Google Sheets integration with MongoDB for data retrieval. Updated data loading functions to support NBA and NFL datasets, adjusted caching mechanisms, and streamlined user interface for sport selection. Removed deprecated code related to Google Sheets and improved data handling for player projections.

app.py

@@ -2,34 +2,22 @@ import pulp
 import numpy as np
 import pandas as pd
 import streamlit as st
-import gspread
+import pymongo
 from itertools import combinations
 import time
 
 @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": st.secrets["sheets_api_connect_pk"],
-            "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"
-          }
+        uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        nba_db = client["NBA_DFS"]
+        nfl_db = client["NFL_Database"]
 
-          gc = gspread.service_account_from_dict(credentials)
-          return gc
+        return nba_db, nfl_db
 
 st.set_page_config(layout="wide")
 
-gc = init_conn()
+nba_db, nfl_db = init_conn()
 
 wrong_acro = ['WSH', 'AZ']
 right_acro = ['WAS', 'ARI']
@@ -47,91 +35,102 @@ expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'}
 
 all_dk_player_projections = st.secrets["NFL_data"]
 
-@st.cache_resource(ttl=300)
+@st.cache_resource(ttl=60)
 def init_baselines():
-    sh = gc.open_by_url(all_dk_player_projections)
-    worksheet = sh.worksheet('DK_SD_ROO')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    load_display['Salary'] = load_display['Salary'] / 1.5
-    dk_roo_raw = load_display.loc[load_display['Median'] > 0]
+    collection = nba_db["Player_SD_Range_Of_Outcomes"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
+                               'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp']]
+    raw_display['player_id'] = raw_display['Player'] ##### Need to fix this later on
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.sort_values(by='Median', ascending=False)
+    nba_dk_sd_raw = raw_display[raw_display['site'] == 'Draftkings']
+    nba_fd_sd_raw = raw_display[raw_display['site'] == 'Fanduel']
     
-    dk_ids = dict(zip(dk_roo_raw['Player'], dk_roo_raw['player_id']))
+    collection = nfl_db["DK_SD_NFL_ROO"] 
+    cursor = collection.find()
 
-    worksheet = sh.worksheet('FD_SD_ROO')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    fd_roo_raw = load_display.loc[load_display['Median'] > 0]
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    nfl_dk_sd_raw = raw_display.sort_values(by='Median', ascending=False)
     
-    fd_ids = dict(zip(fd_roo_raw['Player'], fd_roo_raw['player_id']))
+    collection = nfl_db["FD_SD_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    nfl_fd_sd_raw = raw_display.sort_values(by='Median', ascending=False)
 
-    return dk_roo_raw, fd_roo_raw, dk_ids, fd_ids
+    nba_timestamp = nba_dk_sd_raw['timestamp'].values[0]
+    nfl_dk_timestamp = nfl_dk_sd_raw['timestamp'].values[0]
+
+    nba_dk_id_dict = dict(zip(nba_dk_sd_raw['player_id'], nba_dk_sd_raw['player_id']))
+    nfl_dk_id_dict = dict(zip(nfl_dk_sd_raw['player_id'], nfl_dk_sd_raw['player_id']))
+    nba_fd_id_dict = dict(zip(nba_fd_sd_raw['player_id'], nba_fd_sd_raw['player_id']))
+    nfl_fd_id_dict = dict(zip(nfl_fd_sd_raw['player_id'], nfl_fd_sd_raw['player_id']))
+    
+    return nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict
 
-dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = init_baselines()
+nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines()
 
 @st.cache_data
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-tab1, tab2, tab3 = st.tabs(['Range of Outcomes', 'Optimizer', 'Uploads and Info'])
-
-with tab3:
-    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'rush_yards', 'rec', 'Median', and 'Own'. For the purposes of this showdown optimizer, only include FLEX positions, salaries, and medians. The optimizer logic will handle the rest!")
-    col1, col2 = st.columns([1, 5])
-
-    with col1:
-        proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
-    
-        if proj_file is not None:
-                  try:
-                            proj_dataframe = pd.read_csv(proj_file)
-                            proj_dataframe = proj_dataframe.loc[proj_dataframe['Median'] > 0]
-                            try: 
-                                  proj_dataframe['Own'] = proj_dataframe['Own'].str.replace('%', '').astype(float)
-                            except:
-                                  pass
-                  except:
-                            proj_dataframe = pd.read_excel(proj_file)
-                            proj_dataframe = proj_dataframe.loc[proj_dataframe['Median'] > 0]
-                            try: 
-                                  proj_dataframe['Own'] = proj_dataframe['Own'].str.replace('%', '').astype(float)
-                            except:
-                                  pass
-    with col2:
-        if proj_file is not None:  
-                  st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimizer'])
 
 with tab1:
     col1, col2 = st.columns([1, 5])
     with col1:
         if st.button("Load/Reset Data", key='reset2'):
               st.cache_data.clear()
-              dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = init_baselines()
-        slate_var2 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'User'), key='slate_var2')
+              nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines()
+        sport_var2 = st.radio("What sport are you loading?", ('NBA', 'NFL'), key='sport_var2')
+        if sport_var2 == 'NBA':
+            dk_roo_raw = nba_dk_sd_raw
+            fd_roo_raw = nba_fd_sd_raw
+        elif sport_var2 == 'NFL':
+            dk_roo_raw = nfl_dk_sd_raw
+            fd_roo_raw = nfl_fd_sd_raw
+        slate_var2 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'Paydirt (Auxiliary)'), key='slate_var2')
         site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
-        if slate_var2 == 'User':
-            raw_baselines = proj_dataframe
-        elif slate_var2 != 'User':
-            if site_var2 == 'Draftkings':
-                if slate_var2 == 'Paydirt (Main)':
-                    raw_baselines = dk_roo_raw
-                    raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
-                elif slate_var2 == 'Paydirt (Secondary)':
-                    raw_baselines = dk_roo_raw
-                    raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
-                    
-            elif site_var2 == 'Fanduel':
-                if slate_var2 == 'Paydirt (Main)':
-                    raw_baselines = fd_roo_raw
-                    raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
-                elif slate_var2 == 'Paydirt (Secondary)':
-                    raw_baselines = fd_roo_raw
-                    raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
+        if site_var2 == 'Draftkings':
+            if slate_var2 == 'Paydirt (Main)':
+                raw_baselines = dk_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
+            elif slate_var2 == 'Paydirt (Secondary)':
+                raw_baselines = dk_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
+            elif slate_var2 == 'Paydirt (Auxiliary)':
+                raw_baselines = dk_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
+                
+        elif site_var2 == 'Fanduel':
+            if slate_var2 == 'Paydirt (Main)':
+                raw_baselines = fd_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
+            elif slate_var2 == 'Paydirt (Secondary)':
+                raw_baselines = fd_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
+            elif slate_var2 == 'Paydirt (Auxiliary)':
+                raw_baselines = fd_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
     
     with col2:
         hold_container = st.empty()
         
-        display_Proj = raw_baselines[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+        if sport_var2 == 'NBA':
+            display_Proj = raw_baselines[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX']]
+        elif sport_var2 == 'NFL':
+            display_Proj = raw_baselines[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
         display_Proj = display_Proj.set_index('Player')
         display_Proj = display_Proj.sort_values(by='Median', ascending=False)
   
@@ -152,32 +151,41 @@ with tab2:
     with col1:
         if st.button("Load/Reset Data", key='reset1'):
               st.cache_data.clear()
-              dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = init_baselines()
+              nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines()
               for key in st.session_state.keys():
                   del st.session_state[key]
-        slate_var1 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'User'), key='slate_var1')
+        sport_var1 = st.radio("What sport are you optimizing?", ('NBA', 'NFL'), key='sport_var1')
+        if sport_var1 == 'NBA':
+            dk_roo_raw = nba_dk_sd_raw
+            fd_roo_raw = nba_fd_sd_raw
+        elif sport_var1 == 'NFL':
+            dk_roo_raw = nfl_dk_sd_raw
+            fd_roo_raw = nfl_fd_sd_raw
+        slate_var1 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'Paydirt (Auxiliary)'), key='slate_var1')
         site_var1 = st.selectbox("What site is the showdown on?", ('Draftkings', 'Fanduel'), key='site_var1')
         if site_var1 == 'Draftkings':
-              if slate_var1 == 'User':
-                  raw_baselines = proj_dataframe
-              elif slate_var1 == 'Paydirt (Main)':
-                  raw_baselines = dk_roo_raw
-                  raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
-              elif slate_var1 == 'Paydirt (Secondary)':
-                  raw_baselines = dk_roo_raw
-                  raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
+            if slate_var1 == 'Paydirt (Main)':
+                raw_baselines = dk_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
+            elif slate_var1 == 'Paydirt (Secondary)':
+                raw_baselines = dk_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
+            elif slate_var1 == 'Paydirt (Auxiliary)':
+                raw_baselines = dk_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
         elif site_var1 == 'Fanduel':
-              if slate_var1 == 'User':
-                  st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
-                  raw_baselines = proj_dataframe
-              elif slate_var1 == 'Paydirt (Main)':
-                  st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
-                  raw_baselines = fd_roo_raw
-                  raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
-              elif slate_var1 == 'Paydirt (Secondary)':
-                  st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
-                  raw_baselines = fd_roo_raw
-                  raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
+            if slate_var1 == 'Paydirt (Main)':
+                st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
+                raw_baselines = fd_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
+            elif slate_var1 == 'Paydirt (Secondary)':
+                st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
+                raw_baselines = fd_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
+            elif slate_var1 == 'Paydirt (Auxiliary)':
+                st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
+                raw_baselines = fd_roo_raw
+                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
                   
         contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var1')
         lock_var1 = st.multiselect("Are there any players you want to use in all lineups in the CAPTAIN (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var1')
@@ -195,58 +203,30 @@ with tab2:
         elif site_var1 == 'Fanduel':
             min_sal1 = st.number_input('Min Salary', min_value = 45000, max_value = 59900, value = 59000, step = 100, key='min_sal1')
             max_sal1 = st.number_input('Max Salary', min_value = 45000, max_value = 60000, value = 60000, step = 100, key='max_sal1')
-    
+
         if contest_var1 == 'Small Field GPP':
-            if site_var1 == 'Draftkings':
-                ownframe = raw_baselines.copy()
-                ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own'])
-                ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
-                ownframe['Own%'] = np.where(ownframe['Own%'] > 85, 85, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (600 / ownframe['Own%'].sum())
-                cpt_div = 6
-            elif site_var1 == 'Fanduel':
-                ownframe = raw_baselines.copy()
-                ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/50) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own'])
-                ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/150) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
-                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (500 / ownframe['Own%'].sum())
-                cpt_div = 5
+            ownframe = raw_baselines.copy()
+            if sport_var1 == 'NBA':
+                ownframe['Own'] = ownframe['Small_Own']
+            elif sport_var1 == 'NFL':
+                ownframe['Own'] = ownframe['Small_Field_Own']
         elif contest_var1 == 'Large Field GPP':
-            if site_var1 == 'Draftkings':
-                ownframe = raw_baselines.copy()
-                ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (2.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own'])
-                ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (2.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
-                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (600 / ownframe['Own%'].sum())
-                cpt_div = 6
-            elif site_var1 == 'Fanduel':
-                ownframe = raw_baselines.copy()
-                ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (2.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/50) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own'])
-                ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (2.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/150) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
-                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (500 / ownframe['Own%'].sum())
-                cpt_div = 5
+            ownframe = raw_baselines.copy()
+            if sport_var1 == 'NBA':
+                ownframe['Own'] = ownframe['Large_Own']
+            elif sport_var1 == 'NFL':
+                ownframe['Own'] = ownframe['Large_Field_Own']
         elif contest_var1 == 'Cash':
-            if site_var1 == 'Draftkings':
-                ownframe = raw_baselines.copy()
-                ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own'])
-                ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
-                ownframe['Own%'] = np.where(ownframe['Own%'] > 90, 90, ownframe['Own%'])
-                ownframe['Own'] = ownframe['Own%'] * (600 / ownframe['Own%'].sum())
-                cpt_div = 6
-            elif site_var1 == 'Fanduel':
-                ownframe = raw_baselines.copy()
-                ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/50) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own'])
-                ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/150) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
-                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-                cpt_div = 5
-                ownframe['Own'] = ownframe['Own%'] * (500 / ownframe['Own%'].sum())
-        export_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
+            ownframe = raw_baselines.copy()
+            if sport_var1 == 'NBA':
+                ownframe['Own'] = ownframe['Cash_Own']
+            elif sport_var1 == 'NFL':
+                ownframe['Own'] = ownframe['Cash_Field_Own']
+        export_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'CPT_Own', 'player_id']]
         export_baselines['CPT_Proj'] = export_baselines['Median'] * 1.5
         export_baselines['CPT_Salary'] = export_baselines['Salary'] * 1.5
-        export_baselines['ID'] = export_baselines['Player'].map(dkid_dict)
-        display_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
-        display_baselines['CPT Own'] = display_baselines['Own'] / cpt_div
+        export_baselines['ID'] = export_baselines['player_id']
+        display_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'CPT_Own']]
         display_baselines = display_baselines.sort_values(by='Median', ascending=False)
         display_baselines['cpt_lock'] = np.where(display_baselines['Player'].isin(lock_var1), 1, 0)
         display_baselines['lock'] = np.where(display_baselines['Player'].isin(lock_var2), 1, 0)
@@ -525,12 +505,20 @@ with tab2:
                             final_outcomes_export['FLEX4'] = split_portfolio['FLEX4']
                             final_outcomes_export['FLEX5'] = split_portfolio['FLEX5']
                             
-                            final_outcomes_export['CPT'].replace(dkid_dict, inplace=True)
-                            final_outcomes_export['FLEX1'].replace(dkid_dict, inplace=True)
-                            final_outcomes_export['FLEX2'].replace(dkid_dict, inplace=True)
-                            final_outcomes_export['FLEX3'].replace(dkid_dict, inplace=True)
-                            final_outcomes_export['FLEX4'].replace(dkid_dict, inplace=True)
-                            final_outcomes_export['FLEX5'].replace(dkid_dict, inplace=True)
+                            if sport_var1 == 'NFL':
+                                final_outcomes_export['CPT'].replace(nfl_dk_id_dict, inplace=True)
+                                final_outcomes_export['FLEX1'].replace(nfl_dk_id_dict, inplace=True)
+                                final_outcomes_export['FLEX2'].replace(nfl_dk_id_dict, inplace=True)
+                                final_outcomes_export['FLEX3'].replace(nfl_dk_id_dict, inplace=True)
+                                final_outcomes_export['FLEX4'].replace(nfl_dk_id_dict, inplace=True)
+                                final_outcomes_export['FLEX5'].replace(nfl_dk_id_dict, inplace=True)
+                            elif sport_var1 == 'NBA':
+                                final_outcomes_export['CPT'].replace(nba_dk_id_dict, inplace=True)
+                                final_outcomes_export['FLEX1'].replace(nba_dk_id_dict, inplace=True)
+                                final_outcomes_export['FLEX2'].replace(nba_dk_id_dict, inplace=True)
+                                final_outcomes_export['FLEX3'].replace(nba_dk_id_dict, inplace=True)
+                                final_outcomes_export['FLEX4'].replace(nba_dk_id_dict, inplace=True)
+                                final_outcomes_export['FLEX5'].replace(nba_dk_id_dict, inplace=True)
                             final_outcomes_export['Salary'] = final_outcomes['Cost']
                             final_outcomes_export['Own'] = final_outcomes['Own']
                             final_outcomes_export['Proj'] = final_outcomes['Proj']
@@ -557,11 +545,18 @@ with tab2:
                             final_outcomes_export['FLEX3'] = split_portfolio['FLEX3']
                             final_outcomes_export['FLEX4'] = split_portfolio['FLEX4']
                             
-                            final_outcomes_export['MVP'].replace(fdid_dict, inplace=True)
-                            final_outcomes_export['FLEX1'].replace(fdid_dict, inplace=True)
-                            final_outcomes_export['FLEX2'].replace(fdid_dict, inplace=True)
-                            final_outcomes_export['FLEX3'].replace(fdid_dict, inplace=True)
-                            final_outcomes_export['FLEX4'].replace(fdid_dict, inplace=True)
+                            if sport_var1 == 'NFL':
+                                final_outcomes_export['MVP'].replace(nfl_fd_id_dict, inplace=True)
+                                final_outcomes_export['FLEX1'].replace(nfl_fd_id_dict, inplace=True)
+                                final_outcomes_export['FLEX2'].replace(nfl_fd_id_dict, inplace=True)
+                                final_outcomes_export['FLEX3'].replace(nfl_fd_id_dict, inplace=True)
+                                final_outcomes_export['FLEX4'].replace(nfl_fd_id_dict, inplace=True)
+                            elif sport_var1 == 'NBA':
+                                final_outcomes_export['MVP'].replace(nba_fd_id_dict, inplace=True)
+                                final_outcomes_export['FLEX1'].replace(nba_fd_id_dict, inplace=True)
+                                final_outcomes_export['FLEX2'].replace(nba_fd_id_dict, inplace=True)
+                                final_outcomes_export['FLEX3'].replace(nba_fd_id_dict, inplace=True)
+                                final_outcomes_export['FLEX4'].replace(nba_fd_id_dict, inplace=True)
                             final_outcomes_export['Salary'] = final_outcomes['Cost']
                             final_outcomes_export['Own'] = final_outcomes['Own']
                             final_outcomes_export['Proj'] = final_outcomes['Proj']