Refactor app.py to streamline database connections and enhance data retrieval methods. Removed hardcoded credentials and replaced them with environment variables. Updated functions to include a 'split' parameter for limiting data fetched from MongoDB. Added auxiliary seed frame functions for both DraftKings and FanDuel. Improved simulation logic for contest entries and ownership calculations. Cleaned up unused variables and optimized data handling for better performance.

app.py

@@ -8,47 +8,13 @@ import time
 
 @st.cache_resource
 def init_conn():
-        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
-
-        credentials = {
-          "type": "service_account",
-          "project_id": "model-sheets-connect",
-          "private_key_id": st.secrets['model_sheets_connect_pk'],
-          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
-          "client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
-          "client_id": "100369174533302798535",
-          "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%40model-sheets-connect.iam.gserviceaccount.com"
-        }
-        
-        credentials2 = {
-          "type": "service_account",
-          "project_id": "sheets-api-connect-378620",
-          "private_key_id": st.secrets['sheets_api_connect_pk'],
-          "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)
-     
-        NFL_Data = st.secrets['NFL_Data']
-
-        NBA_Data = st.secrets['NBA_Data']
 
-        gc = gspread.service_account_from_dict(credentials)
-        gc2 = gspread.service_account_from_dict(credentials2)
-
-        return gc, gc2, client, NFL_Data, NBA_Data
+        return client
     
-gcservice_account, gcservice_account2, client, NFL_Data, NBA_Data = init_conn()
+client = init_conn()
 
 percentages_format = {'Exposure': '{:.2%}'}
 freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
@@ -56,14 +22,14 @@ dk_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'pro
 fd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
 @st.cache_data(ttl = 599)
-def init_DK_seed_frames(sport): 
+def init_DK_seed_frames(sport, split): 
         if sport == 'NFL':
-            db = client["testing_db"]
+            db = client["NFL_Database"]
         elif sport == 'NBA':
             db = client["NBA_DFS"]
         
         collection = db[f"DK_{sport}_SD_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
@@ -72,32 +38,49 @@ def init_DK_seed_frames(sport):
         return DK_seed
     
 @st.cache_data(ttl = 599)
-def init_DK_secondary_seed_frames(sport):  
+def init_DK_secondary_seed_frames(sport, split):  
         
         if sport == 'NFL':
-            db = client["testing_db"]
+            db = client["NFL_Database"]
         elif sport == 'NBA':
             db = client["NBA_DFS"]
     
         collection = db[f"DK_{sport}_Secondary_SD_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
         DK_second_seed = raw_display.to_numpy()
 
         return DK_second_seed
+
+@st.cache_data(ttl = 599)
+def init_DK_auxiliary_seed_frames(sport, split):  
+        
+        if sport == 'NFL':
+            db = client["NFL_Database"]
+        elif sport == 'NBA':
+            db = client["NBA_DFS"]
+    
+        collection = db[f"DK_{sport}_Auxiliary_SD_seed_frame"] 
+        cursor = collection.find().limit(split)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        DK_auxiliary_seed = raw_display.to_numpy()
+
+        return DK_auxiliary_seed
     
 @st.cache_data(ttl = 599)
-def init_FD_seed_frames(sport):  
+def init_FD_seed_frames(sport, split):  
         
         if sport == 'NFL':
-            db = client["testing_db"]
+            db = client["NFL_Database"]
         elif sport == 'NBA':
             db = client["NBA_DFS"]
     
         collection = db[f"FD_{sport}_SD_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
@@ -106,15 +89,15 @@ def init_FD_seed_frames(sport):
         return FD_seed
     
 @st.cache_data(ttl = 599)
-def init_FD_secondary_seed_frames(sport):  
+def init_FD_secondary_seed_frames(sport, split):  
         
         if sport == 'NFL':
-            db = client["testing_db"]
+            db = client["NFL_Database"]
         elif sport == 'NBA':
             db = client["NBA_DFS"]
     
         collection = db[f"FD_{sport}_Secondary_SD_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
@@ -122,54 +105,90 @@ def init_FD_secondary_seed_frames(sport):
 
         return FD_second_seed
 
+@st.cache_data(ttl = 599)
+def init_FD_auxiliary_seed_frames(sport, split):  
+        
+        if sport == 'NFL':
+            db = client["NFL_Database"]
+        elif sport == 'NBA':
+            db = client["NBA_DFS"]
+    
+        collection = db[f"FD_{sport}_Auxiliary_SD_seed_frame"] 
+        cursor = collection.find().limit(split)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        FD_auxiliary_seed = raw_display.to_numpy()
+
+        return FD_auxiliary_seed
+
 @st.cache_data(ttl = 599)
 def init_baselines(sport):
     if sport == 'NFL':
-        try:
-            sh = gcservice_account.open_by_url(NFL_Data)
-        except:
-            sh = gcservice_account2.open_by_url(NFL_Data)
-        
-        worksheet = sh.worksheet('DK_SD_ROO')
-        load_display = pd.DataFrame(worksheet.get_all_records())
-        load_display.replace('', np.nan, inplace=True)
-        load_display['STDev'] = load_display['Median'] / 4
-        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        db = client["NFL_Database"] 
+        collection = db['DK_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['Small_Field_Own'] = raw_display['Large_Field_Own']
+        raw_display['small_CPT_Own_raw'] = (raw_display['Small_Field_Own'] / 2) * ((100 - (100-raw_display['Small_Field_Own']))/100)
+        small_cpt_own_var = 300 / raw_display['small_CPT_Own_raw'].sum()
+        raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var
+        raw_display['cpt_Median'] = raw_display['Median'] * 1.25
+        raw_display['STDev'] = raw_display['Median'] / 4
+        raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
         
-        dk_raw = load_display.dropna(subset=['Median'])
+        dk_raw = raw_display.dropna(subset=['Median'])
         
-        worksheet = sh.worksheet('FD_SD_ROO')
-        load_display = pd.DataFrame(worksheet.get_all_records())
-        load_display.replace('', np.nan, inplace=True)
-        load_display['STDev'] = load_display['Median'] / 4
-        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        collection = 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['Small_Field_Own'] = raw_display['Large_Field_Own']
+        raw_display['small_CPT_Own'] = raw_display['CPT_Own']
+        raw_display['cpt_Median'] = raw_display['Median']
+        raw_display['STDev'] = raw_display['Median'] / 4
+        raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
         
-        fd_raw = load_display.dropna(subset=['Median'])
+        fd_raw = raw_display.dropna(subset=['Median'])
     
     elif sport == 'NBA':
-         
-        try:
-            sh = gcservice_account.open_by_url(NBA_Data)
-        except:
-            sh = gcservice_account2.open_by_url(NBA_Data)
-        
-        worksheet = sh.worksheet('Player_Level_SD_ROO')
-        load_display = pd.DataFrame(worksheet.get_all_records())
-        load_display.replace('', np.nan, inplace=True)
-        load_display['STDev'] = load_display['Median'] / 4
-        load_display = load_display[load_display['site'] == 'Draftkings']
-        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        db = client["NBA_DFS"] 
+        collection = 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', 'player_id']]
+        raw_display = raw_display[raw_display['site'] == 'Draftkings']
+        raw_display['Small_Field_Own'] = raw_display['Small_Own']
+        raw_display['small_CPT_Own_raw'] = (raw_display['Small_Field_Own'] / 2) * ((100 - (100-raw_display['Small_Field_Own']))/100)
+        small_cpt_own_var = 300 / raw_display['small_CPT_Own_raw'].sum()
+        raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var
+        raw_display['cpt_Median'] = raw_display['Median'] * 1.25
+        raw_display['STDev'] = raw_display['Median'] / 4
+        raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
         
-        dk_raw = load_display.dropna(subset=['Median'])
+        dk_raw = raw_display.dropna(subset=['Median'])
         
-        worksheet = sh.worksheet('Player_Level_SD_ROO')
-        load_display = pd.DataFrame(worksheet.get_all_records())
-        load_display.replace('', np.nan, inplace=True)
-        load_display['STDev'] = load_display['Median'] / 4
-        load_display = load_display[load_display['site'] == 'Fanduel']
-        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        collection = 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', 'player_id']]
+        raw_display = raw_display[raw_display['site'] == 'Fanduel']
+        raw_display['Small_Field_Own'] = raw_display['Large_Own']
+        raw_display['small_CPT_Own'] = raw_display['CPT_Own']
+        raw_display['cpt_Median'] = raw_display['Median']
+        raw_display['STDev'] = raw_display['Median'] / 4
+        raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
         
-        fd_raw = load_display.dropna(subset=['Median'])
+        fd_raw = raw_display.dropna(subset=['Median'])
 
     return dk_raw, fd_raw
 
@@ -193,25 +212,32 @@ def calculate_FD_value_frequencies(np_array):
     return combined_array
 
 @st.cache_data
-def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
+def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size):
     SimVar = 1
     Sim_Winners = []
-    fp_array = seed_frame[:sharp_split, :]
     
     # Pre-vectorize functions
     vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
+    vec_cpt_projection_map = np.vectorize(maps_dict['cpt_projection_map'].__getitem__)
     vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
+    vec_cpt_stdev_map = np.vectorize(maps_dict['cpt_STDev_map'].__getitem__)
     
     st.write('Simulating contest on frames')
     
     while SimVar <= Sim_size:
-        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+        fp_random = seed_frame[np.random.choice(seed_frame.shape[0], Contest_Size)]
             
         sample_arrays1 = np.c_[
-            fp_random, 
+            fp_random,
             np.sum(np.random.normal(
-                loc=vec_projection_map(fp_random[:, :-7]),
-                scale=vec_stdev_map(fp_random[:, :-7])),
+                loc=np.concatenate([
+                    vec_cpt_projection_map(fp_random[:, 0:1]),  # Apply cpt_projection_map to first column
+                    vec_projection_map(fp_random[:, 1:-7])  # Apply original projection to remaining columns
+                ], axis=1),
+                scale=np.concatenate([
+                    vec_cpt_stdev_map(fp_random[:, 0:1]),  # Apply cpt_projection_map to first column
+                    vec_stdev_map(fp_random[:, 1:-7])  # Apply original projection to remaining columns
+                ], axis=1)),
             axis=1)
         ]
 
@@ -224,6 +250,8 @@ def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
         
     return Sim_Winners
 
+dk_raw, fd_raw = init_baselines('NFL')
+
 tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
 with tab2:
     col1, col2 = st.columns([1, 7])
@@ -235,17 +263,11 @@ with tab2:
               dk_raw, fd_raw = init_baselines('NFL')
         
         sport_var1 = st.radio("What sport are you working with?", ('NBA', 'NFL'), key='sport_var1')
-        dk_raw, fd_raw = init_baselines(sport_var1)
-        slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown'), key='slate_var1')
+        slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='slate_var1')
+        sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
             
         site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
         if site_var1 == 'Draftkings':
-            if slate_var1 == 'Showdown':
-                DK_seed = init_DK_seed_frames(sport_var1)
-            elif slate_var1 == 'Secondary Showdown':
-                DK_seed = init_DK_secondary_seed_frames(sport_var1)
-            raw_baselines = dk_raw
-            column_names = dk_columns
             
             team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
             if team_var1 == 'Specific Teams':
@@ -260,12 +282,6 @@ with tab2:
                     stack_var2 = [5, 4, 3, 2, 1, 0]
                     
         elif site_var1 == 'Fanduel':
-            if slate_var1 == 'Showdown':
-                FD_seed = init_FD_seed_frames(sport_var1)
-            elif slate_var1 == 'Secondary Showdown':
-                FD_seed = init_FD_secondary_seed_frames(sport_var1)
-            raw_baselines = fd_raw
-            column_names = fd_columns
             
             team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
             if team_var1 == 'Specific Teams':
@@ -281,11 +297,58 @@ with tab2:
         
 
         if st.button("Prepare data export", key='data_export'):
+            if 'working_seed' in st.session_state:
                 data_export = st.session_state.working_seed.copy()
-                st.download_button(
-                    label="Export optimals set",
-                    data=convert_df(data_export),
-                    file_name='NFL_SD_optimals_export.csv',
+            elif 'working_seed' not in st.session_state:
+                if site_var1 == 'Draftkings':
+                    if slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    raw_baselines = dk_raw
+                    column_names = dk_columns
+                elif site_var1 == 'Fanduel':
+                    if slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
+            data_export = st.session_state.working_seed.copy()
+            for col in range(6):
+                data_export[:, col] = np.array([export_id_dict.get(x, x) for x in data_export[:, col]])
+            st.download_button(
+                label="Export optimals set",
+                data=convert_df(data_export),
+                file_name='NFL_SD_optimals_export.csv',
                     mime='text/csv',
                 )
             
@@ -297,7 +360,24 @@ with tab2:
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
                 elif 'working_seed' not in st.session_state:
-                    st.session_state.working_seed = DK_seed.copy()
+                    if slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)]
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
@@ -308,7 +388,26 @@ with tab2:
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
                 elif 'working_seed' not in st.session_state:
-                    st.session_state.working_seed = FD_seed.copy()
+                    if slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
@@ -327,32 +426,28 @@ with tab1:
               dk_raw, fd_raw = init_baselines('NFL')
         sim_sport_var1 = st.radio("What sport are you working with?", ('NBA', 'NFL'), key='sim_sport_var1')
         dk_raw, fd_raw = init_baselines(sim_sport_var1)
-        sim_slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown'), key='sim_slate_var1')
+        sim_slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='sim_slate_var1')
         sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
         if sim_site_var1 == 'Draftkings':
-            if sim_slate_var1 == 'Showdown':
-                DK_seed = init_DK_seed_frames(sim_sport_var1)
-            elif sim_slate_var1 == 'Secondary Showdown':
-                DK_seed = init_DK_secondary_seed_frames(sim_sport_var1)
             raw_baselines = dk_raw
             column_names = dk_columns
         elif sim_site_var1 == 'Fanduel':
-            if sim_slate_var1 == 'Showdown':
-                FD_seed = init_FD_seed_frames(sim_sport_var1)
-            elif sim_slate_var1 == 'Secondary Showdown':
-                FD_seed = init_FD_secondary_seed_frames(sim_sport_var1)
             raw_baselines = fd_raw
             column_names = fd_columns
-            
         contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
         if contest_var1 == 'Small':
             Contest_Size = 1000
+            st.write("Small field size is 1,000 entrants.")
+            raw_baselines['Own'] = raw_baselines['Small_Field_Own']
+            raw_baselines['CPT_Own'] = raw_baselines['small_CPT_Own']
         elif contest_var1 == 'Medium':
             Contest_Size = 5000
+            st.write("Medium field size is 5,000 entrants.")
         elif contest_var1 == 'Large':
             Contest_Size = 10000
+            st.write("Large field size is 10,000 entrants.")
         elif contest_var1 == 'Custom':
-            Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...")
+            Contest_Size = st.number_input("Insert contest size", value=100, min_value=1, max_value=100000)
         strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'))
         if strength_var1 == 'Not Very':
             sharp_split = 500000
@@ -371,13 +466,16 @@ with tab1:
             if 'working_seed' in st.session_state:
                 maps_dict = {
                         'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)),
                         'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                         'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
                         'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                        'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
                         'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
-                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
+                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
+                        'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
                         }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size)
+                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, Contest_Size)
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                 
                 #st.table(Sim_Winner_Frame)
@@ -404,18 +502,59 @@ with tab1:
                 
             else:
                 if sim_site_var1 == 'Draftkings':
-                    st.session_state.working_seed = DK_seed.copy()
+                    if sim_slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_DK_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif sim_slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_DK_secondary_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif sim_slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_DK_auxiliary_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    raw_baselines = dk_raw
+                    column_names = dk_columns
                 elif sim_site_var1 == 'Fanduel':
-                    st.session_state.working_seed = FD_seed.copy()
+                    if sim_slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_FD_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif sim_slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_FD_secondary_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif sim_slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_FD_auxiliary_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
                 maps_dict = {
                         'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)),
                         'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                         'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
                         'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                        'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
                         'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
-                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
+                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
+                        'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
                         }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size)
+                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, Contest_Size)
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                 
                 #st.table(Sim_Winner_Frame)
@@ -424,10 +563,86 @@ with tab1:
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
                 Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
                 Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+                # Add percent rank columns for ownership at each roster position
+                # Calculate Dupes column for Fanduel
+                if sim_site_var1 == 'Fanduel':
+                    dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank']
+                    own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own']
+                    calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc']
+                    Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True)
+                    Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True)
+                    Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True)
+                    Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True)
+                    Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True)
+                    Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100
+                    Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100
+                    Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100
+                    Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100
+                    Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100
+                    
+                    # Calculate ownership product and convert to probability
+                    Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1)) + 0.0001
+                    
+                    # Calculate average of ownership percent rank columns
+                    Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
+                    
+                    # Calculate dupes formula
+                    Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 59800) / 100)
+                    
+                    # Round and handle negative values
+                    Sim_Winner_Frame['Dupes'] = np.where(
+                        np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
+                        0, 
+                        np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1
+                    )
+                    Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2
+                elif sim_site_var1 == 'Draftkings':
+                    dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
+                    own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own']
+                    calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc']
+                    Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True)
+                    Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True)
+                    Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True)
+                    Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True)
+                    Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True)
+                    Sim_Winner_Frame['FLEX5_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']).rank(pct=True)
+                    Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100
+                    Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100
+                    Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100
+                    Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100
+                    Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100
+                    Sim_Winner_Frame['FLEX5_Own'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']) / 100
+
+                    # Calculate ownership product and convert to probability
+                    Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1))
+                    
+                    # Calculate average of ownership percent rank columns
+                    Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
+                    
+                    # Calculate dupes formula
+                    Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 49800) / 100)
+                    
+                    # Round and handle negative values
+                    Sim_Winner_Frame['Dupes'] = np.where(
+                        np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
+                        0, 
+                        np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1
+                    )
+                    Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2
+                Sim_Winner_Frame['Dupes'] = np.round(Sim_Winner_Frame['Dupes'], 0)
+                Sim_Winner_Frame['Dupes'] = np.where(
+                        np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
+                        0, 
+                        np.round(Sim_Winner_Frame['dupes_calc'], 0)
+                    )
+                Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=dup_count_columns)
+                Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=own_columns)
+                Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=calc_columns)
+
                 Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
                 
                 # Type Casting
-                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
+                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32, 'Dupes': int}
                 Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
                 
                 # Sorting
@@ -436,6 +651,7 @@ with tab1:
                 
                # Data Copying
                 st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                st.session_state.Sim_Winner_Export.iloc[:, 0:6] = st.session_state.Sim_Winner_Export.iloc[:, 0:6].apply(lambda x: x.map(export_id_dict))
                 
                 # Data Copying
                 st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
@@ -450,7 +666,10 @@ with tab1:
             freq_working['Freq'] = freq_working['Freq'].astype(int)
             freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map'])
             if sim_site_var1 == 'Draftkings':
-                freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
+                if sim_sport_var1 == 'NFL':
+                    freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
+                elif sim_sport_var1 == 'NBA':
+                    freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
             elif sim_site_var1 == 'Fanduel':
                 freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
             freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100
@@ -462,15 +681,16 @@ with tab1:
             if sim_site_var1 == 'Draftkings':
                 cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                cpt_own_div = 600
             elif sim_site_var1 == 'Fanduel':
                 cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                cpt_own_div = 500
             cpt_working['Freq'] = cpt_working['Freq'].astype(int)
             cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
-            cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
-            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['Own_map']) / cpt_own_div
+            if sim_sport_var1 == 'NFL':
+                cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
+            elif sim_sport_var1 == 'NBA':
+                cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) * 1.5
+            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100
             cpt_working['Exposure'] = cpt_working['Freq']/(1000)
             cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
             cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map'])
@@ -487,10 +707,13 @@ with tab1:
             flex_working['Freq'] = flex_working['Freq'].astype(int)
             flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map'])
             if sim_site_var1 == 'Draftkings':
-                flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5
+                if sim_sport_var1 == 'NFL':
+                    flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5
+                elif sim_sport_var1 == 'NBA':
+                    flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
             elif sim_site_var1 == 'Fanduel':
                 flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
-            flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['Own_map']) / cpt_own_div)
+            flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_Own_map']) / 100)
             flex_working['Exposure'] = flex_working['Freq']/(1000)
             flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
             flex_working['Team'] = flex_working['Player'].map(maps_dict['Team_map'])
@@ -505,16 +728,6 @@ with tab1:
             team_working['Freq'] = team_working['Freq'].astype(int)
             team_working['Exposure'] = team_working['Freq']/(1000)
             st.session_state.team_freq = team_working.copy()
-
-            if sim_site_var1 == 'Draftkings':
-                stack_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,10:11].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                stack_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,9:10].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            stack_working['Freq'] = stack_working['Freq'].astype(int)
-            stack_working['Exposure'] = stack_working['Freq']/(1000)
-            st.session_state.stack_freq = stack_working.copy()
             
         with st.container():
             if st.button("Reset Sim", key='reset_sim'):