Enhance support for WNBA alongside NBA in data loading and lineup initialization; refactor column management and statistics calculations for both leagues.

app.py

@@ -13,109 +13,168 @@ def init_conn():
         uri = st.secrets['mongo_uri']
         client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
         db = client["NBA_DFS"]
+        wnba_db = client["WNBA_DFS"]
 
-        return db
+        return db, wnba_db
     
-db = init_conn()
+db, wnba_db = init_conn()
 
-dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
-dk_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
-fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
-fd_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+dk_nba_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+dk_nba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_nba_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_nba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+
+dk_wnba_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+dk_wnba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_wnba_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_wnba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
 roo_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', '6x%': '{:.2%}', 'GPP%': '{:.2%}'}
 
 @st.cache_data(ttl=60)
-def load_overall_stats():
-    collection = db["DK_Player_Stats"] 
+def load_overall_stats(league: str):
+    if league == 'NBA':
+        collection = db["DK_Player_Stats"] 
+    elif league == 'WNBA':
+        collection = wnba_db["DK_Player_Stats"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
-                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
-    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    if league == 'NBA':
+        raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
+                                'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
+        raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    elif league == 'WNBA':
+        raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "DK_Proj": "Median", "DK_ID": "ID", "DK_Pos": "Position", "DK_Salary": "Salary", "DK_Own": "Own"})
     raw_display = raw_display.loc[raw_display['Median'] > 0]
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     dk_raw = raw_display.sort_values(by='Median', ascending=False)
     
-    collection = db["FD_Player_Stats"] 
+    if league == 'NBA':
+        collection = db["FD_Player_Stats"] 
+    elif league == 'WNBA':
+        collection = wnba_db["FD_Player_Stats"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
-                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
-    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    if league == 'NBA':
+        raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
+                                'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
+        raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    elif league == 'WNBA':
+        raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "FD_Proj": "Median", "FD_ID": "ID", "FD_Pos": "Position", "FD_Salary": "Salary", "FD_Own": "Own"})
     raw_display = raw_display.loc[raw_display['Median'] > 0]
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     fd_raw = raw_display.sort_values(by='Median', ascending=False)
     
-    collection = db["Secondary_DK_Player_Stats"] 
+    if league == 'NBA':
+        collection = db["Secondary_DK_Player_Stats"] 
+    elif league == 'WNBA':
+        collection = wnba_db["Secondary_DK_Player_Stats"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
-                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
-    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    if league == 'NBA':
+        raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
+                                'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
+        raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    elif league == 'WNBA':
+        raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "DK_Proj": "Median", "DK_ID": "ID", "DK_Pos": "Position", "DK_Salary": "Salary", "DK_Own": "Own"})
     raw_display = raw_display.loc[raw_display['Median'] > 0]
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     dk_raw_sec = raw_display.sort_values(by='Median', ascending=False)
     
-    collection = db["Secondary_FD_Player_Stats"] 
+    if league == 'NBA':
+        collection = db["Secondary_FD_Player_Stats"] 
+    elif league == 'WNBA':
+        collection = wnba_db["Secondary_FD_Player_Stats"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
-                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
-    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    if league == 'NBA':
+        raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
+                                'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
+        raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    elif league == 'WNBA':
+        raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "FD_Proj": "Median", "FD_ID": "ID", "FD_Pos": "Position", "FD_Salary": "Salary", "FD_Own": "Own"})
     raw_display = raw_display.loc[raw_display['Median'] > 0]
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     fd_raw_sec = raw_display.sort_values(by='Median', ascending=False)
 
-    collection = db["Player_SD_Range_Of_Outcomes"] 
+    if league == 'NBA':
+        collection = db["Player_SD_Range_Of_Outcomes"] 
+    elif league == 'WNBA':
+        collection = wnba_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['Median'] = raw_display['Median'].replace('', 0).astype(float)
     raw_display = raw_display.rename(columns={"player_id": "player_ID"})
     raw_display = raw_display.loc[raw_display['Median'] > 0]
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     sd_raw = raw_display.sort_values(by='Median', ascending=False)
+    dk_sd_raw = sd_raw[sd_raw['site'] == 'Draftkings']
+    fd_sd_raw = sd_raw[sd_raw['site'] == 'Fanduel']
+    fd_sd_raw['player_ID'] = fd_sd_raw['player_ID'].astype(str)
+    fd_sd_raw['player_ID'] = fd_sd_raw['player_ID'].str.rsplit('-', n=1).str[0].astype(str)
 
     print(sd_raw.head(10))
 
-    collection = db["Player_Range_Of_Outcomes"] 
+    if league == 'NBA':
+        collection = db["Player_Range_Of_Outcomes"] 
+    elif league == 'WNBA':
+        collection = wnba_db["Player_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']]
+    try:
+        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']]
+    except:
+        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.rename(columns={"player_id": "player_ID"})
+    raw_display['Median'] = raw_display['Median'].replace('', 0).astype(float)
     raw_display = raw_display.loc[raw_display['Median'] > 0]
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     roo_raw = raw_display.sort_values(by='Median', ascending=False)
 
     timestamp = raw_display['timestamp'].values[0]
     
-    return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp
+    return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp
 
 @st.cache_data(ttl = 60)
-def init_DK_lineups(slate_desig: str):  
+def init_DK_lineups(slate_desig: str, league: str):  
         
         if slate_desig == 'Main Slate':
-            collection = db['DK_NBA_name_map']
+            if league == 'NBA':
+                collection = db['DK_NBA_name_map']
+            elif league == 'WNBA':
+                collection = wnba_db['DK_WNBA_name_map']
             cursor = collection.find()
             raw_data = pd.DataFrame(list(cursor))
             names_dict = dict(zip(raw_data['key'], raw_data['value']))
-        
-            collection = db["DK_NBA_seed_frame"] 
+
+            if league == 'NBA':
+                collection = db["DK_NBA_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["DK_WNBA_seed_frame"] 
             cursor = collection.find().limit(10000)
         elif slate_desig == 'Secondary':
-            collection = db['DK_NBA_Secondary_name_map']
+            if league == 'NBA':
+                collection = db['DK_NBA_Secondary_name_map']
+            elif league == 'WNBA':
+                collection = wnba_db['DK_WNBA_Secondary_name_map']
             cursor = collection.find()
             raw_data = pd.DataFrame(list(cursor))
             names_dict = dict(zip(raw_data['key'], raw_data['value']))
-        
-            collection = db["DK_NBA_Secondary_seed_frame"] 
+
+            if league == 'NBA':
+                collection = db["DK_NBA_Secondary_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["DK_WNBA_Secondary_seed_frame"] 
             cursor = collection.find().limit(10000)
         elif slate_desig == 'Auxiliary':
             collection = db['DK_NBA_Auxiliary_name_map']
@@ -127,8 +186,13 @@ def init_DK_lineups(slate_desig: str):
             cursor = collection.find().limit(10000) 
 
         raw_display = pd.DataFrame(list(cursor))
-        raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-        dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
+        if league == 'NBA':
+            raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
+        elif league == 'WNBA':
+            raw_display = raw_display[['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
+
         for col in dict_columns:
             raw_display[col] = raw_display[col].map(names_dict)
         DK_seed = raw_display.to_numpy()
@@ -136,16 +200,22 @@ def init_DK_lineups(slate_desig: str):
         return DK_seed
 
 @st.cache_data(ttl = 60)
-def init_DK_SD_lineups(slate_desig: str):
+def init_DK_SD_lineups(slate_desig: str, league: str):
 
         if slate_desig == 'Main Slate':
-            collection = db["DK_NBA_SD_seed_frame"] 
+            if league == 'NBA':
+                collection = db["DK_NBA_SD_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["DK_WNBA_SD_seed_frame"] 
         elif slate_desig == 'Secondary':
-            collection = db["DK_NBA_Secondary_SD_seed_frame"] 
+            if league == 'NBA':
+                collection = db["DK_NBA_Secondary_SD_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["DK_WNBA_Secondary_SD_seed_frame"] 
         elif slate_desig == 'Auxiliary':
             collection = db["DK_NBA_Auxiliary_SD_seed_frame"] 
 
-        cursor = collection.find().limit(10000)
+        cursor = collection.find({"Team_count": {"$lt": 6}}).limit(10000)
     
         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']]
@@ -154,23 +224,35 @@ def init_DK_SD_lineups(slate_desig: str):
         return DK_seed
 
 @st.cache_data(ttl = 60)
-def init_FD_lineups(slate_desig: str):  
+def init_FD_lineups(slate_desig: str, league: str):  
         
         if slate_desig == 'Main Slate':
-            collection = db['FD_NBA_name_map']
+            if league == 'NBA':
+                collection = db['FD_NBA_name_map']
+            elif league == 'WNBA':
+                collection = wnba_db['FD_WNBA_name_map']
             cursor = collection.find()
             raw_data = pd.DataFrame(list(cursor))
             names_dict = dict(zip(raw_data['key'], raw_data['value']))
         
-            collection = db["FD_NBA_seed_frame"] 
+            if league == 'NBA':
+                collection = db["FD_NBA_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["FD_WNBA_seed_frame"] 
             cursor = collection.find().limit(10000)
         elif slate_desig == 'Secondary':
-            collection = db['FD_NBA_Secondary_name_map']
+            if league == 'NBA':
+                collection = db['FD_NBA_Secondary_name_map']
+            elif league == 'WNBA':
+                collection = wnba_db['FD_WNBA_Secondary_name_map']
             cursor = collection.find()
             raw_data = pd.DataFrame(list(cursor))
             names_dict = dict(zip(raw_data['key'], raw_data['value']))
         
-            collection = db["FD_NBA_Secondary_seed_frame"] 
+            if league == 'NBA':
+                collection = db["FD_NBA_Secondary_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["FD_WNBA_Secondary_seed_frame"] 
             cursor = collection.find().limit(10000)
         elif slate_desig == 'Auxiliary':
             collection = db['FD_NBA_Auxiliary_name_map']
@@ -182,8 +264,12 @@ def init_FD_lineups(slate_desig: str):
             cursor = collection.find().limit(10000) 
     
         raw_display = pd.DataFrame(list(cursor))
-        raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-        dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
+        if league == 'NBA':
+            raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
+        elif league == 'WNBA':
+            raw_display = raw_display[['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
         for col in dict_columns:
             raw_display[col] = raw_display[col].map(names_dict)
         FD_seed = raw_display.to_numpy()
@@ -191,16 +277,22 @@ def init_FD_lineups(slate_desig: str):
         return FD_seed
 
 @st.cache_data(ttl = 60)
-def init_FD_SD_lineups(slate_desig: str):
+def init_FD_SD_lineups(slate_desig: str, league: str):
 
         if slate_desig == 'Main Slate':
-            collection = db["FD_NBA_SD_seed_frame"] 
+            if league == 'NBA':
+                collection = db["FD_NBA_SD_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["FD_WNBA_SD_seed_frame"] 
         elif slate_desig == 'Secondary':
-            collection = db["FD_NBA_Secondary_SD_seed_frame"] 
+            if league == 'NBA':
+                collection = db["FD_NBA_Secondary_SD_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["FD_WNBA_Secondary_SD_seed_frame"] 
         elif slate_desig == 'Auxiliary':
             collection = db["FD_NBA_Auxiliary_SD_seed_frame"] 
 
-        cursor = collection.find().limit(10000)
+        cursor = collection.find({"Team_count": {"$lt": 6}}).limit(10000)
     
         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']]
@@ -216,15 +308,23 @@ def convert_df(array):
     array = pd.DataFrame(array, columns=column_names)
     return array.to_csv().encode('utf-8')
 
-dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp = load_overall_stats()
+dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA')
 salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
 id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
 salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
-id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID))
-dk_lineups = pd.DataFrame(columns=dk_columns)
-dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns)
-fd_lineups = pd.DataFrame(columns=fd_columns)
-fd_sd_lineups = pd.DataFrame(columns=fd_sd_columns)
+dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
+fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
+
+dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns)
+dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns)
+fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
+fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
+
+dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
+dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
+fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
+fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
+
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
 tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimals'])
@@ -242,24 +342,33 @@ with tab1:
             with col2:
                 if st.button("Load/Reset Data", key='reset1'):
                     st.cache_data.clear()
-                    dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp = load_overall_stats()
+                    dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA')
                     salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
                     id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
                     salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
-                    id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID))
-                    dk_lineups = pd.DataFrame(columns=dk_columns)
-                    dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns)
-                    fd_lineups = pd.DataFrame(columns=fd_columns)
-                    fd_sd_lineups = pd.DataFrame(columns=fd_sd_columns)
+                    dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
+                    fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
+                    dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns)
+                    dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns)
+                    fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
+                    fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
+
+                    dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
+                    dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
+                    fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
+                    fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
                     t_stamp = f"Last Update: " + str(timestamp) + f" CST"
                     for key in st.session_state.keys():
                         del st.session_state[key]
-        col1, col2, col3, col4, col5 = st.columns(5)
+        col1, col2, col3, col4, col5, col6 = st.columns(6)
         with col1:
             view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2')
         with col2:
-            slate_type_var2 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'), key='slate_type_var2')
+            league_var = st.radio("What League to load:", ('NBA', 'WNBA'), key='league_var')
+            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats(league_var)
         with col3:
+            slate_type_var2 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'), key='slate_type_var2')
+        with col4:
             site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')
 
             # Process site selection
@@ -273,7 +382,7 @@ with tab1:
                     site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
                 elif slate_type_var2 == 'Showdown':
                     site_baselines = sd_raw[sd_raw['site'] == 'Fanduel']
-        with col4:
+        with col5:
             slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary'), key='slate_split')
 
             if slate_split == 'Main Slate':
@@ -287,7 +396,7 @@ with tab1:
                 elif slate_type_var2 == 'Showdown':
                     raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2']
         
-        with col5:
+        with col6:
             split_var2 = st.radio("Slate Range", ('Full Slate Run', 'Specific Games'), key='split_var2')
             if split_var2 == 'Specific Games':
                 team_var2 = st.multiselect('Select teams for ROO', options=raw_baselines['Team'].unique(), key='team_var2')
@@ -339,36 +448,50 @@ with tab2:
     with st.expander("Info and Filters"):
         if st.button("Load/Reset Data", key='reset2'):
             st.cache_data.clear()
-            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp = load_overall_stats()
+            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA')
             salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
             id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
             salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
-            id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID))
-            dk_lineups = pd.DataFrame(columns=dk_columns)
-            dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns)
-            fd_lineups = pd.DataFrame(columns=fd_columns)
-            fd_sd_lineups = pd.DataFrame(columns=fd_sd_columns)
+            dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
+            fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
+            dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns)
+            dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns)
+            fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
+            fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
+
+            dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
+            dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
+            fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
+            fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
             t_stamp = f"Last Update: " + str(timestamp) + f" CST"
             for key in st.session_state.keys():
                 del st.session_state[key]
     
-        col1, col2, col3, col4, col5 = st.columns(5)
+        col1, col2, col3, col4, col5, col6 = st.columns(6)
         with col1:
-            slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
+            league_var2 = st.radio("What League to load:", ('NBA', 'WNBA'), key='league_var2')
         with col2:
+            slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
+        with col3:
             site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
             if 'working_seed' in st.session_state:
                 del st.session_state['working_seed']
-        with col3:
-            slate_type_var1 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'))
         with col4:
-            lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
+            slate_type_var1 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'))
         with col5:
+            lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
+        with col6:
             if site_var1 == 'Draftkings':
-                if slate_type_var1 == 'Regular':
-                    column_names = dk_columns
-                elif slate_type_var1 == 'Showdown':
-                    column_names = dk_sd_columns
+                if league_var2 == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        column_names = dk_nba_columns
+                    elif slate_type_var1 == 'Showdown':
+                        column_names = dk_nba_sd_columns
+                elif league_var2 == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        column_names = dk_wnba_columns
+                    elif slate_type_var1 == 'Showdown':
+                        column_names = dk_wnba_sd_columns
                 
                 player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
                 if player_var1 == 'Specific Players':
@@ -377,10 +500,16 @@ with tab2:
                         player_var2 = dk_raw.Player.values.tolist()
                         
             elif site_var1 == 'Fanduel':
-                if slate_type_var1 == 'Regular':
-                    column_names = fd_columns
-                elif slate_type_var1 == 'Showdown':
-                    column_names = fd_sd_columns
+                if league_var2 == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        column_names = fd_nba_columns
+                    elif slate_type_var1 == 'Showdown':
+                        column_names = fd_nba_sd_columns
+                elif league_var2 == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        column_names = fd_wnba_columns
+                    elif slate_type_var1 == 'Showdown':
+                        column_names = fd_wnba_sd_columns
                 
                 player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
                 if player_var1 == 'Specific Players':
@@ -388,19 +517,42 @@ with tab2:
                 elif player_var1 == 'Full Slate':
                         player_var2 = fd_raw.Player.values.tolist()
         if st.button("Prepare data export", key='data_export'):
-            data_export = st.session_state.working_seed.copy()
+    
             if site_var1 == 'Draftkings':
-                for col_idx in range(8):
-                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
+                if slate_type_var1 == 'Regular':
+                    data_export = init_DK_lineups(slate_var1, league_var2)
+                    data_export_names = data_export.copy()
+                    for col_idx in range(8):
+                        data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
+                elif slate_type_var1 == 'Showdown':
+                    data_export = init_DK_SD_lineups(slate_var1, league_var2)
+                    data_export_names = data_export.copy()
+                    for col_idx in range(6):
+                        data_export[:, col_idx] = np.array([dk_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
+                
             elif site_var1 == 'Fanduel':
-                for col_idx in range(9):
-                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
+                if slate_type_var1 == 'Regular':
+                    data_export = init_FD_lineups(slate_var1, league_var2)
+                    data_export_names = data_export.copy()
+                    for col_idx in range(9):
+                        data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
+                elif slate_type_var1 == 'Showdown':
+                    data_export = init_FD_SD_lineups(slate_var1, league_var2)
+                    data_export_names = data_export.copy()
+                    for col_idx in range(6):
+                        data_export[:, col_idx] = np.array([fd_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
+            st.download_button(
+                label="Export optimals (Names)",
+                data=convert_df(data_export_names),
+                file_name='NBA_optimals_export.csv',
+                mime='text/csv',
+            )
             st.download_button(
-                label="Export optimals set",
+                label="Export optimals (IDs)",
                 data=convert_df(data_export),
                 file_name='NBA_optimals_export.csv',
                 mime='text/csv',
-        )
+            )
             
 
     if site_var1 == 'Draftkings':
@@ -414,17 +566,17 @@ with tab2:
 
         elif 'working_seed' not in st.session_state:
             if slate_type_var1 == 'Regular':
-                st.session_state.working_seed = init_DK_lineups(slate_var1)
+                st.session_state.working_seed = init_DK_lineups(slate_var1, league_var2)
             elif slate_type_var1 == 'Showdown':
-                st.session_state.working_seed = init_DK_SD_lineups(slate_var1)
+                st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var2)
             st.session_state.working_seed = st.session_state.working_seed
             if player_var1 == 'Specific Players':
                 st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
             elif player_var1 == 'Full Slate':
                 if slate_type_var1 == 'Regular':
-                    st.session_state.working_seed = init_DK_lineups(slate_var1)
+                    st.session_state.working_seed = init_DK_lineups(slate_var1, league_var2)
                 elif slate_type_var1 == 'Showdown':
-                    st.session_state.working_seed = init_DK_SD_lineups(slate_var1)
+                    st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var2)
             st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
         
     elif site_var1 == 'Fanduel':
@@ -438,17 +590,17 @@ with tab2:
         
         elif 'working_seed' not in st.session_state:
             if slate_type_var1 == 'Regular':
-                st.session_state.working_seed = init_FD_lineups(slate_var1)
+                st.session_state.working_seed = init_FD_lineups(slate_var1, league_var2)
             elif slate_type_var1 == 'Showdown':
-                st.session_state.working_seed = init_FD_SD_lineups(slate_var1)
+                st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var2)
             st.session_state.working_seed = st.session_state.working_seed
             if player_var1 == 'Specific Players':
                 st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
             elif player_var1 == 'Full Slate':
                 if slate_type_var1 == 'Regular':
-                    st.session_state.working_seed = init_FD_lineups(slate_var1)
+                    st.session_state.working_seed = init_FD_lineups(slate_var1, league_var2)
                 elif slate_type_var1 == 'Showdown':
-                    st.session_state.working_seed = init_FD_SD_lineups(slate_var1)
+                    st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var2)
             st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
 
     export_file = st.session_state.data_export_display.copy()
@@ -457,30 +609,42 @@ with tab2:
             for col_idx in range(8):
                 export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
         elif slate_type_var1 == 'Showdown':
-            for col_idx in range(5):
-                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict_sd)
+            for col_idx in range(6):
+                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(dk_id_dict_sd)
     elif site_var1 == 'Fanduel':
         if slate_type_var1 == 'Regular':
             for col_idx in range(9):
                 export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
         elif slate_type_var1 == 'Showdown':
-            for col_idx in range(5):
-                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict_sd)
+            for col_idx in range(6):
+                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(fd_id_dict_sd)
             
     with st.container():
         if st.button("Reset Optimals", key='reset3'):
             for key in st.session_state.keys():
                 del st.session_state[key]
             if site_var1 == 'Draftkings':
-                if slate_type_var1 == 'Regular':
-                    st.session_state.working_seed = dk_lineups.copy()
-                elif slate_type_var1 == 'Showdown':
-                    st.session_state.working_seed = dk_sd_lineups.copy()
+                if league_var2 == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        st.session_state.working_seed = dk_nba_lineups.copy()
+                    elif slate_type_var1 == 'Showdown':
+                        st.session_state.working_seed = dk_nba_sd_lineups.copy()
+                elif league_var2 == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        st.session_state.working_seed = dk_wnba_lineups.copy()
+                    elif slate_type_var1 == 'Showdown':
+                        st.session_state.working_seed = dk_wnba_sd_lineups.copy()
             elif site_var1 == 'Fanduel':
-                if slate_type_var1 == 'Regular':
-                    st.session_state.working_seed = fd_lineups.copy()
-                elif slate_type_var1 == 'Showdown':
-                    st.session_state.working_seed = fd_sd_lineups.copy()
+                if league_var2 == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        st.session_state.working_seed = fd_nba_lineups.copy()
+                    elif slate_type_var1 == 'Showdown':
+                        st.session_state.working_seed = fd_nba_sd_lineups.copy()
+                elif league_var2 == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        st.session_state.working_seed = fd_wnba_lineups.copy()
+                    elif slate_type_var1 == 'Showdown':
+                        st.session_state.working_seed = fd_wnba_sd_lineups.copy()
         if 'data_export_display' in st.session_state:
             st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
         st.download_button(
@@ -494,96 +658,188 @@ with tab2:
         if 'working_seed' in st.session_state:
             # Create a new dataframe with summary statistics
             if site_var1 == 'Draftkings':
-                if slate_type_var1 == 'Regular':
-                    summary_df = pd.DataFrame({
-                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,8]),
-                            np.mean(st.session_state.working_seed[:,8]),
-                            np.max(st.session_state.working_seed[:,8]),
-                            np.std(st.session_state.working_seed[:,8])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,9]),
-                            np.mean(st.session_state.working_seed[:,9]),
-                            np.max(st.session_state.working_seed[:,9]),
-                            np.std(st.session_state.working_seed[:,9])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,14]),
-                            np.mean(st.session_state.working_seed[:,14]),
-                            np.max(st.session_state.working_seed[:,14]),
-                            np.std(st.session_state.working_seed[:,14])
-                        ]
-                    })
-                elif slate_type_var1 == 'Showdown':
-                    summary_df = pd.DataFrame({
-                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,6]),
-                            np.mean(st.session_state.working_seed[:,6]),
-                            np.max(st.session_state.working_seed[:,6]),
-                            np.std(st.session_state.working_seed[:,6])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,7]),
-                            np.mean(st.session_state.working_seed[:,7]),
-                            np.max(st.session_state.working_seed[:,7]),
-                            np.std(st.session_state.working_seed[:,7])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,12]),
-                            np.mean(st.session_state.working_seed[:,12]),
-                            np.max(st.session_state.working_seed[:,12]),
-                            np.std(st.session_state.working_seed[:,12])
-                        ]
-                    })
+                if league_var2 == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,8]),
+                                np.mean(st.session_state.working_seed[:,8]),
+                                np.max(st.session_state.working_seed[:,8]),
+                                np.std(st.session_state.working_seed[:,8])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,9]),
+                                np.mean(st.session_state.working_seed[:,9]),
+                                np.max(st.session_state.working_seed[:,9]),
+                                np.std(st.session_state.working_seed[:,9])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,14]),
+                                np.mean(st.session_state.working_seed[:,14]),
+                                np.max(st.session_state.working_seed[:,14]),
+                                np.std(st.session_state.working_seed[:,14])
+                            ]
+                        })
+                    elif slate_type_var1 == 'Showdown':
+                        summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,6]),
+                                np.mean(st.session_state.working_seed[:,6]),
+                                np.max(st.session_state.working_seed[:,6]),
+                                np.std(st.session_state.working_seed[:,6])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,7]),
+                                np.mean(st.session_state.working_seed[:,7]),
+                                np.max(st.session_state.working_seed[:,7]),
+                                np.std(st.session_state.working_seed[:,7])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,12]),
+                                np.mean(st.session_state.working_seed[:,12]),
+                                np.max(st.session_state.working_seed[:,12]),
+                                np.std(st.session_state.working_seed[:,12])
+                            ]
+                        })
+                elif league_var2 == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,6]),
+                                np.mean(st.session_state.working_seed[:,6]),
+                                np.max(st.session_state.working_seed[:,6]),
+                                np.std(st.session_state.working_seed[:,6])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,7]),
+                                np.mean(st.session_state.working_seed[:,7]),
+                                np.max(st.session_state.working_seed[:,7]),
+                                np.std(st.session_state.working_seed[:,7])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,12]),
+                                np.mean(st.session_state.working_seed[:,12]),
+                                np.max(st.session_state.working_seed[:,12]),
+                                np.std(st.session_state.working_seed[:,12])
+                            ]
+                        })
+                    elif slate_type_var1 == 'Showdown':
+                        summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,6]),
+                                np.mean(st.session_state.working_seed[:,6]),
+                                np.max(st.session_state.working_seed[:,6]),
+                                np.std(st.session_state.working_seed[:,6])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,7]),
+                                np.mean(st.session_state.working_seed[:,7]),
+                                np.max(st.session_state.working_seed[:,7]),
+                                np.std(st.session_state.working_seed[:,7])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,12]),
+                                np.mean(st.session_state.working_seed[:,12]),
+                                np.max(st.session_state.working_seed[:,12]),
+                                np.std(st.session_state.working_seed[:,12])
+                            ]
+                        })
                 
             elif site_var1 == 'Fanduel':
-                if slate_type_var1 == 'Regular':
-                    summary_df = pd.DataFrame({
+                if league_var2 == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        summary_df = pd.DataFrame({
+                            'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,9]),
+                                np.mean(st.session_state.working_seed[:,9]),
+                                np.max(st.session_state.working_seed[:,9]),
+                                np.std(st.session_state.working_seed[:,9])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,10]),
+                                np.mean(st.session_state.working_seed[:,10]),
+                                np.max(st.session_state.working_seed[:,10]),
+                                np.std(st.session_state.working_seed[:,10])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,15]),
+                                np.mean(st.session_state.working_seed[:,15]),
+                                np.max(st.session_state.working_seed[:,15]),
+                                np.std(st.session_state.working_seed[:,15])
+                            ]
+                        })
+                    elif slate_type_var1 == 'Showdown':
+                        summary_df = pd.DataFrame({
                         'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,9]),
-                            np.mean(st.session_state.working_seed[:,9]),
-                            np.max(st.session_state.working_seed[:,9]),
-                            np.std(st.session_state.working_seed[:,9])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,10]),
-                            np.mean(st.session_state.working_seed[:,10]),
-                            np.max(st.session_state.working_seed[:,10]),
-                            np.std(st.session_state.working_seed[:,10])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,15]),
-                            np.mean(st.session_state.working_seed[:,15]),
-                            np.max(st.session_state.working_seed[:,15]),
-                            np.std(st.session_state.working_seed[:,15])
-                        ]
-                    })
-                elif slate_type_var1 == 'Showdown':
-                    summary_df = pd.DataFrame({
-                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,6]),
-                            np.mean(st.session_state.working_seed[:,6]),
-                            np.max(st.session_state.working_seed[:,6]),
-                            np.std(st.session_state.working_seed[:,6])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,7]),
-                            np.mean(st.session_state.working_seed[:,7]),
-                            np.max(st.session_state.working_seed[:,7]),
-                            np.std(st.session_state.working_seed[:,7])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,12]),
-                            np.mean(st.session_state.working_seed[:,12]),
-                            np.max(st.session_state.working_seed[:,12]),
-                            np.std(st.session_state.working_seed[:,12])
-                        ]
-                    })
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,6]),
+                                np.mean(st.session_state.working_seed[:,6]),
+                                np.max(st.session_state.working_seed[:,6]),
+                                np.std(st.session_state.working_seed[:,6])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,7]),
+                                np.mean(st.session_state.working_seed[:,7]),
+                                np.max(st.session_state.working_seed[:,7]),
+                                np.std(st.session_state.working_seed[:,7])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,12]),
+                                np.mean(st.session_state.working_seed[:,12]),
+                                np.max(st.session_state.working_seed[:,12]),
+                                np.std(st.session_state.working_seed[:,12])
+                            ]
+                        })
+                elif league_var2 == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        summary_df = pd.DataFrame({
+                            'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,7]),
+                                np.mean(st.session_state.working_seed[:,7]),
+                                np.max(st.session_state.working_seed[:,7]),
+                                np.std(st.session_state.working_seed[:,7])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,8]),
+                                np.mean(st.session_state.working_seed[:,8]),
+                                np.max(st.session_state.working_seed[:,8]),
+                                np.std(st.session_state.working_seed[:,8])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,13]),
+                                np.mean(st.session_state.working_seed[:,13]),
+                                np.max(st.session_state.working_seed[:,13]),
+                                np.std(st.session_state.working_seed[:,13])
+                            ]
+                        })
+                    elif slate_type_var1 == 'Showdown':
+                        summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,6]),
+                                np.mean(st.session_state.working_seed[:,6]),
+                                np.max(st.session_state.working_seed[:,6]),
+                                np.std(st.session_state.working_seed[:,6])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,7]),
+                                np.mean(st.session_state.working_seed[:,7]),
+                                np.max(st.session_state.working_seed[:,7]),
+                                np.std(st.session_state.working_seed[:,7])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,12]),
+                                np.mean(st.session_state.working_seed[:,12]),
+                                np.max(st.session_state.working_seed[:,12]),
+                                np.std(st.session_state.working_seed[:,12])
+                            ]
+                        })
 
             # Set the index of the summary dataframe as the "Metric" column
             summary_df = summary_df.set_index('Metric')
@@ -600,16 +856,29 @@ with tab2:
         tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
         with tab1:
             if 'data_export_display' in st.session_state:
-                if slate_type_var1 == 'Regular':
-                    if site_var1 == 'Draftkings':
-                        player_columns = st.session_state.data_export_display.iloc[:, :8]
-                    elif site_var1 == 'Fanduel':
-                        player_columns = st.session_state.data_export_display.iloc[:, :9]
-                elif slate_type_var1 == 'Showdown':
-                    if site_var1 == 'Draftkings':
-                        player_columns = st.session_state.data_export_display.iloc[:, :5]
-                    elif site_var1 == 'Fanduel':
-                        player_columns = st.session_state.data_export_display.iloc[:, :5]
+                if league_var2 == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.data_export_display.iloc[:, :8]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.data_export_display.iloc[:, :9]
+                    elif slate_type_var1 == 'Showdown':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.data_export_display.iloc[:, :5]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.data_export_display.iloc[:, :5]
+                elif league_var2 == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.data_export_display.iloc[:, :7]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.data_export_display.iloc[:, :8]
+                    elif slate_type_var1 == 'Showdown':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.data_export_display.iloc[:, :5]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.data_export_display.iloc[:, :5]
+                
                 
                 # Flatten the DataFrame and count unique values
                 value_counts = player_columns.values.flatten().tolist()
@@ -640,16 +909,28 @@ with tab2:
                 )
         with tab2:
             if 'working_seed' in st.session_state:
-                if slate_type_var1 == 'Regular':
-                    if site_var1 == 'Draftkings':
-                        player_columns = st.session_state.working_seed[:, :8]
-                    elif site_var1 == 'Fanduel':
-                        player_columns = st.session_state.working_seed[:, :9]
-                elif slate_type_var1 == 'Showdown':
-                    if site_var1 == 'Draftkings':
-                        player_columns = st.session_state.working_seed[:, :5]
-                    elif site_var1 == 'Fanduel':
-                        player_columns = st.session_state.working_seed[:, :5]
+                if league_var2 == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.working_seed[:, :8]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.working_seed[:, :9]
+                    elif slate_type_var1 == 'Showdown':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.working_seed[:, :5]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.working_seed[:, :5]
+                elif league_var2 == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.working_seed[:, :7]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.working_seed[:, :8]
+                    elif slate_type_var1 == 'Showdown':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.working_seed[:, :5]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.working_seed[:, :5]
                 
                 # Flatten the DataFrame and count unique values
                 value_counts = player_columns.flatten().tolist()