restrucuring to NBA

src/database.py

@@ -8,7 +8,7 @@ def init_conn():
     if not uri:
         uri = st.secrets['mongo_uri']
     client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
-    db = client["NFL_Database"]
+    db = client["NBA_Database"]
     return db
 
 # Initialize the database connection

src/sim_func_hold/regular_functions.py

@@ -30,8 +30,8 @@ def init_DK_seed_frames(slate_var, sharp_split):
     cursor = collection.find().limit(sharp_split)
 
     raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+    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']
     for col in dict_columns:
         raw_display[col] = raw_display[col].map(names_dict)
     DK_seed = raw_display.to_numpy()
@@ -39,7 +39,7 @@ def init_DK_seed_frames(slate_var, sharp_split):
     return DK_seed
 
 @st.cache_data(ttl = 599)
-def init_FD_seed_frames(slate_var, sharp_split): 
+def init_FD_seed_frames(slate_var, sharp_split):
 
     if slate_var == 'Main Slate':
         collection = db['FD_NFL_name_map']
@@ -64,8 +64,8 @@ def init_FD_seed_frames(slate_var, sharp_split):
         cursor = collection.find().limit(sharp_split)
 
     raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+    raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+    dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
     for col in dict_columns:
         raw_display[col] = raw_display[col].map(names_dict)
     FD_seed = raw_display.to_numpy()
@@ -74,24 +74,20 @@ def init_FD_seed_frames(slate_var, sharp_split):
 
 @st.cache_data(ttl = 599)
 def init_baselines(slate_var):
-    collection = db["DK_NFL_ROO"] 
+    collection = db["Player_Range_Of_Outcomes"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
     raw_display = raw_display[raw_display['slate'] == slate_var]
     raw_display = raw_display[raw_display['version'] == 'overall']
-    dk_raw = 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']]
+    dk_raw = raw_display[raw_display['site'] == 'Draftkings']
+    dk_raw = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
+                          'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_ID', 'site']]
     dk_raw['STDev'] = (dk_raw['Ceiling'] - dk_raw['Floor']) / 4
 
-    collection = db["FD_NFL_ROO"] 
-    cursor = collection.find()
-
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[raw_display['slate'] == slate_var]
-    raw_display = raw_display[raw_display['version'] == 'overall']
-    fd_raw = 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']]
+    fd_raw = raw_display[raw_display['site'] == 'FanDuel']
+    fd_raw = fd_raw[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
+                          'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_ID', 'site']]
     fd_raw['STDev'] = (fd_raw['Ceiling'] - fd_raw['Floor']) / 4
 
     return dk_raw, fd_raw
@@ -103,7 +99,7 @@ def convert_df(array):
 
 @st.cache_data
 def calculate_DK_value_frequencies(np_array):
-    unique, counts = np.unique(np_array[:, :9], return_counts=True)
+    unique, counts = np.unique(np_array[:, :8], return_counts=True)
     frequencies = counts / len(np_array)  # Normalize by the number of rows 
     combined_array = np.column_stack((unique, frequencies))  
     return combined_array 
@@ -116,7 +112,7 @@ def calculate_FD_value_frequencies(np_array):
     return combined_array
 
 @st.cache_data
-def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size):
+def sim_contest(site_var, Sim_size, seed_frame, maps_dict, Contest_Size):
     SimVar = 1
     Sim_Winners = []
     fp_array = seed_frame.copy()
@@ -139,7 +135,10 @@ def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size):
 
         sample_arrays = sample_arrays1
 
-        final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
+        if site_var == 'Draftkings':
+            final_array = sample_arrays[sample_arrays[:, 9].argsort()[::-1]]
+        elif site_var == 'FanDuel':
+            final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
         best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
         Sim_Winners.append(best_lineup)
         SimVar += 1

src/sim_func_hold/showdown_functions.py

@@ -30,7 +30,7 @@ def init_FD_SD_seed_frames(slate, split, translation_dict):
 
 @st.cache_data(ttl = 599)
 def init_SD_baselines(slate_var):
-    collection = db['DK_SD_NFL_ROO'] 
+    collection = db['Player_SD_Range_Of_Outcomes'] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
@@ -46,25 +46,10 @@ def init_SD_baselines(slate_var):
     raw_display['STDev'] = raw_display['Median'] / 4
     raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
     
-    dk_raw = raw_display.dropna(subset=['Median'])
-    
-    collection = db['FD_SD_NFL_ROO'] 
-    cursor = collection.find()
+    raw_display = raw_display.dropna(subset=['Median'])
 
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[raw_display['version'] == 'overall']
-    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
-                                'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
-    raw_display = raw_display.rename(columns={'player_id': 'player_ID'})
-    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
-    
-    fd_raw = raw_display.dropna(subset=['Median'])
+    dk_raw = raw_display[raw_display['site'] == 'Draftkings']
+    fd_raw = raw_display[raw_display['site'] == 'FanDuel']
 
     return dk_raw, fd_raw
 

src/streamlit_app.py

@@ -15,18 +15,18 @@ from sim_func_hold.showdown_functions import *
 
 percentages_format = {'Exposure': '{:.2%}'}
 freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
-dk_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
-fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C', '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_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
 showdown_selections = ['Showdown #1', 'Showdown #2', 'Showdown #3', 'Showdown #4', 'Showdown #5', 'Showdown #6', 'Showdown #7', 'Showdown #8', 'Showdown #9', 'Showdown #10', 'Showdown #11', 'Showdown #12', 'Showdown #13', 'Showdown #14', 'Showdown #15']
-dk_db_showdown_selections = ['DK_NFL_SD_seed_frame_Showdown #1', 'DK_NFL_SD_seed_frame_Showdown #2', 'DK_NFL_SD_seed_frame_Showdown #3', 'DK_NFL_SD_seed_frame_Showdown #4', 'DK_NFL_SD_seed_frame_Showdown #5', 'DK_NFL_SD_seed_frame_Showdown #6',
-'DK_NFL_SD_seed_frame_Showdown #7', 'DK_NFL_SD_seed_frame_Showdown #8', 'DK_NFL_SD_seed_frame_Showdown #9', 'DK_NFL_SD_seed_frame_Showdown #10', 'DK_NFL_SD_seed_frame_Showdown #11', 'DK_NFL_SD_seed_frame_Showdown #12', 'DK_NFL_SD_seed_frame_Showdown #13',
-'DK_NFL_SD_seed_frame_Showdown #14', 'DK_NFL_SD_seed_frame_Showdown #15']
-fd_db_showdown_selections = ['FD_NFL_SD_seed_frame_Showdown #1', 'FD_NFL_SD_seed_frame_Showdown #2', 'FD_NFL_SD_seed_frame_Showdown #3', 'FD_NFL_SD_seed_frame_Showdown #4', 'FD_NFL_SD_seed_frame_Showdown #5', 'FD_NFL_SD_seed_frame_Showdown #6',
-'FD_NFL_SD_seed_frame_Showdown #7', 'FD_NFL_SD_seed_frame_Showdown #8', 'FD_NFL_SD_seed_frame_Showdown #9', 'FD_NFL_SD_seed_frame_Showdown #10', 'FD_NFL_SD_seed_frame_Showdown #11', 'FD_NFL_SD_seed_frame_Showdown #12', 'FD_NFL_SD_seed_frame_Showdown #13',
-'FD_NFL_SD_seed_frame_Showdown #14', 'FD_NFL_SD_seed_frame_Showdown #15']
+dk_db_showdown_selections = ['DK_NBA_SD_seed_frame_Showdown #1', 'DK_NBA_SD_seed_frame_Showdown #2', 'DK_NBA_SD_seed_frame_Showdown #3', 'DK_NBA_SD_seed_frame_Showdown #4', 'DK_NBA_SD_seed_frame_Showdown #5', 'DK_NBA_SD_seed_frame_Showdown #6',
+'DK_NBA_SD_seed_frame_Showdown #7', 'DK_NBA_SD_seed_frame_Showdown #8', 'DK_NBA_SD_seed_frame_Showdown #9', 'DK_NBA_SD_seed_frame_Showdown #10', 'DK_NBA_SD_seed_frame_Showdown #11', 'DK_NBA_SD_seed_frame_Showdown #12', 'DK_NBA_SD_seed_frame_Showdown #13',
+'DK_NBA_SD_seed_frame_Showdown #14', 'DK_NBA_SD_seed_frame_Showdown #15']
+fd_db_showdown_selections = ['FD_NBA_SD_seed_frame_Showdown #1', 'FD_NBA_SD_seed_frame_Showdown #2', 'FD_NBA_SD_seed_frame_Showdown #3', 'FD_NBA_SD_seed_frame_Showdown #4', 'FD_NBA_SD_seed_frame_Showdown #5', 'FD_NBA_SD_seed_frame_Showdown #6',
+'FD_NBA_SD_seed_frame_Showdown #7', 'FD_NBA_SD_seed_frame_Showdown #8', 'FD_NBA_SD_seed_frame_Showdown #9', 'FD_NBA_SD_seed_frame_Showdown #10', 'FD_NBA_SD_seed_frame_Showdown #11', 'FD_NBA_SD_seed_frame_Showdown #12', 'FD_NBA_SD_seed_frame_Showdown #13',
+'FD_NBA_SD_seed_frame_Showdown #14', 'FD_NBA_SD_seed_frame_Showdown #15']
 
 dk_showdown_db_translation = dict(zip(showdown_selections, dk_db_showdown_selections))
 fd_showdown_db_translation = dict(zip(showdown_selections, fd_db_showdown_selections))
@@ -80,9 +80,10 @@ st.markdown("""
 
 @st.cache_resource(ttl=60)
 def define_dk_showdown_slates():
-    collection = db["DK_SD_NFL_ROO"]
+    collection = db["Player_SD_Range_Of_Outcomes"]
     cursor = collection.find()
     raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[raw_display['site'] == 'Draftkings']
     unique_slates = raw_display['slate'].unique()
 
     slate_names = []
@@ -97,9 +98,10 @@ def define_dk_showdown_slates():
 
 @st.cache_resource(ttl=60)
 def define_fd_showdown_slates():
-    collection = db["FD_SD_NFL_ROO"]
+    collection = db["Player_SD_Range_Of_Outcomes"]
     cursor = collection.find()
     raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[raw_display['site'] == 'FanDuel']
     unique_slates = raw_display['slate'].unique()
 
     slate_names = []
@@ -268,101 +270,116 @@ if selected_tab == "Regular Slate Contest Sims":
             st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()    
 
         if sim_site_var1 == 'Draftkings':
-            freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
+            pg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
         elif sim_site_var1 == 'Fanduel':
-            freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
+            pg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:2].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-        freq_working['Freq'] = freq_working['Freq'].astype(int)
-        freq_working['Position'] = freq_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-        freq_working['Salary'] = freq_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-        freq_working['Proj Own'] = freq_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-        freq_working['Exposure'] = freq_working['Freq']/(1000)
-        freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
-        freq_working['Team'] = freq_working['Player'].map(st.session_state.maps_dict['Team_map'])
-        st.session_state.player_freq = freq_working.copy()
-
+        pg_working['Freq'] = pg_working['Freq'].astype(int)
+        pg_working['Position'] = pg_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        pg_working['Salary'] = pg_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        pg_working['Proj Own'] = pg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        pg_working['Exposure'] = pg_working['Freq']/(1000)
+        pg_working['Edge'] = pg_working['Exposure'] - pg_working['Proj Own']
+        pg_working['Team'] = pg_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.pg_freq = pg_working.copy()
+        
         if sim_site_var1 == 'Draftkings':
-            qb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
+            sg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:2].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
         elif sim_site_var1 == 'Fanduel':
-            qb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
+            sg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:4].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-        qb_working['Freq'] = qb_working['Freq'].astype(int)
-        qb_working['Position'] = qb_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-        qb_working['Salary'] = qb_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-        qb_working['Proj Own'] = qb_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-        qb_working['Exposure'] = qb_working['Freq']/(1000)
-        qb_working['Edge'] = qb_working['Exposure'] - qb_working['Proj Own']
-        qb_working['Team'] = qb_working['Player'].map(st.session_state.maps_dict['Team_map'])
-        st.session_state.qb_freq = qb_working.copy()
+        sg_working['Freq'] = sg_working['Freq'].astype(int)
+        sg_working['Position'] = sg_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        sg_working['Salary'] = sg_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        sg_working['Proj Own'] = sg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        sg_working['Exposure'] = sg_working['Freq']/(1000)
+        sg_working['Edge'] = sg_working['Exposure'] - sg_working['Proj Own']
+        sg_working['Team'] = sg_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.sg_freq = sg_working.copy()
         
         if sim_site_var1 == 'Draftkings':
-            rbwrte_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:7].values, return_counts=True)),
+            sf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:3].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
         elif sim_site_var1 == 'Fanduel':
-            rbwrte_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:7].values, return_counts=True)),
+            sf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:6].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-        rbwrte_working['Freq'] = rbwrte_working['Freq'].astype(int)
-        rbwrte_working['Position'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-        rbwrte_working['Salary'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-        rbwrte_working['Proj Own'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-        rbwrte_working['Exposure'] = rbwrte_working['Freq']/(1000)
-        rbwrte_working['Edge'] = rbwrte_working['Exposure'] - rbwrte_working['Proj Own']
-        rbwrte_working['Team'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Team_map'])
-        st.session_state.rbwrte_freq = rbwrte_working.copy()
+        sf_working['Freq'] = sf_working['Freq'].astype(int)
+        sf_working['Position'] = sf_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        sf_working['Salary'] = sf_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        sf_working['Proj Own'] = sf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        sf_working['Exposure'] = sf_working['Freq']/(1000)
+        sf_working['Edge'] = sf_working['Exposure'] - sf_working['Proj Own']
+        sf_working['Team'] = sf_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.sf_freq = sf_working.copy()
         
         if sim_site_var1 == 'Draftkings':
-            rb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:3].values, return_counts=True)),
+            pf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:4].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
         elif sim_site_var1 == 'Fanduel':
-            rb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:3].values, return_counts=True)),
+            pf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:8].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-        rb_working['Freq'] = rb_working['Freq'].astype(int)
-        rb_working['Position'] = rb_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-        rb_working['Salary'] = rb_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-        rb_working['Proj Own'] = rb_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-        rb_working['Exposure'] = rb_working['Freq']/(1000)
-        rb_working['Edge'] = rb_working['Exposure'] - rb_working['Proj Own']
-        rb_working['Team'] = rb_working['Player'].map(st.session_state.maps_dict['Team_map'])
-        st.session_state.rb_freq = rb_working.copy()
+        pf_working['Freq'] = pf_working['Freq'].astype(int)
+        pf_working['Position'] = pf_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        pf_working['Salary'] = pf_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        pf_working['Proj Own'] = pf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        pf_working['Exposure'] = pf_working['Freq']/(1000)
+        pf_working['Edge'] = pf_working['Exposure'] - pf_working['Proj Own']
+        pf_working['Team'] = pf_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.pf_freq = pf_working.copy()
         
         if sim_site_var1 == 'Draftkings':
-            wr_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:6].values, return_counts=True)),
+            c_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:5].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
         elif sim_site_var1 == 'Fanduel':
-            wr_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:6].values, return_counts=True)),
+            c_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-        wr_working['Freq'] = wr_working['Freq'].astype(int)
-        wr_working['Position'] = wr_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-        wr_working['Salary'] = wr_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-        wr_working['Proj Own'] = wr_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-        wr_working['Exposure'] = wr_working['Freq']/(1000)
-        wr_working['Edge'] = wr_working['Exposure'] - wr_working['Proj Own']
-        wr_working['Team'] = wr_working['Player'].map(st.session_state.maps_dict['Team_map'])
-        st.session_state.wr_freq = wr_working.copy()
+        c_working['Freq'] = c_working['Freq'].astype(int)
+        c_working['Position'] = c_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        c_working['Salary'] = c_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        c_working['Proj Own'] = c_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        c_working['Exposure'] = c_working['Freq']/(1000)
+        c_working['Edge'] = c_working['Exposure'] - c_working['Proj Own']
+        c_working['Team'] = c_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.c_freq = c_working.copy()
         
         if sim_site_var1 == 'Draftkings':
-            te_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
+            g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,5:6].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
         elif sim_site_var1 == 'Fanduel':
-            te_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
+            g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:4].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-        te_working['Freq'] = te_working['Freq'].astype(int)
-        te_working['Position'] = te_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-        te_working['Salary'] = te_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-        te_working['Proj Own'] = te_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-        te_working['Exposure'] = te_working['Freq']/(1000)
-        te_working['Edge'] = te_working['Exposure'] - te_working['Proj Own']
-        te_working['Team'] = te_working['Player'].map(st.session_state.maps_dict['Team_map'])
-        st.session_state.te_freq = te_working.copy()
+        g_working['Freq'] = g_working['Freq'].astype(int)
+        g_working['Position'] = g_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        g_working['Salary'] = g_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        g_working['Proj Own'] = g_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        g_working['Exposure'] = g_working['Freq']/(1000)
+        g_working['Edge'] = g_working['Exposure'] - g_working['Proj Own']
+        g_working['Team'] = g_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.g_freq = g_working.copy()
         
         if sim_site_var1 == 'Draftkings':
-            flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
+            f_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
         elif sim_site_var1 == 'Fanduel':
+            f_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        f_working['Freq'] = f_working['Freq'].astype(int)
+        f_working['Position'] = f_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        f_working['Salary'] = f_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        f_working['Proj Own'] = f_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        f_working['Exposure'] = f_working['Freq']/(1000)
+        f_working['Edge'] = f_working['Exposure'] - f_working['Proj Own']
+        f_working['Team'] = f_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.f_freq = f_working.copy()
+
+        if sim_site_var1 == 'Draftkings':
             flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
         flex_working['Freq'] = flex_working['Freq'].astype(int)
         flex_working['Position'] = flex_working['Player'].map(st.session_state.maps_dict['Pos_map'])
         flex_working['Salary'] = flex_working['Player'].map(st.session_state.maps_dict['Salary_map'])
@@ -371,24 +388,9 @@ if selected_tab == "Regular Slate Contest Sims":
         flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
         flex_working['Team'] = flex_working['Player'].map(st.session_state.maps_dict['Team_map'])
         st.session_state.flex_freq = flex_working.copy()
-        
-        if sim_site_var1 == 'Draftkings':
-            dst_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
-                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-        elif sim_site_var1 == 'Fanduel':
-            dst_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
-                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-        dst_working['Freq'] = dst_working['Freq'].astype(int)
-        dst_working['Position'] = dst_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-        dst_working['Salary'] = dst_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-        dst_working['Proj Own'] = dst_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-        dst_working['Exposure'] = dst_working['Freq']/(1000)
-        dst_working['Edge'] = dst_working['Exposure'] - dst_working['Proj Own']
-        dst_working['Team'] = dst_working['Player'].map(st.session_state.maps_dict['Team_map'])
-        st.session_state.dst_freq = dst_working.copy()
 
         if sim_site_var1 == 'Draftkings':
-            team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
+            team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.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':
             team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
@@ -397,215 +399,112 @@ if selected_tab == "Regular Slate Contest Sims":
         team_working['Exposure'] = team_working['Freq']/(1000)
         st.session_state.team_freq = team_working.copy()
         
-    with st.container():
-        if st.button("Reset Sim", key='reset_sim'):
-            for key in st.session_state.keys():
-                del st.session_state[key]
-        if 'player_freq' in st.session_state: 
-            player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
-            if player_split_var2 == 'Specific Players':
-                        find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
-            elif player_split_var2 == 'Full Players':
-                        find_var2 = st.session_state.player_freq.Player.values.tolist()
-
-            if player_split_var2 == 'Specific Players':
-                        st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
-            if player_split_var2 == 'Full Players':
-                        st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
-        if 'Sim_Winner_Display' in st.session_state:
-            st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-        if 'Sim_Winner_Export' in st.session_state:
-            st.download_button(
-                label="Export Full Frame",
-                data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
-                file_name='MLB_consim_export.csv',
-                mime='text/csv',
-            )  
-    tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics'])
-    
-    with tab1:
-        if 'Sim_Winner_Display' in st.session_state:
-            # Create a new dataframe with summary statistics
-            summary_df = pd.DataFrame({
-                'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                'Salary': [
-                    st.session_state.Sim_Winner_Display['salary'].min(),
-                    st.session_state.Sim_Winner_Display['salary'].mean(),
-                    st.session_state.Sim_Winner_Display['salary'].max(),
-                    st.session_state.Sim_Winner_Display['salary'].std()
-                ],
-                'Proj': [
-                    st.session_state.Sim_Winner_Display['proj'].min(),
-                    st.session_state.Sim_Winner_Display['proj'].mean(),
-                    st.session_state.Sim_Winner_Display['proj'].max(),
-                    st.session_state.Sim_Winner_Display['proj'].std()
-                ],
-                'Own': [
-                    st.session_state.Sim_Winner_Display['Own'].min(),
-                    st.session_state.Sim_Winner_Display['Own'].mean(),
-                    st.session_state.Sim_Winner_Display['Own'].max(),
-                    st.session_state.Sim_Winner_Display['Own'].std()
-                ],
-                'Fantasy': [
-                    st.session_state.Sim_Winner_Display['Fantasy'].min(),
-                    st.session_state.Sim_Winner_Display['Fantasy'].mean(),
-                    st.session_state.Sim_Winner_Display['Fantasy'].max(),
-                    st.session_state.Sim_Winner_Display['Fantasy'].std()
-                ],
-                'GPP_Proj': [
-                    st.session_state.Sim_Winner_Display['GPP_Proj'].min(),
-                    st.session_state.Sim_Winner_Display['GPP_Proj'].mean(),
-                    st.session_state.Sim_Winner_Display['GPP_Proj'].max(),
-                    st.session_state.Sim_Winner_Display['GPP_Proj'].std()
-                ]
-            })
-
-            # Set the index of the summary dataframe as the "Metric" column
-            summary_df = summary_df.set_index('Metric')
-
-            # Display the summary dataframe
-            st.subheader("Winning Frame Statistics")
-            st.dataframe(summary_df.style.format({
-                'Salary': '{:.2f}',
-                'Proj': '{:.2f}',
-                'Fantasy': '{:.2f}',
-                'GPP_Proj': '{:.2f}'
-            }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own', 'Fantasy', 'GPP_Proj']), use_container_width=True)
-
-    with tab2:
-        if 'Sim_Winner_Display' in st.session_state:
-            # Apply position mapping to FLEX column
-            flex_positions = st.session_state.freq_copy['FLEX'].map(st.session_state.maps_dict['Pos_map'])
-            
-            # Count occurrences of each position in FLEX
-            flex_counts = flex_positions.value_counts()
-            
-            # Calculate average statistics for each FLEX position
-            flex_stats = st.session_state.freq_copy.groupby(flex_positions).agg({
-                'proj': 'mean',
-                'Own': 'mean',
-                'Fantasy': 'mean',
-                'GPP_Proj': 'mean'
-            })
-            
-            # Combine counts and average statistics
-            flex_summary = pd.concat([flex_counts, flex_stats], axis=1)
-            flex_summary.columns = ['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
-            flex_summary = flex_summary.reset_index()
-            flex_summary.columns = ['Position', 'Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
-            
-            # Display the summary dataframe
-            st.subheader("FLEX Position Statistics")
-            st.dataframe(flex_summary.style.format({
-                'Count': '{:.0f}',
-                'Avg Proj': '{:.2f}',
-                'Avg Fantasy': '{:.2f}',
-                'Avg GPP_Proj': '{:.2f}'
-            }).background_gradient(cmap='RdYlGn', axis=0, subset=['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']), use_container_width=True)
-            
-        else:
-            st.write("Simulation data or position mapping not available.")
-    with st.container():
-        tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB-WR-TE Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures', 'Team Exposures'])
-        with tab1:
-            if 'player_freq' in st.session_state:
-                
-                st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+        with st.container():
+            if st.button("Reset Sim", key='reset_sim'):
+                for key in st.session_state.keys():
+                    del st.session_state[key]
+            if 'player_freq' in st.session_state: 
+                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
+                if player_split_var2 == 'Specific Players':
+                            find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
+                elif player_split_var2 == 'Full Players':
+                            find_var2 = st.session_state.player_freq.Player.values.tolist()
+
+                if player_split_var2 == 'Specific Players':
+                            st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
+                if player_split_var2 == 'Full Players':
+                            st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
+            if 'Sim_Winner_Display' in st.session_state:
+                st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+            if 'Sim_Winner_Export' in st.session_state:
                 st.download_button(
-                    label="Export Exposures",
-                    data=st.session_state.player_freq.to_csv().encode('utf-8'),
-                    file_name='player_freq_export.csv',
+                    label="Export Full Frame",
+                    data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
+                    file_name='MLB_consim_export.csv',
                     mime='text/csv',
-                    key='overall'
-                )
+                )  
+        tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics'])
+        
+        with tab1:
+            if 'Sim_Winner_Display' in st.session_state:
+                # Create a new dataframe with summary statistics
+                summary_df = pd.DataFrame({
+                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                    'Salary': [
+                        st.session_state.Sim_Winner_Display['salary'].min(),
+                        st.session_state.Sim_Winner_Display['salary'].mean(),
+                        st.session_state.Sim_Winner_Display['salary'].max(),
+                        st.session_state.Sim_Winner_Display['salary'].std()
+                    ],
+                    'Proj': [
+                        st.session_state.Sim_Winner_Display['proj'].min(),
+                        st.session_state.Sim_Winner_Display['proj'].mean(),
+                        st.session_state.Sim_Winner_Display['proj'].max(),
+                        st.session_state.Sim_Winner_Display['proj'].std()
+                    ],
+                    'Own': [
+                        st.session_state.Sim_Winner_Display['Own'].min(),
+                        st.session_state.Sim_Winner_Display['Own'].mean(),
+                        st.session_state.Sim_Winner_Display['Own'].max(),
+                        st.session_state.Sim_Winner_Display['Own'].std()
+                    ],
+                    'Fantasy': [
+                        st.session_state.Sim_Winner_Display['Fantasy'].min(),
+                        st.session_state.Sim_Winner_Display['Fantasy'].mean(),
+                        st.session_state.Sim_Winner_Display['Fantasy'].max(),
+                        st.session_state.Sim_Winner_Display['Fantasy'].std()
+                    ],
+                    'GPP_Proj': [
+                        st.session_state.Sim_Winner_Display['GPP_Proj'].min(),
+                        st.session_state.Sim_Winner_Display['GPP_Proj'].mean(),
+                        st.session_state.Sim_Winner_Display['GPP_Proj'].max(),
+                        st.session_state.Sim_Winner_Display['GPP_Proj'].std()
+                    ]
+                })
+
+                # Set the index of the summary dataframe as the "Metric" column
+                summary_df = summary_df.set_index('Metric')
+
+                # Display the summary dataframe
+                st.subheader("Winning Frame Statistics")
+                st.dataframe(summary_df.style.format({
+                    'Salary': '{:.2f}',
+                    'Proj': '{:.2f}',
+                    'Fantasy': '{:.2f}',
+                    'GPP_Proj': '{:.2f}'
+                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own', 'Fantasy', 'GPP_Proj']), use_container_width=True)
+
         with tab2:
-            if 'qb_freq' in st.session_state:
-                
-                st.dataframe(st.session_state.qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=st.session_state.qb_freq.to_csv().encode('utf-8'),
-                    file_name='qb_freq.csv',
-                    mime='text/csv',
-                    key='qb'
-                )
-        with tab3:
-            if 'rbwrte_freq' in st.session_state:
-                
-                st.dataframe(st.session_state.rbwrte_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=st.session_state.rbwrte_freq.to_csv().encode('utf-8'),
-                    file_name='rbwrte_freq.csv',
-                    mime='text/csv',
-                    key='rbwrte'
-                )
-        with tab4:
-            if 'rb_freq' in st.session_state:
-                
-                st.dataframe(st.session_state.rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=st.session_state.rb_freq.to_csv().encode('utf-8'),
-                    file_name='rb_freq.csv',
-                    mime='text/csv',
-                    key='rb'
-                )
-        with tab5:
-            if 'wr_freq' in st.session_state:
-                
-                st.dataframe(st.session_state.wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=st.session_state.wr_freq.to_csv().encode('utf-8'),
-                    file_name='wr_freq.csv',
-                    mime='text/csv',
-                    key='wr'
-                )
-        with tab6:
-            if 'te_freq' in st.session_state:
-                
-                st.dataframe(st.session_state.te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=st.session_state.te_freq.to_csv().encode('utf-8'),
-                    file_name='te_freq.csv',
-                    mime='text/csv',
-                    key='te'
-                )
-        with tab7:
-            if 'flex_freq' in st.session_state:
-                
-                st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=st.session_state.flex_freq.to_csv().encode('utf-8'),
-                    file_name='flex_freq.csv',
-                    mime='text/csv',
-                    key='flex'
-                )
-        with tab8:
-            if 'dst_freq' in st.session_state:
-                
-                st.dataframe(st.session_state.dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=st.session_state.dst_freq.to_csv().encode('utf-8'),
-                    file_name='dst_freq.csv',
-                    mime='text/csv',
-                    key='dst'
-                )
-        with tab9:
-            if 'team_freq' in st.session_state:
+            if 'Sim_Winner_Display' in st.session_state:
+                st.write("Yeah man that's crazy")
                 
-                st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=st.session_state.team_freq.to_csv().encode('utf-8'),
-                    file_name='team_freq.csv',
-                    mime='text/csv',
-                    key='team'
-                )
+            else:
+                st.write("Simulation data or position mapping not available.")
+        with st.container():
+            tab1, tab2 = st.tabs(['Overall Exposures', 'Team Exposures'])
+            with tab1:
+                if 'player_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.player_freq.to_csv().encode('utf-8'),
+                        file_name='player_freq_export.csv',
+                        mime='text/csv',
+                        key='overall'
+                    )
+            
+            with tab2:
+                if 'team_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.team_freq.to_csv().encode('utf-8'),
+                        file_name='team_freq.csv',
+                        mime='text/csv',
+                        key='team'
+                    )
 
 if selected_tab == "Showdown Contest Sims":
     dk_sd_raw, fd_sd_raw = init_SD_baselines('Showdown #1')