Add support for secondary and auxiliary slates in lineup initialization; refactor data loading and display logic for DraftKings and FanDuel

app.py

@@ -19,7 +19,9 @@ def init_conn():
 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']
 
 roo_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', '6x%': '{:.2%}', 'GPP%': '{:.2%}'}
 
@@ -69,40 +71,61 @@ def load_overall_stats():
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     fd_raw_sec = raw_display.sort_values(by='Median', ascending=False)
 
-    collection = db["Player_Range_Of_Outcomes"] 
+    collection = db["Player_SD_Range_Of_Outcomes"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
     raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
-                               'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_ID']]
+                               '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 = 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)
+    sd_raw = raw_display.sort_values(by='Median', ascending=False)
 
-    timestamp = raw_display['timestamp'].values[0]
+    print(sd_raw.head(10))
 
-    collection = db["Range_Of_Outcomes_Backlog"] 
+    collection = 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', 'Date']]
-    roo_backlog = raw_display.sort_values(by='Date', ascending=False)
-    roo_backlog = roo_backlog[roo_backlog['slate'] == 'Main Slate']
+                               'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_ID']]
+    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, timestamp, roo_backlog
+    return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp
 
 @st.cache_data(ttl = 60)
-def init_DK_lineups():  
+def init_DK_lineups(slate_desig: str):  
         
-        collection = db['DK_NBA_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"] 
-        cursor = collection.find().limit(10000)
-    
+        if slate_desig == 'Main Slate':
+            collection = db['DK_NBA_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"] 
+            cursor = collection.find().limit(10000)
+        elif slate_desig == 'Secondary':
+            collection = db['DK_NBA_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"] 
+            cursor = collection.find().limit(10000)
+        elif slate_desig == 'Auxiliary':
+            collection = db['DK_NBA_Auxiliary_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_Auxiliary_seed_frame"] 
+            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']
@@ -113,16 +136,51 @@ def init_DK_lineups():
         return DK_seed
 
 @st.cache_data(ttl = 60)
-def init_FD_lineups():  
-        
-        collection = db['FD_NBA_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"] 
+def init_DK_SD_lineups(slate_desig: str):
+
+        if slate_desig == 'Main Slate':
+            collection = db["DK_NBA_SD_seed_frame"] 
+        elif slate_desig == 'Secondary':
+            collection = db["DK_NBA_Secondary_SD_seed_frame"] 
+        elif slate_desig == 'Auxiliary':
+            collection = db["DK_NBA_Auxiliary_SD_seed_frame"] 
+
         cursor = collection.find().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']]
+        DK_seed = raw_display.to_numpy()
+
+        return DK_seed
+
+@st.cache_data(ttl = 60)
+def init_FD_lineups(slate_desig: str):  
+        
+        if slate_desig == 'Main Slate':
+            collection = db['FD_NBA_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"] 
+            cursor = collection.find().limit(10000)
+        elif slate_desig == 'Secondary':
+            collection = db['FD_NBA_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"] 
+            cursor = collection.find().limit(10000)
+        elif slate_desig == 'Auxiliary':
+            collection = db['FD_NBA_Auxiliary_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_Auxiliary_seed_frame"] 
+            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']
@@ -132,6 +190,24 @@ def init_FD_lineups():
 
         return FD_seed
 
+@st.cache_data(ttl = 60)
+def init_FD_SD_lineups(slate_desig: str):
+
+        if slate_desig == 'Main Slate':
+            collection = db["FD_NBA_SD_seed_frame"] 
+        elif slate_desig == 'Secondary':
+            collection = db["FD_NBA_Secondary_SD_seed_frame"] 
+        elif slate_desig == 'Auxiliary':
+            collection = db["FD_NBA_Auxiliary_SD_seed_frame"] 
+
+        cursor = collection.find().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']]
+        DK_seed = raw_display.to_numpy()
+
+        return DK_seed
+
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
@@ -140,171 +216,78 @@ 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, timestamp, roo_backlog = load_overall_stats()
+dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp = load_overall_stats()
 salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
-
-try:
-    dk_lineups = init_DK_lineups()
-    fd_lineups = init_FD_lineups()
-except:
-    dk_lineups = pd.DataFrame(columns=dk_columns)
-    fd_lineups = pd.DataFrame(columns=fd_columns)
+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)
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
 tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimals'])
 
-with st.sidebar:
-    st.header("Quick Builder")
-    st.info("This is a quick hand building helper to give you some basic info about player combos and lineup feasibility")
-    sidebar_site = st.selectbox("What site are you running?", ('Draftkings', 'Fanduel'), key='sidebar_site')
-    sidebar_slate = st.selectbox("What slate are you running?", ('Main Slate', 'Secondary Slate'), key='sidebar_slate')
-
-    if sidebar_site == 'Draftkings':
-        roo_sample = roo_raw[roo_raw['slate'] == str(sidebar_slate)]
-        roo_sample = roo_sample[roo_sample['site'] == 'Draftkings']
-        roo_sample = roo_sample.sort_values(by='Own', ascending=False)
-        selected_pg = []
-        selected_sg = []
-        selected_sf = []
-        selected_pf = []
-        selected_c = []
-        selected_g = []
-        selected_f = []
-        selected_flex = []
-    elif sidebar_site == 'Fanduel':
-        roo_sample = roo_raw[roo_raw['slate'] == str(sidebar_slate)]
-        roo_sample = roo_sample[roo_sample['site'] == 'Fanduel']
-        roo_sample = roo_sample.sort_values(by='Own', ascending=False)
-        selected_pg1 = []
-        selected_pg2 = []
-        selected_sg1 = []
-        selected_sg2 = []
-        selected_sf1 = []
-        selected_sf2 = []
-        selected_pf1 = []
-        selected_pf2 = []
-        selected_c1 = []
-
-    # Get unique players by position from dk_roo_raw
-    pgs = roo_sample[roo_sample['Position'].str.contains('PG')]['Player'].unique()
-    sgs = roo_sample[roo_sample['Position'].str.contains('SG')]['Player'].unique()
-    sfs = roo_sample[roo_sample['Position'].str.contains('SF')]['Player'].unique()
-    pfs = roo_sample[roo_sample['Position'].str.contains('PF')]['Player'].unique()
-    centers = roo_sample[roo_sample['Position'].str.contains('C')]['Player'].unique()
-    guards = roo_sample[roo_sample['Position'].str.contains('G')]['Player'].unique()
-    forwards = roo_sample[roo_sample['Position'].str.contains('F')]['Player'].unique()
-    flex = roo_sample['Player'].unique()
-
-    if sidebar_site == 'Draftkings':
-        selected_pgs = st.multiselect('Select PG:', list(pgs), default=None, placeholder='Select PG', label_visibility='collapsed', key='pg1')
-        selected_sgs = st.multiselect('Select SG:', list(sgs), default=None, placeholder='Select SG', label_visibility='collapsed', key='sg1')
-        selected_sfs = st.multiselect('Select SF:', list(sfs), default=None, placeholder='Select SF', label_visibility='collapsed', key='sf1')
-        selected_pfs = st.multiselect('Select PF:', list(pfs), default=None, placeholder='Select PF', label_visibility='collapsed', key='pf1')
-        selected_cs = st.multiselect('Select C:', list(centers), default=None, placeholder='Select C', label_visibility='collapsed', key='c1')
-        selected_g = st.multiselect('Select G:', list(guards), default=None, placeholder='Select G', label_visibility='collapsed', key='g')
-        selected_f = st.multiselect('Select F:', list(forwards), default=None, placeholder='Select F', label_visibility='collapsed', key='f')
-        selected_flex = st.multiselect('Select Flex:', list(flex), default=None, placeholder='Select Flex', label_visibility='collapsed', key='flex')
-
-        # Combine all selected players
-        all_selected = selected_pgs + selected_sgs + selected_sfs + selected_pfs + selected_cs + selected_g + selected_f + selected_flex
-    
-    elif sidebar_site == 'Fanduel':
-        selected_pg1 = st.multiselect('Select PG1:', list(pgs), default=None, placeholder='Select PG1', label_visibility='collapsed', key='pg1')
-        selected_pg2 = st.multiselect('Select PG2:', list(pgs), default=None, placeholder='Select PG2', label_visibility='collapsed', key='pg2')
-        selected_sg1 = st.multiselect('Select SG1:', list(sgs), default=None, placeholder='Select SG1', label_visibility='collapsed', key='sg1')
-        selected_sg2 = st.multiselect('Select SG2:', list(sgs), default=None, placeholder='Select SG2', label_visibility='collapsed', key='sg2')
-        selected_sf1 = st.multiselect('Select SF1:', list(sfs), default=None, placeholder='Select SF1', label_visibility='collapsed', key='sf1')
-        selected_sf2 = st.multiselect('Select SF2:', list(sfs), default=None, placeholder='Select SF2', label_visibility='collapsed', key='sf2')
-        selected_pf1 = st.multiselect('Select PF1:', list(pfs), default=None, placeholder='Select PF1', label_visibility='collapsed', key='pf1')
-        selected_pf2 = st.multiselect('Select PF2:', list(pfs), default=None, placeholder='Select PF2', label_visibility='collapsed', key='pf2')
-        selected_c1 = st.multiselect('Select C1:', list(centers), default=None, placeholder='Select C1', label_visibility='collapsed', key='c1')
-
-        # Combine all selected players
-        all_selected = selected_pg1 + selected_pg2 + selected_sg1 + selected_sg2 + selected_sf1 + selected_sf2 + selected_pf1 + selected_pf2 + selected_c1
-
-    if all_selected:
-        # Get stats for selected players
-        selected_stats = roo_sample[roo_sample['Player'].isin(all_selected)]
-        
-        # Calculate sums
-        salary_sum = selected_stats['Salary'].sum()
-        median_sum = selected_stats['Median'].sum()
-        own_sum = selected_stats['Own'].sum()
-        levx_sum = selected_stats['LevX'].sum()
-
-        # Display sums
-        st.write('---')
-        if sidebar_site == 'Draftkings':
-            if salary_sum > 50000:
-                st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $50,000')
-            else:
-                st.write(f'Total Salary: ${salary_sum:.2f}')
-        elif sidebar_site == 'Fanduel':
-            if salary_sum > 60000:
-                st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $60,000')
-            else:
-                st.write(f'Total Salary: ${salary_sum:.2f}')
-        st.write(f'Total Median: {median_sum:.2f}')
-        st.write(f'Total Ownership: {own_sum:.2f}%')
-        st.write(f'Total LevX: {levx_sum:.2f}')
-
 with tab1:
-    with st.container():
-        st.info("Advanced view includes all stats and thresholds, simple includes just basic columns for ease of use on mobile")
-    with st.container():
-        # First row - timestamp and reset button
-        col1, col2 = st.columns([3, 1])
-        with col1:
-            st.info(t_stamp)
-        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, timestamp, roo_backlog = load_overall_stats()
-                id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
-                dk_lineups = init_DK_lineups()
-                fd_lineups = init_FD_lineups()
-                t_stamp = f"Last Update: " + str(timestamp) + f" CST"
-                for key in st.session_state.keys():
-                    del st.session_state[key]
-
-        # Second row - main options
-        col1, col2, col3, col4 = st.columns(4)
+    
+    with st.expander("Info and Filters"):
+        with st.container():
+            st.info("Advanced view includes all stats and thresholds, simple includes just basic columns for ease of use on mobile")
+        with st.container():
+            # First row - timestamp and reset button
+            col1, col2 = st.columns([3, 1])
+            with col1:
+                st.info(t_stamp)
+            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()
+                    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)
+                    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)
         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')
+        with col3:
             site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')
 
             # Process site selection
             if site_var2 == 'Draftkings':
-                site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
-                site_backlog = roo_backlog[roo_backlog['site'] == 'Draftkings']
+                if slate_type_var2 == 'Regular':
+                    site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
+                elif slate_type_var2 == 'Showdown':
+                    site_baselines = sd_raw[sd_raw['site'] == 'Draftkings']
             elif site_var2 == 'Fanduel':
-                site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
-                site_backlog = roo_backlog[roo_backlog['site'] == 'Fanduel']
-        with col3:
-            slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary', 'Backlog'), key='slate_split')
+                if slate_type_var2 == 'Regular':
+                    site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
+                elif slate_type_var2 == 'Showdown':
+                    site_baselines = sd_raw[sd_raw['site'] == 'Fanduel']
+        with col4:
+            slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary'), key='slate_split')
 
             if slate_split == 'Main Slate':
-                raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
+                if slate_type_var2 == 'Regular':
+                    raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
+                elif slate_type_var2 == 'Showdown':
+                    raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #1']
             elif slate_split == 'Secondary':
-                raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
-            elif slate_split == 'Backlog':
-                raw_baselines = site_backlog
-                # Third row - backlog options
-                col1, col2 = st.columns(2)
-                with col1:
-                    view_all = st.checkbox("View all dates?", key='view_all')
-                with col2:
-                    if not view_all:
-                        date_var2 = st.date_input("Select date", key='date_var2')
-                
-                if view_all:
-                    raw_baselines = raw_baselines.sort_values(by=['Median', 'Date'], ascending=[False, False])
-                else:
-                    raw_baselines = raw_baselines[raw_baselines['Date'] == date_var2.strftime('%m-%d-%Y')]
-                    raw_baselines = raw_baselines.sort_values(by='Median', ascending=False)
+                if slate_type_var2 == 'Regular':
+                    raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
+                elif slate_type_var2 == 'Showdown':
+                    raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2']
         
-        with col4:
+        with col5:
             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')
@@ -312,10 +295,16 @@ with tab1:
                 team_var2 = raw_baselines.Team.values.tolist()
 
         pos_var2 = st.selectbox('Position Filter', options=['All', 'PG', 'SG', 'SF', 'PF', 'C'], key='pos_var2')
+        col1, col2 = st.columns(2)
+        with col1:
+            low_salary = st.number_input('Enter Lowest Salary', min_value=300, max_value=15000, value=300, step=100, key='low_salary')
+        with col2:
+            high_salary = st.number_input('Enter Highest Salary', min_value=300, max_value=25000, value=25000, step=100, key='high_salary')
     
     display_container_1 = st.empty()
     display_dl_container_1 = st.empty()
     display_proj = raw_baselines[raw_baselines['Team'].isin(team_var2)]
+    display_proj = display_proj[display_proj['Salary'].between(low_salary, high_salary)]
     if view_var2 == 'Advanced':
         display_proj = display_proj[['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']]
@@ -323,9 +312,8 @@ with tab1:
         display_proj = display_proj[['Player', 'Position', 'Salary', 'Median', 'GPP%', 'Own']]
     export_data = display_proj.copy()
 
-
-    display_proj = display_proj.set_index('Player')
-    st.session_state.display_proj = display_proj
+    # display_proj = display_proj.set_index('Player')
+    st.session_state.display_proj = display_proj.set_index('Player', drop=True)
         
     with display_container_1:
         display_container = st.empty()
@@ -334,7 +322,8 @@ with tab1:
                 st.session_state.display_proj = st.session_state.display_proj
             elif pos_var2 != 'All':
                 st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)]
-            st.dataframe(st.session_state.display_proj.style.set_properties(**{'font-size': '6pt'}).background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(roo_format, precision=2), height=1000, use_container_width = True)
+            st.dataframe(st.session_state.display_proj.style.set_properties(**{'font-size': '6pt'}).background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(roo_format, precision=2),
+            height=1000, use_container_width = True)
     
     with display_dl_container_1:
             display_dl_container = st.empty()
@@ -347,50 +336,57 @@ with tab1:
                 )
 
 with tab2:
-    col1, col2 = st.columns([1, 7])
-    with col1:
+    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, timestamp, roo_backlog = load_overall_stats()
-            dk_lineups = init_DK_lineups()
-            fd_lineups = init_FD_lineups()
+            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp = load_overall_stats()
+            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)
             t_stamp = f"Last Update: " + str(timestamp) + f" CST"
             for key in st.session_state.keys():
                 del st.session_state[key]
-              
-        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate'))
-        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
-        lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
-
-        if site_var1 == 'Draftkings':
-            raw_baselines = dk_raw
-            ROO_slice = roo_raw[roo_raw['site'] == 'Draftkings']
-            id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
-            # Get the minimum and maximum ownership values from dk_lineups
-            min_own = np.min(dk_lineups[:,14])
-            max_own = np.max(dk_lineups[:,14])
-            column_names = dk_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':
-                    player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique())
-            elif player_var1 == 'Full Slate':
-                    player_var2 = dk_raw.Player.values.tolist()
-                    
-        elif site_var1 == 'Fanduel':
-            raw_baselines = fd_raw
-            ROO_slice = roo_raw[roo_raw['site'] == 'Fanduel']
-            id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
-            min_own = np.min(fd_lineups[:,15])
-            max_own = np.max(fd_lineups[:,15])
-            column_names = fd_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':
-                    player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique())
-            elif player_var1 == 'Full Slate':
-                    player_var2 = fd_raw.Player.values.tolist()
-
+    
+        col1, col2, col3, col4, col5 = st.columns(5)
+        with col1:
+            slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
+        with col2:
+            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)
+        with col5:
+            if site_var1 == 'Draftkings':
+                if slate_type_var1 == 'Regular':
+                    column_names = dk_columns
+                elif slate_type_var1 == 'Showdown':
+                    column_names = dk_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':
+                        player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique())
+                elif player_var1 == 'Full Slate':
+                        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
+                
+                player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
+                if player_var1 == 'Specific Players':
+                        player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique())
+                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':
@@ -404,74 +400,103 @@ with tab2:
                 data=convert_df(data_export),
                 file_name='NBA_optimals_export.csv',
                 mime='text/csv',
-            )
-    with col2:
-        
-        if site_var1 == 'Draftkings':
-            if 'working_seed' in st.session_state:
-                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':
-                    st.session_state.working_seed = dk_lineups.copy()
-                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
-            elif 'working_seed' not in st.session_state:
-                st.session_state.working_seed = dk_lineups.copy()
-                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':
-                    st.session_state.working_seed = dk_lineups.copy()
-                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+        )
             
-        elif site_var1 == 'Fanduel':
-            if 'working_seed' in st.session_state:
-                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':
-                    st.session_state.working_seed = fd_lineups.copy()
-                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
-            elif 'working_seed' not in st.session_state:
-                st.session_state.working_seed = fd_lineups.copy()
+
+    if site_var1 == 'Draftkings':
+        if 'working_seed' in st.session_state:
+            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':
                 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':
-                    st.session_state.working_seed = fd_lineups.copy()
                 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()
-        if site_var1 == 'Draftkings':
+        elif 'working_seed' not in st.session_state:
+            if slate_type_var1 == 'Regular':
+                st.session_state.working_seed = init_DK_lineups(slate_var1)
+            elif slate_type_var1 == 'Showdown':
+                st.session_state.working_seed = init_DK_SD_lineups(slate_var1)
+            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)
+                elif slate_type_var1 == 'Showdown':
+                    st.session_state.working_seed = init_DK_SD_lineups(slate_var1)
+            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+        
+    elif site_var1 == 'Fanduel':
+        if 'working_seed' in st.session_state:
+            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':
+                st.session_state.working_seed = st.session_state.working_seed
+            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+        
+        elif 'working_seed' not in st.session_state:
+            if slate_type_var1 == 'Regular':
+                st.session_state.working_seed = init_FD_lineups(slate_var1)
+            elif slate_type_var1 == 'Showdown':
+                st.session_state.working_seed = init_FD_SD_lineups(slate_var1)
+            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)
+                elif slate_type_var1 == 'Showdown':
+                    st.session_state.working_seed = init_FD_SD_lineups(slate_var1)
+            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()
+    if site_var1 == 'Draftkings':
+        if slate_type_var1 == 'Regular':
             for col_idx in range(8):
                 export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
-        elif site_var1 == 'Fanduel':
+        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)
+    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)
-                
-        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':
+        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)
+            
+    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 site_var1 == 'Fanduel':
+                elif slate_type_var1 == 'Showdown':
+                    st.session_state.working_seed = dk_sd_lineups.copy()
+            elif site_var1 == 'Fanduel':
+                if slate_type_var1 == 'Regular':
                     st.session_state.working_seed = fd_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(
-                label="Export display optimals",
-                data=convert_df(export_file),
-                file_name='NBA_display_optimals.csv',
-                mime='text/csv',
-            )
-        
-        with st.container():
-            if 'working_seed' in st.session_state:
-                # Create a new dataframe with summary statistics
-                if site_var1 == 'Draftkings':
+                elif slate_type_var1 == 'Showdown':
+                    st.session_state.working_seed = fd_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(
+            label="Export display optimals",
+            data=convert_df(export_file),
+            file_name='NBA_display_optimals.csv',
+            mime='text/csv',
+        )
+    
+    with st.container():
+        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'],
+                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                         'Salary': [
                             np.min(st.session_state.working_seed[:,8]),
                             np.mean(st.session_state.working_seed[:,8]),
@@ -491,7 +516,31 @@ with tab2:
                             np.std(st.session_state.working_seed[:,14])
                         ]
                     })
-                elif site_var1 == 'Fanduel':
+                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({
                         'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                         'Salary': [
@@ -513,84 +562,118 @@ with tab2:
                             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])
+                        ]
+                    })
 
-                # Set the index of the summary dataframe as the "Metric" column
-                summary_df = summary_df.set_index('Metric')
+            # Set the index of the summary dataframe as the "Metric" column
+            summary_df = summary_df.set_index('Metric')
 
-                # Display the summary dataframe
-                st.subheader("Optimal Statistics")
-                st.dataframe(summary_df.style.format({
-                    'Salary': '{:.2f}',
-                    'Proj': '{:.2f}',
-                    'Own': '{:.2f}'
-                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)
+            # Display the summary dataframe
+            st.subheader("Optimal Statistics")
+            st.dataframe(summary_df.style.format({
+                'Salary': '{:.2f}',
+                'Proj': '{:.2f}',
+                'Own': '{:.2f}'
+            }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)
 
-        with st.container():
-            tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
-            with tab1:
-                if 'data_export_display' in st.session_state:
+    with st.container():
+        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]
-                    
-                    # Flatten the DataFrame and count unique values
-                    value_counts = player_columns.values.flatten().tolist()
-                    value_counts = pd.Series(value_counts).value_counts()
-                    
-                    percentages = (value_counts / lineup_num_var * 100).round(2)
-                    
-                    # Create a DataFrame with the results
-                    summary_df = pd.DataFrame({
-                        'Player': value_counts.index,
-                        'Salary': [salary_dict.get(player, player) for player in value_counts.index],
-                        'Frequency': value_counts.values,
-                        'Percentage': percentages.values                        
-                    })
-                    
-                    # Sort by frequency in descending order
-                    summary_df = summary_df.sort_values('Frequency', ascending=False)
-                    
-                    # Display the table
-                    st.write("Player Frequency Table:")
-                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
+                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()
+                value_counts = pd.Series(value_counts).value_counts()
+                
+                percentages = (value_counts / lineup_num_var * 100).round(2)
                 
-                    st.download_button(
-                        label="Export player frequency",
-                        data=convert_df_to_csv(summary_df),
-                        file_name='NBA_player_frequency.csv',
-                        mime='text/csv',
-                    )
-            with tab2:
-                if 'working_seed' in st.session_state:
+                # Create a DataFrame with the results
+                summary_df = pd.DataFrame({
+                    'Player': value_counts.index,
+                    'Salary': [salary_dict.get(player, player) for player in value_counts.index],
+                    'Frequency': value_counts.values,
+                    'Percentage': percentages.values                        
+                })
+                
+                # Sort by frequency in descending order
+                summary_df = summary_df.sort_values('Frequency', ascending=False)
+                
+                # Display the table
+                st.write("Player Frequency Table:")
+                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
+            
+                st.download_button(
+                    label="Export player frequency",
+                    data=convert_df_to_csv(summary_df),
+                    file_name='NBA_player_frequency.csv',
+                    mime='text/csv',
+                )
+        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]
-                    
-                    # Flatten the DataFrame and count unique values
-                    value_counts = player_columns.flatten().tolist()
-                    value_counts = pd.Series(value_counts).value_counts()
-                    
-                    percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
-                    # Create a DataFrame with the results
-                    summary_df = pd.DataFrame({
-                        'Player': value_counts.index,
-                        'Salary': [salary_dict.get(player, player) for player in value_counts.index],
-                        'Frequency': value_counts.values,
-                        'Percentage': percentages.values                        
-                    })
-                    
-                    # Sort by frequency in descending order
-                    summary_df = summary_df.sort_values('Frequency', ascending=False)
-                    
-                    # Display the table
-                    st.write("Seed Frame Frequency Table:")
-                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
+                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()
+                value_counts = pd.Series(value_counts).value_counts()
                 
-                    st.download_button(
-                        label="Export seed frame frequency",
-                        data=convert_df_to_csv(summary_df),
-                        file_name='NBA_seed_frame_frequency.csv',
-                        mime='text/csv',
-                    )
+                percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
+                # Create a DataFrame with the results
+                summary_df = pd.DataFrame({
+                    'Player': value_counts.index,
+                    'Salary': [salary_dict.get(player, player) for player in value_counts.index],
+                    'Frequency': value_counts.values,
+                    'Percentage': percentages.values                        
+                })
+                
+                # Sort by frequency in descending order
+                summary_df = summary_df.sort_values('Frequency', ascending=False)
+                
+                # Display the table
+                st.write("Seed Frame Frequency Table:")
+                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
+            
+                st.download_button(
+                    label="Export seed frame frequency",
+                    data=convert_df_to_csv(summary_df),
+                    file_name='NBA_seed_frame_frequency.csv',
+                    mime='text/csv',
+                )