Enhance metric calculations and player data handling in app.py

- Introduced logic to calculate 'stack' and 'stack_size' metrics for the 'Showdown' game type, improving data analysis capabilities.
- Added functionality to identify and count duplicate player combinations, enhancing data integrity.
- Updated player column creation to exclude specific columns, streamlining data processing and improving clarity.

app.py

@@ -84,6 +84,7 @@ with tab2:
     if 'Contest' in st.session_state and 'projections_df' in st.session_state:
         col1, col2 = st.columns([1, 8])
         excluded_cols = ['BaseName', 'EntryCount']
+        player_columns = [col for col in st.session_state['Contest'].columns if col not in excluded_cols]
         
         # Create mapping dictionaries
         map_dict = {
@@ -131,7 +132,27 @@ with tab2:
             working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
             working_df['median'] = working_df.apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
             working_df['Own'] = working_df.apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
+            working_df['sorted'] = working_df[player_columns].apply(
+                lambda row: ','.join(sorted(row.values)),
+                axis=1
+            )
+            working_df['dupes'] = working_df.groupby('sorted').transform('size')
+            working_df = working_df.drop('sorted', axis=1)
         elif type_var == 'Showdown':
+            working_df['stack'] = working_df.apply(
+                lambda row: Counter(
+                    map_dict['team_map'].get(player, '') for player in row
+                    if map_dict['team_map'].get(player, '') != ''
+                ).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row) else '',
+                axis=1
+            )
+            working_df['stack_size'] = working_df.apply(
+                lambda row: Counter(
+                    map_dict['team_map'].get(player, '') for player in row
+                    if map_dict['team_map'].get(player, '') != ''
+                ).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row) else '',
+                axis=1
+            )
             working_df['salary'] = working_df.apply(
                 lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + 
                             sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
@@ -147,6 +168,12 @@ with tab2:
                             sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
                 axis=1
             )
+            working_df['sorted'] = working_df[player_columns].apply(
+                lambda row: row[0] + '|' + ','.join(sorted(row[1:].values)),
+                axis=1
+            )
+            working_df['dupes'] = working_df.groupby('sorted').transform('size')
+            working_df = working_df.drop('sorted', axis=1)
 
         # Initialize pagination in session state if not exists
         if 'current_page' not in st.session_state: