Enhance portfolio calculations in app.py: implement sport-specific logic for CS2 to differentiate salary, median, and ownership calculations for Captain and FLEX positions, improving accuracy in player statistics and overall portfolio analysis.

app.py

@@ -787,11 +787,34 @@ with tab2:
             }
         
         if type_var == 'Classic':
-            st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
-            st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
-            st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
-            if stack_dict is not None:
-                st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].index.map(stack_dict)
+            if sport_var == 'CS2':
+                # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
+                st.session_state['portfolio']['salary'] = st.session_state['portfolio'].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:]),
+                    axis=1
+                )
+                
+                # Calculate median (CPT uses cpt_proj_map, others use proj_map)
+                st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
+                    lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + 
+                            sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
+                    axis=1
+                )
+                
+                # Calculate ownership (CPT uses cpt_own_map, others use own_map)
+                st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
+                    lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + 
+                            sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
+                    axis=1
+                )
+
+            elif sport_var != 'CS2':
+                st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
+                st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
+                st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
+                if stack_dict is not None:
+                    st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].index.map(stack_dict)
         elif type_var == 'Showdown':
             # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
             st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
@@ -1066,22 +1089,55 @@ with tab2:
                             'Avg Lineup Edge': st.session_state['portfolio'][flex_mask]['Lineup Edge'].mean(),
                         })
             else:
-                # Original Classic format processing
-                for player in player_names:
-                    player_mask = st.session_state['portfolio'][player_columns].apply(
-                        lambda row: player in list(row), axis=1
-                    )
-                    
-                    if player_mask.any():
-                        player_stats.append({
-                            'Player': player,
-                            'Lineup Count': player_mask.sum(),
-                            'Avg Median': st.session_state['portfolio'][player_mask]['median'].mean(),
-                            'Avg Own': st.session_state['portfolio'][player_mask]['Own'].mean(),
-                            'Avg Dupes': st.session_state['portfolio'][player_mask]['Dupes'].mean(),
-                            'Avg Finish %': st.session_state['portfolio'][player_mask]['Finish_percentile'].mean(),
-                            'Avg Lineup Edge': st.session_state['portfolio'][player_mask]['Lineup Edge'].mean(),
-                        })
+                if sport_var == 'CS2':
+                    # Handle Captain positions
+                    for player in player_names:
+                        # Create mask for lineups where this player is Captain (first column)
+                        cpt_mask = st.session_state['portfolio'][player_columns[0]] == player
+                        
+                        if cpt_mask.any():
+                            player_stats.append({
+                                'Player': f"{player} (CPT)",
+                                'Lineup Count': cpt_mask.sum(),
+                                'Avg Median': st.session_state['portfolio'][cpt_mask]['median'].mean(),
+                                'Avg Own': st.session_state['portfolio'][cpt_mask]['Own'].mean(),
+                                'Avg Dupes': st.session_state['portfolio'][cpt_mask]['Dupes'].mean(),
+                                'Avg Finish %': st.session_state['portfolio'][cpt_mask]['Finish_percentile'].mean(),
+                                'Avg Lineup Edge': st.session_state['portfolio'][cpt_mask]['Lineup Edge'].mean(),
+                            })
+                        
+                        # Create mask for lineups where this player is FLEX (other columns)
+                        flex_mask = st.session_state['portfolio'][player_columns[1:]].apply(
+                            lambda row: player in list(row), axis=1
+                        )
+                        
+                        if flex_mask.any():
+                            player_stats.append({
+                                'Player': f"{player} (FLEX)",
+                                'Lineup Count': flex_mask.sum(),
+                                'Avg Median': st.session_state['portfolio'][flex_mask]['median'].mean(),
+                                'Avg Own': st.session_state['portfolio'][flex_mask]['Own'].mean(),
+                                'Avg Dupes': st.session_state['portfolio'][flex_mask]['Dupes'].mean(),
+                                'Avg Finish %': st.session_state['portfolio'][flex_mask]['Finish_percentile'].mean(),
+                                'Avg Lineup Edge': st.session_state['portfolio'][flex_mask]['Lineup Edge'].mean(),
+                            })
+                elif sport_var != 'CS2':
+                    # Original Classic format processing
+                    for player in player_names:
+                        player_mask = st.session_state['portfolio'][player_columns].apply(
+                            lambda row: player in list(row), axis=1
+                        )
+                        
+                        if player_mask.any():
+                            player_stats.append({
+                                'Player': player,
+                                'Lineup Count': player_mask.sum(),
+                                'Avg Median': st.session_state['portfolio'][player_mask]['median'].mean(),
+                                'Avg Own': st.session_state['portfolio'][player_mask]['Own'].mean(),
+                                'Avg Dupes': st.session_state['portfolio'][player_mask]['Dupes'].mean(),
+                                'Avg Finish %': st.session_state['portfolio'][player_mask]['Finish_percentile'].mean(),
+                                'Avg Lineup Edge': st.session_state['portfolio'][player_mask]['Lineup Edge'].mean(),
+                            })
             
             player_summary = pd.DataFrame(player_stats)
             player_summary = player_summary.sort_values('Lineup Count', ascending=False)