Enhance player summary generation in app.py: refactor logic to handle Captain and FLEX positions separately for improved accuracy in lineup statistics, and ensure consistent display of player performance metrics in the summary table.

app.py

@@ -1144,11 +1144,44 @@ with tab2:
                 use_container_width=True
             )
             
-            # Create player summary dataframe
-            player_stats = []
-            player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
-            
-            if type_var == 'Showdown':
+        # Create player summary dataframe
+        player_stats = []
+        player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
+        
+        if type_var == 'Showdown':
+            # 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(),
+                    })
+        else:
+            if sport_var == 'CS2':
                 # Handle Captain positions
                 for player in player_names:
                     # Create mask for lineups where this player is Captain (first column)
@@ -1180,71 +1213,38 @@ with tab2:
                             'Avg Finish %': st.session_state['portfolio'][flex_mask]['Finish_percentile'].mean(),
                             'Avg Lineup Edge': st.session_state['portfolio'][flex_mask]['Lineup Edge'].mean(),
                         })
-            else:
-                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)
-            
-            st.subheader("Player Summary")
-            st.dataframe(
-                player_summary.style
-                .background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Avg Finish %', 'Avg Own', 'Avg Dupes'])
-                .format({
-                    'Avg Median': '{:.2f}',
-                    'Avg Own': '{:.2f}',
-                    'Avg Dupes': '{:.2f}',
-                    'Avg Finish %': '{:.2%}',
-                    'Avg Lineup Edge': '{:.2%}'
-                }),
-                height=400,
-                use_container_width=True
-            )
+            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)
+        
+        st.subheader("Player Summary")
+        st.dataframe(
+            player_summary.style
+            .background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Avg Finish %', 'Avg Own', 'Avg Dupes'])
+            .format({
+                'Avg Median': '{:.2f}',
+                'Avg Own': '{:.2f}',
+                'Avg Dupes': '{:.2f}',
+                'Avg Finish %': '{:.2%}',
+                'Avg Lineup Edge': '{:.2%}'
+            }),
+            height=400,
+            use_container_width=True
+        )