ping

app.py

@@ -2908,10 +2908,8 @@ if selected_tab == 'Manage Portfolio':
                                     stacking_sports
                                 )
                                 
-                                # Update the original dataframe with the modified rows
                                 parsed_frame.loc[containing_mask] = modified_rows.values
                                 
-                                # Use consolidated calculation function for export
                                 parsed_frame = calculate_lineup_metrics(
                                     parsed_frame, 
                                     st.session_state['player_columns'], 
@@ -2960,9 +2958,6 @@ if selected_tab == 'Manage Portfolio':
                         )
                         
                         st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
-                        
-                        # st.session_state['working_frame'] = predict_dupes(st.session_state['working_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
-                        # Load Default base from compressed storage for reassess_edge
                         default_base = load_base_frame('Default')
                         st.session_state['working_frame'] = reassess_edge(st.session_state['working_frame'], default_base, st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
                         team_dict = dict(zip(st.session_state['portfolio_inc_proj']['player_names'], st.session_state['portfolio_inc_proj']['team']))
@@ -2997,7 +2992,6 @@ if selected_tab == 'Manage Portfolio':
                             st.session_state['portfolio_inc_proj']
                         )
 
-                        
                         st.session_state['export_base'] = parsed_frame.reset_index(drop=True)
                         default_base = load_base_frame('Default')
                         st.session_state['export_base'] = reassess_edge(st.session_state['export_base'], default_base, st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)

global_func/optimize_lineup.py

@@ -103,20 +103,19 @@ def optimize_single_lineup(
     # Constraint 1: Each open column gets exactly one player
     for j in range(num_open_cols):
         solver.Add(sum(x[i, j] for i in range(num_players)) == 1)
-    
-    # Constraint 2: Each player can only be used once across all open columns
+
+    # Constraint 2: Each player can only be used AT MOST once across all open columns
     for i in range(num_players):
-        solver.Add(sum(x[i, j] for j in range(num_player_columns)) <= 1)
+        solver.Add(sum(x[i, j] for j in range(num_open_cols)) <= 1)
 
-    # Constraint 3: Players already in ANY position in the row cannot be selected again
-    all_players_in_row = set(row[col] for col in player_columns)  # Get ALL players in row
+    # Constraint 3: Players already LOCKED in the row cannot be selected again
+    # (only check locked_player_names, not all players in row)
     for i, player in enumerate(player_list):
         player_name = player['player_names']
-        if player_name in all_players_in_row:
-            # This player is already somewhere in the row, can't use them again
+        if player_name in locked_player_names:  # ✅ Only check locked players
             for j in range(num_open_cols):
                 solver.Add(x[i, j] == 0)
-    
+
     # Constraint 4: Position eligibility
     for i, player in enumerate(player_list):
         player_positions = player['position'].split('/')