Refactor player summary generation in app.py to correctly iterate over results_dict using game number keys. Updated player summary aggregation logic to ensure team names and positions are accurately reflected. This change enhances data accuracy and improves the clarity of player statistics during simulations.

app.py

@@ -354,7 +354,8 @@ if st.button("Run"):
     team_data, opp_boost, results_dict = init_team_data(selected_team, selected_opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date)
 
     player_summary = pd.DataFrame()
-    for game_count, game_df in results_dict.items():
+    for game_num in range(game_count):
+        game_df = results_dict[f'game {game_num}']  # Use correct dictionary key format
         # Remove 'game X' from playernames if present
         clean_df = game_df.copy()
         clean_df['playername'] = clean_df['playername'].str.split(' game ').str[0]
@@ -363,9 +364,12 @@ if st.button("Run"):
             player_summary = clean_df
         else:
             # Add the stats to existing players
-            player_summary.update(clean_df)  # Update teamname and position if needed
             for col in ['Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']:
                 player_summary[col] += clean_df[col]
+            # Update teamname and position if needed
+            player_summary['teamname'].update(clean_df['teamname'])
+            player_summary['position'].update(clean_df['position'])
+    
     player_summary = player_summary.set_index('playername')
 
     # Create simulated percentiles