Refactor player summary generation in app.py to utilize results_dict for improved performance metrics aggregation. Updated the init_team_data function to return results_dict instead of player_summary, and added logic to clean player names for better clarity. This change enhances the overall organization of player statistics and streamlines the data handling process during simulations.

app.py

@@ -346,22 +346,27 @@ def init_team_data(team, opponent, win_loss_settings, kill_predictions, death_pr
             results_dict[f'game {game + 1}'] = team_data
             team_data['playername'] = team_data['playername'] + f' game {game + 1}'
 
-        player_summary = pd.DataFrame()
-        for game_count, game_df in results_dict.items():
-            if player_summary.empty:
-                player_summary = game_df
-            else:
-                player_summary.update(game_df)
-                for col in ['Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']:
-                    player_summary[col] += game_df[col]
-            player_summary = player_summary.set_index('playername')
-
         overall_team_data = pd.concat([overall_team_data, team_data])
 
-        return overall_team_data.dropna().set_index('playername'), opp_boosts, player_summary
+        return overall_team_data.dropna().set_index('playername'), opp_boosts, results_dict
 
 if st.button("Run"):
-    team_data, opp_boost, player_summary = init_team_data(selected_team, selected_opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date)
+    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_num, game_df in results_dict.items():
+        # Remove 'game X' from playernames if present
+        clean_df = game_df.copy()
+        clean_df['playername'] = clean_df['playername'].str.split(' game ').str[0]
+        
+        if player_summary.empty:
+            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]
+        player_summary = player_summary.set_index('playername')
 
     # Create simulated percentiles
     sim_results = []