Refactor team data retrieval in init_player_data function to filter by 'position' instead of 'teamname'. This change improves data accuracy for team statistics calculations, enhancing the integrity of player performance metrics during simulations.

app.py

@@ -413,8 +413,8 @@ def init_player_data(game_count, players, opponent, win_loss_settings, kill_pred
                 for stats in calc_columns:
                     playername_win_stats[stats] = tables[tables['result'] == 1].groupby(['playername'])[stats].mean().to_dict()
                     playername_loss_stats[stats] = tables[tables['result'] == 0].groupby(['playername'])[stats].mean().to_dict()
-                    teamname_win_stats[stats] = tables[(tables['result'] == 1) & (tables['teamname'] == 'team')].groupby(['teamname'])[stats].mean().to_dict()
-                    teamname_loss_stats[stats] = tables[(tables['result'] == 0) & (tables['teamname'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() 
+                    teamname_win_stats[stats] = tables[(tables['result'] == 1) & (tables['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict()
+                    teamname_loss_stats[stats] = tables[(tables['result'] == 0) & (tables['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() 
 
                 for stat in calc_columns: