Enhance team data processing in init_player_data function by adding retrieval of team statistics. Introduced a new query to fetch team data based on unique team names, improving the accuracy of team performance metrics. Updated the logic to conditionally apply team statistics calculations, ensuring proper integration with player data. This change enhances the overall integrity of performance metrics during simulations.

app.py

@@ -391,10 +391,15 @@ def init_player_data(game_count, players, opponent, win_loss_settings, kill_pred
         cursor = collection.find({"playername": {"$in": players}, "date": {"$gte": start_datetime, "$lte": end_datetime}})
         raw_display = pd.DataFrame(list(cursor))
 
+        teams = raw_display['teamname'].unique()
+
+        cursor = collection.find({"teamname": {"$in": teams}, "date": {"$gte": start_datetime, "$lte": end_datetime}})
+        raw_team = pd.DataFrame(list(cursor))
+
         cursor = collection.find({"date": {"$gte": start_datetime, "$lte": end_datetime}})
         raw_opponent = pd.DataFrame(list(cursor))
 
-        tables_to_loop = [raw_display, raw_opponent]
+        tables_to_loop = [raw_display, raw_opponent, raw_team]
 
         for loop in range(len(tables_to_loop)):
             tables = tables_to_loop[loop]
@@ -430,17 +435,18 @@ def init_player_data(game_count, players, opponent, win_loss_settings, kill_pred
                         axis=1
                     )
 
-                    column_name = f'teamname_avg_{stat}_win'
-                    tables[column_name] = tables.apply(
-                        lambda row: teamname_win_stats[stat].get(row['teamname'], 0), 
-                        axis=1
-                    )
-
-                    column_name = f'teamname_avg_{stat}_loss'
-                    tables[column_name] = tables.apply(
-                        lambda row: teamname_loss_stats[stat].get(row['teamname'], 0), 
-                        axis=1
-                    )
+                    if loop == 2:
+                        column_name = f'teamname_avg_{stat}_win'
+                        tables[column_name] = tables.apply(
+                            lambda row: teamname_win_stats[stat].get(row['teamname'], 0), 
+                            axis=1
+                        )
+
+                        column_name = f'teamname_avg_{stat}_loss'
+                        tables[column_name] = tables.apply(
+                            lambda row: teamname_loss_stats[stat].get(row['teamname'], 0), 
+                            axis=1
+                        )
             
                 tables['playername_avg_kill_share_win'] = tables['playername_avg_kills_win'] / tables['teamname_avg_kills_win']
                 tables['playername_avg_death_share_win'] = tables['playername_avg_deaths_win'] / tables['teamname_avg_deaths_win']