removed opponent elo from stat collection

ml-agents/mlagents/trainers/ghost/trainer.py

                 self.current_elo, result
             )
             self.change_current_elo(change)
-            # opponents = np.array(self.policy_elos, dtype=np.float32)
-            # self._stats_reporter.add_stat(
-            #    "Self-play/Mean Opponent ELO", opponents.mean()
-            # )
-            # self._stats_reporter.add_stat("Self-play/Std Opponent ELO", opponents.std())
 
     def advance(self) -> None:
         """
                 except AgentManagerQueue.Empty:
                     pass
 
+        self.learning_team = self.controller.get_learning_team(self.ghost_step)
+
         if self.ghost_step - self.last_save > self.steps_between_save:
             self._save_snapshot(self.trainer.policy)
             self.last_save = self.ghost_step
             self.last_swap = self.ghost_step
 
-        self.learning_team = self.controller.get_learning_team(self.ghost_step)
-
     def end_episode(self):
         self.trainer.end_episode()
 
             self._save_snapshot(policy)  # Need to save after trainer initializes policy
             self.learning_team = team_id
             self.wrapped_trainer_team = team_id
-            self._stats_reporter.add_property(StatsPropertyType.SELF_PLAY_TEAM, team_id)
         else:
             # for saving/swapping snapshots
             policy.init_load_weights()
             self.current_opponent = -1 if x == "current" else x
             logger.debug(
                 "Step {}: Swapping snapshot {} to id {} with {} learning".format(
-                    self.get_step, x, parsed_behavior_id.behavior_id, self.learning_team
+                    self.ghost_step,
+                    x,
+                    parsed_behavior_id.behavior_id,
+                    self.learning_team,
                 )
             )
             policy = self.get_policy(parsed_behavior_id.behavior_id)

ml-agents/mlagents/trainers/stats.py

             )
             if self.self_play and "Self-play/ELO" in values:
                 elo_stats = values["Self-play/ELO"]
-                #        mean_opponent_elo = values["Self-play/Mean Opponent ELO"]
-                #        std_opponent_elo = values["Self-play/Std Opponent ELO"]
-                logger.info(
-                    "{} Team {}: ELO: {:0.3f}. ".format(
-                        category, self.self_play_team, elo_stats.mean
-                    )
-                    #                mean_opponent_elo.mean,
-                    #                std_opponent_elo.mean,
-                )
+                logger.info("{} ELO: {:0.3f}. ".format(category, elo_stats.mean))
         else:
             logger.info(
                 "{}: Step: {}. No episode was completed since last summary. {}".format(
         elif property_type == StatsPropertyType.SELF_PLAY:
             assert isinstance(value, bool)
             self.self_play = value
-        elif property_type == StatsPropertyType.SELF_PLAY_TEAM:
-            assert isinstance(value, int)
-            self.self_play_team = value
 
     def _dict_to_str(self, param_dict: Dict[str, Any], num_tabs: int) -> str:
         """