The statistics behind the game

Central player dynamics

The Central Player Dynamics team works with all of our games and focuses on identifying disruption in communication between players. Messages related to texts or voice chat in the games are evaluated using the team’s system. Game-specific reports that involve things like inactivity or intentional feeding are the responsibility of the game’s teams.

As for reports due to texts, the majority were handled by the Central Player Dynamics team – there were over 120 million games with at least one report, which in turn resulted in 13 million games being found to have violations. These violations resulted in consequences ranging from warnings to suspensions for more than 365 days (depending on the severity of each violation and the player’s past violations).

League of Legends and Teamfight Tactics

Our League team currently imposes about 700,000 penalties per month via text detection, inactivity detection, and intentional feeding detection.

LeaverBuster, our inactivity detection system, monitors every game to ensure that players who leave games early, affecting their teams, receive appropriate punishment.

We use classes to more severely penalize players who are inactive more often. And as far as inactive players in ranked matches, there’s an option to quit early and a mitigation of LP loss to make sure you don’t get penalized for a team member’s freak out.

However, inactivity is only one option that angry players resort to – the other is intentional feeding. Detecting this behavior can be a bit more difficult, which is why we rely on a learning model that monitors seven different data points from all champions to clearly determine whether a player is intentionally feeding or simply playing poorly. And because we continuously update this system, false positives have become very rare.

If you’d like to learn more about how the League team works on player behavior, you can read through this post published earlier in 2022.

VALORANT

In addition to voice chat, the VALORANT social and player dynamics team is also focusing on inactivity and feeding. Currently, out of 1,000 VALORANT players, about 27 are inactive during matches. And some of these are bots that just want to dust off ERF. In the meantime, however, these bots increasingly end up in lobbies with other bots, and if no damage is done, no one gets experience.

As for players who sit in front of their keyboards and lose games on purpose, our feeding detection system already has a method to expose them, and will soon add another.

The current method takes all input into account and decides after the game whether a player played poorly or intentionally fed. However, this method catches offenders only after the violation and does not help anyone who is already 11 rounds behind and understandably not having much fun.

For this reason, the VALORANT team is also working on real-time feeding detection. However, this problem has lots of gray areas, since poor performance can have many reasons and intentional feeding is only a small percentage of them. Once the VALORANT team has reduced the amount of false positives to a small number, we will implement this new method in addition to post-game detection.

Wild Rift

Wild Rift’s processes have evolved in 2022. Previously, inactivity detection simply checked to see if players were providing any input at all. But as some players have bypassed this simple detection system, we have introduced new layers to really ensure that players are participating in the game and providing useful input, rather than just moving around.

However, 2022 also saw the introduction of a new feeding system for Wild Rift that uses machine learning to ensure players are intentionally playing poorly. This feeding system has already detected nearly 2,000 instances of players intentionally sabotaging games since March 2022. As the machine continues to learn, this number will likely increase as more players are detected as feeders.

Last but not least, there is a detection system for trading wins. This system is based on several factors, one of which is the so-called “together against each other” players. These are players who permanently play with and against the same group of players. The detection system looks at the patterns of “Together Against Each Other” players, the length of games, and the win-loss histories of “Together Against Each Other” lobbies to identify swapping wins.

The importance of transparency

The move from one game to multiple has brought all sorts of new challenges. And with more games in development, we’re working to integrate the concepts of player dynamics into the earliest stages of game design to foster better communities from the start.

At the same time, we also believe it’s important to transparently communicate the data we collect in all of our games. These are complicated problems and there is no way to solve them completely. That said, we’re working hard to improve the game experience for all of our players, and we’ll be posting more regular updates about what we’re doing to achieve that goal.

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