Behavioral Player Rating in Competitive Online Shooter Games

TL;DR

This paper develops behavioral player ratings in online shooter games by analyzing in-game statistics, leading to more accurate and interpretable skill assessments that enhance matchmaking and player experience.

Contribution

It introduces a novel behavioral rating system based on in-game features, outperforming traditional rating systems in predicting player ranks.

Findings

Behavioral ratings predict player ranks more accurately.

Behavioral ratings maintain interpretability of player skills.

Using behavioral ratings improves matchmaking quality.

Abstract

Competitive online games use rating systems for matchmaking; progression-based algorithms that estimate the skill level of players with interpretable ratings in terms of the outcome of the games they played. However, the overall experience of players is shaped by factors beyond the sole outcome of their games. In this paper, we engineer several features from in-game statistics to model players and create ratings that accurately represent their behavior and true performance level. We then compare the estimating power of our behavioral ratings against ratings created with three mainstream rating systems by predicting rank of players in four popular game modes from the competitive shooter genre. Our results show that the behavioral ratings present more accurate performance estimations while maintaining the interpretability of the created representations. Considering different aspects of the playing behavior of players and using behavioral ratings for matchmaking can lead to match-ups that are more aligned with players' goals and interests, consequently resulting in a more enjoyable gaming experience.