# Behavioral Player Rating in Competitive Online Shooter Games

arXiv: https://arxiv.org/abs/2207.00528 · HTML: https://mlsanity.com/paper/2207.00528/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Competitive Balance in Team Sports Games](https://mlsanity.com/paper/2006.13763/) (2020)
- [The Evaluation of Rating Systems in Online Free-for-All Games](https://mlsanity.com/paper/2008.06787/) (2020)
- [Data-Driven Game Development: Ethical Considerations](https://mlsanity.com/paper/2006.10808/) (2020)
- [Evaluating Team Skill Aggregation in Online Competitive Games](https://mlsanity.com/paper/2106.11397/) (2021)
- [EOMM: An Engagement Optimized Matchmaking Framework](https://mlsanity.com/paper/1702.06820/) (2017)
- [The Evaluation of Rating Systems in Team-based Battle Royale Games](https://mlsanity.com/paper/2105.14069/) (2021)
- [Player Modeling using Behavioral Signals in Competitive Online Games](https://mlsanity.com/paper/2112.04379/) (2021)
- [Using user's local context to support local news](https://mlsanity.com/paper/2205.12408/) (2022)

## Abstract (arXiv)

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.