# Player Modeling using Behavioral Signals in Competitive Online Games

arXiv: https://arxiv.org/abs/2112.04379 · HTML: https://mlsanity.com/paper/2112.04379/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 7 of 7)

- ["It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games](https://mlsanity.com/paper/2009.00541/) (2020)
- [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)

## Built on by (most cited first, 2 of 2)

- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Behavioral Player Rating in Competitive Online Shooter Games](https://mlsanity.com/paper/2207.00528/) (2022)

## Abstract (arXiv)

Competitive online games use rating systems to match players with similar skills to ensure a satisfying experience for players. In this paper, we focus on the importance of addressing different aspects of playing behavior when modeling players for creating match-ups. To this end, we engineer several behavioral features from a dataset of over 75,000 battle royale matches and create player models based on the retrieved features. We then use the created models to predict ranks for different groups of players in the data. The predicted ranks are compared to those of three popular rating systems. Our results show the superiority of simple behavioral models over mainstream rating systems. Some behavioral features provided accurate predictions for all groups of players while others proved useful for certain groups of players. The results of this study highlight the necessity of considering different aspects of the player's behavior such as goals, strategy, and expertise when making assignments.