# EOMM: An Engagement Optimized Matchmaking Framework

arXiv: https://arxiv.org/abs/1702.06820 · HTML: https://mlsanity.com/paper/1702.06820/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2017.

## Built on by (most cited first, 3 of 3)

- [Player Modeling via Multi-Armed Bandits](https://mlsanity.com/paper/2102.05264/) (2021)
- [Player Modeling using Behavioral Signals in Competitive Online Games](https://mlsanity.com/paper/2112.04379/) (2021)
- [Behavioral Player Rating in Competitive Online Shooter Games](https://mlsanity.com/paper/2207.00528/) (2022)

## Abstract (arXiv)

Matchmaking connects multiple players to participate in online player-versus-player games. Current matchmaking systems depend on a single core strategy: create fair games at all times. These systems pair similarly skilled players on the assumption that a fair game is best player experience. We will demonstrate, however, that this intuitive assumption sometimes fails and that matchmaking based on fairness is not optimal for engagement. In this paper, we propose an Engagement Optimized Matchmaking (EOMM) framework that maximizes overall player engagement. We prove that equal-skill based matchmaking is a special case of EOMM on a highly simplified assumption that rarely holds in reality. Our simulation on real data from a popular game made by Electronic Arts, Inc. (EA) supports our theoretical results, showing significant improvement in enhancing player engagement compared to existing matchmaking methods.