EOMM: An Engagement Optimized Matchmaking Framework

TL;DR

This paper introduces EOMM, a novel matchmaking framework that prioritizes maximizing player engagement over fairness, challenging traditional equal-skill pairing assumptions, and demonstrating improved engagement through simulations.

Contribution

The paper proposes a new engagement-optimized matchmaking framework and proves its advantages over traditional fairness-based methods using real-world data.

Findings

EOMM outperforms existing matchmaking methods in engagement metrics.

Equal-skill matchmaking is a special case of EOMM under simplified assumptions.

Simulation results show significant engagement improvements with EOMM.

Abstract

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.