  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.
