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Beyond Skill Rating: Advanced Matchmaking in Ghost Recon Online

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6 Author(s)
Olivier Delalleau ; Department of Computer Science and Operations Research, University of Montreal, Montreal, QC, Canada ; Emile Contal ; Eric Thibodeau-Laufer ; Raul Chandias Ferrari
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Player satisfaction is particularly difficult to ensure in online games, due to interactions with other players. In adversarial multiplayer games, matchmaking typically consists in trying to match together players of similar skill level. However, this is usually based on a single-skill value, and assumes the only factor of “fun” is the game balance. We present a more advanced matchmaking strategy developed for Ghost Recon Online, an upcoming team-focused first-person shooter (FPS) from Ubisoft (Montreal, QC, Canada). We first show how incorporating more information about players than their raw skill can lead to more balanced matches. We also argue that balance is not the only factor that matters, and present a strategy to explicitly maximize the players' fun, taking advantage of a rich player profile that includes information about player behavior and personal preferences. Ultimately, our goal is to ask players to provide direct feedback on match quality through an in-game survey. However, because such data were not available for this study, we rely here on heuristics tailored to this specific game. Experiments on data collected during Ghost Recon Online's beta tests show that neural networks can effectively be used to predict both balance and player enjoyment.

Published in:

IEEE Transactions on Computational Intelligence and AI in Games  (Volume:4 ,  Issue: 3 )