Transcription of TrueSkill 2: An improved Bayesian skill rating system
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TrueSkill 2: An improved Bayesian skill rating system
TrueSkill 2: An improved Bayesian skill rating systemTom MinkaMicrosoft ResearchRyan ClevenThe CoalitionYordan ZaykovMicrosoft ResearchMarch 22, 2018 AbstractOnline multiplayer games, such asGears of WarandHalo, use skill -based matchmakingto give players fair and enjoyable matches. They depend on a skill rating system to inferaccurate player skills from historical data. TrueSkill is a popular and effective skill ratingsystem, working from only the winner and loser of each game. This paper presents anextension to TrueSkill that incorporates additional information that is readily available inonline shooters, such as player experience, membership in a squad, the number of kills aplayer scored, tendency to quit, and skill in other game modes. This extension, which wecall TrueSkill2, is shown to significantly improve the accuracy of skill ratings computedfromHalo 5matches.
drawn from a Gaussian distribution with mean m 0 and variance v 0. This sampling process is denoted skillt 0 i ˘N(m 0;v 0) (1) where t 0 is the time of the player’s rst match and (m 0;v 0) are tunable parameters. After each match, the player’s skill changes by a random amount, also drawn from a Gaussian:
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