TeamSkill Evolved: Mixed Classification Schemes for Team-Based Multi-player Games

  • Colin DeLong
  • Jaideep Srivastava
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301)


In this paper, we introduce several approaches for maintaining weights over the aggregate skill ratings of subgroups of teams during the skill assessment process and extend our earlier work in this area to include game-specific performance measures as features alongside aggregate skill ratings as part of the online prediction task. We find that the inclusion of these game-specific measures do not improve prediction accuracy in the general case, but do when competing teams are considered evenly matched. As such, we develop a “mixed” classification method called TeamSkill-EVMixed which selects a classifier based on a threshold determined by the prior probability of one team defeating another. This mixed classification method outperforms all previous approaches in most evaluation settings and particularly so in tournament environments. We also find that TeamSkill-EVMixed’s ability to perform well in close games is especially useful early on in the rating process where little game history is available.


Player rating systems competitive gaming perceptron passive aggressive algorithm confidence-weighted learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Colin DeLong
    • 1
  • Jaideep Srivastava
    • 1
  1. 1.Department of Computer ScienceUniversity of MinnesotaUSA

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