Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Expectation Propagation for Rating Players in Sports Competitions
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength
CG '08 Proceedings of the 6th international conference on Computers and Games
TeamSkill: modeling team chemistry in online multi-player games
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
TeamSkill and the NBA: applying lessons from virtual worlds to the real-world
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Evaluating simulation software components with player rating systems
Proceedings of the 6th International ICST Conference on Simulation Tools and Techniques
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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.