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
A study of UCT and its enhancements in an artificial game
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
TeamSkill evolved: mixed classification schemes for team-based multi-player games
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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
Hi-index | 0.00 |
Whole-History Rating (WHR) is a new method to estimate the time-varying strengths of players involved in paired comparisons. Like many variations of the Elo rating system, the whole-history approach is based on the dynamic Bradley-Terry model. But, instead of using incremental approximations, WHR directly computes the exact maximum a posteriori over the whole rating history of all players. This additional accuracy comes at a higher computational cost than traditional methods, but computation is still fast enough to be easily applied in real time to large-scale game servers (a new game is added in less than 0.001 second). Experiments demonstrate that, in comparison to Elo, Glicko, TrueSkill, and decayed-history algorithms, WHR produces better predictions.