"The Bradley-Terry model for obtaining individual skill from paired comparisons has been
popular in many areas. In machine learning, this model is related to multi-class probability
estimates by coupling all pairwise classification results. Error correcting output codes
(ECOC) are a general framework to decompose a multi-class problem to several binary
problems. To obtain probability estimates under this framework, this paper introduces a
generalized Bradley-Terry model in which paired individual comparisons are extended to
paired team comparisons. We propose a simple algorithm with convergence proofs to solve
the model and obtain individual skill. Experiments on synthetic and real data demonstrate
that the algorithm is useful for obtaining multi-class probability estimates. Moreover, we
discuss four extensions of the proposed model: 1) weighted individual skill, 2) home-field
advantage, 3) ties, and 4) comparisons with more than two teams"
