Ranking Individuals by Group Comparisons

We discuss the problem of ranking individuals from their group competition results. Many real-world problems are of this type. For example, ranking players from team games is important in some sports. In machine learning, this is closely related to multi-class classification and probability estimates. We propose new models for estimating individuals' abilities, and hence rankings of individuals. We develop easy and effective solution procedures. Experiments on real bridge records and multi-class classification demonstrate the viability of the proposed models.