A Probabilistic Rating System for Team Competitions with Individual Contributions

We study the problem of constructing a probabilistic rating system for team competitions. Unlike previous studies, we consider a setting where the competition can be broken down into relatively small individual tasks, and it is reasonable to assume that each task is done by a single team member. We begin with a simplistic naive Bayes approach which is this case reduces to logistic regression and then develop it into a more complex model with latent variables trained by expectation-maximization. We show experimental results that validate our approach.