Learning to rank sports teams on a graph

Abstract

To improve the prediction ability of ranking models in sports, a generalized PageRank model is introduced. In the model, a game graph is constructed from the perspective of Bayesian correction with game results. In the graph, nodes represent teams, and a link function is used to synthesize the information of each game to calculate the weight on the graph's edge. The parameters of the model are estimated by minimizing the loss function, which measures the gap between the predicted rank obtained by the model and the actual rank. The application to the National Basketball Association (NBA) data shows that the proposed model can achieve better prediction performance than the existing ranking models.