![]() The resulting ratings are then used as a covariate in a simple logit model. ![]() Unlike what happens for the standard paired comparisons class (where the rates or latent abilities only change at time t for those players involved in the matches at time t), the use of a centrality measure allows the ratings of the whole set of players to vary every time there is a new match. We propose a measure based on eigenvector centrality. In this paper, we extend this latter class of models by using network indicators for the predictions. In particular, paired comparison approaches make use of latent ability estimates or rating calculations to determine the probability that a player will win a match. The use of statistical tools for predicting the winner in tennis matches has enjoyed an increase in popularity over the last two decades and, currently, a variety of methods are available.
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