A new model for predicting the winner in tennis based on the eigenvector centrality

Alberto Arcagni (), Vincenzo Candila () and Rosanna Grassi ()
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Alberto Arcagni: Sapienza University of Rome
Vincenzo Candila: Sapienza University of Rome
Rosanna Grassi: University of Milano-Bicocca

Annals of Operations Research, 2023, vol. 325, issue 1, No 26, 615-632

Abstract: Abstract 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. 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. In this paper, we extend this latter class of models by using network indicators for the predictions. We propose a measure based on eigenvector centrality. 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. The resulting ratings are then used as a covariate in a simple logit model. Evaluating the proposed approach with respect to some popular competing specifications, we find that the centrality-based approach largely and consistently outperforms all the alternative models considered in terms of the prediction accuracy. Finally, the proposed method also achieves positive betting results.

Keywords: Network; Eigenvector centrality; Tennis; Forecasting (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-022-04594-7

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