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- Title
A new model for predicting the winner in tennis based on the eigenvector centrality.
- Authors
Arcagni, Alberto; Candila, Vincenzo; Grassi, Rosanna
- 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.
- Subjects
CENTRALITY; TENNIS tournaments; TENNIS; LOGISTIC regression analysis; TENNIS players; RATE setting
- Publication
Annals of Operations Research, 2023, Vol 325, Issue 1, p615
- ISSN
0254-5330
- Publication type
Article
- DOI
10.1007/s10479-022-04594-7