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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<jats:italic>t<\/jats:italic>for those players involved in the matches at time<jats:italic>t<\/jats:italic>), 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.<\/jats:p>","DOI":"10.1007\/s10479-022-04594-7","type":"journal-article","created":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T16:06:47Z","timestamp":1646669207000},"page":"615-632","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A new model for predicting the winner in tennis based on the eigenvector centrality"],"prefix":"10.1007","volume":"325","author":[{"given":"Alberto","family":"Arcagni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincenzo","family":"Candila","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5292-1723","authenticated-orcid":false,"given":"Rosanna","family":"Grassi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,7]]},"reference":[{"key":"4594_CR1","doi-asserted-by":"crossref","unstructured":"Anderson, E., Bai, Z., Bischof, C., Blackford, L.S., Demmel, J., Dongarra, J., Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A., & Sorensen, D. 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