Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/55014
Title: A Bayesian asymmetric logistic model of factors underlying team success in top-level basketball in Spain
Authors: Pérez-Sánchez, José María 
Salmerón-Gómez, Román
Ocaña-Peinado, Francisco M.
Keywords: Game-Related Statistics
Elite Basketball
Performance
Players
Season, et al
Issue Date: 2019
Publisher: 0039-0402
Journal: Statistica Neerlandica 
Abstract: This paper analyses the factors underlying the victories and defeats of the Spanish basketball teams Real Madrid and Barcelona in the national league, ACB. The following research questions were addressed: (a) Is it possible to identify the factors underlying these results? (b) Can knowledge of these factors increase the probability of winning and thus help coaches take better decisions? We analysed 80 and 79 games played in the 2013-2014 season by Real Madrid and Barcelona, respectively. Logistic regression analysis was performed to predict the probability of the team winning. The models were estimated by standard (frequentist) and Bayesian methods, taking into account the asymmetry of the data, that is, the fact that the database contained many more wins than losses. Thus, the analysis consisted of an asymmetric logistic regression. From the Bayesian standpoint, this model was considered the most appropriate, as it highlighted relevant factors that might remain undetected by standard logistic regression. The prediction quality of the models obtained was tested by application to the results produced in the following season (2014-2015). Again, asymmetric logistic regression achieved the best results. In view of the study findings, we make various practical recommendations to improve decision making in this field. In short, asymmetric logistic regression is a valuable tool that can help coaches improve their game strategies.
URI: http://hdl.handle.net/10553/55014
ISSN: 0039-0402
DOI: 10.1111/stan.12127
Source: Statistica Neerlandica[ISSN 0039-0402],v. 73, p. 22-43
Appears in Collections:Artículos
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