A Bayesian asymmetric logistic model of factors underlying team success in top-level basketball in Spain

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.