We propose a multitask, regression-based approach for predicting future performances of soccer players. The multitask approach allows us to simultaneously learn individual player models as offsets to a general model. We devise multitask variants of ridge regression and ε-support vector regression. Together with a hashed joint feature space, the generalized models can be optimized using standard techniques. Relevant features for the prediction are identified by a modified recursive feature elimination strategy. We report on extensive empirical results using real data from the German Bundesliga. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016.
CITATION STYLE
Arndt, C., & Brefeld, U. (2016). Predicting the future performance of soccer players. Statistical Analysis and Data Mining, 9(5), 373–382. https://doi.org/10.1002/sam.11321
Mendeley helps you to discover research relevant for your work.