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Predicting the future performance of soccer players
Author(s) -
Arndt Cornelius,
Brefeld Ulf
Publication year - 2016
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11321
Subject(s) - computer science , machine learning , artificial intelligence , feature (linguistics) , regression , feature vector , data mining , regression analysis , feature selection , multi task learning , statistics , mathematics , task (project management) , engineering , philosophy , linguistics , systems engineering
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