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Predicting the Potential of Professional Soccer Players.

, , , and . MLSA@PKDD/ECML, volume 1971 of CEUR Workshop Proceedings, page 1-10. CEUR-WS.org, (2017)

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Relational Learning for Football-Related Predictions., and . ILP (Late Breaking Papers), page 237-244. Imperial College Press / World Scientific, (2011)Evaluating Sports Analytics Models: Challenges, Approaches, and Lessons Learned., , , , , , and . EBeM@IJCAI, volume 3169 of CEUR Workshop Proceedings, CEUR-WS.org, (2022)Lifted Generative Learning of Markov Logic Networks, , , and . Machine Learning, (2015)(to appear).Player Chemistry: Striving for a Perfectly Balanced Soccer Team., and . CoRR, (2020)Actions Speak Louder than Goals: Valuing Player Actions in Soccer., , , and . KDD, page 1851-1861. ACM, (2019)Mining Hierarchical Pathology Data Using Inductive Logic Programming., , , , , , , and . AIME, volume 9105 of Lecture Notes in Computer Science, page 76-85. Springer, (2015)Relational approaches for learning, transferring and mining ; Relationele benaderingen voor leren, overdragen en mijnen.. Katholieke Universiteit Leuven, Belgium, (2016)base-search.net (ftunivleuven:oai:lirias.kuleuven.be:123456789/557862).Predicting the Potential of Professional Soccer Players., , , and . MLSA@PKDD/ECML, volume 1971 of CEUR Workshop Proceedings, page 1-10. CEUR-WS.org, (2017)Distinguishing Between Roles of Football Players in Play-by-Play Match Event Data., and . MLSA@PKDD/ECML, volume 2284 of CEUR Workshop Proceedings, page 31-41. CEUR-WS.org, (2018)Measuring Football Players' On-the-Ball Contributions from Passes During Games., and . MLSA@PKDD/ECML, volume 2284 of CEUR Workshop Proceedings, page 3-15. CEUR-WS.org, (2018)