Data mining approaches to the characterizations of nominees for FIFA Ballon d´Or award

In this paper, data mining methods are applied to a data set of recent football players in order to characterize the players who are nominated for FIFA Ballon d´Or award. We assume there is an impartial criterion for the nomination considering only player´s in-game statistics. Under this assumption, we applied four data mining algorithms, possessing distinctive features each other: OneR, C4.5, MLEM2 and DOMLEM. The results obtained by each method are discussed and compared among them in order to evaluate how accurate they are according to a football expert´s opinion.