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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.