The recruitment of athletes with limited resources is a global problem in professional sports. In US Major League Baseball, the experience of the Oakland Athletics' general manager in the last decade turned his "Moneyball" model into a synonym of quantitative analysis in the transfer market of baseball players. His strategy focused on hiring players with outstanding technical skills but relatively low market value. This study adapted this model to the framework of a multiple criteria decision aid (MCDA), by selecting undervalued players who have complementary abilities. The novelty here refers to the joint use of four algorithms explored by the composition of probabilistic preferences (CPP) (i.e., ranking, classification, dynamic evaluation and regularity analysis) and their application to soccer player performance evaluation. The new model analysed the recent transfer of a left-back soccer player to Europe. The results indicated 12 opportunities for better investment, among 32 left and right-back players considered. Two years later, the value of the same player was considerably lower. He played only five matches in the 2018-2019 season, without scoring or providing any assists. On the other hand, the players better classified by the CPP-MB model presented higher performances and market values.