In most research studies, much of the gathered information is qualitative in nature. This article focuses on items for which there are multiple rankings that should be optimally combined. More specifically, it describes a supervised stochastic approach, driven by a Boltzmann machine capable of ranking elements related to each other by order of importance. Unlike classic statistical ranking techniques, the algorithm does not need a voting rule for decision-making. The experimental results indicate that the proposed model outperforms two reference rank aggregation algorithms, ELECTRE IV and VIKOR, and it behaves more stable when encountering noisy data. a This research was supported by the program Cátedras Franco-Brasileiras no Estado de São Paulo, an initiative of the French consulate and the state of São Paulo (Brazil). We thank our colleagues Rémi Souriau for his helpful comments and Laurence Le-Moyec who supervise the data acquisition with the Institut national du sport, de l'expertise et de la performance (INSEP). b