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Coaches and sports scientists are looking for a way to predict performance in complex team sports such as basketball. However, concerning knowing what type of player’s profile is needed to win the competition, there is not too much information in the literature. Hence, our study had two aims: (i) to identify how the individual game-related statistics discriminate between winning and losing among different player positions through a cluster analysis; (ii) to elaborate predictive models that explain better performance through a decision tree analysis. 335 matches of the men’s Spanish League 2018/2019 were analysed, with a total of 7,345 individual statistics performances. The cluster analysis identified 3 performance groups formed by foreigners with both low (FLC; 23.8% shooting-guards) and high contributions (FHC; 32.1% centres) and Spanish with low contribution (SLC; 32.9% shooting-forwards). The decision tree analysis revealed that having players of SLC and FHC profiles predicts better results in the competition. Coaches can apply these profiles to build team composition.

Determination of basketball players’ high-performance profiles in the Spanish League