In the study of López-Valenciano, a total of 132 male professional soccer and handball players underwent pre-season screening evaluation which included personal, psychological and neuromuscular measures. In addition, injury surveillance was employed to all musculoskeletal injuries during the season. The authors employed different learning techniques to check their accuracy in injury prediction.
Their results showed that the machine learning algorithms presented moderate accuracy for identifying players at risk of injury. From other studies, we know that ML accuracy can be improved with more data entered in the analysis. Nevertheless, the novelty of the study of López-Valenciano and colleagues is they showed that machine learning can assist in solving problems like the identification of players at risk. However, one should bear in mind that ML algorithms work well for the population they were created and we cannot predict what will happen with another set of data.