Real world research worth exploring: Machine learning algorithms in injury prevention

Today, I would like to comment on a recent paper reporting the use of machine learning algorithms to estimate injury risk in team sports athletes (López-Valenciano et al., 2018). Machine learning (ML) is a relatively new approach in sports medicine and science that applies certain algorithms, mostly without pre-defined assumptions, to solve complex problems like the sports injury prediction. As the name indicates, machine learning attempts to make computers "learn" and produce more and more accurate algorithms. As a discipline it integrates statistics with computer science.

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.