Predicting special forces dropout via explainable machine learning

Selecting the right individuals for a sports team, organization, or military unit has a large influence on the achievements of the organization. However, the approaches commonly used for selection are either not reporting predictive performance or not explainable (i.e., black box models). In the present study, we introduce a novel approach to selection research, using various machine learning models. We examined 274 special forces recruits, of whom 196 dropped out, who performed a set of physical and psychological tests. On this data, we compared four machine learning models on their predictive performance, explainability, and stability. We found that a stable rule-based (SIRUS) model was most suitable for classifying dropouts from the special forces selection program. With an averaged area under the curve score of 0.70, this model had good predictive performance, while remaining explainable and stable. Furthermore, we found that both physical and psychological variables were related to dropout. More specifically, a higher score on the 2800 m time, need for connectedness, and skin folds was most strongly associated with dropping out. We discuss how researchers and practitioners can benefit from these insights in sport and performance contexts. In high-stakes contexts such as elite sports and the military, selecting the right individuals has a large influence on individual and organizational achievements. We compare four machine learning models, with various levels of explainability, on a dataset of 274 Dutch special forces recruits. A fully explainable SIRUS model could achieve an average area under the curve score of 0.70 for predicting dropouts from the program. Both physical and psychological variables were related to dropout, especially the 2800 m time, need for connectedness, and skin folds.