Risk of Future Injuries in Mid/Long-Range Runners: Feasible Rashomon Set of Sparse Decision Trees

In sports research, highly optimised machine learning (ML) models often fail to be applied “in the field“. Although useful and with strong performances, they offer limited flexibility to experts to “interact” with the model. This study proposes TREEFARMS+, a Rashomon set-based framework (a collection of prediction models that exhibit near-optimal accuracy) that addresses the aforementioned limitation of traditional ML approaches, using reasonable resources. TREEFARMS+ is jointly presented with an interactive tool which facilitates the user's trade-offs between performance, complexity, interpretation and expertise-based knowledge. Although presented here in a sports science context, showing beneficial effects to the prediction of future injuries in mid/long-range runners, this method can be easily integrated into different fields.