Pace my race: recommendations for marathon running

Abstract

We propose marathon running as a novel domain for recommender systems and machine learning. Using high-resolution marathon performance data from multiple marathon races (n = 7931), we build in-race recommendations for runners. We show that we can outperform the existing techniques which are currently employed for in-race finish-time prediction, and we demonstrate how such predictions may be used to make real time recommendations to runners. The recommendations are made at critical points in the race to provide personalised guidance so the runner can adjust their race strategy. Through the association of model features and the expert domain knowledge of marathon runners we generate explainable, adaptable pacing recommendations which can guide runners to their best possible finish time and help them avoid the potentially catastrophic effects of hitting the wall.