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Trail running is an endurance sport in which athletes face severe physicalchallenges. Due to the growing number of participants, the organization oflimited staff, equipment, and medical support in these races now plays a keyrole. Monitoring runner's performance is a difficult task that requiresknowledge of the terrain and of the runner's ability. In the past, choices weresolely based on the organizers' experience without reliance on data. However,this approach is neither scalable nor transferable. Instead, we propose a firmstatistical methodology to perform this task, both before and during the race.Our proposed framework, Trail Running Assessment of Performance (TRAP), studies(1) the the assessment of the runner's ability to reach the next checkpoint,(2) the prediction of the runner's expected passage time at the nextcheckpoint, and (3) corresponding prediction intervals for the passage time. Toobtain data on the ability of runners, we introduce a Python package,ScrapITRA, to access the race history of runners from the International TrailRunning Association (ITRA). We apply our methodology, using the ITRA data alongwith checkpoint and terrain-level information, to the "holy grail" ofultra-trail running, the Ultra-Trail du Mont-Blanc (UTMB) race, demonstratingthe predictive power of our methodology.

TRAP: A Predictive Framework for Trail Running Assessment of Performance