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TRAP: a predictive framework for the Assessment of Performance in Trail Running

  • Riccardo Fogliato EMAIL logo , Natalia L. Oliveira und Ronald Yurko
Veröffentlicht/Copyright: 30. September 2020
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Journal of Quantitative Analysis in Sports
Aus der Zeitschrift Journal of Quantitative Analysis in Sports Band 17 Heft 2

Abstract

Trail running is an endurance sport in which athletes face severe physical challenges. Due to the growing number of participants, the organization of limited staff, equipment, and medical support in these races now plays a key role. Monitoring runner’s performance is a difficult task that requires knowledge of the terrain and of the runner’s ability. In the past, choices were solely based on the organizers’ experience without reliance on data. However, this approach is neither scalable nor transferable. Instead, we propose a firm statistical methodology to perform this task, both before and during the race. Our proposed framework, Trail Running Assessment of Performance (TRAP), studies (1) the assessment of the runner’s ability to reach the next checkpoint, (2) the prediction of the runner’s expected passage time at the next checkpoint, and (3) corresponding prediction intervals for the passage time. We apply our methodology, using the race history of runners from the International Trail Running Association (ITRA) along with checkpoint and terrain-level information, to the “holy grail” of ultra-trail running, the Ultra-Trail du Mont-Blanc (UTMB) race, demonstrating the predictive power of our methodology.

Keywords: ITRA; random forests; trail running; UTMB
Corresponding author: Riccardo Fogliato, Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA, E-mail:

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/jqas-2020-0013).

Received: 2020-01-30
Accepted: 2020-09-18
Published Online: 2020-09-30
Published in Print: 2021-06-25

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/jqas-2020-0013
Schlagwörter für diesen Artikel
ITRA; random forests; trail running; UTMB

Artikel in diesem Heft

  1. Frontmatter
  2. Research articles
  3. A Bayesian marked spatial point processes model for basketball shot chart
  4. How well do Elo-based ratings predict professional tennis matches?
  5. Algorithmically deconstructing shot locations as a method for shot quality in hockey
  6. An iterative Markov rating method
  7. TRAP: a predictive framework for the Assessment of Performance in Trail Running
  8. The influence of field size, goal size and number of players on the average number of goals scored per game in variants of football and hockey: the Pi-theorem applied to team sports
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