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

Author

Listed:
  • Fogliato Riccardo

    (Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA)

  • Oliveira Natalia L.

    (Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA)

  • Yurko Ronald

    (Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA)

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.

Suggested Citation

  • Fogliato Riccardo & Oliveira Natalia L. & Yurko Ronald, 2021. "TRAP: a predictive framework for the Assessment of Performance in Trail Running," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 129-143, June.
    • Handle: RePEc:bpj:jqsprt:v:17:y:2021:i:2:p:129-143:n:5
    DOI: 10.1515/jqas-2020-0013
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    References listed on IDEAS

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    1. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Bartolucci Francesco & Murphy Thomas Brendan, 2015. "A finite mixture latent trajectory model for modeling ultrarunners’ behavior in a 24-hour race," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(4), pages 193-203, December.
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