TRAP：一个用于性能测试的预测框架
TRAP: A Predictive Framework for Trail Running Assessment of Performance
摘要：
越野跑是一项耐力运动，运动员面临着严峻的身体挑战。由于参赛者人数不断增加，在这些比赛中，有限的工作人员、设备和医疗支持的组织现在起着关键作用。监控跑步者的表现是一项艰巨的任务，需要了解地形和跑步者的能力。在过去，选择完全基于组织者的经验，而不依赖于数据。然而，这种方法既不能扩展，也不能转移。相反，我们提出了一个坚定的统计方法学来执行这项任务，无论是在比赛之前和比赛期间。我们提出的框架，越野跑性能评估（TRAP），研究（1）的评估，跑步者的能力，以达到下一个检查点，（2）预测的跑步者的预期通过时间在下一个检查点，和（3）相应的预测间隔的通过时间。为了获得有关跑步者能力的数据，我们引入了一个Python包ScrapITRA，以访问国际越野跑协会（ITRA）的跑步者的比赛历史。我们应用我们的方法，使用ITRA的数据以及检查点和地形级别的信息，以“圣杯”的超径运行，超径杜勃朗峰（UTMB）的比赛，展示了我们的方法的预测能力。
英文摘要：
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 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. To obtain data on the ability of runners, we introduce a Python package, ScrapITRA, to access the race history of runners from the International Trail Running Association (ITRA). We apply our methodology, using the ITRA data 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
