论文标题
陷阱：越野跑步评估的预测框架
TRAP: A Predictive Framework for Trail Running Assessment of Performance
论文作者
论文摘要
越野跑是一种耐力运动，运动员面临严重的身体挑战。由于参与者的数量越来越大，这些种族中的员工，设备和医疗支持的组织现在起着关键作用。监视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。过去，选择仅基于组织者的经验而不依赖数据。但是，这种方法既不可扩展也不可转移。取而代之的是，我们提出了一种牢固的统计方法来执行比赛之前和期间执行此任务。我们提出的框架，性能评估（陷阱），研究（1）评估跑步者达到下一个检查站的能力，（2）预测跑步者在下一个检查站的预期通行时间，以及（3）通行时间的相应预测间隔。为了获取有关跑步者能力的数据，我们引入了一个Python软件包，Scrapitra，以访问国际越野跑协会（ITRA）的跑步者的种族历史。我们将我们的方法（使用ITRA数据以及检查站和地形级别的信息）应用于Ultra-Trail Running的“圣杯”，即Ultra-Trail du Mont-Blanc（UTMB）种族，展示了我们方法论的预测能力。
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.
