Department of Economics, Monash University, Melbourne, Australia.
Med Sci Sports Exerc. 2011 Dec;43(12):2405-11. doi: 10.1249/MSS.0b013e3182245367.
We demonstrate a methodology that uncovers an athlete's true pacing strategy from high-frequency (≤1 km) split field data, even if affected by high gradient variation on course. The method thus opens up the analysis of many previously opaque but popular undulating professional and amateur races to scientific scrutiny.
The method is relatively simple to use in any standard statistical package, and execution only requires the addition of the altitude-distance trace of the event to a runner's split times (e.g., as automatically collected by a modern Global Positioning System-enabled wristwatch). In addition, as opposed to assuming a pacing function (e.g., "J shaped," "U shaped," "all-out") and testing this function on the data, the method uses a preliminary discovery step to suggest the most appropriate pacing function(s) to test on the data (if any).
The method is demonstrated with two novel case studies: Gebrselassie's world-record Berlin marathon (September 2008) and a unique data set taken from several years of the Six Foot Track Ultramarathon (45 km, Sydney, Australia).
In both cases, the method reveals highly variable pacing strategies on a microscale despite remarkable symmetry on a macroscale in one case adding weight to the recent complex system perspective of the neural regulator.
我们展示了一种方法，即使在赛道上存在高坡度变化的情况下，也可以从高频（≤1 公里）分段数据中揭示运动员的真实配速策略。该方法为分析许多以前不透明但受欢迎的起伏专业和业余比赛开辟了道路，以便进行科学审查。
该方法在任何标准统计软件包中都相对简单易用，执行时只需将比赛的海拔-距离轨迹添加到跑步者的分段时间中（例如，由现代全球定位系统启用的腕戴式设备自动收集）。此外，与假设配速函数（例如“J 形”、“U 形”、“全力以赴”）并在数据上测试该函数不同，该方法使用初步发现步骤来建议最适合在数据上测试的配速函数（如果有）。
该方法通过两个新的案例研究进行了演示：格布雷塞拉西在 2008 年 9 月创造的柏林马拉松世界纪录和来自多年六英尺赛道超级马拉松（45 公里，澳大利亚悉尼）的独特数据集。
在这两种情况下，该方法都揭示了微观尺度上高度可变的配速策略，尽管在一种情况下宏观尺度上存在显着的对称性，这为最近的神经调节复杂系统观点增加了分量。
