NRCD：采用统一性能标准化的大学跑步公开数据库
NRCD: An Open Database of Collegiate Running with Unified Performance Standardization
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中文总结 AI 辅助
本研究推出首个大规模公开大学跑步数据集NRCD，含12万余条表现数据，发布统一性能标准化框架，可降低运动员跨赛事表现变异性，支持体育相关研究。
中文摘要 AI 辅助
美国大学跑步运动（越野跑和田径项目）每年产生数千场比赛结果，但一直没有大规模公开数据集供研究使用。现有网站如MileSplit和TFRRS虽托管了比赛结果，但不支持批量下载，导致此前的分析仅能使用约500项表现数据，且研究常偏向男性运动员。我们推出了国家跑步俱乐部数据库（National Running Club Database, NRCD），这是首个公开可用的大规模大学跑步数据集：包含2004年至2026年间1336场赛事中28913名运动员的128963项经核准的表现数据，涵盖越野跑（XC）、室内田径、室外田径和公路赛四项运动，其中女性占比36.3%。在同一份导出数据中，2023年8月及之后的赛事包含完整的赛道距离、海拔升降、比赛时天气以及赛道场地元数据（97.7%的越野跑记录带有天气字段）；2004年之前的赛季元数据则较为稀疏。NRCD由社区治理，通过公开提交和专家审核运作，作为实时数据库维护，赛事数量逐年增长。我们发布了统一性能标准化框架，该框架将既定的距离调整、海拔调整和分组调整整合为一条流程。此外，我们建议采用性别分层建模。在越野跑项目中，与原始时间相比，完整标准化将运动员跨赛事的中位数变异性降低了51.0%（女性）和34.4%（男性）。我们依据FAIR原则发布了该数据集和配套的Python包`nrcd`，支持运动员纵向建模、环境混淆因素研究以及大学体育中的性别平等研究。
英文摘要
Collegiate running in the United States generates thousands of race results annually in cross country and track and field, yet no large-scale dataset has been publicly available for research. Existing websites such as Athletic.net, MileSplit, and TFRRS host results but do not support bulk download, restricting prior analyses to ~500 performances, often skewing studies toward male athletes. We introduce the National Running Club Database (NRCD), the first openly available collegiate running dataset at scale: 143,868 approved performances from 31,351 athletes across 1,423 meets in four sports (cross country (XC), indoor and outdoor track, and road races), 36.2% women, spanning 2003-2026. Meets from August 2023 onward carry comprehensive course distance, elevation gain and loss, weather at race time, and track venue metadata (99.9% of XC rows with weather fields). NRCD is community-governed through open submission and expert approval and is maintained as a live database whose meet volume has grown yearly. We release a unified performance standardization framework that operationalizes established distance, elevation, and heat adjustments in one pipeline; XC-only validation; heat is a Hadley-band heuristic. We recommend gender-stratified modeling. On XC, full standardization lowers median within-athlete cross-meet variability by 51.1% (women) and 35.4% (men) versus raw times. We release the dataset and pipeline with a Python package `nrcd' under FAIR principles, supporting longitudinal athlete modeling, environmental-confounder studies, and gender-equity research in collegiate sport.
发表机构
- University of Notre Dame（圣母大学）
- National Running Club Database（全国跑步俱乐部数据库）
- University of Virginia（弗吉尼亚大学）
- Virginia Tech（弗吉尼亚理工大学）
- University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
机构由 AI 辅助整理，请以论文原文为准。
