首个大规模公开大学生跑步数据集，含标准化性能分析工具。
NRCD: An Open Database of Collegiate Running with Unified Performance Standardization
- 构建14.4万条运动员成绩数据库，覆盖4类项目和2003-2026年赛事。
- 统一标准化后，男女运动员跨比赛时间波动分别降低51.1%和35.4%。
- 支持长期追踪、环境影响与性别公平研究，适合体育数据分析者使用。
美国大学生越野跑与田径每年产生数千条比赛成绩，但此前缺乏公开的大规模数据集。现有网站如Athletic.net、MileSplit和TFRRS虽有记录，却不支持批量下载，导致以往研究仅能基于约500条成绩，且多偏向男性运动员。本文推出国家跑步俱乐部数据库（NRCD），首个可公开获取的大学生跑步大规模数据集：包含143,868条经审核成绩，来自31,351名运动员，在1,423场赛事中完成，涵盖越野跑（XC）、室内与室外田径及公路赛，女性占比36.2%，时间跨度为2003–2026年。自2023年8月起，所有赛事均附完整赛道距离、海拔升降、比赛时天气及场地元数据（99.9%的越野跑记录含天气字段）。NRCD由社区共建，通过开放提交与专家审核维护，赛事数量逐年增长。我们发布统一性能标准化框架，集成距离、海拔与热力调整，采用哈德利带启发式方法处理热力影响；仅对越野跑进行验证。建议分性别建模。在越野跑中，全量标准化使男女运动员跨赛事成绩波动中位数分别下降51.1%和35.4%。数据集与标准化流程以Python包`nrcd`形式发布，遵循FAIR原则，支持运动员长期追踪、环境混杂因素研究及性别平等分析。
原文摘要 · Abstract (English)
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.
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