Artificial Intelligence · 18.08.2026, 13:55 UTC
NRCD: An Open Database of Collegiate Running with Unified Performance Standardization
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | arXiv cs.LG ↗ |
| Veröffentlicht | 18.08.2026 UTC |
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arXiv:2608.14776v1 Announce Type: new Abstract: 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: 128,963 approved performances from 28,913 athletes across 1,336 meets in four sports (cross country (XC), indoor and outdoor track, and road races), 36.3% women, spanning 2004 through 2026. Within that single export, meets from August 2023 onward carry comprehensive course distance, elevation gain and loss, weather at race time, and track venue metadata (97.7% of XC rows with weather fields); earlier seasons back to 2004 are included with sparser metadata. 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. Furthermore, we recommend gender-stratified modeling. On XC, full standardization lowers median within-athlete cross-meet variability by 51.0% (women) and 34.4% (men) versus raw times. We release the dataset and pipeline with a python package `nrcd' under FAIR principles, …
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