NRCD: An Open Database of Collegiate Running with Unified Performance Standardization

[2608.14776] NRCD: An Open Database of Collegiate Running with Unified Performance Standardization Skip to main content Search Submit Donate Log in Computer Science > Machine Learning arXiv:2608.14776 (cs) [Submitted on 14 Aug 2026 ( v1 ), last revised 26 Aug 2026 (this version, v2)] Title: NRCD: An Open Database of Collegiate Running with Unified Performance Standardization Authors: Jonathan A. Karr Jr. , Ryan M. Fryer , Ben Darden , Nicholas Pell , Kayla Ambrose , Evan Hall , Ramzi K. Bualuan , Nitesh V. Chawla View PDF HTML (experimental) 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 this http URL , 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. Comments: Accepted to CIKM'26 Resources Paper - Main Conference Oral Presentation Subjects: Machine Learning (cs.LG) ; Computers and Society (cs.CY); Information Retrieval (cs.IR) Cite as: arXiv:2608.14776 [cs.LG] (or arXiv:2608.14776v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14776 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jonathan Karr Jr [ view email ] [v1] Fri, 14 Aug 2026 17:36:02 UTC (161 KB) [v2] Wed, 26 Aug 2026 18:06:34 UTC (163 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CY cs.IR References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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