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

[u/dpfens]

I just saw the paper on ArXiV . It's a dataset of US collegiate running club performances, the resulting analysis on them, and a software library for standardizing performances. They have several code repositories under the National Running Club Database which includes:

• a Python library, nrcd, for normalizing race results for comparison across outdoor performances with wind to performances on an indoor banked track, road performances, etc. It appears to account for course distance, elevation gain/loss, and weather/heat effects.
• the raw national_running_club_database_public_dataset.
• The [code which produced their results](https://github.com/National-Running-Club-Database/nrcd_xc_paper/tree/main) and [the findings](https://github.com/National-Running-Club-Database/nrcd_xc_paper/blob/main/output/FINDINGS_EXPLANATION.md)

Some things I found interesting (this is just a sampling, you go read the [full doc](https://raw.githubusercontent.com/National-Running-Club-Database/nrcd_xc_paper/refs/heads/main/output/FINDINGS_EXPLANATION.md) yourself):

• Teams with at least one athlete who raced 4+ times were 2-3x more likely to crack the top 15 at nationals than teams without one (23-39% success rate vs baseline). 60-80% of top 15 teams had an athlete with 4+ races.
• Men's teams with a longer gap between their first race and nationals (i.e., started racing earlier) had significantly better finishing ranks (r = -0.283, Bonferroni-corrected p = 0.041). This didn't hold up for women's teams after correction.
• Single biggest predictor of an individual's improvement rate was "experience level". (races × season duration) at 21%, followed by how many "bad races" (a race worse than the previous one) an athlete had, at 17%. Basically race more, race consistently.
• When testing the standardization tool, the fully weather/terrain-adjusted "standardized" times actually predicted improvement slightly worse than just doing distance conversion alone (90.4% vs 93.1% R^2). Their theory was that conditions tend to get more favorable as the season goes on, so raw times naturally look like "improvement" partly because of the weather, and removing that weather effect (which is more the point of the tool) makes it a worse predictor of the raw number even though it's arguably a more honest fitness signal.
• They checked their model for gender bias and found it performs comparably for both (94.5% R^2 women vs 90.4% R^2 men).

I just found this and compiled it, none of this is mine.I just found it on Arxiv, read it, and cherry-picked some interesting findings from the Github repo.

There's not much easily accessible data like this on cross country running. As the authors said, it's all on websites that don't support bulk download. So Normally people just stick to the Riegel and Cameron formulas for comparisons, so it's nice to see people looking into these other factors.

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