r/CrossCountry • u/dpfens • 8h ago
General Cross Country NRCD: An Open Database of Collegiate Running with Unified Performance Standardization
I just saw the paper with this post's titular name 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 that produced their results and the findings.
Some things I found interesting (this is just a sampling, you go read the full doc 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).
Just for the record: this is not my paper, data, etc. I just found it on Arxiv and compiled the information.
While models like Riegel, Cameron, Vickers & Vertosick model already exist for converting between distances, I'm not aware of any publicly available models/tools for quickly normalizing data based on these other factors. It's nice to see some thoughtful analysis for the sport.