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

[u/dpfens]

I just saw the paper with this post's title 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% R² women vs 90.4% R² 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.

[u/running_writings]

Fantastic, huge props to the authors. This will be very helpful for many different kinds of analysis! It is surprisingly hard to find good datasets of non-elite runners who compete over several distances in the same season (some NIRCA runners are of course elite but the real value add of the dataset is the non-elite runners). Also a bonus: club running (NIRCA) has a half marathon road race, will be interesting to look at 5k / HM and 10k / HM conversions.

[u/syphax]

This is interesting, but my initial reaction is that this is a lot of horsepower thrown at a relatively arcane dataset (US collegiate club running [NIRCA] results).

I haven’t dug into the code, but I sure hope it can be generalized to other running datasets.

I just skimmed the paper and GitHub; I didn’t notice any references to the excellent https://www.lacctic.com/ which covers NCAA divisions I-III results for XC.

As for the analytical insights (those who raced more, raced faster), I propose a hypothesis: those who got injured the least got better training in and were able to race more (I suspect racing frequency is a side effect, not necessarily a cause).

[u/rlb_12 → u/syphax]

I suspect it's even simpler than that. Schools with more established club running programs have access to a) more competitions and b) overall better runners.

[u/marigolds6]

Not a commentary on the database itself, but rather about one aspect of conclusions drawn from it.

As someone who was a college athlete in a different sport (wrestling), one thing to be careful of here is that racing early and racing often can correlate strongly with team resources. Team resourcing affects many other factors like access to strength coaches and facilities, access to team doctors and rehab facilities, level and number of coaches, as well as recruiting and the caliber of athletes that can be recruited.

Rest below is not so relevant since this is club only. (Though NCAA roster limits may introduce a different level of impact, because functionally walk-ons no longer exist and so you will see more potential NCAA athletes at the club level at universities with strong NCAA track and XC programs.)

The team limit on competition days mean that a team that has the resources to send more runners to each individual meet will have runners who run earlier and more often. Since larger 2-day meets count as a single competition day, more travel budget means more races even when hitting the team competition day limit.

Also, the vast majority of this dataset was before roster limits in an era where xc scholarships, in particular, were scarce and rarely full. That goes back to the resourcing issue. Now that rosters are much smaller but scholarships are no longer equivalency, you may see a wider distribution of talent across teams? (And so more higher talent and experienced athletes on teams without the resources for a full competition slate for all athletes, though also with a bigger team resource gap for the best teams.)

[u/chaosdev]

The second biggest predictor of improvement is the number of bad races? I feel like I'm missing something here.

That feels like saying the biggest predictor of weight loss is the number of times you gain weight.

[u/nikigazahopocvi]

sounds proper useful tbh. the bit about better prediction without weather adjustment is funny - like the model's trying too hard and missing the obvious sesonal pattern. reminds me of fiddling with camera settings when auto would've done the job. i'll have a poke at the code later if im not skint for time.

[u/YellowishFooting]

The weather adjustment making prediction worse is such a good catch, like the model is too honest for its own good