Stephen Royle

RE: https://biologists.social/@steveroyle/116358027583114171

I found this in my drafts. Don't know why I didn't post it earlier... now it's April (a big marathon month) it's a good time!

I crunched the numbers on the 2025 NY Marathon to work out how to optimally pace a marathon. There's a general rule that works if you're going for sub-2:20 or trying to make it round in 6 hours.

#running #RunnersOfMastodon

Apr 6 at 1:44pmWeb
Show threadAnke1d ago

@steveroyle

Getting @ionica vibes from this cool analysis! (In a good way!)

Show threadParker Morse1d ago
@steveroyle I am impressed with this analysis of how people *actually* run marathons. (I've been hoping the advent of chip timing would ultimately lead to this kind of large-dataset analysis!) What I question, though, is whether a (very thorough) analysis of how marathons *get* run tells us much about how they *should be* run? This seems to be saying, "Forget about an *optimal* pace, here's how to compensate for the *sub-optimal* pace you're going to run despite your plans."
Show threadStephen Royle23h ago
@flashesofpanic I agree. In fact this was what I wanted to get out of the data. The dataset does have other waypoints to get closer to an answer. What’s missing is knowing what each runner was trying to do vs what happened to them. This analysis assumes the runners went off at goal pace. I suspect the answer of ideal pace will be highly individualised.
Show threadParker Morse23h ago
@steveroyle We need a chip data set that includes intended race plans! (Or goal finish times, at the very least.) We should ask the major marathons to start collecting this data. (And why not? The WMM has always been about sharing organizational experience; why not leverage that for data analysis?)
Show threadStephen Royle23h ago
@flashesofpanic it might be possible to deduce it from chip start times because the runners are started in bins according to goal time…