Jarrett Phillips writes:
I came across this recent preprint by Drummond and Popinga (2021) on applying Bayesian modeling to assess climbing ability. Drummond is foremost a computational evolutionary biologist (as am I) who is also an experienced climber. The work looks quite interesting. I was previously unaware of such an application and thought others may also appreciate it.
I’m not a climber at all, but it’s always fun to see new applications of Stan. In their revision, I hope the authors can collaborate with someone who’s experienced in Bayesian data visualization and can help them make some better graphs. I don’t mean they should just take their existing plots and make them prettier; I mean that I’m pretty sure there are some more interesting and informative ways to display their data and fitted models. Maybe Jonah or Yair could help—they live in Colorado so they might be climbers, right? Or Aleks would be perfect: he’s from Slovenia where everyone climbs, and he makes pretty graphs, and then the paper would have 3 authors named some form of Alex. So that’s my recommendation.
Interesting, in many ways it’s similar to this work
https://www.microsoft.com/en-us/research/publication/trueskilltm-a-bayesian-skill-rating-system/
recently updated into
https://www.microsoft.com/en-us/research/uploads/prod/2018/03/trueskill2.pdf – in some sense it’s like
cool! fun application.
I would also wonder if the slope varies by crag (ie climbing location). V8 in Joe’s Valley is not the same as V8 in Joshua Tree or V8 in Hueco Tanks. So, I wonder if the slope also varies. In some places, the lower grade climbs are clearly sandbagged (given grades lower than they should) while the hard climbs are given a ‘typical grade’. This would change the slope. I think using data across a large number of crags and fit a multilevel model (I didn’t readily find if they did this; maybe they did).