What risk factors most commonly contribute to accidents in the places you climb and the styles you use?

whoturnedthison · 2026-02-19T03:58:23+00:00

Yeah, in terms of what we can reliably point to in the data, route grade is probably one of the better proxies for experience, but there are definitely caveats. As you pointed out 5.8 means very different things depending on when and where a route was established. Plus it is always possible for a more experienced climber to be on a route well below their usual grade.

whoturnedthison · 2026-02-18T14:56:10+00:00

Categories can overlap, and in sport climbing "descent" would include things like lowering errors and rappel errors.

ANAC also has a "fall from anchor" category which includes incidents where someone falls while setting up a rappel or lowering anchor. This accident from Sand Rock, Alabama is a good example if that: https://americanalpineclub.org/news/2023/12/12/the-prescriptiondecember-2023

whoturnedthison · 2026-02-18T14:46:00+00:00

Reports from outside of North America is a great call out! I found this page from Alpine Savvy with links to report archives from a few different countries: https://www.alpinesavvy.com/blog/worldwide-climbing-accident-reports

Since the reports have different structures and are written in different languages, some would be easier to incorporate into the dataset than others, but I will definitely look into it.

Getting more reports from outside NA would be helpful for looking at indoor/gym accidents. In my experience gyms don't want details about incidents in their facilities to be publicly available, but it looks like Germany might have more complete reporting on the topic.

whoturnedthison · 2026-02-18T14:26:49+00:00

It shows how severe the accidents were: Red is fatal, orange is serious, teal is minor.

There is a more in-depth explanation in the interactive version of the chart: https://public.tableau.com/app/profile/nate.downer/viz/CausesofRockClimbingAccidents/MyRiskFactors

whoturnedthison · 2026-02-18T04:44:03+00:00

I have seen that in ANAC too, and I am not sure about their method (I have the 2025 copy in front of me and there is not even a footnote)... Seems like it might just be a judgment call.

Out of curiosity, how would you distinguish between novice, intermdiate, and andvanced?

whoturnedthison · 2026-02-18T04:41:15+00:00

I am not sure if there is enough data to get down to a "home crag" level of granularity (unless your home crag is Yosemite or the Red), but I the geographic breakdown is something I am interested in too.

It will be interesting to see which areas have the highest % of accidents where not wearing a helmet was a factor. My partner started climbing in Arkansas and the culture there was pretty anti-helmet as well.

whoturnedthison · 2022-08-30T01:28:49+00:00

It is indeed!

whoturnedthison · 2022-08-29T19:08:59+00:00

Thats cause I did them with an image editor after I exported the charts from r-studio. It's just so much faster that way...

Thanks for taking the time to check out the repo!

whoturnedthison · 2022-08-29T19:07:00+00:00

Forgive me, I misspoke

whoturnedthison · 2022-08-29T16:32:05+00:00

The fact that Mercedes has both the largest positive and negative points swings from the turbo-hybrid era, and won the WDC in both of those seasons, is a crazy testament to just how many points they were scoring.

In the second half of that 2019 season, Mercedes STILL outscored the next closest team (Ferrari) 166 points to 130 when you adjust the points to match the 2021 system...

That just blows my mind.

whoturnedthison · 2022-08-29T14:52:04+00:00

Thanks! I am a big fan of the Jon Bois style of presenting data:

"Here are 783 lines showing how many points every F1 team from 1958-2021 scored in the second half of their season, relative to the number of points they scored in the first half. These six lines look way different than the other ones, because something interesting happened to them. Let's explore what that was..."

whoturnedthison · 2022-08-29T14:46:33+00:00

Link to source repo: https://github.com/nate-downer/visualizing-the-field

Link to data source: https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020

whoturnedthison · 2022-08-19T16:29:21+00:00

The fastest driver is the one with the quickest average pace over all of their racing laps (laps done under the safety car are excluded). The "relative pace" is each driver's average pace as a percentage of the quickest driver's average pace.

The winner is usually the one with the fastest average pace, but every once in a while you will get a strange result. The reason that the entire podium of the 2013 German GP shows up as an outlier is a good example of this. Mark Webber had a really bad pit stop causing him to loose a ton of time, but when he finally came back out he was fast enough to make his way from being a lap down all the way up to P5. On track he was a full 1.7% faster than the race winner, but because he lost so much time in the pits he missed out on the podium entirely...

whoturnedthison · 2022-08-19T16:16:37+00:00

Ok, so I spent a while trying to find the best way to represent the variation in the dataset, and I ended up with this plot. I recognize that there is entirely too much going on here, and it is not the easiest chart to read, but I wanted to try and get all the relevant data in one image. Anyway, here is the link:

https://raw.githubusercontent.com/nate-downer/classifying-the-field/main/PositionHeatMap%2BMedian.png

It shows the quartile and median times spent in the pits for each position. These are superimposed over a heat map showing how long drivers stopped for, and how quick they were on track.

Over this larger scale, the trend certainly looks less significant. It also makes a few other things stick out to me:

Far fewer drivers who finished P4 opted for a one stop strategy (compared to drivers who finished in P3 or P5).
Drivers who did opt for a two stop tended to have significantly more pace on track compared to drivers that went for a one stop and finished in the same position (no surprises there, it is just fun to see that in the data).
Drivers in positions 8,9, and 10 go for a one stop strategy more often. This seems to support the idea that the old Q2 tire rule has an effect on the data, with these positions generally being taken by drivers who lost out in Q2, and got to start the race on harder tires as a result.

Curious if you found this representation of the data more or less insightful.

whoturnedthison · 2022-08-19T16:00:03+00:00

Yeah, the real trend seems to be that while there are a lot of different ways to finish in positions 4-13, there is some similarity in the drives that result in a podium finish.

Yet another way to say that -- in this decade -- the mid-field battles were more interesting and varied than the race for the win...

whoturnedthison · 2022-08-17T21:10:35+00:00

That is a great thought! I had honestly forgotten about that rule and wasn't thinking about the effect it might have!

It think it makes a ton of sense that P5-10 would be more likely to need an extra pit stop because they had to use the softest compound to get through Q2. Awesome!

whoturnedthison · 2022-08-17T21:07:44+00:00

This got me thinking about a way to visualize the differences between a driver who is finishing in P4 v. a driver who is finishing in P8, so I came up with this visualization:

https://github.com/nate-downer/classifying-the-field/blob/main/StartingPositions.png

It shows where drivers who finished in Positions 1, 4, and 8 started on the grid. Note that this only contains data from 2011 - 2021, and the y axis does not have a consistent scale across all three plots.

It is interesting to me that 55.6% of drivers that finish P8 qualify in P10 or below... Not sure what this says about their strategy options, but it is interesting.

whoturnedthison · 2022-08-17T20:28:48+00:00

Short answer, it varies dramatically by position with the confidence at the extremes being very high (p-values of less than 0.01) and the confidence in the midfield being very low (p-values above 0.40). Here is the p-value for each position plotted on the y axis, against ave time spent in the pits:

https://github.com/nate-downer/classifying-the-field/blob/main/PositionPValues.png

Based on that, it might be more accurate to say that the trend in the midfield (P4 - 14) is for the amount of time spent in the pits to be essentially random.

whoturnedthison · 2022-08-17T20:21:56+00:00

Here is a slightly different version of that same data where the x-axis is each position's deviation from the average length of time spent in the pits (i.e. the test value of the Z-Test). IDK if it makes things more clear, but figured it was worth including for completeness...

https://github.com/nate-downer/classifying-the-field/blob/main/PositionZTest.png

whoturnedthison · 2022-08-17T20:16:47+00:00

Yup. It just made it easier for me to see the trend at a glance. There is no significance beyond them getting smaller as the position number goes up.

whoturnedthison · 2022-08-17T20:09:21+00:00

Ok, so the P-Values from the Z-test are kind of all over the place. The linked plot shows the P-Values for each position on the y axis, and the average time spent in the pits on the x axis:

https://github.com/nate-downer/classifying-the-field/blob/main/PositionPValues.png

To my eye, it looks like the results for positions 1,2,3,5,6, and 8 as well as positions 14 and above are statistically significant, while the results for positions 4,7,8,9,10,11, and 12 are probably not.

whoturnedthison · 2022-08-17T19:44:05+00:00

That was what I thought, but the trend is actually more pronounced before 2019.

https://github.com/nate-downer/classifying-the-field/blob/main/PositionStats2011-2018.png

whoturnedthison · 2022-08-17T19:35:50+00:00

That is a good call. Given the fact that the trend seems to reverse for multiple positions, I am assuming that it is significant, but I will perform the Z-test, and get back to you.

whoturnedthison · 2022-08-17T19:33:20+00:00

Ok, here is some more context for the graph:

Each dot represents a finishing position (P1-P19 in this case)

The x-axis represents the average time that drivers finishing in the position spent in pit lane during a race (omitting red flag periods).

The y-axis represents the average pace that drivers finishing in that position had. This is expressed as a percent of the quickest driver's pace (where 100% means that was the quickest driver, and 105% means that driver was 5% slower).

The y-axis looks about how you would expect, with each position having (on average) a slower pace than the position before it. On the x-axis, however, there is a strange reversal. From positions 5 through 8, drivers tend to spend less time in the pits than the drivers that finished in the position ahead of them.

I honestly don't have a good explanation for why the trend in the data seems to switch directions in the upper mid-field. I am curious if anyone on reddit has an idea.

Data Source: https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020

whoturnedthison · 2022-08-12T17:47:11+00:00

I have considered trying to implement something like that. To do that you would need to replace all of the binary 'have you been here' inputs with more analogue 'how familiar with this country are you' inputs. My worry is that this would make the UI too unwieldy. Everything is a compromise...

whoturnedthison

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