Does this even make sense?

I’ve been crawling around the internet as of late and generally have been interested in how the world of climbing works.

Recently, I thought I had solved a problem in regards to pulley systems and generally thought that I was good at physics. Turns out no?

I ended up having a really good idea, however, after a bit of introspection. After browsing through the data on the 8a.nu kaggle database, I decided to run a query that would inform this article. Here’s what I did:

Top routes in the database (That have a name)

I ran a query to see what the top routes were in the world in terms of sends. The graph above contains 20 odd routes that are the most ticked in the world according to 8a.nu. Of course, it doesn’t matter if they are or not, I just needed tick data for all of these.

Cortomaltèse: the beginning

The first obvious route that I decided to pick was one in Fontainbleau called Cortomalthese. If you’re unfamiliar with the route see the below video:

Basically a one move wonder?

A V5 that seems to me like it contains one very obvious crux. Holding on to slopers. This excites me.

A quick note why I didn’t choose other routes: boulder problems tend to focus on pure strength and I was worried that choosing one of the Kalymnos routes would be too strange. I also chose this one as it was close to Paris and in Europe, something that would be less influenced by the “traveler effect” of people going during their vacations. I wanted to make sure that this route was the least effected by outside forces. Also this problem is quite special in how little moves it actually has that focus on movement. It’s one big sloper.

Why did it excite me? The obvious thing here is that I wanted to see if there was some relationship between humity, temperature and ease of sending a V6 pure sloper problem. So first I decided to smash up some data.

I requested a ton of hourly data from this site: https://www7.ncdc.noaa.gov/CDO/cdopoemain.cmd?datasetabbv=DS3505. The DS3505 data sets are hourly data sets based on any station you require. I chose Orly since it seemed that this airport data was within an hour of the sending site. If anyone who lives in France wants to let me know how bad this data set is (or good) I’d love to know. I didn’t have boots on the ground for this one and couldn’t verify if it was accurate for the boulder problem.

I then realized that having hourly data made it quite difficult in deciding how to plot all this. Do I do daily averages? Or do I do a specific time? I decided on filtering out all data points during the evening and so I had my mean set on hours between 7:00 and 21:00.

Then I matched everything up and got this nice little graph:

Temperature vs. Humidity for all sends,

Already i knew this showed promise so I decided to see what my new friend, Seaborn, could do. Seaborn is a plotting function that some genius made for Python. I used it recently when I did the box plots for BMI vs. Grades in my other post. Turns out there is a Gaussian plot called Kde plot that is perfect for visualizing density.

Think of it as a sinkhole, the darkest being the most common

What ends up being interesting here is that the most common sending occurs at 80 RHx and 12 degrees Celsius (53 freedom degrees). However, what’s more interesting is that as you move up in temperature you must go down in relative humidity to stay on one contour.

I wanted to make sure this wasn’t really influence by grade so I decided to parse out each user’s send max grade onto one big billboard to see if I could visually make out anything. I know PCA would have been a good choice here but i did not want to start learning more stuff for this analysis. To come!

Below is the max grade someone has sent separated in each graph.

See my BMI explanation on grades. 44=V5, 70=V15. Some additional grades due to french system has more gradations.

It didn’t really seem like there was much here. I don’t actually think there is much of an effect here because I am sure that the harder you climb the more you tend to pick your days. That said, the spread into the higher temps seems quite obvious, the strong guys still send in higher temperatures. I decided to drop trying to correlate the max grade

What’s obvious as well is that through the grades is that higher temps means lower humidity and lower temps means higher possible humidity. It seems like there is a hard cutoff almost where it starts getting much harder. Moving North-East on the graph seems way harder than South-East (easiest) or South west (easy as well).

I was on the upper end of humidity and a friend asked “What if you looked at a place that is a bit less soupy in terms of conditions.” Fine.

Bishop: Hulk, Soul Slinger, Seven Spanish Angels.

Bit drier here.

I decided to start with the Hulk. It was the most common problem (see first graph) in bishop and so might be nice in terms of what it will output:

The banana graph

Here is where I started thinking of stuff in terms of my new theory: The banana graph. Unfortunately, font is much too humid to ever have the top of the banana visible. However, here in Bishop is was more than obvious.

What was also interesting was that the temperatures seemed similar, but the humidity was much lower in this one. Was it a case of people just expecting better conditions in Bishop or just that there were not really any days with a relative humidity of 80 and a temperature of +8? Probably the latter, and so maybe humidity wasn’t so important. However it did seem to be correlated.

What’s frustrating is this: grades are absolutely not a great way of thinking about rock climbs. Especially here where I can say that we’re probably seeing a case of if both of these problems were in Bishop, I feel like the Font problem would be actually graded easier due to consistent temps. But that I cannot prove and so I’ll just throw my hands up and give up in correlating grades and temps.

Hulk grades

Well no I wont. I decided to run something a bit more related to our first problem. I’ll take Seven Spanish Angels.

Seven Spanish Angels

SSA is a pretty climb that is what one would call a “sloper” problem. This made me believe that it could be similar to Font. So let’s see what the outputs were:

Another Banana, but temps were much more in line with Font

Looks like slopers really live and die by that idealized 10 degrees C. I am thinking that perhaps people just cannot really climb problems that are sloper based without this 7-12 degree window being available. That said, let’s go see the grade distribution.

What do i even do with this…

Seems like, once again, all over the place. However, there are a bunch of ultra strong fellas that can send even when temps are in the mid 20s. Not like those soft ultra-ultra strong guys (grade 66=V13, only climb in the cold!).

Soul Slinger

I decided to, even after my best judgement, continue down this old country road. I decided to look at a much harder climb: Soul Slinger. This V9 is ultra crimpy and my hypothesis was that it would require cooler temps in general.

Much colder, not much less humid.

So generally this problem is being sent at a bit colder temps. What I think is maybe happening now is really the general effect of grades where they are very area based. Everything seems to have that sweet spot of 40-50 RHx but the temp is really what varies.

That being said, my idea of a banana graph really seems to hold up. It looks like it is some sort of line can be drawn (see title picture) which correlates to a sort of hardness line. Like some line that could be drawn to travel between humidity and temperature. Such as a humidity of 60 and temperature of 0C on Soul Slinger would be roughly equal to a humidity of 30 and a temperature of 10C.

I’m also sort of fretting because I wish I could do more problems like this. However, I have just recently been able to standardize my code in such a way that it takes me 10 minutes per area + whatever the query time for the NOAA data to come in is. Generally this means i have a delay of about half an hour. This means that to do every area in the USA, i think it would take me days of work.

This is also why I decided to focus on two areas. I feel like perhaps I should look into more Font problems but at this point I don’t have much more to go on to continue this query.

All of one area (Bishop vs. Font)



Ran both of all the send in each area

So here I decided to run all the sends regardless of which route it was. What seems to happen is that both sort of follow, to a point, the roues that were listed. Maybe this means that it is more in line with rock type and area than it is with individual problems? That said, we did see slight variations in all the problems in Bishop, so it’s hard to think that it is just a data error.

To close all this off, I guess I don’t know much more than I did before I started this. I think I know these things:

There’s some sort of link between humidity and sending

There’s a link between temperature and sending, Seems bouldering generally likes to happen at 7-12 degrees C.

Areas seem to differ in terms of send conditions

Problems seem to also differ (much slighter than areas that we examined) but to some point.

Not sure there’s a link between hardest send and conditions. You’d think people would be able to climb hard in any conditions if they climbed V15, but it may not be that possible to climb in +20 and 100% RHx.

Anyhow, let me know if any of this was interesting at all, if I should use some other time scale range, if you think I should examine another problem or something like that.

Thanks!

-CC