Online Logbook Data Science: Part 1
By: Charlie Andrews-Jubelt
Tags: Sociology, Statistics, Climbing, Editors Pick, Data Science

Online Logbook Data Science: Part 1

Is downgrading climbs more common than upgrading? Do climbers begin to prefer longer routes over shorter ones as they get older? What route will you enjoy the most at a new climbing area?

In the past, these questions have been difficult or impossible to answer objectively. In general, we remain largely in the dark about the underlying mechanics of climber preferences and our habits of grading climbs. However, tens of thousands of climbers have added millions of ascents to “online logbooks,” which I define as web applications for keeping track of which climbs you have done. This presents an exciting opportunity for the social science of climbing to answer questions that require a lot of data to answer.

The goal of this project is to leverage the data available on online logbooks to gain a better understanding of climber preferences and route grading. In collaboration with thecrag.com and Beta Angel, I have analyzed a dataset of 1.5 million ascents logged by climbers from all over the world. I plan to release results in batches that will address the following questions:

Do longer climbs appeal to older climbers? (The subject of this post) Can a route’s grade be decided by an algorithm? Who is downgrading climbs, and how often does downgrading happen? Can machine learning be used to recommend climbs like Netflix recommends movies?

The backstory

About three years ago I was cooped up in my AirBnB on a bouldering trip in Hueco Tanks, Texas. It was a day some friends and I were forced to rest, due to some winter storms. We were getting cagey, and a little tired of watching climbing videos — like any group of cooped-up college student climbers on their last few days of winter break, we just wanted to climb.

Someone was listing off the latest routes that had been downgraded on 8a.nu, an online logbook website that was popular among some of the group’s more ambitious grade chasers. And then someone said, “Wouldn’t it be cool to use all that data on 8a.nu to do some Machine Learning on what people like to climb?” We loved the idea. (Yes, my group was hopelessly nerdy.) Right then, the idea for Online Logbook Data Science was born.

Wouldn’t it be cool to use all that data on 8a.nu to do some machine learning on what people like to climb?

After some excited brainstorming, the literal storm cleared, and just as you’d expect, we dropped everything and flocked to the boulders. But the idea stayed with me… and today I’m excited to announce the first batch of results!

Image by the author

Our approach

The goal of this project is to gain a deeper understanding of climber preferences and habits of grading climbs. The project is deliberately open-ended. We did not run an experiment with any single hypothesis in mind. Instead, we are starting with the data, and exploring the space of possible questions that might be answerable from it.

Let’s get more concrete about what it means to do data science on an online logbook.

What is an online logbook?

I define an online logbook as a web application where climbers keep track of which climbs they’ve done. 8a.nu, the logbook mentioned in the project backstory, is just one example — there’s also thecrag.com, Mountain Project, 27crags, and several others.

Why do data science on online logbooks?

On each of the major online logbook platforms, tens of thousands of climbers have logged over a million ascents. That’s exciting, but you can’t even open an Excel file with that many rows. How are we ever going to spot patterns in that much data?

Data science.

If you’re not familiar with the phrase, think statistics, but with computer programming mixed in. And you are already familiar with data science…read on! My goal for this series is to write for beginners and experts alike.

Hasn’t this been done already?

Interestingly, soon after the idea of Online Logbook Data Science was floated on that Hueco Trip I described above, David Cohen independently scraped 8a.nu and posted the resulting dataset on Kaggle, leading to some interesting spinoff research. Some highlights:

Steve Bachmeier used the dataset to investigate demographic factors for climber performance. ( Tl;dr : experience helps, height doesn’t.)

: experience helps, height doesn’t.) Durand D’souza posted an interesting notebook showing how long it takes climbers to reach the next grade (a few months to a year before the data thins out).

However, David Cohen’s script was taken down, and the author himself was threatened with legal action (scraping is a dangerous game). Although the data is still available on Kaggle, it has not been “refreshed” in some time, and some may argue that it’s ethically questionable to use it at this point (at least since 8a.nu’s parent company objected to its use).

Fortunately, 8a.nu isn’t the only online logbook in the game; neither is their data the best-suited to data science, necessarily. One issue is that routes are reportedly keyed by route name, so misspellings during logging can lead to messier data.

On the other hand, I was excited to find that the team at theCrag.com promotes collaborations with data scientists and keeps their data relatively clean and structured. For example, all ascents of the same route are linked by a route ID, even if the climbers who logged the route misspelled the route’s name.

Where we are today

Courtesy of Ulf Fuchsleuger and the team at theCrag.com, one of the largest online logbook platforms (and the largest in Australia), I was permitted to access a sizable dataset consisting of around a million and a half ascents. From several excited conversations with my project mentor Taylor Reed, we have almost as many ideas for research questions as we have rows of data.

Since our goal is to research continuously and let the data speak for itself, I have decided to release our research results in small-ish batches, rather than allow insights to accumulate over a long period of time.

Results Batch One

We began with a question that I thought was a bit of a long shot: do longer climbs appeal to older climbers?

Though it may sound funny when stated that way, I think the assumption that age has something to do with route length preference is relatively common. Imagine hearing this at the crag: “That route is just 10 meters, and incredibly crimpy. It’s like one long boulder problem. I’ll leave it to the gym kids and save my tendons for a nice long 40-meter adventure.” In my mind, the climber voicing this opinion has a few more gray hairs than those gym kids. But does the data support the stereotype?

The answer, surprisingly, is yes.

To reach that conclusion, I started by formulating the question as one that could be answered from the available data. The precise question I chose was “Do longer routes get a higher proportion of older ascentionists?” For example, if 75% of the climbers who logged a 35m route were over 50 years old, but the typical route on thecrag.com had only 20% of its ascents logged by climbers in that demographic, then that climb could be said to get a higher proportion of older ascentionists.

Do longer routes get a higher proportion of older ascentionists?

Notably lacking in rigor is the phrase “older ascentionists” — what qualifies as old? Choosing a threshold for “old” required a look at the data of who’s logging their climbs on thecrag.com

Figure 1 — Image by the author

From the histogram in Figure 1, it’s clear that thecrag.com users are a young crowd, with a median age of just 27. As a result, I chose “over 35” as my definition of “older climber” for the purposes of this problem. Please don’t be offended if you are over 35! I do not consider you “old” if you are 36. I just needed your data ;). To spot a subtle difference between two groups, you need as many “observations” (climbers’ log records) as you can get for both of them, and 35 was the highest age for which there were still a reasonable number of climbs logged in the dataset (about 22% of log records).

Next, I chose to consider only sport climbing routes under 40m, also due to data availability. thecrag.com has about twice as many sport climbing ascents as the next route gear style (trad), and as shown in Figure 2, the number of routes logged also drops off significantly with climbs above 40m. 40m also happens to be a good proxy for “single-pitch” sport routes, so this works out nicely.

Figure 2 — Image by the author

To answer the main research question I needed just two more steps. First, I grouped routes by length, assigning each route to a five-meter bucket and combining their ascents. (In other words, all ascents of routes between 20 and 25m were counted in the same bucket.) Then, I took the fraction of all climbers who logged a route in that bucket who were over 35, and plotted them against route length. Figure 3 demonstrates the result, which I found surprisingly coherent.

Figure 3 — Image by the author

So for routes around 5 meters, about 20% of their climbers are above 35, whereas for routes around 35 meters, that number is about 25%. A simple linear regression on the data from Figure 3 yields a p-value of about 0.0015. For reference, 0.05 is considered “statistically significant,” so this is pretty good! If p-values are unfamiliar to you, suffice it to say that if there were no relationship between route length and climber age, this result would have been very unlikely.

I repeated the experiment with a slightly different formulation, looking at mean climber age vs route length bucket. I got a similar result, shown in Figure 4. I did another linear regression on the mean age vs route length data and got a p-value of about 0.003. Still well below the threshold for statistical significance!

Figure 4 (the boring kind, not the fun climbing move) — Image by the author

So in what turned out to be kind of an extra-mathy, climber-edition Mythbusters style exercise of checking an admittedly niche “myth”, I get to declare this one…

Mythbusters calls this “CONFIRMED” . Image by the author, inspired by mythbusters.

Though of course, take it with a grain of salt. The average climber logging a route gets about 10 days older per meter.

Sneak Peek at Results Batch Two

Next on the agenda is an exploration of downgrading as a social phenomenon. Looking at hundreds of thousands of routes logged gives us an exciting chance to spot trends in who is downgrading climbs, and how often it takes place.

My next post in this series will focus on how to write an algorithm that will identify when a climb has been downgraded with no human in the loop. After that, we’ll do some social science on the climbs we identified as having been downgraded. Maybe we’ll even tackle another climbing myth while we’re at it!

Acknowledgements

Thanks again to the team at thecrag.com for providing data and support. Thanks Taylor Reed of Beta Angel for mentoring me and listening to many, many oscillations of excitement and disappointment as I worked through different research directions, software bugs, procrastination and stress. You guys are the best!

Thanks also to the Data Science team at Lark, who offered some feedback on my analysis. I’m going to post a followup soon to discuss their comments and go deeper on the “why” questions of the route length vs climber age correlation that was the subject of this article.