UTMB race data
To mentally prepare for an upcoming trail running race I scraped data from the largest trail running organization, the UTMB. In this post I will briefly expand on the data gathered, then give some insights, and finally provide a tool that you can use in choosing and preparing for a race. I also provided the data if you want to have a go at it.
UTMB.world content
Trail running races are much like regular road running races except that they are off-road by definition, and generally longer than your typical road race. The premier global trail running race is the Ultra-Trail du Mont-Blanc, held in Chamonix-Mont-Blanc, France. It is a 174 km (108 mi) race with 10,000 m (33,000 ft) of elevation gain that is won in about 20 hours. This race is organized by the UTMB Group. In addition to the UTMB, they organize 40+ prominent races across Asia, Oceania, Europe, Africa, and the Americas called the “UTMB World Series”. Their website UTMB.world contains the results of these events. But to my surprise, it also contains the results of thousands of other trail running races. Since their website displays results using simple pagination, and not state-based systems, it was easy to scrape data from the site using Beautiful Soup.
How this data can help
Trail running races vary a lot more than road running races. This makes estimating the challenge of a race more difficult. Factors you will have to consider in addition to the distance include the elevation gain, terrain type, and climate. Setting the right expectations can substantially improve race preparation and race outcome.
Click here to access the UTMB Dashboard in full screen.
For example, this data would have helped me in my first UTMB race, the X-Traversée. The X-Traversée starts at 8 am, and I was hoping to finish the 76km race before sunset, in about 12 hours, a reasonable 6km/hour, right? This data, however, would have told me that this would have put me in the top 4% of runners, pretty ambitious for a first-timer from a flat country. The data would have told me that finishers take 17 hours on average. The unsuspected climb and finish in the dark were mental blows that this data could have prevented
Quick facts
| Category | Race name | Amount | Link |
|---|---|---|---|
| Steepest average incline | Vertikal K3 Bei 2019 | 30.6% | 🔗 |
| Longest distance | Great Himal Race 2017 | 1,355 km | 🔗 |
| Shortest distance | Amangeldy Race 2023 | 6 km | 🔗 |
| Most elevation gain | Great Himal Race 2017 | 80,230 m | 🔗 |
| Longest mean finish time | Great Himal Race 2017 | 415.3 hours | 🔗 |
| Shortest mean finish time | GIIR DI MONT 2022 | 0.9 hours | 🔗 |
| Highest DNF Rate | Mad Fox Ultra 2019 | 89.3% | 🔗 |
| Largest portion of female participants | QUEEN of the JUNGLE 2017 | 85.7% | 🔗 |
| Most participants | La SaintéLyon 2017 | 6,740 | 🔗 |
| Longest race time recorded | Great Himal Race 2017 | 500.0 hours | 🔗 |
| Most international race | UTMB® Mont Blanc 2022 | 83 countries | 🔗 |
Exclusion criteria: < 10 participants, < 10 finishers, uncategorized races, female exclusive races, < 1km race distance, mean finish time of 0 hours, last finish time < 10 * mean finish time
Data
Here’s a download utmb_sheet.csv (3.7MB)
- Columns: Race UID, year, Race Title, N Participants, Race Category, Distance, Elevation Gain, Mean Finish Time, Winning Time, Last Time, N DNF, N Women, N Countries
- The data set consists of 19,894 races (=unique Race UIDs) and 38,461 events (=unique Race UID & year), held between 2014 and 2024 from utmb.world
- Each row can be traced back using the URL: https://utmb.world/utmb-index/races/RACE_UID..YEAR, e.g., https://utmb.world/utmb-index/races/10001..2017
- In this data set, there are no other genders than male or female so you can assume that “N Men” = “N Participants” - “N Women”
- Participants that DNF’d were excluded from the mean finish time.
The source file to this cleaned file is 187MB. In addition to the content of this file, it contains all finish times and the frequency of countries of origin. I could share it if there’s a specific reason you’d need it.
