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Unlock the secrets behind your UTMB Index! Ever wondered how your trail running performance is measured and compared globally? This video breaks down the powerful methodology used by over 7,000 races worldwide.

Discover how your best scores over the last three years are calculated, offering a true reflection of your trail running ability. We dive into how individual race scores are determined, using real-world examples to illustrate the process.

See how statistical models predict runner performance and how actual race results are analyzed to identify outstanding efforts. Learn why identical finish times can lead to different scores, considering crucial external factors like weather and trail conditions.

Understand the data-driven approach that converts relative performances into a definitive score, contributing to your overall UTMB index. Get ready to see your race results in a whole new light!

#UTMBIndex #TrailRunning #RaceScores
Transcript
00:00Ever wondered how your UTMB index is calculated? The UTMB index is a reference
00:05indicator used to measure and compare performances in trail running. More than
00:107,000 independent races worldwide use this shared methodology. Your UTMB
00:17index is calculated from your best scores achieved in races over the past
00:21three years. So, to understand the UTMB index, we first need to understand how
00:28race scores are calculated. Let's take the example of the CCC 2024. More than 1,600
00:37runners crossed the finish line that edition. First, for each runner, we look
00:43at all their past results in similar races and use our statistical model to
00:49predict their score on this race. For example, on this distance, we know that
00:55Bob usually scores around 700, well ahead of Lisa, who normally scores around 580.
01:04We then look at the actual race results of the CCC 2024. And this time, Lisa and Bob
01:12crossed the finish line at the same time. But how do we know whether Lisa delivered
01:17an outstanding performance or Bob simply had an off day? With just two runners, it's
01:24difficult to tell. But what if the comparison isn't made between only two
01:28athletes, but across all finishers at every performance level? With enough
01:34historical data, we can build a comprehensive and highly accurate picture
01:39of each athlete's expected performance on that specific day. This method takes into
01:45account external factors that influence the finishing times of all runners, such as weather
01:51or trail conditions. For example, at the CCC 2024, overall runner speeds were lower than
01:59in 2022, mostly due to the extreme heat on race day. That's why identical finishing times
02:07on two different editions of a race can produce different scores. And that's it. Using all the
02:13data points from all the runners, we can now convert these relative performances into a score.
02:20The analysis reveals that Lisa had an exceptional race. She earns a score of 660. It's her highest score
02:28ever, so it will contribute positively to her UTMB index. Congratulations, Lisa!
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