Trail Mix | How researchers are using data and statistics to predict trail-running performances
- A team of data scientists are creating a framework to predict trail running performances and the probability of a runner dropping out of the race
One of the most beautiful things about trail running is its free-form, non-standardised nature. No two racecourses are the same and different athletes excel on different terrains, from mountainous to runnable. This makes competitions, from the local to the international level, quite unpredictable – and that is where a lot of the fun lies.
Still, there’s value in trying to ascribe some method to the trail madness, even if only for curiosity’s sake.
Now, a team of statistics and data science researchers have devised a predictive framework for assessing trail running performance. Riccardo Fogliato, Natalia L. Oliveira and Ronald Yurko, PhD candidates in statistics and data science at Carnegie Mellon University in Pittsburgh, propose a framework called Trail Running Assessment of Performance (TRAP), which assesses runners’ performance both before and during a race.
The framework takes into account three factors: the runner’s ability to reach the next checkpoint (or put another way, their probability of dropping out); the runner’s expected passage time at the next checkpoint; and predicted intervals for the passage time.
The researchers observe that drop-out rates in trail running races are relatively high, even among experienced competitors. For example, in Ultra Trail du Mont Blanc races that they examined, rates range from 30 per cent to 42 per cent – slightly higher for women compared to men and lower for younger runners compared to older runners.
The researchers also observe that more than half of all runners gain at least one position in the overall ranking at almost every checkpoint throughout a race. This means that a smaller fraction of runners lose multiple positions in the ranking throughout the race, implying that they slow down substantially as they tire.
