Greetings, and welcome to the RLRH newsletter!
There is something slightly strange about the way we study the marathon.
In the laboratory, researchers can control almost everything: treadmill speed, temperature, nutrition, airflow, and when a blood sample is taken. That control is the great strength of laboratory science. But it also creates a problem. Marathon performance unfolds over several hours, under changing environmental conditions, after months or years of training, and in the presence of thousands of other runners. Reproducing all of that in a lab is… well… impossible.
Then there is the sample-size problem. You cannot ask 100,000 runners to complete multiple all-out marathons while randomly changing their training, pacing, altitude, heat exposure, or air pollution. It would be impractical, not to mention costly.
Luckily, runners perform this experiment for us every weekend!
A new review by Daniel Muniz-Pumares and colleagues argues that the marathon can and should be treated as a giant natural experiment. The researchers do not assign the exposure. Instead, they study what happens when large groups of runners encounter different environments, adopt different behaviors, or arrive with different training histories.
The data are messy and the “experiment” is uncontrolled, but this type of analysis can reveal patterns that a small, tightly controlled study would never be able to detect.
The conclusion is not that big data should replace the laboratory. It is that the two answer different parts of the same question: What actually determines marathon performance in the real world? That’s what today’s newsletter is about.
The physiological boundary that matters
To understand the marathon, it helps to begin with critical speed.
Critical speed is the speed associated with the upper boundary of sustainable, steady-state metabolism. Run meaningfully faster than this boundary, and you enter the severe-intensity domain, where oxygen uptake continues to rise, important muscle metabolites (like lactate) become progressively disturbed, and exhaustion arrives within minutes rather than hours.
A marathon therefore needs to be run close to, but below, critical speed. The question is how close.
Traditionally, determining critical speed requires several maximal efforts lasting roughly two to 15 minutes. Researchers graph speed against duration and estimate a runner’s maximum sustainable speed (or critical speed) from that relationship. It is informative, but it is also demanding and time-consuming.
Wearable data offer another route. In more than 25,000 recreational runners, researchers estimated critical speed from each runner’s best training performances over distances from 400 to 5,000 meters. The resulting critical-speed estimate predicted marathon performance with an average error of about 7.7%.
Across the group, runners completed the marathon at roughly 85% of their predicted critical speed. But that average conceals the most interesting result:
The fastest recreational runners sustained more than 90% of critical speed.
The fraction declined progressively as finishing time increased.
Elite runners in separate data completed the marathon at approximately 96% of critical speed.
Faster marathoners, in other words, do not merely have a higher physiological ceiling. They also spend the race closer to it.
That distinction points toward another quality that I’ve talked about a lot in this newsletter: durability.
The marathon exposes durability
Most physiological testing describes a runner while fresh. A marathon asks whether those same characteristics still exist after two, three, or four hours of running.
That ability to preserve physiological function during prolonged exercise is often called durability. A durable runner experiences less deterioration in the traits that support performance. A less durable runner may look strong in a short laboratory test but lose more of that capacity as fatigue accumulates.
One way to observe this deterioration in the field is to compare internal workload with external workload. Heart rate is the internal cost; speed is the external output. If heart rate rises while speed stays the same—or speed falls despite a similar heart rate—the two measures have “decoupled.”
In an analysis of 82,303 recreational marathoners, runners with high decoupling had a heart-rate-to-speed ratio at the end of the race that was at least 30% higher than during the 5-to-10-kilometer segment. The gap between heart rate and speed widened as the race went on! They finished in an average of 3:58. Runners with low decoupling, defined as an increase of less than 10%, averaged 3:37.
Critical speed alone predicted finishing time with an error of about 6.5%. Adding decoupling improved the prediction, reducing the error to about 5.2%.
So here, among a large group of marathoners, a fresh measure of capacity did not tell the whole story. Knowing how the relationship between effort and output changed during the race improved the ability to predict a runner’s finishing time.
