What is the most reliable predictor of your marathon time?

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A few years ago, I wrote an article about a high-tech marathon prediction study that broke Strava data from 25,000 runners. They extracted the fastest training segments for each runner at distances from 400m to 5k, plotted the data as a curve of hyperbolic velocity versus duration, used this curve to calculate the critical velocity for runners, and used the critical velocity to predict marathon time.

If none of that makes sense to you, or if you don’t have a GPS watch, or if you simply don’t bother to load all your training data into an algorithm that sees it all, I have a different marathon prediction study for you. In the European Journal of Applied PhysiologyJapanese researchers led by Akihiko Yamaguchi look at simpler variables like how much and how often you run, and come up with some big-picture insights worth considering the next time you run 26.2 miles.

Researchers surveyed nearly 500 runners about their training habits before the Hokkaido Marathon, focusing on their monthly training volume, the number of days they run per week, the average running distance, and the longest running distance. (According to the newspaper, Japanese runners and running media generally track their training volume by month, rather than the more popular weekly totals in North America.)

Seasoned readers will note that these variables are interrelated: If you know your running frequency and average running distance, you’ve already determined your monthly training volume. This is what makes this type of analysis difficult. Lots of previous studies have tried to figure out which training variables are the best predictor of marathon time. But if total training volume, for example, is a good indicator, it’s hard to tell if it’s because running every day is the most important thing, or whether getting some really long runs is key, or whether it’s the total distance The lump is what counts, no matter how you stack it up.

To get around this, the researchers divided the runners into subgroups. For example, they created four subsets of monthly mileage: those who ran less than 100,000 (62 miles) per month; 101 to 150 K; from 151 to 200 K; And more than 200 thousand. inside Each of these groups, monthly mileage didn’t have the ability to predict who would run the fastest marathon, because everyone was doing the same mileage. Then you can ask about the variables an act Anticipate marathon time. Is it the operating frequency? Average run distance? Longest running distance? The answer, interestingly, is that none of them have any significant predictive power. For people who run similar distances, the other training variables don’t tell you anything useful.

They followed a procedure similar to iteration of training, divided the subjects into homogeneous groups running one to two times per week, three to four times, and five to seven times, and then analyzed the effect of other variables. In this case, the strongest predictor was monthly mileage: for a given running frequency, the more you ran, the better. Average running distance was also an indicator, but that doesn’t add anything new: If you run the same number of days a week, those with a higher average running distance will also have higher monthly miles.

Subgrouping the other two variables (average running distance and longest running distance) yielded similar results: In each case, total monthly mileage was the best predictor of marathon time within each subgroup. But this relationship was designed only for people whose average run was at least six miles and whose longest run was at least 12 miles. Below a certain minimum training level, all expectations are off.

Yet, this might seem painfully obvious: Those who run more miles race marathons faster. But subgroup analysis allows us to draw some stronger conclusions. More importantly, it doesn’t seem to matter how those miles stack up: a group of short runs or several long courses lead to similar results. That parallels findings I found earlier this summer in JAMA Internal Medicine about the health benefits of being a so-called weekend warrior: Long-term mortality depends on how much exercise you do, but it doesn’t matter whether you spread out your workout throughout the week or pack it in the weekend.

If you delve into the subgroup analyses, you’ll also find that longer range was a better predictor than average range. As a result, the researchers concluded that at a certain mileage level, it is better to do one long run and several short distances than to do all your running at a similar distance. This is also in keeping with the marathon dogma that says there is no substitute for long distance running.

Compared to the Strava Study of 25,000 runners, this study has a lot of shortcomings. It’s very small, the training data is self-reported (and as a result) doesn’t include any accelerometer, people are very lightly trained (averaging 93 miles) Per month, or roughly 23 miles per week, with an average finish time of 4:20). If you’re looking to qualify for the next Olympics, or even Boston, look no further than any secrets here: You must pile on size. And the repeat And the In the long run, don’t try to figure out which variables you can neglect.

But there are times in every runner’s life when training slips down a few notches on your priority list. In these situations, the rule of thumb from this study seems more useful than the formula for how to calculate critical velocity from your Strava data. The rule is: accumulate as many miles as possible, whenever you can, and in what dose you can get. Sometimes the distances can be shorter or less frequent than you’d like, but come race day, that all counts.


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