A few years ago, I wrote an article about a high-tech marathon prediction study that collected Strava data from 25,000 runners. They extracted each runner’s fastest training segments at distances ranging from 400 meters to 5K, plotted the data as a hyperbolic speed-time curve, used this curve to calculate the runner’s critical speed, and used the critical speed to estimate marathon times.

If none of this makes sense to you, or if you don’t have a GPS watch or can’t deal with uploading all your training data to an all-seeing algorithm, then there’s a different kind. marathon prediction work for you. Inside *European Journal of Applied Physiology*Japanese researchers led by Akihiko Yamaguchi look at simpler variables like how far and how often you run, and uncover some big-picture insights worth keeping in mind on your next 26.2-mile run.

Researchers surveyed nearly 500 runners on their training habits en route to the Hokkaido Marathon and focused on monthly training volume, number of running days per week, average running distance, and longest running distance. (Japanese runners and running media often track training volumes by month, rather than weekly totals more common in North America, according to the newspaper.)

Intelligent readers will notice that these variables are interconnected: if you know the running frequency and the average running distance, then you have already specified the monthly training volume. This is what makes this type of analysis difficult. Many previous studies have attempted to find out which training variables are the best predictors of marathon duration. But if let’s say total training volume is a good predictor, it’s because it’s hard to know if running every day is the most important thing, or if really long runs are key, or if total mileage matters. How do you collect?

To circumvent this, the researchers divided their runners into subgroups. For example, they created four subgroups for monthly mileage: those who run less than 100,000 (62 miles) per month; 101 to 150K; 151 to 200K; and over 200K. *Inside* each of these groups did not have the power to predict who would run the fastest marathon by mile per month, because everyone was doing similar mileage. Then what variables can you ask *to do* Estimate the marathon time. Is the frequency working? Average running distance? Longest running distance? The answer, interestingly, is that none of them have significant predictive power. For people running similar overall mileage, the other training variables won’t tell you anything useful.

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

Subgrouping the other two variables (average running distance and longest running distance) yielded similar results: in all cases, total monthly mileage was the best predictor of marathon duration within each subgroup. However, this relationship was only valid for people with an average run of at least six miles and the longest run of at least 12 miles. Below a certain minimum education level, all predictions are off.

By now, this may seem painfully obvious: Those who run more kilometers run marathons faster. However, subgroup analysis allows us to draw some stronger conclusions. Most importantly, it doesn’t seem to matter how you accumulate that mileage: a bunch of short runs or a few long runs yield similar results. This is in line with findings 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 if you spread your exercise over the week or pack on the weekend.

If you dig deeper into the subgroup analyzes, you’ll also find that the longest run is a better predictor than the average run. As a result, the researchers conclude that at a given mileage level, it’s better to do one long run and several short runs than to do all your runs at a similar distance. This fits with the marathon orthodoxy, which says that nothing can replace long runs.

Compared to the Strava study of 25,000 runners, this one has many shortcomings. Very small, training data is self-reported and (as a result) does not include any speed measures, subjects train very lightly (93 mph on average). *monthly*or approximately 23 miles per week with an average finishing time of 4:20). If you want to qualify for the next Olympics, or even Boston, don’t look for any secrets here: you must be accumulating volume.* and* Frequency *and *long runs, not trying to figure out which variables you can neglect.

But there are times in every runner’s life when training drops a few places on your priority list. In these cases, the rule of thumb in this study seems more useful than the formula for how to calculate critical velocity from your Strava data. The rule is this: Accumulate as many kilometers as possible, when and in what dose you get. Sometimes runs can be shorter or less frequent than you’d like, but when race day arrives, everything counts.

Join me for more Sweat Science excitement and Facebook, sign up for the email newsletter and check out my book *Withstand: Mind, Body, and the Bizarre Elastic Limits of Human Performance*.

## Comments

## Post a Comment