A few weeks ago I wrote about an attempt to retrospectively predict athletic success with the help of DNA tests. It failed miserably, and I reworked a great line from sports scientist Carl Foster, as David Epstein said in his book The Sports Gene: “If you want to know if your child is going to be fast, the best genetic test right now is a stopwatch. Take him to the playground and have him face the other children. “
That seems like solid, reasonable advice – but it’s actually not science. Indeed, the accuracy of the stopwatch as a predictor of future athletic greatness has been a topic of great debate over the past few decades, which has resulted in major discussions about the nature of talent, the 10,000-hour rule, and the benefits and dangers of early specialization. It seems high time, therefore, to take a look at a recently published study of Belgian cyclists testing the thesis that a child’s behavior when “facing the other children” is a good indicator of championship potential.
The study appears in the European Journal of Sport Science under the direction of Mireille Mostaert from Ghent University. Mostaert and her colleagues have looked through the records of national and regional cycling championships in Belgium in three age groups: under 15, under 17 and under 19. They identified 307 male cyclists born between 1990 and 1993 who competed in all three age groups and had at least one top ten championship result. Of these 307 cyclists, 32 had successful careers and competed at the continental level or above for at least four years.
The main research question is straightforward: Did the later professionals dominate the youth ranks? The most important measure of success was the percentage of races in which the athlete finished in the top ten. The graph below shows the success rate for the “top performers” (who became successful professionals) and “non-performers” (everyone else) between the ages of 12 and 18. The solid lines are average results for each group. The dashed lines show the standard deviation.
(Figure: European Journal of Sport Science)
In the three years of U15 competition, there is no significant difference between the possible pros and non-pros. There is a difference in the U17 category, which is greater in the U19 category. It’s not surprising that the older you get, the more predictive your race results are. It is interesting, however, that U15 results have essentially no predictive value, a result that is largely in line with other research, although it differs from sport to sport.
You can see some ups and downs in the trend lines. If the athletes move up into a new age group, for example as 15-year-olds in the U17 category, their success rate drops. Then it increases again once they are a year older but are still in the same category. Again, this is not surprising, but it is a reminder that subtle age differences are important when comparing young people who have not yet reached their physical maturity.
In fact, the differences can be significant within a year of birth, a much discussed phenomenon known as the relative age effect. Mostaert and her colleague divided the athletes into four groups, depending on the month of birth, and recorded the results again. This is what it looked like for the later non-professionals:
(Figure: European Journal of Sport Science)
In the youngest age group, those born in the first quarter of the year far outnumbered those born in the third or fourth quarter. The differences disappear in the U17 and U19 categories. (There’s a similar pattern among the later pros, but the sample is too small to get a meaningful picture if you split the group into four groups.) This provides more evidence that the U15 race results are less interesting Factors such as the month of birth are reflected as the ultimate future potential.
I think it’s fair to say that Carl Foster is still right that the stopwatch (or its equivalent in other sports) is the best test of future potential we have. However, these results confirm that even the stopwatch is not very good. At the age of 18, even the future professionals only managed top ten placements against their local colleagues in 27 percent of the cases. As you try to pick future stars from a group of 18 year olds, even if you rely on the best science available, you will inevitably pick a few types – and, perhaps more importantly, some athletes with the potential to become To develop world bats.
The effects of all of this on talent identification and development are complex and varied. (For a good overview, see Ross Tucker’s video series on the subject.) On the surface, the lesson you might extract is that there is no point in identifying talent before age 15 (or whatever threshold for sport or the activity that you are applying is concerned with). In reality, the incentives are not that simple. For example, if you fail to identify the (seemingly) talented 14-year-olds and assign them to a select squad and give them top coaching and a fancy uniform, etc., another team – or sport – will do so.
So you have a system that everyone knows is buggy but that you have to use anyway. It recalls an anecdote from Nobel Prize winner Kenneth Arrow, who worked as a statistician in the military’s weather department during World War II. He found that the long-term projections they made were no better than the numbers pulled out of a hat – but when he suggested they stop, he received the answer: “The commanding general is aware that the projections are not good. However, he needs them for planning purposes. “
We will inevitably keep trying to predict which child will be a star – for planning purposes of course. And the stopwatch is as good as we are, certainly much better than a DNA test. The most important lesson to remember, however, is that those kids who don’t look like world bats at 14, 16, or even 18 may still get there. Keep as many children as possible involved in the sport, well trained and motivated to discover their own limits and you never know how the story will end.
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Main photo: Angela Lumsden / Stocksy