With the rise of sports science and analytics, there’s a tendency to hold these fields as bearers of absolute truth. If the science or data proclaim a fact, then who are we to question it?

The notion that you need statistical significance or a double blind placebo controlled study to find the actual truth is a notion that is indoctrinated into your mind as a young science student at University. It’s repeated so often, that you actually start to believe it, quoting studies, performing data analysis, in search of a truth.

Long gone are the days of simply “knowing.” Sports science, data analytics, sabermetrics are the new norm in sport.

As someone with a proclivity towards science, it’s a welcome change from the older anti-science days. Yet, as a coach, it’s concerning. As the pendulum swings back and forth between a scientific mindset dominance and intuition based coaching truisms, the danger becomes swinging too far in one direction. These seemingly opposing forces- intuitive feel and experience-based understanding and scientific knowledge- counterbalance each other. They ensure that we don’t tip too far in one direction, ignoring a wealth of understanding that can guide us towards better performance.

For decades, the scales tipped in favor of experience, especially in sports where tradition ran deep. Since the turn of the century, the pendulum has rapidly swung in the other direction. Sports science, statistic gurus, and the use of science has become a requirement.

The popular notion was: why should we guess anymore when now we can know.

With the popularization of Moneyball, statistics gurus became a mainstay in major sports teams. Tour de France teams became reliant on such notions of “marginal gains” and applying a lab testing like rigor to performance. Even college sports have bought into the notion of tracking every bit of data possible, from sleep patterns, to algorithms that spit out “readiness” scores, to GPS data on how far and fast each player moved.

These innovations, the modernization of sport, have brought forth a belief that the only way to truly know what is right is through this process.

But, not unlike most major shifts, it appears we’ve swung too far in one direction.

The GPS data ‘lies’ over short spurts (as any runner can tell you), those readiness scores might not mean what we think they do, and the statistics pointing towards correlation and predictions of performance can be as flawed as in any aspect of science. Just as we are having popular theories in the world of psychology research questioned and overturned, the promise of fundamental truths in sport through sabermetrics and data.

In a fantastic article, Josh Levin traces the history and promise of the analytics movement in major league baseball. You can see the hubris with which the statisticians believed that they were light years ahead of the dinosaur like scouts who predominated baseball at the time. The modernization of scouting with the use of data was supposed to usher in a new era where guessing took a back seat to truth.

Historical truths of baseball were attacked, and nebulous sounding ideals such as clubhouse chemistry, hustle, clutch players, were all attacked as pseudo-scientific explanations that held little truth. With “science” we could dispense of such soft characterizations in favor of knowing.

Yet, over a decade later, with some much-needed perspective, we’ve realized that even the data analysts could be wrong. As mentioned in Levin’s article, Pat Neyer, a man on the forefront of the statistics revolution, reflected: “When you’re trying to change things, when there’s a revolution, all the nuance does get lost. You’re going to end up taking simplistic positions.”

In fact, the father of the statistics movement in baseball, Bill James, has similar feelings now. James “groans whenever he hears people discount leadership or team chemistry or heart because they cannot find such things in the data. He has done this himself in the past … and regrets it.”

When we begin to rely on one kind of knowledge, when we let the pendulum swing too far in one direction, we lose perspective. We miss the nuance of the arguments, and forget the assumptions we make. We trap ourselves in a box where the alure of fundamental truths clouds our judgement. In other words, we become blind.

In baseball, the data revolution brought much-needed innovation, but it also caused a perspective losing swinging of the pendulum too far in one direction. The stats gurus didn’t hold all the answers. They too could be wrong, just as the intuition based scouts before them.

Knowledge:

Knowledge and truth aren’t reserved for deep analytical thinking. There are various  ways we accumulate understanding. Science, faith, intuition, don’t have a monopoly on what is considered truth. They can all, in their own way, aid us in discovering and refining our best practices to apply in the field.

This doesn’t mean we should abandon science or data, or that we should rely solely on experience and instinct. Instead, the answer is to be aware. Aware of the limitations of both sides. Aware of where the pendulum is and in what direction it is now swinging. And aware that neither side has a monopoly on truth.

As I like to remind myself, whenever the pendulum is swinging quickly in one direction, it’s time to turn around and look the other way. Knowledge is a tricky thing.

 

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The Battle Between Sport Science, Analytics, and Intuitive Coaching
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2 thoughts on “The Battle Between Sport Science, Analytics, and Intuitive Coaching

  • October 16, 2016 at 2:44 am
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    Another solid perspective on coaching, Magness. I’ve benefitted a lot from your generosity, as have my runners. I like to think of the topic of this post from the perspective of modeling. Science gives us models to try, but good science uses large samples to wash out the individuality. Personal experience helps me choose parts of a science model for unique runners. As Lou Duesing once told me, “Coach the runner not the race.” With time, I should have a unique training model for each runner. Built with a lot of input from the runner. The question becomes, how do I build a model from my experience, and how do I use the science already available as constants in that model. Observe,observe, observe.

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