0.5%: The Margin Between Good and Great, and How to Find It
As sports have become high stakes, global competitions, the performance margins that differentiate good, great and legendary have shrunk dramatically. Fortunately, cutting edge science has shone a light on the best path to peak performance, and it contradicts the most popular notions about skill acquisition, like the famed “10,000-hours Rule.” That argument says that only accumulated hours of practice matter to success. In fact, though, future experts start off practicing less in their eventual discipline than their peers. David Epstein explains just what it is that future elites are doing during that time that primes them for later (and greater) success. He also dissects how — once at the top competitive level — athletes are using “small data” to find what factors most matters for performance, and which of those they can change in the pursuit of the final 0.5% of performance. The conclusions from elite sports can guide any individual or team in the search to find their personal 0.5%.
Turning Silver (and Bronze) into Gold: Finding What Matters, and What You Can Change
The famed biologist and author Stephen Jay Gould observed that, in any complex system, a “spread of excellence” occurs over time. That is, the longer people strive in a field — whether that be baseball, chess, computer programming, or stock trading — the higher the performance bar is raised and the greater the number of elite performers who find the keys to success. Consequently, performances at the top converge, until the point when it can be difficult to discern what separates best from second-best. Perhaps no area of human endeavor more easily exemplifies this than sports, where athletes now routinely approach the limit of what is humanly possible. In some cases, elite athletes have converged so greatly that “photo-finishes” no longer work because the camera’s margin of error is larger than the difference between athletes. Thus, it is more important than ever for coaches, sports scientists and athletes to hone in on the tiny, undiscovered advantages that can separate them from their similarly talented and similarly trained peers. David Epstein draws on cutting edge sports science from around the world to describe how elite performers are finding those hidden advantages, and how similar tactics can be applied beyond sports in the hunt for peak performance.
Capitalizing on Small Data to Make Big-Data Work
What do champagne, diapers, and razor blades have in common? Those three items comprise one of the most common purchase patterns of identity thieves. More importantly, fraud detectors at major credit card companies now know this. They learned it not by mounting a massive investigation of the products that are easy to flip on the black market (everyone drinks and shaves…hopefully not at the same time), or with an expensive sociological study of how thieves try to appear nonthreatening at the checkout counter (add diapers to your cart). The lesson came instead from one of the single most important business innovations of the decade: big data. Simply cross reference instances of known fraud with purchase patterns, and boom: catch the thieves more efficiently.
And yet, when it comes to improving how individuals and teams learn and perform, big data often falls short. That’s because it isn’t supplemented by “small data,” the kind of analysis that finds out not just what variables matter, but, more importantly, which of those can be most easily exploited to achieve goals:
- Olympic sports scientists start with a big data approach to determine that only three variables matter to long-jump performance, and then find that one of those can quickly and easily be changed to optimize performance. Their athlete wins the gold medal
- A boxer’s support team discovers that training time is constantly lost to a single repeatedly bruised knuckle. With that knowledge, they put sensors in the athlete’s gloves and find that by changing how his hands are taped they can better disperse the forces on his fingers. A month of training is saved
- A cycling team’s data reveals that one rider has abnormally large week-to-week performance fluctuations. They study his lifestyle and find that the amount of caffeine he imbibes each week predicts his performance. They cap the rider’s weekly espressos, and improve his performance
- The Weather Channel’s 2 billion sensors provide prototypical big data on stormy weather. But it takes IBM to discern a ground level business application. IBM analysis of the impact of weather on businesses finds that auto insurers pay about $25 per policyholder per year for hail damage in regions that regularly get frozen rain. So IBM applies The Weather Channel’s data to the problem, creating a tool for auto insurers to warn customers when they should move their cars. Both insurers and their customers save money.
Big data finds patterns of variables that are statistically significant. Small data creates advantages by revealing which of those are functionally significant. With stories from some of the most competitive entities on the planet—elite sports teams—supplemented by analogies from the business world, Epstein reveals how any person, team or company can leverage small data to get more out of big data.
Late Specialization: The Counterintuitive Key to Expertise
Every ambitious person wants a head start. Why, then, have scientists found that the most elite performers in sports — as well as in other areas, like music — actually get off to a slower start than their peers who get out of the gate quickly but who also plateau before they ever reach elite performance? It turns out that an important component of learning any skill involves delaying specialized training so that the trainee has a chance to go through an “implicit learning” phase. That is, they must first have an environment that allows them to “learn like a baby,” free from certain types of coaching that become important later on. While early, specialized training may be good for a head start, David Epstein explains that if a learner is to maximize their capabilities, a coach, mentor, or supervisor must resist the urge to sacrifice long term development for that head start, and instead follow the optimal path of skill acquisition as determined by cutting edge science.
How the 10,000-Hour Rule Hinders Peak Performance
Every high performer has heard of the 10,000-hours rule—the idea that voluminous, task-specific training is both necessary and sufficient for expert performance. But few know that it originates in a tiny study of violinists who were so highly pre-screened that they had already gained admission to a world famous music academy. This would be like looking at NBA centers, noticing they had practiced a lot, and ignoring the fact that being seven feet tall also helped. The danger in the “rule” is that it prevents the kind of talent identification and individualized development that truly leads to peak performance. Through stories and cutting edge scientific findings that range from research on chess masters and musicians to Kenyan marathoners, David Epstein explodes the 10,000 hours myth and, as Daryl Morey, Houston Rockets general manager and co-founder of the MIT Sloan Sports Analytics Conference, puts it: “Epstein reveals the true complexity behind excellence.” Epstein explains how research into talent identification, skill acquisition, and team dynamics can help every individual and team march toward peak performance.
How Elite Teachers and Learners Act
As athletes have gotten better, and as the performance gaps between elites have narrowed, sports have become far more than simple physical competitions — they have morphed into learning contests. Sports scientists search furiously for any way that skills can be taught and learned more rapidly and more completely, and much of what they’ve found applies in any teaching arena.
Epstein shares with audiences specific tips and tools that can applied right away to bolster learning like:
- Chunking: the practice of contextualized learning that makes chess masters appear to have photographic memories, when really they’ve just learned how to quickly analyze groups of pieces the way you can analyze words in a language you speak fluently.
- Self-regulatory behavior: a pattern of reflective self-analysis — which can be taught — that sports scientists have used to accurately predict which 12-year-old children will avoid dreaded learning-plateaus and go on to become professional athletes in sports from hockey to soccer. Amazingly, self-regulatory behavior also predicted which students would outperform their peers in the classroom!
- Implicit learning: sports scientists have found that rigorous technical instruction for young learners often backfires, slowing the rate of learning. Instead, they have developed systems to help performers “learn like a baby,” and enhance their rate and depth of learning with strategies that make use of indirect instruction.
These are just a few of the cutting edge findings from the science of skill acquisition in sports that Epstein will share. With the concrete instructional examples he includes, any teacher will walk away with something new to try.