Major League Vision and the Greatest Child Athlete Sample Ever
The Hardware and Software Paradigm
The Hardware and Software Paradigm
In 1992, his first year of research on the Los Angeles Dodgers, Louis J. Rosenbaum met with an unexpected problem. The players were literally off the charts.
Rosenbaum had been the team ophthalmologist for the NFL’s Phoenix Cardinals since 1988, and now he was in Dodgertown, the spring training facility in Vero Beach, Florida, to test eighty-seven players in the Dodgers organization, major league players as well as minor leaguers hoping to earn their spot in the show.
From eight A.M. to five P.M., Rosenbaum tested players for traditional visual acuity, dynamic visual acuity (the ability to see detail in moving objects), stereoacuity (the ability to detect fine differences in the depth of objects), and contrast sensitivity (the ability to differentiate fine gradations of light and dark). For the visual acuity test, instead of the usual eye chart with the big E on top, Rosenbaum and his colleagues used Landolt rings—circles with a gap in one section that the viewer must pick out as the rings get progressively smaller toward the bottom of the chart.
The trouble was that Rosenbaum used commercially available Landolt ring charts, which tested visual acuity down to 20/15.* Nearly every player maxed out the test.
Fortunately, the other vision tests were successful. So when gruffly skeptical Tommy Lasorda, the Dodgers’ legendary manager, challenged Rosenbaum to predict which minor leaguer would thrive in the majors, Rosenbaum had plenty of data to pore over. He did not have the players’ baseball statistics and so had to rely purely on the vision testing data. He chose a minor league first baseman with outstanding scores.
The player was Eric Karros, a mere sixth-round pick in the 1988 draft. By ’92, though, Karros was starting at first base for the Dodgers and won the National League Rookie of the Year award. It was his first of thirteen full seasons as a major leaguer.
The following spring, Rosenbaum returned to Dodgertown with a custom-made visual acuity test that went down to 20/8. Given the size and shape of particular photoreceptor cells, or cones, in the eye, 20/8 is around the theoretical limit of human visual acuity.
One’s maximum visual acuity is determined by the density of cones in the macula, an oval-shaped spot in the retina of the eye. Cone density in humans is akin to the megapixel rating in digital cameras, and it is highly variable between people. Scientists who have collected retinas from deceased adults, ages twenty to forty-five, found a range from 100,000 cones/mm2 to 324,000 cones/mm2. (If one’s cone density is below 20,000 cones/mm2, a magnifying glass will be needed to read the newspaper.) As Michael A. Peters, author of See to Play and an eye doctor who works with pro baseball and hockey players, puts it: the number of cones appears to be “genetically predetermined for each of us.”
Armed with a custom test at the 1993 spring training, Rosenbaum could finally measure how well pro ballplayers see. Again, Lasorda challenged Rosenbaum to predict which minor leaguer would make a distinguished pro. This time, the player whose vision tests stood out to Rosenbaum was Mike Piazza, a lightly regarded catcher.
Piazza had been picked by the Dodgers five years earlier in the sixty-second round of the draft, the 1,390th player taken overall, and only because Piazza’s father was a childhood friend of Lasorda’s. Nonetheless, Piazza would make good on Rosenbaum’s prediction. He won the National League Rookie of the Year in 1993 and went on to become the greatest hitting catcher in baseball history.
Over four years of testing, and 387 minor and major league players, Rosenbaum and his team found an average visual acuity around 20/13. Position players (players who have to hit) had better vision than pitchers, and major league players had better vision than minor leaguers. Major league position players had an average right eye visual acuity of 20/11 and an average left eye visual acuity of 20/12. In the test of fine depth perception, 58 percent of the baseball players scored “superior,” compared with 18 percent of a control population. In tests of contrast sensitivity, the pro players scored better than collegiate baseball players had in previous research, and collegiate players scored better than young people in the general population. In each eye test, pro baseball players were better than nonathletes, and major league players were better than minor league players. “Half the guys on the Dodgers’ major league roster were 20/10 uncorrected,” Rosenbaum says.
The two largest population studies of visual acuity, one from India and one from China, give a sense of just how rare 20/10 vision might be. In the Indian study, out of 9,411 tested eyes, one single eye had 20/10 vision. In the Beijing Eye Study, only 22 out of 4,438 eyes tested at 20/17 or better.
Smaller studies focused only on young people, though, have documented average vision that is better than the standard 20/20. Seventeen- and eighteen-year-olds in a Swedish study had average visual acuity around 20/16. So we should expect that Major League Baseball hitters—their average age is around twenty-eight—would have better than 20/20 vision just because they are young, but not an average of 20/11. (Coincidentally, or perhaps not, twenty-nine often is the age at which visual acuity starts to deteriorate and the age when hitters, as a group, begin to decline.)
Mark Kipnis shared with me his first recollection of his baseball-playing son Jason’s visual acuity. It was during a ski vacation when Jason was twelve years old. The Kipnis family was sitting in a large restaurant in a lodge and Mark wanted to see the score of a football game on a television in the far corner. He was tired, so he asked Jason to get up, walk over to the television, and tell him the score. “He just turned his head and told me the score,” Mark says, “and a little light went off in my head.” A decade later, Jason was selected by the Cleveland Indians in the second round of the 2009 draft. By 2011, he was starting at second base.
Ted Williams, the last man to hit .400 over a major league season, used to insist that he only saw ducks on the horizon before his hunting partners because he was “intent on seeing them.” Perhaps. But Williams’s 20/10 vision, discovered during his World War II pilot’s exam, probably didn’t hurt either.*
About 2 percent of the players in the Dodgers organization dipped below 20/9, flirting with the theoretical limit of the human eye. Daniel M. Laby, an ophthalmologist who worked on the Dodgers study and later with the Boston Red Sox, says that he encounters a few players at that level every year in spring training. “I can pretty comfortably say that in twenty years of caring for people’s eyes I’ve never seen someone outside pro athletics achieve that, and I’ve seen over twenty thousand people,” Laby says. David G. Kirschen, an optometrist who also works with professional athletes and is chief of binocular vision and orthoptic services at the Jules Stein Eye Institute at UCLA’s medical school, says that he has seen a few patients outside of elite sports with 20/9 vision, “but you can count them on one hand over thirty years.”
So while major league hitters might not have any faster reaction time than you or I do, they do have the superior vision that can help them pick up the anticipatory cues they need earlier, making raw reaction speed less important.*
Baseball players have to know before the final two hundred milliseconds of a pitch where to swing, so the earlier they pick up anticipatory cues the better. One such cue, as psychologist Mike Stadler writes in The Psychology of Baseball, is the “flicker” of a pitch, or the indication of the spin of the ball by the flashing pattern of rotating red seams. Two-seam fastballs and curveballs are foretold by signature red stripes on the side of the ball. A four-seam slider shows the batter a bright red dot in the center of a white circle. “That circle right out of the [pitcher’s] hand, you identify in your brain, ‘Oh, okay, slider,’” Keith Hernandez, the five-time All-Star first baseman, once said in television commentary of a Mets game. “If you didn’t have those little red seams on the ball, you’d be in a world of trouble.”
The importance of picking up ball rotation has been demonstrated in virtual-reality batting studies in which baseball players were asked to identify or to swing at digital pitches. When players picked up the rotation of the ball, they identified pitches more accurately and executed more precise swings. Hitters performed better when the red seams of the ball were accentuated, and worse when the seams were covered with white paint.
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It’s easy to understand why an athlete with outstanding visual acuity but without the mental database of what to look for is as useless as Albert Pujols facing Jennie Finch. But once the data is downloaded into the brain, it’s advantageous to see those signals as clearly and as early as possible, all the better not to have to rely on pure reaction speed.* Al Goldis, a longtime major league scout who studied motor learning in grad school, says: “If a player has better visual skills, he can pick up the pitch while it’s five feet or ten feet closer to the pitcher. If he doesn’t, his mechanics might be outstanding but he reacts so late that he breaks his bat because the ball is in on his hands. It’s not the bat speed, it’s the visual skills. That little bit is the difference between ordinary and extraordinary.”
When Laby and Kirschen studied U.S. Olympians from the 2008 Beijing Games, they found that the softball team had an average visual acuity of 20/11, outstanding depth perception, and better contrast sensitivity than athletes from any other sport. Olympic archers also had exceptional visual acuity—they scored similarly to the Dodgers—but not particularly good depth perception. That makes sense, Laby says, because the target is far away, but it’s also flat. Fencers, who must make rapid use of tiny, close-range variations in distance, scored very well on depth perception. Athletes who track flying objects at a distance—softball players and to a lesser extent soccer and volleyball players—scored well on contrast sensitivity, which is “probably set at a certain ability you’re born with,” Laby says.*
Clearly, visual hardware interacts with the particular sports task at hand. Plus, visual hardware becomes increasingly critical the faster the ball is moving. In a study of catching skill among Belgian college students, some of whom had normal depth perception and others who had weak depth perception, there was little difference in catching ability at low ball speeds. But at high speeds, there was a tremendous difference in catching skill. Depth perception differentiated people only when the ball was whistling.
A clever follow-up study by an international team of scientists recruited a group of young women, all with normal visual acuity but some who had poor depth perception and others with good depth perception. Each woman had a catching pretest—in which she had to snag tennis balls shot out of a machine—followed by more than 1,400 practice catches over two weeks, and then a posttest. The women with good depth perception improved rapidly during the training, while the women with poor depth perception didn’t improve at all. Better hardware sped the download of sport-specific software. Conversely, a 2009 Emory medical school study suggested that children with poor depth perception start self-selecting out of Little League baseball and softball by age ten. As Gobet found with chess players, when it comes to intercepting flying objects, some catchers are more readily trainable than others.
While physical hardware alone—like depth perception or visual acuity—is as useless as a laptop with an operating system but no programs, innate traits have value in determining who will have a better computer once the sport-specific software is downloaded. Pro baseball players and Olympic softball players have outstanding vision, and Louis J. Rosenbaum was able to use tests of visual hardware to predict two straight NL Rookies of the Year—though two successes do not constitute a scientific study.
Other tests of hardware might detect the potential for greatness much earlier in life.
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Psychologist Wolfgang Schneider had no idea in 1978 that he was being handed the study sample of a lifetime when the German Tennis Federation helped him and a University of Heidelberg research team recruit 106 of the most adroit eight-to-twelve-year-old tennis players in Germany.
The federation was fervent in its assistance because its officials were curious to learn whether, even among a sample restricted to kids who were already highly proficient players, the scientists could predict who might go on to be an elite adult player. Schneider’s sample turned out to be quite possibly the greatest single sample of child athletes ever studied. Of 106 kids, 98 ultimately made it to the professional level, 10 rose to the top 100 players in the world, and a few climbed all the way to the top 10.
Each year for five years, the scientists gauged the children first on tennis-specific skills and then on measures of general athleticism. Schneider’s expectation was that tennis-specific skills acquired through practice—like the accuracy with which a player could return a ball back to a specific target—would have predictive value for how highly ranked the children would be as adults. And he was correct. When the researchers eventually fit their data to the actual rankings of the players later on, the children’s tennis-specific skill scores predicted 60 to 70 percent of the variance in their eventual adult tennis ranking. But another finding surprised Schneider.
The tests of general athleticism—for example, a thirty-meter sprint and start-and-stop agility drills—influenced which children would acquire the tennis-specific skills most rapidly. “When we omitted these motor abilities, our model no longer fit the ranking data,” Schneider says. “So we said, okay, we have to keep that in our model.” In other words, over the five years of the study, the kids who were better all-around athletes were better at acquiring tennis-specific skills. As with the study that examined depth perception and the ability to learn a catching skill, superior hardware was speeding the download of tennis-skill software. Schneider’s study received significant attention in Germany, but because it was published in German, it garnered scant notice in the rest of the world.
Ten years later, Schneider replicated the entire study with one hundred more child tennis players. He was not nearly so fortunate with the second sample—no future world top one hundreds this time around. But the finding that general athleticism impacted tennis skill acquisition held strong. “This may not be generalizable to other sports,” says Schneider, who later became president of the International Society for the Study of Behavioral Development. “But for tennis, I think it’s a rather stable phenomenon.”
Among the children in the original study were two, both under twelve when the testing began, who would eventually become pretty familiar in the tennis world: Boris Becker and Steffi Graf, two of the most dominant players in history. “We called Steffi Graf the perfect tennis talent,” Schneider says. “She outperformed the others in tennis-specific skills and basic motor skills, and we also predicted from her lung capacity that she could have ended up as the European champion in the 1500-meters.”
Graf was at the top of every single test, from measures of her competitive desire to her ability to sustain concentration to her running speed. Years later, when Graf was the best tennis player in the world, she would train for endurance alongside Germany’s Olympic track runners.
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The most thorough tracking of athletes from youth en route to the pros tells yet another hardware-plus-software story. As part of the “Groningen talent studies,” four scientists from the University of Groningen in the Netherlands tested soccer players who were in pro-team development pipelines each year for a decade, starting in 2000 with twelve-year-old boys.
The Netherlands, despite a population of just 16.7 million, is a juggernaut in the planet’s most popular team sport. The country has made the final game of the World Cup three times, including in 2010, and all of the Netherlands’ professional teams have talent development programs for youth players. By 2011, sixty-eight of the hundreds of players studied had reached the professional level, nineteen of them in the Eredivisie, the premier professional league in the Netherlands.
When the study began, “I would go down on my knees and ask, ‘Please can we do the testing with your players?’” recalls Marije Elferink-Gemser, of the University of Groningen’s Center for Human Movement Sciences. But the work has turned out to be so valuable in predicting which players will develop best in the long term that “now clubs are coming to us and asking if we can also test their players,” Elferink-Gemser says. “Now there are more clubs than we can handle.”
Some of the traits that help predict the future pros are behavioral. The future pros not only tend to practice more, but they take responsibility for practicing better. Says Elferink-Gemser, “We see already when we first test them at the age of twelve that they are the players who will go up and ask the trainer, ‘Why should I do this?’ if they don’t agree with the training.”
But even among the youth soccer players—already highly prescreened by professional clubs—small variations in physical traits at age twelve delineate the haves and the have-nots. “What we see in the shuttle sprints,” Elferink-Gemser says, “is that the ones signing a professional contract later are the ones that are on average 0.2 seconds faster when they are younger, at the age of twelve, thirteen, fourteen, fifteen, and sixteen. They are always on a group average about 0.2 seconds faster than the ones who end up on the amateur level. That really gives some indication that it is important to be fast. You need a minimum speed. If you’re really slow, then you cannot catch up, and speed is really hard for them to train.”*
This theme isn’t exactly breaking news to sports scientists. Justin Durandt, manager of the Discovery High Performance Centre at the Sports Science Institute of South Africa, is in the business of testing for speed as he scours the country for rugby players. The fastest runner he ever tested was a natural. “A sixteen-year-old boy who came from a rural area and never had a day of professional training in his life,” Durandt says. The boy ran 4.68 seconds for forty meters, which would be in the 4.2-second range in the NFL-style forty-yard dash, on par with the fastest NFL players ever. It’s what Durandt hasn’t seen, though, that is telling. “We’ve tested over ten thousand boys,” he says, “and I’ve never seen a boy who was slow become fast.”
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In August 2004, a small group of scientists at the venerable Australian Institute of Sport (AIS) bet all their chips on the primacy of general, non-sport-specific athleticism.
The AIS scientists had a year and a half to try to qualify a woman for the 2006 Winter Olympics in Turin, Italy, in the winter sport of skeleton, in which the athlete begins by running down the ice with one or two hands on a sled and then, in a leap fairly like the disco move “the worm,” gets on board and careens down an ice-coated track face-first on her stomach at more than seventy miles per hour.
The Aussie scientists had never even seen the sport, but they had learned that the beginning sprint accounts for about half of the variation in total race time. So they announced a nationwide call for women who could fit snugly on a tiny sled and who could sprint. Thus began Australia’s Winter Olympics equivalent of American Idol, and it would draw commensurate media attention Down Under.
Based on written applications, twenty-six athletes were invited to the AIS in Canberra in southeastern Australia to undergo physical tests in the hope of earning one of ten funded training spots. The women came from track, gymnastics, water skiing, and surf lifesaving, a popular sport in Australia that mixes open-water rowing and kayaking, surf paddling, swimming, and footraces in the sand. Not one woman had heard of skeleton, much less tried it.
Five of the ten spots were filled solely based on the 30-meter sprint, the other five by consensus of the scientists and AIS coaches, based on how well the athletes did in a dry land test during which they had to jump on a sled fitted with wheels.
As far as the world skeleton community was concerned, the project was a doomed sideshow. “Everyone in the sport told us, ‘You guys will never succeed,’” says Jason Gulbin, then a physiologist at the AIS. “They told us, ‘It’s a real feel thing. It’s an art. You need time in this sport.’ The biggest naysayers were really the coaches from other countries.”
The women of the AIS project certainly had no feel for the ice, but they were outstanding all-around athletes. Melissa Hoar had won a world championship title in the beach-racing category of surf lifesaving. Emma Sheers had been a world water skiing champion. “It was a real curiosity,” Gulbin says, “to dump basically beach babes in skeleton who had never done it before.”
After selection, it was time to find out whether the women could actually get down the ice, bones intact. The scientists swallowed their nerves and headed to Calgary at the start of the winter season for the first runs on ice. It didn’t take a Ph.D. to evaluate the results.
Within three slides, the newbies were recording the fastest runs in Australian history, faster than the previous national record holder, who had had years of training. “That first week on the track, it was all over,” says Gulbin. “The writing was on the wall.”
So much for needing a feel for the ice. Suddenly, the initial helpfulness became standoffishness as rival skeleton athletes and coaches realized they stood to be displaced or embarrassed by women they had previously viewed as rank novices.
Ten weeks after she first set foot on ice, Melissa Hoar bested about half the field at the world under-twenty-three skeleton championships. (She won the title in her next try.) And beach sprinter Michelle Steele made it all the way to the Winter Olympics in Italy.
The AIS scientists chronicled the program’s success in an aptly titled paper: “Ice Novice to Winter Olympian in 14 Months.”
Australia, a world sports powerhouse, has thrived off talent identification and “talent transfer,” the switching of athletes between sports. In 1994, as part of the run-up to the 2000 Sydney Olympics, the country launched its National Talent Search program. Children ages fourteen to sixteen were examined in school for body size and tested for general athleticism. Australia, home to 19.1 million people at the time, won 58 medals in Sydney. That’s 3.03 medals for every million citizens, nearly ten times the relative haul of the United States, which took home 0.33 medals per million Americans.
As part of the Australian talent search, some athletes were ushered away from the sports in which they had experience into unfamiliar ones that better suited them. In 1994, Alisa Camplin, who had previously competed in gymnastics, track and field, and sailing, was converted into an aerial skier. Camplin was an outstanding all-around athlete but had never even seen snow. On her first jump ever she broke a rib. On her second, she hit a tree. “Everyone thought it was a joke,” Camplin told Australia’s Channel Nine television network. “They told me I was too old. They told me I started too late.” But by 1997, Camplin was competing on the World Cup circuit. At the 2002 Winter Olympics in Salt Lake City, despite breaking both her ankles six weeks earlier, Camplin won the gold medal. Even after that victory, watching the sparsely experienced Camplin on skis was like watching a giraffe on roller skates. She crushed her victory flowers when she fell trying to ski down the mountain to the gold medal winner’s press conference.
The successes with talent transfer attest to the fact that a nation succeeds in a sport not only by having many athletes who practice prodigiously at sport-specific skills, but also by getting the best all-around athletes into the right sports in the first place. Members of the Belgian men’s national field hockey team, for instance, were found to average just greater than 10,000 hours of accumulated practice, thousands more than players on the Dutch team. But the Belgian team is consistently mediocre—the Cleveland Browns of world field hockey—while the Dutch, who draw superior athletes to the sport, are a perennial world powerhouse.
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The truth is, even at the most basic level, it’s always a hardware and software story. The hardware is useless without the software, just as the reverse is true. Sport skill acquisition does not happen without both specific genes and a specific environment, and often the genes and the environment must coincide at a specific time.
Yet another remarkable finding of the chess studies of Guillermo Campitelli and Fernand Gobet was that the chance of reaching the international master level was drastically reduced if the player did not start serious chess by age twelve. It didn’t necessarily matter exactly how early they started, as long as it was before twelve. Some players who start later do still reach the international master level, but their chances drop precipitously. So perhaps twelve is an approximate critical age by which certain chunks must be learned and certain neuronal connections reinforced lest the opportunity be lost.
It was once thought that as we grow and learn our brain forms neurons. But it now appears that we are born overflowing with neurons and that the ones we don’t use early on are pruned away, and those that we do use are strengthened and interconnected. The brain becomes less broadly flexible but more narrowly efficient.
In his book Why Michael Couldn’t Hit, neurologist Harold Klawans argues that, despite his transcendent athleticism, Michael Jordan was never going to learn to hit a baseball at the major league level (following his first retirement from the NBA) because the neurons he needed to learn the appropriate anticipatory skills had been pruned long ago, while he was busy playing basketball.*
This is one reason why advocates of the strict deliberate practice approach suggest that training should begin as early as possible. But it is unclear which sports truly require early childhood specialization in return for elite performance. Certainly, female gymnasts must start early. But a large and growing body of scientific evidence says that early specialization not only is not required to make it to the highest level in many sports, but should perhaps be actively avoided.
In sprinting, early training that is heavy and specific can be an impediment to speed development when it results in the dreaded “speed plateau.” That is, the athlete gets stuck at a certain top speed and running rhythm that seems to be ingrained from early training. According to a scientific report published by the International Association of Athletics Federations (IAAF), the governing body of world track and field, “the speed plateau most often occurs in beginners who are introduced to narrowly sport-specific training too early, at the expense of general development.” Says Justin Durandt, of South Africa’s Sports Science Institute: “With Ericsson’s 10,000-hours model, it’s not that we don’t believe in training, but what’s happening now is that people are overtraining athletes.”
A 2011 study of 243 Danish athletes found that early specialization was either entirely unnecessary or actually detrimental to ultimate development. The athletes were divided into elites, who had competed at the top level in their field, like the Olympics, and lesser, near-elites. The study focused solely on “cgs sports”—sports measured in centimeters, grams, or seconds, like cycling, track and field, sailing, swimming, skiing, and weight lifting. Both elites and near-elites “sampled” a number of sports in childhood, but near-elites—the lesser of the two groups—could be identified by a certain quality indicative of early specialization: they practiced more than the elites by age fifteen. It was only after age fifteen that the elites accelerated their practice pace and by age eighteen had surpassed their near-elite peers in training hours. The counterintuitive, counter-10,000-hours title of the study: “Late Specialization: The Key to Success in Centimeters, Grams, or Seconds (cgs) Sports.”
The consistency of the results in those sports led South African sports physiologist and writer Ross Tucker to suggest that the elites were probably more gifted all along and simply did not have to work as hard as the near-elites early in their careers. “Their natural talent takes them to that point with less training than their peers,” Tucker says. “At the age of sixteen or seventeen, when most children have matured physically, they can begin to see that they have a future in the sport and must increase training volume.”*
In several popular books that give short shrift to the importance of genes, Tiger Woods is put forth as the apotheosis of the 10,000-hours model. His father facilitated colossal amounts of early childhood practice. But, by Woods’s account, that was in response to his own desire to play. “To this day,” Woods said in 2000, “my dad has never asked me to go play golf. I ask him. It’s the child’s desire to play that matters, not the parent’s desire to have the child play.” With Woods, one oft-omitted fact about his childhood is that, at six months old, when most infants are just beginning their struggle to stand, he could balance on his father Earl’s palm as Earl walked around the house. Not to say that this necessarily destined Woods for superhuman coordination or strength as an adult, but at the very least it would seem to have given him an opportunity to start practicing earlier than other children so that he was hitting balls at eleven months. Perhaps another case of physical hardware facilitating the download of sport-specific software.
The “practice only” narrative to explain Tiger Woods has an obvious attraction: it appeals to our hope that anything is possible with the right environment, and that children are lumps of clay with infinite athletic malleability. In short, it has the strongest possible self-help angle and it preserves more free will than any alternative explanation. But narratives that shun the contributions of innate talent can have negative side effects in exercise science.
Sports scientists who do genetic work occasionally told me that their research has a public relations problem stemming from the idea that genes are rigidly deterministic, and that they negate free will or the ability to improve one’s athletic station. Some genes—like the ones that give you two eyeballs or the one for the degenerative brain disease Huntington’s—are rather deterministic. If you have the genetic defect for Huntington’s, you will get the disease. Many other genes, however, are not biological destiny, but simply tilt one’s physical predispositions. Unfortunately, that moderate message is often entirely lost in a mainstream press that heralds each study of a new gene as if it completely supplants some aspect of human agency.
Jason Gulbin, the physiologist who worked on Australia’s Olympic skeleton experiment, says that the word “genetics” has become so taboo in his talent-identification field that “we actively changed our language here around genetic work that we’re doing from ‘genetics’ to ‘molecular biology and protein synthesis.’ It was, literally, ‘Don’t mention the g-word.’ Any research proposals we put in, we don’t mention the genetics if we can help it. It’s: ‘Oh, well, if you’re doing molecular biology and protein synthesis, well, that’s all right.’” Never mind that it’s the same thing.
Several sports psychologists I interviewed told me that they publicly support a view that marginalizes genes because they believe it sends a positive social message. “But maybe it’s dangerous too,” one eminent sports psychologist told me, “to say that you’re stuck where you are because you’re not working hard enough.” Either way, the social message has no bearing on the scientific truth.
Janet Starkes, whose work, along with Ericsson’s, helped usher in the era of “software not hardware,” always believed that genetic differences played a part in sport skill, but in the past she was reticent to say so publicly. “Thirty-five years ago, people very easily accepted that there are underlying innate abilities,” Starkes says. “As the [learned] perceptual cognitive approach became more acceptable, it allowed me to be more centrist. It’s really been very much of a pendulum swing. . . . Darts is the most closed motor skill you can get, but practice still cannot explain all the variance. And to hit [a baseball] you’ve got to have a modicum of visual acuity, and it’s better if it’s better, and you also definitely need the software to go with it.”
Starkes has contributed as much to the study of skill practice as any sports scientist alive. Her work forms a full vertebra in the backbone of the strict 10,000-hours view—that only practice determines success in sports. And yet, even when she was afraid to say it, Starkes knew that without genes, the picture of sports expertise is woefully incomplete.
After all, Starkes adds, if only accumulated hours of practice matter, then why do we separate men and women in athletic competition?
It’s a good question.
Rosenbaum had been the team ophthalmologist for the NFL’s Phoenix Cardinals since 1988, and now he was in Dodgertown, the spring training facility in Vero Beach, Florida, to test eighty-seven players in the Dodgers organization, major league players as well as minor leaguers hoping to earn their spot in the show.
From eight A.M. to five P.M., Rosenbaum tested players for traditional visual acuity, dynamic visual acuity (the ability to see detail in moving objects), stereoacuity (the ability to detect fine differences in the depth of objects), and contrast sensitivity (the ability to differentiate fine gradations of light and dark). For the visual acuity test, instead of the usual eye chart with the big E on top, Rosenbaum and his colleagues used Landolt rings—circles with a gap in one section that the viewer must pick out as the rings get progressively smaller toward the bottom of the chart.
The trouble was that Rosenbaum used commercially available Landolt ring charts, which tested visual acuity down to 20/15.* Nearly every player maxed out the test.
Fortunately, the other vision tests were successful. So when gruffly skeptical Tommy Lasorda, the Dodgers’ legendary manager, challenged Rosenbaum to predict which minor leaguer would thrive in the majors, Rosenbaum had plenty of data to pore over. He did not have the players’ baseball statistics and so had to rely purely on the vision testing data. He chose a minor league first baseman with outstanding scores.
The player was Eric Karros, a mere sixth-round pick in the 1988 draft. By ’92, though, Karros was starting at first base for the Dodgers and won the National League Rookie of the Year award. It was his first of thirteen full seasons as a major leaguer.
The following spring, Rosenbaum returned to Dodgertown with a custom-made visual acuity test that went down to 20/8. Given the size and shape of particular photoreceptor cells, or cones, in the eye, 20/8 is around the theoretical limit of human visual acuity.
One’s maximum visual acuity is determined by the density of cones in the macula, an oval-shaped spot in the retina of the eye. Cone density in humans is akin to the megapixel rating in digital cameras, and it is highly variable between people. Scientists who have collected retinas from deceased adults, ages twenty to forty-five, found a range from 100,000 cones/mm2 to 324,000 cones/mm2. (If one’s cone density is below 20,000 cones/mm2, a magnifying glass will be needed to read the newspaper.) As Michael A. Peters, author of See to Play and an eye doctor who works with pro baseball and hockey players, puts it: the number of cones appears to be “genetically predetermined for each of us.”
Armed with a custom test at the 1993 spring training, Rosenbaum could finally measure how well pro ballplayers see. Again, Lasorda challenged Rosenbaum to predict which minor leaguer would make a distinguished pro. This time, the player whose vision tests stood out to Rosenbaum was Mike Piazza, a lightly regarded catcher.
Piazza had been picked by the Dodgers five years earlier in the sixty-second round of the draft, the 1,390th player taken overall, and only because Piazza’s father was a childhood friend of Lasorda’s. Nonetheless, Piazza would make good on Rosenbaum’s prediction. He won the National League Rookie of the Year in 1993 and went on to become the greatest hitting catcher in baseball history.
Over four years of testing, and 387 minor and major league players, Rosenbaum and his team found an average visual acuity around 20/13. Position players (players who have to hit) had better vision than pitchers, and major league players had better vision than minor leaguers. Major league position players had an average right eye visual acuity of 20/11 and an average left eye visual acuity of 20/12. In the test of fine depth perception, 58 percent of the baseball players scored “superior,” compared with 18 percent of a control population. In tests of contrast sensitivity, the pro players scored better than collegiate baseball players had in previous research, and collegiate players scored better than young people in the general population. In each eye test, pro baseball players were better than nonathletes, and major league players were better than minor league players. “Half the guys on the Dodgers’ major league roster were 20/10 uncorrected,” Rosenbaum says.
The two largest population studies of visual acuity, one from India and one from China, give a sense of just how rare 20/10 vision might be. In the Indian study, out of 9,411 tested eyes, one single eye had 20/10 vision. In the Beijing Eye Study, only 22 out of 4,438 eyes tested at 20/17 or better.
Smaller studies focused only on young people, though, have documented average vision that is better than the standard 20/20. Seventeen- and eighteen-year-olds in a Swedish study had average visual acuity around 20/16. So we should expect that Major League Baseball hitters—their average age is around twenty-eight—would have better than 20/20 vision just because they are young, but not an average of 20/11. (Coincidentally, or perhaps not, twenty-nine often is the age at which visual acuity starts to deteriorate and the age when hitters, as a group, begin to decline.)
Mark Kipnis shared with me his first recollection of his baseball-playing son Jason’s visual acuity. It was during a ski vacation when Jason was twelve years old. The Kipnis family was sitting in a large restaurant in a lodge and Mark wanted to see the score of a football game on a television in the far corner. He was tired, so he asked Jason to get up, walk over to the television, and tell him the score. “He just turned his head and told me the score,” Mark says, “and a little light went off in my head.” A decade later, Jason was selected by the Cleveland Indians in the second round of the 2009 draft. By 2011, he was starting at second base.
Ted Williams, the last man to hit .400 over a major league season, used to insist that he only saw ducks on the horizon before his hunting partners because he was “intent on seeing them.” Perhaps. But Williams’s 20/10 vision, discovered during his World War II pilot’s exam, probably didn’t hurt either.*
About 2 percent of the players in the Dodgers organization dipped below 20/9, flirting with the theoretical limit of the human eye. Daniel M. Laby, an ophthalmologist who worked on the Dodgers study and later with the Boston Red Sox, says that he encounters a few players at that level every year in spring training. “I can pretty comfortably say that in twenty years of caring for people’s eyes I’ve never seen someone outside pro athletics achieve that, and I’ve seen over twenty thousand people,” Laby says. David G. Kirschen, an optometrist who also works with professional athletes and is chief of binocular vision and orthoptic services at the Jules Stein Eye Institute at UCLA’s medical school, says that he has seen a few patients outside of elite sports with 20/9 vision, “but you can count them on one hand over thirty years.”
So while major league hitters might not have any faster reaction time than you or I do, they do have the superior vision that can help them pick up the anticipatory cues they need earlier, making raw reaction speed less important.*
Baseball players have to know before the final two hundred milliseconds of a pitch where to swing, so the earlier they pick up anticipatory cues the better. One such cue, as psychologist Mike Stadler writes in The Psychology of Baseball, is the “flicker” of a pitch, or the indication of the spin of the ball by the flashing pattern of rotating red seams. Two-seam fastballs and curveballs are foretold by signature red stripes on the side of the ball. A four-seam slider shows the batter a bright red dot in the center of a white circle. “That circle right out of the [pitcher’s] hand, you identify in your brain, ‘Oh, okay, slider,’” Keith Hernandez, the five-time All-Star first baseman, once said in television commentary of a Mets game. “If you didn’t have those little red seams on the ball, you’d be in a world of trouble.”
The importance of picking up ball rotation has been demonstrated in virtual-reality batting studies in which baseball players were asked to identify or to swing at digital pitches. When players picked up the rotation of the ball, they identified pitches more accurately and executed more precise swings. Hitters performed better when the red seams of the ball were accentuated, and worse when the seams were covered with white paint.
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It’s easy to understand why an athlete with outstanding visual acuity but without the mental database of what to look for is as useless as Albert Pujols facing Jennie Finch. But once the data is downloaded into the brain, it’s advantageous to see those signals as clearly and as early as possible, all the better not to have to rely on pure reaction speed.* Al Goldis, a longtime major league scout who studied motor learning in grad school, says: “If a player has better visual skills, he can pick up the pitch while it’s five feet or ten feet closer to the pitcher. If he doesn’t, his mechanics might be outstanding but he reacts so late that he breaks his bat because the ball is in on his hands. It’s not the bat speed, it’s the visual skills. That little bit is the difference between ordinary and extraordinary.”
When Laby and Kirschen studied U.S. Olympians from the 2008 Beijing Games, they found that the softball team had an average visual acuity of 20/11, outstanding depth perception, and better contrast sensitivity than athletes from any other sport. Olympic archers also had exceptional visual acuity—they scored similarly to the Dodgers—but not particularly good depth perception. That makes sense, Laby says, because the target is far away, but it’s also flat. Fencers, who must make rapid use of tiny, close-range variations in distance, scored very well on depth perception. Athletes who track flying objects at a distance—softball players and to a lesser extent soccer and volleyball players—scored well on contrast sensitivity, which is “probably set at a certain ability you’re born with,” Laby says.*
Clearly, visual hardware interacts with the particular sports task at hand. Plus, visual hardware becomes increasingly critical the faster the ball is moving. In a study of catching skill among Belgian college students, some of whom had normal depth perception and others who had weak depth perception, there was little difference in catching ability at low ball speeds. But at high speeds, there was a tremendous difference in catching skill. Depth perception differentiated people only when the ball was whistling.
A clever follow-up study by an international team of scientists recruited a group of young women, all with normal visual acuity but some who had poor depth perception and others with good depth perception. Each woman had a catching pretest—in which she had to snag tennis balls shot out of a machine—followed by more than 1,400 practice catches over two weeks, and then a posttest. The women with good depth perception improved rapidly during the training, while the women with poor depth perception didn’t improve at all. Better hardware sped the download of sport-specific software. Conversely, a 2009 Emory medical school study suggested that children with poor depth perception start self-selecting out of Little League baseball and softball by age ten. As Gobet found with chess players, when it comes to intercepting flying objects, some catchers are more readily trainable than others.
While physical hardware alone—like depth perception or visual acuity—is as useless as a laptop with an operating system but no programs, innate traits have value in determining who will have a better computer once the sport-specific software is downloaded. Pro baseball players and Olympic softball players have outstanding vision, and Louis J. Rosenbaum was able to use tests of visual hardware to predict two straight NL Rookies of the Year—though two successes do not constitute a scientific study.
Other tests of hardware might detect the potential for greatness much earlier in life.
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Psychologist Wolfgang Schneider had no idea in 1978 that he was being handed the study sample of a lifetime when the German Tennis Federation helped him and a University of Heidelberg research team recruit 106 of the most adroit eight-to-twelve-year-old tennis players in Germany.
The federation was fervent in its assistance because its officials were curious to learn whether, even among a sample restricted to kids who were already highly proficient players, the scientists could predict who might go on to be an elite adult player. Schneider’s sample turned out to be quite possibly the greatest single sample of child athletes ever studied. Of 106 kids, 98 ultimately made it to the professional level, 10 rose to the top 100 players in the world, and a few climbed all the way to the top 10.
Each year for five years, the scientists gauged the children first on tennis-specific skills and then on measures of general athleticism. Schneider’s expectation was that tennis-specific skills acquired through practice—like the accuracy with which a player could return a ball back to a specific target—would have predictive value for how highly ranked the children would be as adults. And he was correct. When the researchers eventually fit their data to the actual rankings of the players later on, the children’s tennis-specific skill scores predicted 60 to 70 percent of the variance in their eventual adult tennis ranking. But another finding surprised Schneider.
The tests of general athleticism—for example, a thirty-meter sprint and start-and-stop agility drills—influenced which children would acquire the tennis-specific skills most rapidly. “When we omitted these motor abilities, our model no longer fit the ranking data,” Schneider says. “So we said, okay, we have to keep that in our model.” In other words, over the five years of the study, the kids who were better all-around athletes were better at acquiring tennis-specific skills. As with the study that examined depth perception and the ability to learn a catching skill, superior hardware was speeding the download of tennis-skill software. Schneider’s study received significant attention in Germany, but because it was published in German, it garnered scant notice in the rest of the world.
Ten years later, Schneider replicated the entire study with one hundred more child tennis players. He was not nearly so fortunate with the second sample—no future world top one hundreds this time around. But the finding that general athleticism impacted tennis skill acquisition held strong. “This may not be generalizable to other sports,” says Schneider, who later became president of the International Society for the Study of Behavioral Development. “But for tennis, I think it’s a rather stable phenomenon.”
Among the children in the original study were two, both under twelve when the testing began, who would eventually become pretty familiar in the tennis world: Boris Becker and Steffi Graf, two of the most dominant players in history. “We called Steffi Graf the perfect tennis talent,” Schneider says. “She outperformed the others in tennis-specific skills and basic motor skills, and we also predicted from her lung capacity that she could have ended up as the European champion in the 1500-meters.”
Graf was at the top of every single test, from measures of her competitive desire to her ability to sustain concentration to her running speed. Years later, when Graf was the best tennis player in the world, she would train for endurance alongside Germany’s Olympic track runners.
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The most thorough tracking of athletes from youth en route to the pros tells yet another hardware-plus-software story. As part of the “Groningen talent studies,” four scientists from the University of Groningen in the Netherlands tested soccer players who were in pro-team development pipelines each year for a decade, starting in 2000 with twelve-year-old boys.
The Netherlands, despite a population of just 16.7 million, is a juggernaut in the planet’s most popular team sport. The country has made the final game of the World Cup three times, including in 2010, and all of the Netherlands’ professional teams have talent development programs for youth players. By 2011, sixty-eight of the hundreds of players studied had reached the professional level, nineteen of them in the Eredivisie, the premier professional league in the Netherlands.
When the study began, “I would go down on my knees and ask, ‘Please can we do the testing with your players?’” recalls Marije Elferink-Gemser, of the University of Groningen’s Center for Human Movement Sciences. But the work has turned out to be so valuable in predicting which players will develop best in the long term that “now clubs are coming to us and asking if we can also test their players,” Elferink-Gemser says. “Now there are more clubs than we can handle.”
Some of the traits that help predict the future pros are behavioral. The future pros not only tend to practice more, but they take responsibility for practicing better. Says Elferink-Gemser, “We see already when we first test them at the age of twelve that they are the players who will go up and ask the trainer, ‘Why should I do this?’ if they don’t agree with the training.”
But even among the youth soccer players—already highly prescreened by professional clubs—small variations in physical traits at age twelve delineate the haves and the have-nots. “What we see in the shuttle sprints,” Elferink-Gemser says, “is that the ones signing a professional contract later are the ones that are on average 0.2 seconds faster when they are younger, at the age of twelve, thirteen, fourteen, fifteen, and sixteen. They are always on a group average about 0.2 seconds faster than the ones who end up on the amateur level. That really gives some indication that it is important to be fast. You need a minimum speed. If you’re really slow, then you cannot catch up, and speed is really hard for them to train.”*
This theme isn’t exactly breaking news to sports scientists. Justin Durandt, manager of the Discovery High Performance Centre at the Sports Science Institute of South Africa, is in the business of testing for speed as he scours the country for rugby players. The fastest runner he ever tested was a natural. “A sixteen-year-old boy who came from a rural area and never had a day of professional training in his life,” Durandt says. The boy ran 4.68 seconds for forty meters, which would be in the 4.2-second range in the NFL-style forty-yard dash, on par with the fastest NFL players ever. It’s what Durandt hasn’t seen, though, that is telling. “We’ve tested over ten thousand boys,” he says, “and I’ve never seen a boy who was slow become fast.”
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In August 2004, a small group of scientists at the venerable Australian Institute of Sport (AIS) bet all their chips on the primacy of general, non-sport-specific athleticism.
The AIS scientists had a year and a half to try to qualify a woman for the 2006 Winter Olympics in Turin, Italy, in the winter sport of skeleton, in which the athlete begins by running down the ice with one or two hands on a sled and then, in a leap fairly like the disco move “the worm,” gets on board and careens down an ice-coated track face-first on her stomach at more than seventy miles per hour.
The Aussie scientists had never even seen the sport, but they had learned that the beginning sprint accounts for about half of the variation in total race time. So they announced a nationwide call for women who could fit snugly on a tiny sled and who could sprint. Thus began Australia’s Winter Olympics equivalent of American Idol, and it would draw commensurate media attention Down Under.
Based on written applications, twenty-six athletes were invited to the AIS in Canberra in southeastern Australia to undergo physical tests in the hope of earning one of ten funded training spots. The women came from track, gymnastics, water skiing, and surf lifesaving, a popular sport in Australia that mixes open-water rowing and kayaking, surf paddling, swimming, and footraces in the sand. Not one woman had heard of skeleton, much less tried it.
Five of the ten spots were filled solely based on the 30-meter sprint, the other five by consensus of the scientists and AIS coaches, based on how well the athletes did in a dry land test during which they had to jump on a sled fitted with wheels.
As far as the world skeleton community was concerned, the project was a doomed sideshow. “Everyone in the sport told us, ‘You guys will never succeed,’” says Jason Gulbin, then a physiologist at the AIS. “They told us, ‘It’s a real feel thing. It’s an art. You need time in this sport.’ The biggest naysayers were really the coaches from other countries.”
The women of the AIS project certainly had no feel for the ice, but they were outstanding all-around athletes. Melissa Hoar had won a world championship title in the beach-racing category of surf lifesaving. Emma Sheers had been a world water skiing champion. “It was a real curiosity,” Gulbin says, “to dump basically beach babes in skeleton who had never done it before.”
After selection, it was time to find out whether the women could actually get down the ice, bones intact. The scientists swallowed their nerves and headed to Calgary at the start of the winter season for the first runs on ice. It didn’t take a Ph.D. to evaluate the results.
Within three slides, the newbies were recording the fastest runs in Australian history, faster than the previous national record holder, who had had years of training. “That first week on the track, it was all over,” says Gulbin. “The writing was on the wall.”
So much for needing a feel for the ice. Suddenly, the initial helpfulness became standoffishness as rival skeleton athletes and coaches realized they stood to be displaced or embarrassed by women they had previously viewed as rank novices.
Ten weeks after she first set foot on ice, Melissa Hoar bested about half the field at the world under-twenty-three skeleton championships. (She won the title in her next try.) And beach sprinter Michelle Steele made it all the way to the Winter Olympics in Italy.
The AIS scientists chronicled the program’s success in an aptly titled paper: “Ice Novice to Winter Olympian in 14 Months.”
Australia, a world sports powerhouse, has thrived off talent identification and “talent transfer,” the switching of athletes between sports. In 1994, as part of the run-up to the 2000 Sydney Olympics, the country launched its National Talent Search program. Children ages fourteen to sixteen were examined in school for body size and tested for general athleticism. Australia, home to 19.1 million people at the time, won 58 medals in Sydney. That’s 3.03 medals for every million citizens, nearly ten times the relative haul of the United States, which took home 0.33 medals per million Americans.
As part of the Australian talent search, some athletes were ushered away from the sports in which they had experience into unfamiliar ones that better suited them. In 1994, Alisa Camplin, who had previously competed in gymnastics, track and field, and sailing, was converted into an aerial skier. Camplin was an outstanding all-around athlete but had never even seen snow. On her first jump ever she broke a rib. On her second, she hit a tree. “Everyone thought it was a joke,” Camplin told Australia’s Channel Nine television network. “They told me I was too old. They told me I started too late.” But by 1997, Camplin was competing on the World Cup circuit. At the 2002 Winter Olympics in Salt Lake City, despite breaking both her ankles six weeks earlier, Camplin won the gold medal. Even after that victory, watching the sparsely experienced Camplin on skis was like watching a giraffe on roller skates. She crushed her victory flowers when she fell trying to ski down the mountain to the gold medal winner’s press conference.
The successes with talent transfer attest to the fact that a nation succeeds in a sport not only by having many athletes who practice prodigiously at sport-specific skills, but also by getting the best all-around athletes into the right sports in the first place. Members of the Belgian men’s national field hockey team, for instance, were found to average just greater than 10,000 hours of accumulated practice, thousands more than players on the Dutch team. But the Belgian team is consistently mediocre—the Cleveland Browns of world field hockey—while the Dutch, who draw superior athletes to the sport, are a perennial world powerhouse.
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The truth is, even at the most basic level, it’s always a hardware and software story. The hardware is useless without the software, just as the reverse is true. Sport skill acquisition does not happen without both specific genes and a specific environment, and often the genes and the environment must coincide at a specific time.
Yet another remarkable finding of the chess studies of Guillermo Campitelli and Fernand Gobet was that the chance of reaching the international master level was drastically reduced if the player did not start serious chess by age twelve. It didn’t necessarily matter exactly how early they started, as long as it was before twelve. Some players who start later do still reach the international master level, but their chances drop precipitously. So perhaps twelve is an approximate critical age by which certain chunks must be learned and certain neuronal connections reinforced lest the opportunity be lost.
It was once thought that as we grow and learn our brain forms neurons. But it now appears that we are born overflowing with neurons and that the ones we don’t use early on are pruned away, and those that we do use are strengthened and interconnected. The brain becomes less broadly flexible but more narrowly efficient.
In his book Why Michael Couldn’t Hit, neurologist Harold Klawans argues that, despite his transcendent athleticism, Michael Jordan was never going to learn to hit a baseball at the major league level (following his first retirement from the NBA) because the neurons he needed to learn the appropriate anticipatory skills had been pruned long ago, while he was busy playing basketball.*
This is one reason why advocates of the strict deliberate practice approach suggest that training should begin as early as possible. But it is unclear which sports truly require early childhood specialization in return for elite performance. Certainly, female gymnasts must start early. But a large and growing body of scientific evidence says that early specialization not only is not required to make it to the highest level in many sports, but should perhaps be actively avoided.
In sprinting, early training that is heavy and specific can be an impediment to speed development when it results in the dreaded “speed plateau.” That is, the athlete gets stuck at a certain top speed and running rhythm that seems to be ingrained from early training. According to a scientific report published by the International Association of Athletics Federations (IAAF), the governing body of world track and field, “the speed plateau most often occurs in beginners who are introduced to narrowly sport-specific training too early, at the expense of general development.” Says Justin Durandt, of South Africa’s Sports Science Institute: “With Ericsson’s 10,000-hours model, it’s not that we don’t believe in training, but what’s happening now is that people are overtraining athletes.”
A 2011 study of 243 Danish athletes found that early specialization was either entirely unnecessary or actually detrimental to ultimate development. The athletes were divided into elites, who had competed at the top level in their field, like the Olympics, and lesser, near-elites. The study focused solely on “cgs sports”—sports measured in centimeters, grams, or seconds, like cycling, track and field, sailing, swimming, skiing, and weight lifting. Both elites and near-elites “sampled” a number of sports in childhood, but near-elites—the lesser of the two groups—could be identified by a certain quality indicative of early specialization: they practiced more than the elites by age fifteen. It was only after age fifteen that the elites accelerated their practice pace and by age eighteen had surpassed their near-elite peers in training hours. The counterintuitive, counter-10,000-hours title of the study: “Late Specialization: The Key to Success in Centimeters, Grams, or Seconds (cgs) Sports.”
The consistency of the results in those sports led South African sports physiologist and writer Ross Tucker to suggest that the elites were probably more gifted all along and simply did not have to work as hard as the near-elites early in their careers. “Their natural talent takes them to that point with less training than their peers,” Tucker says. “At the age of sixteen or seventeen, when most children have matured physically, they can begin to see that they have a future in the sport and must increase training volume.”*
In several popular books that give short shrift to the importance of genes, Tiger Woods is put forth as the apotheosis of the 10,000-hours model. His father facilitated colossal amounts of early childhood practice. But, by Woods’s account, that was in response to his own desire to play. “To this day,” Woods said in 2000, “my dad has never asked me to go play golf. I ask him. It’s the child’s desire to play that matters, not the parent’s desire to have the child play.” With Woods, one oft-omitted fact about his childhood is that, at six months old, when most infants are just beginning their struggle to stand, he could balance on his father Earl’s palm as Earl walked around the house. Not to say that this necessarily destined Woods for superhuman coordination or strength as an adult, but at the very least it would seem to have given him an opportunity to start practicing earlier than other children so that he was hitting balls at eleven months. Perhaps another case of physical hardware facilitating the download of sport-specific software.
The “practice only” narrative to explain Tiger Woods has an obvious attraction: it appeals to our hope that anything is possible with the right environment, and that children are lumps of clay with infinite athletic malleability. In short, it has the strongest possible self-help angle and it preserves more free will than any alternative explanation. But narratives that shun the contributions of innate talent can have negative side effects in exercise science.
Sports scientists who do genetic work occasionally told me that their research has a public relations problem stemming from the idea that genes are rigidly deterministic, and that they negate free will or the ability to improve one’s athletic station. Some genes—like the ones that give you two eyeballs or the one for the degenerative brain disease Huntington’s—are rather deterministic. If you have the genetic defect for Huntington’s, you will get the disease. Many other genes, however, are not biological destiny, but simply tilt one’s physical predispositions. Unfortunately, that moderate message is often entirely lost in a mainstream press that heralds each study of a new gene as if it completely supplants some aspect of human agency.
Jason Gulbin, the physiologist who worked on Australia’s Olympic skeleton experiment, says that the word “genetics” has become so taboo in his talent-identification field that “we actively changed our language here around genetic work that we’re doing from ‘genetics’ to ‘molecular biology and protein synthesis.’ It was, literally, ‘Don’t mention the g-word.’ Any research proposals we put in, we don’t mention the genetics if we can help it. It’s: ‘Oh, well, if you’re doing molecular biology and protein synthesis, well, that’s all right.’” Never mind that it’s the same thing.
Several sports psychologists I interviewed told me that they publicly support a view that marginalizes genes because they believe it sends a positive social message. “But maybe it’s dangerous too,” one eminent sports psychologist told me, “to say that you’re stuck where you are because you’re not working hard enough.” Either way, the social message has no bearing on the scientific truth.
Janet Starkes, whose work, along with Ericsson’s, helped usher in the era of “software not hardware,” always believed that genetic differences played a part in sport skill, but in the past she was reticent to say so publicly. “Thirty-five years ago, people very easily accepted that there are underlying innate abilities,” Starkes says. “As the [learned] perceptual cognitive approach became more acceptable, it allowed me to be more centrist. It’s really been very much of a pendulum swing. . . . Darts is the most closed motor skill you can get, but practice still cannot explain all the variance. And to hit [a baseball] you’ve got to have a modicum of visual acuity, and it’s better if it’s better, and you also definitely need the software to go with it.”
Starkes has contributed as much to the study of skill practice as any sports scientist alive. Her work forms a full vertebra in the backbone of the strict 10,000-hours view—that only practice determines success in sports. And yet, even when she was afraid to say it, Starkes knew that without genes, the picture of sports expertise is woefully incomplete.
After all, Starkes adds, if only accumulated hours of practice matter, then why do we separate men and women in athletic competition?
It’s a good question.
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