Why Kids Need Their Recess Time At School

Ask a group of grade school students to name their favorite class and the overwhelming and immediate response is “recess!”  Kids are not wired to sit still for hours focused on learning math equations or memorizing facts.  They’re built to move and need the time in their day to unplug their brain and restart their legs.  

However, school administrators and teachers are facing growing pressure to reduce this play time in favor of more instruction time to meet tougher academic standards.  Two new research studies argue that would be counterproductive showing that exercise and aerobic fitness are key contributors to cognitive performance.

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From Fighter Pilots To Hockey Players, Cognitive Training Gets Results

“He has great field vision.” “Her court awareness is the difference.” “He seems to have eyes in the back of his head.” Beyond physical talent and technical abilities, some players seem to have this sixth sense of awareness on a court, rink or field that allows them to keep track of their teammates and their opponents so that they can make the perfect pass or step in at the last second to make a defensive stop.  

Coaches often praise and search for this elusive intangible that appears to be a genetic gift but, according to research, is actually a trainable skill.  

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High School Athletes Are Getting Fat And Injured

In the new era of "bigger is better" in youth and high school sports, strength and conditioning programs emphasize muscle development over pure size. However, many kids get the formula wrong and simply bulk up with protein shakes, fast food and not enough movement.

While we don’t typically think of athletes struggling with weight issues, they face the same battle as the general public in making the right choices and understanding their body’s unique metabolism.  Recent research also shows that keeping an athlete’s weight under control can reduce injuries.  Oregon State nutritionist Melinda Manore recommends a “low energy dense” diet for athletes and some tips on managing their nutrition with their training.

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Positive Self Talk Can Boost Your Athletic Endurance

It has become a tradition in football for players to hold up four fingers at the start of the 4th quarter, signifying that they need to dig deep and finish strong.  Even if their legs are dead and they’re ready to quit, they convince themselves to compete for one more quarter.  This type of self-talk motivation is used by many athletes but now its effectiveness has been supported by new research from the University of Kent.

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8 Coaching Keys From Sir Alex Ferguson

The Mount Rushmore of coaching legends finally has its fourth member – Sir Alex Ferguson.  Alongside, say, Vince Lombardi, John Wooden and Scotty Bowman, Ferguson will be remembered as one of the most successful managers of all-time and certainly at the top of the football/soccer ranks.  With his slightly surprising retirement earlier this year, he left behind a Manchester United club that is now valued at $2.3 billion thanks in large part to the 13 English league titles and 25 additional titles won in his 26 years at Old Trafford.

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Balancing The Running Back's Brain

One of Associate Head Coach Burton Burns’ favorite drills for his University of Alabama running backs has them hopping over pads with both feet, teaching his players balance and more importantly how to recover from a stumble. 

One of his many star students was Trent Richardson, who liked the drill. “Even my freshman year when we were against North Texas and I had a long run and I could feel it near the end, someone just hit my feet,” Richardson told AL.com. “We get our feet up, it's better for us to keep our balance.” 

As you watch the video of the drill below, notice the stumbles after the second or third hurdle. Their brain engages in some fast calculations to sense the pending fall and sends signals out to the limbs to adjust for the unexpected body position. How exactly our brain senses a balance problem and how quickly we can adjust are the questions of two new research studies at McGill University and the University of Michigan.

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How Neuroplasticity Helped Get The Red Sox Into The World Series

Its the stuff every young baseball player dreams of - down by a run in the bottom of the 7th inning with the bases loaded in game 6 of the American League Championship Series.  With a chance to become a legend, Red Sox outfielder Shane Victorino tried to focus at the plate.  "I was just trying to tie the game," Victorino told ESPN. "I wasn't thinking grand slam, hit it out of the park, any of that. I was just trying to put the ball in play, to give us another chance."

Instead, he launched an 0-2 pitch from right-handed pitcher Jose Veras over the Green Monster in left field for a grand slam, giving the Sox a 5-2 lead over the Detroit Tigers.  The lead would hold up sending Boston to the World Series against the St. Louis Cardinals

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Geometry Teachers Make Great Football Coaches

For every youth football coach trying to teach angle of pursuit to defensive players, watching the DeAngelo Williams highlight reel from last season’s Panthers-Saints game demonstrates the right way and the wrong way of chasing down a ball-carrier.

Midway through the second quarter, Williams breaks through the Saints line heading for the end zone 65 yards away. Roman Harper, the Saints’ veteran strong safety, takes the proper angle and is able to push Williams out of bounds at the 1-yard line.

In the third quarter, Williams gets his revenge when he takes the ball 54 yards up the middle for a touchdown, leaving the Saints secondary chasing behind. Three defenders underestimated Williams’ speed, and before they could adjust their angles, it was too late.

While it may come as a shock to young players, there is real-life geometry happening in these types of plays, and now researchers from Ohio State University have found that our brains can solve these pursuit puzzles using not only our vision but also our hearing.

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What Happens When Johnny Manziel Sleeps Late

Last month, Johnny Manziel, Heisman Trophy winning quarterback at Texas A&M, made news when he was asked to leave the Manning Passing Academy after he missed a morning meeting and practice.  In his role as a coach/counselor to the future QBs at the camp, he was helping teach the fundamental movements and technique of the position.  

His reason for his absence? “I just overslept”. While some in the media have suggeste other reasons for his “tiredness”, new research reveals that all that sleep may have actually helped him improve his own motor skills for the new season.

Researchers have known for awhile that we all need sleep, not only for rejuvenation, but also to help us consolidate and organize new information and allow the day’s learning to solidify in our brains.  This is especially true for motor tasks, including everything from playing a complicated piano piece to riding a bike to throwing a tight spiral twenty yards down field.

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Baseball Pitchers Dominate With Length

Justin Verlander
“You can’t coach height.”  While that scouting advice is usually heard around high school and college basketball courts, it applies equally well to pitching prospects in baseball. 

The trend towards taller, dominating pitchers has been rising for years.  A quick check of this season’s MLB stats shows the average height of the top 10 pitchers with the most strikeouts this season is 6’ 5” compared to the average height of all MLB players of 6’ 1”. 

In fact, the height of pro pitchers has been on the rise for the last 110 years and they're throwing harder. In the 2009 MLB season, all but two of the fastest 20 pitches thrown came from pitchers 6’ 2” or above. It makes intuitive sense that with greater height usually comes a faster pitch, but now a mechanical engineering professor at Duke has helped to explain why.

Tall pitchers are not alone in their domination of a sport.  World record sprinters have gained an average of 6.4 inches in height since 1900, while champion swimmers have shot up 4.5 inches, compared to the mere mortal average height gain of 1.9 inches.  During the same time, about 7/10 of a second has been shaved off of the 100-meter sprint world record time while over 14 seconds have come off the 100-meter swim record.  Even in golf, the top 10 players in driving distance in 2010 were, on average, 2.5 inches taller than the bottom ten.

What do all of these athletes have in common?  According to Adrian Bejan of Duke University, it is the “falling forward” motion of their athletic task.  The taller the athlete, the more force they can put behind either themselves or an object that they want to propel forward.  It is what Bejan calls the “constructal law” theory of sports, which he describes in this recent Ted Talk.

His latest research is reported in the current online edition of the International Journal of Design & Nature and Ecodynamics.

"Our analysis shows that the constructal-law theory of sports evolution predicts and unites not only speed running and speed swimming, but also the sports where speed is needed for throwing a mass or ball," Bejan said.

Pitching anglesTrebuchet

He compares the pitching motion with that of a trebuchet machine, (ala Science Channel’s Punkin Chunkin).  "According to the constructal law predictions, the larger and taller machine, like a medieval trebuchet, is capable of hurling a large mass farther and faster," Bejan said. “In the case of the human thrower, the height of the mechanism is the height of the ball that is accelerated overhead. This height scales with the size of the athlete, in this case, the shoulder height plus the arm length. The other players on the baseball field do not have to throw a ball as fast, so they tend to be shorter than pitchers, but they too evolve toward more height over time. For pitchers, in particular, height means speed.”

Of course, there are always exceptions to the rule.  Two-time Cy Young award winner Tim Lincecum, all of 5’ 11”, pitched a no-hitter this month.  Still, scouts and manager have learned over the years that taller is better, even if they have no idea what the constructal law says.

Be sure to follow @AxonSports and check out the Axon hitting app for iPad.

Muscle Memory Is Real And Can Help Your Game

You’ll hear the same thing over and over on high school and college football fields this month. “We just have to get our reps in.” “Time to knock the rust off and find our rhythm.” “Its all about timing and getting everyone in sync.”  
The common theme for players is trying to increase the efficiency of their thinking and their movements, better known as muscle memory.  By repeating the same motions and plays, practice may not become perfect but it certainly will improve.  Now, neuroscientists at the University of Pittsburgh School of Medicine have found that brains actually do become more energy efficient after numerous repetitions by decreasing the electrical activity between neurons.

Unlike its meaning in strength conditioning, muscle memory in skill development is also referred to as motor learning.  By stringing together an entire series of micro movements, whether it be a QB throwing a back shoulder pass or a linebacker executing an open field tackle, the recipe for the whole process becomes a procedural memory stored, obviously, in the brain not the muscles.  Located in the brain’s primary motor cortex, this neural network has been shown to decrease in activity as athletes go through the learning process as it finds the most economical connection pathways between neurons.
Neuroscientist Peter Strick, professor in the Department of Neurobiology at the Pitt School of Medicine, wondered if this decline in metabolic activity coincided with a decrease in the number of neurons firing.  He and his research team trained monkeys to do two tasks, one where they had to learn to anticipate a point appearing on a screen and one where they had to learn a short sequence of movements without any visual cues.  The second task simulated a motor learning experience where they had to string together a complete movement, like throwing a bullet into double coverage.
They found that the level of neuron firing was the same with both tasks but the metabolic or connection activity required was lower for the internally remembered task.  The research was just published in Nature Neuroscience.
“This tells us that practicing a skilled movement and the development of expertise leads to more efficient generation of neuron activity in the primary motor cortex to produce the movement. The increase in efficiency could be created by a number of factors such as more effective synapses, greater synchrony in inputs and more finely tuned inputs,” Dr. Strick noted. “What is really important is that our results indicate that practice changes the primary motor cortex so that it can become an important substrate for the storage of motor skills. Thus, the motor cortex is adaptable, or plastic.”
So, those endless drills and repetition really do physically change the structure of the brain.  Getting football movements installed as muscle memory lets the player perform them automatically without thinking about each movement component.
To continue with mental reps even after the two a day practices end, many young QBs are turning to cognitive training tools, like the Axon QB app for iPad.  The sooner the better before the season starts.

Go With The Flow - Part 2

Number-loving sports like baseball and basketball have plenty of individual statistics to measure a team and a player’s performance over time.  Not so for the “beautiful game.”  "In soccer there are relatively few big things that can be counted," said Luis Amaral, professor of chemical and biological engineering at Northwestern University. "You can count how many goals someone scores, but if a player scores two goals in a match, that's amazing. You can really only divide two or three goals or two or three assists among, potentially, eleven players. Most of the players will have nothing to quantify their performance at the end of the match."

Amaral and his colleagues at NU’s McCormick School of Engineering and Applied Science knew that there was plenty of information buried in the data about player movement, passing and timing, if only a logical model could be created to explain the chaos on the field.  Just as we learned in Part 1 about network models in basketball, Amaral, a lifelong soccer fan from Portugal, used his knowledge of social network analysis in biological systems to create a model of soccer ball strategy.

"You can define a network in which the elements of the network are your players," Amaral said. "Then you have connections between the players if they make passes from one to another. Also, because their goal is to score, you can include another element in this network, which is the goal."

Diagram 1 (click to zoom)
Using detailed player event data from the Euro 2008 international tournament, the researchers mapped every pass for each of the 31 matches.  By comparing the passing route that a team used to end in a shot on goal, certain patterns emerge that highlight the players that are most often involved in the build-up to a goal.  Not only could Amaral predict success for the team but also assign value ratings to each player.

"We looked at the way in which the ball can travel and finish on a shot," said Amaral, who also is a member of the Northwestern Institute on Complex Systems (NICO). "The more ways a team has for a ball to travel and finish on a shot, the better that team is. And, the more times the ball goes through a given player to finish in a shot, the better that player performed."

In Diagram 1, take a look at the bold network connections representing the most used passing paths between Euro 2008 players (denoted by their uniform numbers).  Just as in the basketball network paths, finding passing patterns that end with shots on goals can give clues to the most efficient ball movement.

Diagram 2 (click to zoom)
From their initial research paper that appeared in PLOS One, Amaral created a company, Chimu Solutions, dedicated to refining their network model by adding data from thousands of top flight games across multiple leagues.  Their goal is to produce a single metric that measures a player’s value as defined by their contribution to shots on goal.

In fact, by the time Euro 2012 came around last year, the model was able to attach an average player rating for each team.  Diagram 2 shows the table for Spain’s champion roster which most pundits would agree ranks the players in a logical order from top to bottom.

This focus on data analytics is the new standard for sports and those teams that accept the challenge to find athlete monitoring tools that reveal these trends and patterns will have the competitive advantage over those teams that do not.  Indeed, with apologies to Sun, the network is now the sport.

The Secret Ingredient to Sports Success: An Interview With David Epstein On The Sports Gene

The Sports Gene
Maybe its not all about practice.  Since the youth sports world fell in love with the romantic notion that 10,000 hours of structured practice is the magic ingredient to world-class mastery in just about any field, especially sports, we've forgotten or ignored that our genetic endowment may still have something to do with the outcome.  Just watch this video of a young Lionel Messi, who was probably still working towards his 10,000 hour total at the time.  He clearly has something else, something that was already there at age 5 and something that the other kids didn't have.

David Epstein, senior writer at Sports Illustrated, has been on a search for that extra something.  In his new book, The Sports Gene, Epstein launched himself directly into the nature vs. nurture, genes vs. practice and natural vs. self-made debates about athletic greatness.

I recently had a chance to chat with David about his book and found out that there is a complex, misunderstood mixture of variables in the magic formula:

David, congratulations on your new book!  One of my all-time favorite SI articles of yours is the 2010 piece “Sports Genes”.  At the time, you opened many eyes on the influence of genetics on athletic performance.  Is it safe to say that the science and our understanding of it has come a long way in the last three years?

David Epstein
David Epstein: I appreciate that! I think it safe to say that the science has come a very long way in the last three years. At the same time, the studies of genes related to sports performance is still hampered by certain problems. A decade ago, scientists hoped that genetics might be simple; that single traits, like, say, height, might be attributable to a single gene or a small number of genes. But now it’s clear that most traits—and certainly those as complex as athleticism—can involve large numbers of genes, each with a small effect. That can make things particularly tricky for studying elite athletes, because there aren’t very many elite athletes in the world, so studies are often too small to detect the effects of relevant genes. 

Still, using certain innovative methods, like those described in chapter five of my book, scientists are pinpointing some of the genetic influences on an individual’s ability to adapt to a training regimen. And that now looks to be a key component of “talent,” not simply some skill that manifests prior to training, but the very biological setup that makes one athlete better at adapting to a particular training plan. In recent years, both with respect to endurance and strength training, the science has increasingly shown that genes mediate the ability to “respond” to training, and it appears that work will continue to be bolstered. People often say “I’m not very talented in this or that area,” but the genetic work is increasingly showing that we can’t necessarily know if we have talent before we try training.

In the book, you tell the story of Dan McLaughlin, an amateur golfer, who has put his life on hold while he accumulates the infamous 10,000 hours of deliberate practice towards his goal of playing on the PGA Tour.  You document how genetics can offer exclusive physical advantages for sprinters, swimmers or even baseball batters.  However, in sports like golf, dominated more by mental skill than brute physical abilities, does genetics still play a role or is it all about practice?

DE: That’s a great question. For starters, there is less scientific evidence regarding genes that influence skill in very technical sports, like golf, but that is partly because those skills are difficult to study. We have enough trouble finding genes for simple traits, like height, and physiologists don’t even understand everything that makes a great golfer, much less the genes that undergird the particular physiological traits. As Sir Roger Bannister once said: “The human body is centuries in advance of the physiologist, and can perform an integration of heart, lungs, and muscles which is too complex for the scientist to analyze.” No where is this complexity more difficult for scientists to link to specific traits than in sports based on specialized skills. So one reason there’s more known about genes—or innate physiological traits—that influence the more raw athletic skills is simply because scientists more often choose to study athletes engaged in more “raw” sports. The idea is it will be easier to find the biological influences. 

That said, there are mounds of studies that show that when individuals practice motor skills, differences in the rate of progress become apparent in all but extremely simple skills. In some studies, the more complex the skill, the greater the differences between individuals will become as they practice. In other words, there are differences in “trainability.” Which genes are at play here is largely a mystery, but that doesn’t mean they don’t exist. Remember, we don’t know many of the genes that influence height, and yet from studies of families and large populations, we know quite well that differences in the heights of adults in any given population are generally at least 80% inherited. 

To use an example relevant to some of the writing in my book, left-handed people are highly overrepresented among chess masters. We don’t know what the “left handed genes” are, but we know there is a genetic component. Men are about twice as likely to be left handed as women, for example. So it would seem as if certain genes for left-handedness, which of course means brains that influence motor control in the brain, interact with the learning of a skill like chess. As a related aside, Belgian scientist Debbie Van Beisen has shown that competitive table tennis players with mental handicaps fail to learn the anticipatory cues required to return shots as quickly as similarly experienced table tennis players who do not have mental handicaps.

Additionally (and I actually had to trim much of this from the book) there is some interesting work implicating specific genes in motor skill learning. Here’s a snippet I had to cut from the book, as my first draft was WAY over printable length:

“The level of BDNF is elevated in the brain’s motor cortex when people learn a motor skill, and BDNF is one of the neural signals that coordinates the reorganization of the brain when skills are learned. And a 2006 study found that, when people practiced motor skills with their right hand—like putting small pegs in holes as quickly as possible—the area of the activated brain representing the right hand, the neural “motor map,” increased in size with practice only in those people who did not have a met version of the BDNF gene. All of the subjects started with similar sized motor maps, but only the non-met carriers experienced a change with practice.

And in 2010 a group of scientists led by neurologist Steven C. Cramer set out explicitly to test whether the BDNF gene impacts the kind of memory involved in motor skill learning, and their findings suggest that it does. In that study, people drove a car along a digital track 15 separate times in one day. All of the drivers improved as they learned the course, but the met carriers did not improve as much. And when all the drivers were asked back four days hence and made to drive the course once more, the met carriers made more mistakes. When scientists used fMRI to look at the drivers’ brains as they practiced simple motor skills, they found different patterns of activation in the people who had a met version of the BDNF gene.”

Recently, Atlas Sports Genetics has caused a stir in youth sports by offering parents a test for their kids to look for a certain variation of the ACTN3 gene, otherwise known as the “speed gene.”  You mention that this test is only useful to know if your youngster is the next Usain Bolt or Carmelita Jeter, something parents probably already know.  What’s next on the horizon for genetic testing for young athletes?  Are there genes or combinations of genes for traits like reaction time, balance or coordination?

DE: First, just to clarify, the ACTN3 gene is only really useful for telling you that your youngster will not be the next Bolt—if they don’t have the so-called “right” version for sprinting. But it doesn’t even do a very specific job of that, since most people have the “right” version. And, let’s face it, you can take your kid to the playground and have him race the other kids and you’ll get a better idea of his chances of becoming the next Bolt than you would with a genetic test. 

As far as the next frontier of genetic testing for young athletes, I think it will undoubtedly be “injury genes,” before performance genes, and we’re already actually starting to see a bit of that. I spent some time with Brandon Colby, an L.A.-based physician who treats retired NFL players, and—at the behest of parents—he already tests some teenagers for their version of the ApoE gene. As I write in the book, one version of this gene makes an individual more susceptible to brain damage from concussions or the kind of hits to the head to occur on every football play. There are other gene variants that put some athletes at risk of dropping dead on the field, and others that appear to increase the risk of an injury like a ruptured Achilles tendon or torn ACL. 

As I discuss in the book, some of these genes are actually now being used for practical purposes, and I think that we’ll see that increase. As for reaction time, I don’t think we’ll see much there, given that, as I explain in the first chapter, the simple reaction times—the time it takes one to hit a button in response to a light—of elite athletes are no different than those of teachers, lawyers, or college kids. The skills that allow hitters to intercept 100 mph fastballs are learned perceptual skills, not innate reaction abilities. And even if simple reaction time was important, it would be way easier to measure directly—by giving someone a reaction time test—than indirectly by looking at genes.

Here at Sports Are 80 Percent Mental, we talk a lot about the brain’s role in playing sports.  From vision to perception to decision making to emotions, the brain plays a critical role in sports success.  What have we learned about neurogenetics that can influence an athlete’s performance from a cognitive perspective?

DE: One of the most surprising things I learned in my reporting was that scientists know quite well that not only does the dopamine system in the brain—which is involved in the sense of pleasure and reward—respond to physical activity, but it can also drive physical activity. 

One of the scientists I quote in the book suggests that this may be why very active children who take Ritalin, which alters dopamine levels, suddenly have less drive to move around. That’s precisely what he sees when he gives Ritalin to the rodents he breeds for high voluntary running, anyway. And it appears that different versions of genes involved in the dopamine system influence the drive to be active. (Interestingly, native populations that are nomadic and that migrate long distances tend to have a higher prevalence of a particular dopamine receptor gene; the same one that predisposes people to ADHD. I discuss in the book the possible link.) 

One of my takeaways from the research I did for the book was that some traits we think are innate, like the bullet-fast reactions of a Major League hitter, are not, and others that we often portray as entirely voluntary—like the compulsive drive to train—can have important genetic components. Additionally, the section of the book that deals with pain in sports, and discusses the genetics of pain, gets into the fact that the circuitry of pain is shared with circuitry of emotion. (Morphine, after all, doesn’t so much dull pain as make one less upset about it.) And the first genes that are emerging that might allow athletes to deal calmly with pain on the field—like, perhaps, the COMT “warrior/worrier” gene—are genes involved in the metabolism of neurotransmitters in the brain. And, of course, as I mentioned in my longwinded answer to the second question, there are genes that appear to influence motor learning.

David, you were a competitive runner in your college days at Columbia and I understand you still run quite a bit.  Has the research for this book given you any insight or tips that you or other weekend athletes can use?

DE: Indeed I was. I was an 800-meter runner. I still love running, but I’d call what I do now “jogging”! But working on this book gave me certain broad insights that I apply to my own training. In 2007, the prestigious peer-reviewed journal Science listed “human genetic variation” as the breakthrough of the year; the revelation of how truly different we are from one another. And, as J.M. Tanner, the eminent growth expert (and world class hurdler) once put it: “Everyone has a different genotype. Therefore, for optimal development…everyone should have a different environment.” No two people respond to a Tylenol the same way because of their distinct biology, and no two people respond to the medicine of exercise the same way either. 

When I was in college, I had better endurance—at all distances—on a training plan of 35 miles per week that included carefully selected intervals, than I had previously on 85 miles per week of cookie-cutter distance training. If you aren’t taking a scientific approach to your training—and this doesn’t mean cutting edge science, but just paying attention to what you best respond to—then you aren’t getting everything out of yourself. To use track, because it’s just an easy example, in every training group from high school to the pros, you have groups of runners doing identical workouts, and yet never crossing the line at the same time in a race. 

Genetic science is showing us that the most important kind of “talent” isn’t some physical trait that preexists training, but rather that ability to physically adapt to training. And studies I describe in the book make it quite clear that particular genes mediate an individual’s ability to benefit from training such that two people can have drastically different results from the same training plan. 

So if you feel like, for some reason, you aren’t getting results on par with your training partner, you might be right. And the problem might be you, in the very deepest sense. So don’t be afraid to try something different. Several of the athletes I write about in the book weren’t afraid to jump into entirely new activities or training plans, and some came out world champions.

Thank you, David and good luck with the book!

Go With The Flow - Part 1

Back in the mid-90s, Sun Microsystems, the creator of the Java programming language, coined the marketing slogan “The Network Is The Computer.”  They were describing the Internet of twenty years ago, which obviously has grown into every corner of our lives today, as being as important if not more so than individual computers.  The idea that individual nodes of a network can’t succeed on their own but only through communications and coordination sounds a lot like a pre-game pep talk in the locker room about teamwork and passing.
For continuous play sports like basketball and soccer, the optimal flow of the ball across a connected network of players is critical to winning.  It was only a matter of time before network scientists, who were also sports fans, offered their advice on how these in-game connections can be measured and optimized.
In this two-part series, we’ll first take a look at research done at Arizona State University (ASU) on basketball, then, in the next article, an analysis of soccer networking and player metrics created by an engineering professor at Northwestern University.  In fact, we'll see that "the network is the sport".

In traditional basketball offenses, transitions up the court begin with an inbound or outlet pass to the point guard who then becomes the hub of ball movement until his team eventually attempts a shot.  But is that the most ideal strategy from a network flow standpoint? Even though a team’s point guard may be very skilled, would a less predictable ball movement be harder to defend?
Jennifer Fewell, a professor in ASU’s School of Life Sciences in the College of Liberal Arts and Sciences and Dieter Armbruster, math professor at ASU, watched and diagrammed every offensive series from the first round of the 2010 NBA playoffs to build a network model for each team.  Knowing the eventual outcome of the first round and the entire postseason, they were able to correlate network movement with wins and losses.  ”We were able to come up with a hypothesis about strategy and then apply network analysis to that,” said Fewell.
Diagram 1 (click to zoom)
First, take a look at Diagram 1, which represents the cumulative network model for all 16 first round playoff teams.  The width of the arrows indicates the number of times that this network path was taken across the the hundreds of plays analyzed by the researchers. The wider the arrow, the more often those two players connected with a pass.  As you can see, starting an inbound play with the point guard (PG) is very common while rebounds typically start with the big men near the glass, centers (CN), power forwards (PF) and small forwards (SF).
Diagram 2 (click to zoom)
However, Diagram 2 shows the network patterns for four teams with increasing success in the playoffs, the Bulls who lost in the first round, the Cavaliers in the 2nd round, and the Celtics who lost to the Lakers in the NBA Finals.  The Bulls relied heavily on their point guard, Derrick Rose, while the champion Lakers used a more distributed model spreading the ball around in their famous “Triangle Offense.”
In fact, this more unpredictable pattern of the Lakers and the Celtics, which Fewell labeled a team’s entropy, was directly related to higher winning percentages across the playoff teams.

“What that basically says is that the most successful teams are the ones that use a less predictable, more distributed offense and that connect their players more,” said Fewell. “Those were the teams that had actually hired more elite players and allowed them to work together.”
Their research was published in PLOS One.
Network models like these also help coaches evaluate players as part of a team in a way that pure stats such as points, assists and rebounds may not capture.  This is especially true in soccer, where scoring is much more rare than basketball.  In our next article, we’ll take a look at the work of Professor Luis Amaral of Northwestern University and a new soccer stat that he calls, “flow centrality.”

Better Late Than Never For Young Athletes

We’ve all seen the “early bloomers” in youth sports.  Those kids who just seem more physically gifted at an young age allowing them to dominate their leagues.  They fill up the rosters of the best teams and can discourage other players from sticking with their sport until their development takes off.  Now, new research from Indiana University shows that it can be worth the wait and investment to stay focused with good coaching and perseverance.

In his book “Outliers”, author Malcolm Gladwell pointed out a little known anomaly in youth sports that is known as the “Relative-Age Effect.” He reviewed the research of Canadian psychologist Roger Barnsley, who found that a disproportionate percentage of elite hockey players had birthdays in the first quarter of each year.  Indeed, 32% of the NHL players studied had birthdays from January to March, while 16% were born between October and December.  Gladwell included studies from other sports, including baseball, football and soccer with the same uneven pattern.

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Remind Your Brain That You Can Hit This Pitch

Baseball hitting strategy is usually taught as a logical, almost statistical thought process. Depending on the score of the game, runners on base, the number of outs and the current count, the batter can make an educated guess as to what pitch will be thrown next.  This cues the visual system to expect a certain release point, speed and location of the ball.  

But what about the emotions of the game?  Do the possible positive and negative outcomes affect a hitter’s ability to see the right pitch?  According to new research, the reward that you associate with a visual stimuli can help improve your ability to quickly identify that object.

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Medical Moneyball - The Rise Of Injury Analytics

Robert Griffin III
What if?  It’s a question that many of the world’s top teams asked in the last year when faced with ill-timed injuries to key players.  What if Derrick Rose of the Chicago Bulls, Robert Griffin III of the Redskins, Derek Jeter of the Yankees or Lionel Messi of Barcelona could have avoided their season ending injuries?  

Some are just the result of unlucky, violent contact but others have their origin from a combination of fatigue and overuse.  What if athletic trainers and team physicians could find early clues and signals that an athlete was at risk of breaking down?  Now, with the use of data analytics, that crystal ball may have finally arrived.

Stan Conte, VP of medical services for the Los Angeles Dodgers, 
declared last year, "in a post-Moneyball world, injury risk assessment is the final frontier."  At this year’s Sloan Sports Analytics Conference, he presented some surprising data to reinforce the rising toll of injuries;  just over 50% of all starting pitchers in the MLB had some type of injury during last season, lasting an average of 65 days on the disabled list.  Across all MLB players in 2012, the salaries of injured players plus the players that replaced them cost their teams almost $600 million.

Even at the Olympics, the world’s premier athletic showcase, the impact of injuries is significant.  Big names like Paula Radcliffe, Asafa Powell, and Rafael Nadal could not complete their gold medal quest.  Lars Engebretsen, a physician and professor at the University of Oslo and chief physician of the Norwegian Olympic team, has been tracking injuries and illness at the Games for over a decade.  His latest report, released this month on the 2012 London Olympics, recorded 1,361 injuries and 758 illnesses among the 10,568 athletes, which equates to injury and illness rates of 11% and 7%, respectively.  Unfortunately, these percentages are similar with the last two Summer Olympics in Beijing and Athens, highlighting the lack of progress in reducing lost time in competition.

In this Scientific American graphic, Engebretsen’s data from the 2008 Summer Olympics and the 2010 Winter Olympics shows that overuse caused 22% of summer athletes' injuries while 54% of winter athletes were injured in training.

Like the Dodgers, teams across the globe are beginning to search for answers.  As Big Data creeps into all aspects of athlete development, injury analytics is the new secret weapon.  That is what pushed the Leicester Tigers rugby union club to dig into the details.  Leicester, 9-time English champions, faces the challenge of tight budgets that requires keeping the best players on the field.

According to Andy Shelton, Leicester’s head of sports science, strength and conditioning, any competitive edge is worth the investment.  "It gets more competitive every year and our focus must be on helping our players stay injury-free for longer," he told the BBC. "When we have our key players available against the top European sides, we can compete and we will win, so the question is how do we keep key players on the pitch?"

Metrifit Predictive Analytics
Factoring in variables like fatigue, stress, sleep and training intensity into a predictive algorithm can yield what may have been hidden trends and combinations that cause injuries. 

“Similarly we also collect data on previous injuries that they had and what they are doing in the gym, ­basically everything they do from when they walk in the door of the club in the morning and leave in the evening is collected,” Shelton added. “The aim is to be able to affect a player’s lifestyle through the week. For example, if they recorded a very good night’s sleep, then their risk of injury could go down from ‘predicted injured’ to ‘not-predicted injured.’”

Some coaches and trainers still feel that using predictive analytics to create an injury model based on volumes of underlying data seems a little over the top.  But if your job is to develop healthy, productive athletes that win, then any tool that provides an edge is worth a look.

"Traditional baseball types tell me to just give up, that this is a waste of time because injuries are mostly bad luck,” Conte commented. "Twenty-five years ago no one listened to Bill James either."

Andy Shelton agrees, "There is no point in collecting stats unless you can know what to do with it. But by predicting things before they happen is where we can make gains, and considerably enhance performance."

Preventing Burnout In Young Athletes

young athlete burnout
For many elite team coaches, the greater challenge in developing top young athletes is not improving the ones on your team, but rather finding the talented kids that got away from the sport.  Keeping the next Lionel Messi or Michael Phelps involved and motivated from age 7 to 17 is becoming more difficult.  While over 35 million kids between 4 and 14 play organized sports in the U.S., over 70% will drop out by age 13.  According to new research, that drastically reduced talent pool may be caused by the athlete’s own psychological profile and how a coach manages it.

According to a 2004 study by Michigan State’s Institute for the Study of Youth Sport, here are the top ten reasons why boys and girls quit organized sports:
  1. I was no longer interested.
  2. It was no longer fun.
  3. The sport took too much time
  4. The coach played favourites.
  5. The coach was a poor teacher.
  6. I was tired of playing.
  7. There was too much emphasis on winning.
  8. I wanted to participate in other non-sport activity.
  9. I needed more time to study.
  10. There was too much pressure.
  1. I was no longer interested.
  2. It was no longer fun.
  3. I needed more time to study.
  4. There was too much pressure
  5. The coach was a poor teacher.
  6. I wanted to participate in other non-sport activities.
  7. The sport took too much time.
  8. The coach played favourites.
  9. I was tired of playing.
  10. Games and practices were scheduled when I could not attend.
Comparing the lists, when a young athlete loses interest and does not have fun, (partly because the coach applied too much pressure or they were a poor teacher), it may be due to their internal mindset based on an educational psychology concept known as the Achievement Goal Theory (AGT).

Psychologists Carol Dweck and Thomas Nicholls, while they worked together at the University of Michigan, both studied students who failed to learn and their research resulted in parallel tracks, just with different terminology.  For Dweck, she defined two styles of learning motivation as Mastery and Performance.  Take for example, a young soccer player that spends hours in the backyard learning to juggle a ball.  For a player with a Performance mindset, he is practicing because of a desire to be the best juggler on the team or maybe because he is embarrassed by his lack of skill compared to his teammates.

Carol Dweck Mindset DiagramOn the opposite end, a player with a Mastery mindset is motivated to learn simply by the challenge of the skill without any competitive instincts.  Nicholls uses the terms Task to compare with Dweck's Mastery and substitutes Ego instead of Performance.  Both Dweck and Nicholls agree that the player’s perceived ability and competence also affects their motivation to keep trying to learn the new skill.  Here’s a great video overview of the concepts by Professor Jonathan Hilpert of Georgia Southern University.

In fact, in a study this year of 167 junior club soccer players in England, Andrew Hill, sports scientist at the University of Leeds, found that a quarter of the boys experienced symptoms of burnout.
"What we see among the athletes showing symptoms of burnout is emotional and physical exhaustion, a sense that they are not achieving and a sense of devaluation of the sport. Even though they might originally enjoy their sport and be emotionally invested in it, they eventually become disaffected. Participation can be very stressful," Dr. Hill said.

However, the results showed that those players who admitted being more afraid of making mistakes in front of others (a Performance/Ego mindset) were also much more likely to suffer from burnout versus those players that were driven by their own high standards (a Mastery/Task mindset).

"Perfectionism can be a potent energising force but can also carry significant costs for athletes when things don't go well,” Dr. Hill commented. “Perfectionists are stuck in a self-defeating cycle. They are overly dependent on personal accomplishment as a means of establishing a sense of self-esteem but are always dissatisfied with their efforts. Even success can be problematic because they simply become more demanding until they inevitably experience failure.”

His research appears in the Journal of Sport and Exercise Psychology.

A coach can have a significant impact on motivating learning by the type of environment they create, one that rewards players for self-improvement alone or one that rewards improvement compared to others.  Last year, French researchers surveyed 309 young, elite handball players about four things, their perception of their coach’s motivational environment, their own perceived competence, their type of learning motivation and any symptoms of burnout.

They concluded that “young talented athletes perceiving an ego-involving climate had a higher risk of experiencing burnout symptoms at the season’s end. In contrast, players perceiving a high task-involving climate had lower burnout scores when the season concluded.”

Once again the coach-athlete relationship becomes critical to development of elite potential and performances.  The more training data that can be captured and analyzed, the better the subtle hints of burnout can be detected.

Using Rate Of Perceived Exertion As A Training Metric

Coaches invest hours devising training plans that will push their athletes just to the edge, but not over.  Overtraining leads to burnout and injuries but going easy won’t get the right results.  The challenge of walking this fine line is truly understanding the intensity and workload being placed on athletes, whether its real or perceived.  Objective, wearable technology has helped in the form of heart rate and GPS monitors, but can be expensive and doesn’t capture a true sense of the player’s experience.  As an alternative, sport scientists have recently found that a self-reported rate of perceived exertion (RPE) can accurately capture the workload experience.

Two sports that are learning to rely on RPE, soccer and swimming, represent two very different training styles.  Soccer coaches spend considerable time on team drills and scrimmages while interspersing physical fitness into the sessions.  Swimmers, while part of a team, primarily focus on individual times and skills.  Despite the differences, researchers have found plenty of evidence that the athlete’s opinion of their workout difficulty is valid and reliable.

Back in the 1960s, Gunnar Borg, psychology professor at Stockholm University, created the RPE scale, now respectively called the Borg scale.  The original version asked athletes to rate their level of exertion on a range of 6 to 20, with 6 being “very light” and 20 indicating “very difficult.”  While the 6-20 range may seem an odd choice, it actually has some logic.  Borg found that if the rating is multiplied by 10, there is a high correlation to the athlete’s heart rate at that moment (i.e. a rating of 12 typically corresponds with a HR of 120).

Borg also added a 10 point scale, known as the Category(C) Ratio(R) scale or CR-10.  This produces ratings of 1-10 and is used not only in sports training but also in clinical settings to estimate levels of pain.

Last year, Spanish and Italian researchers compared the workout assessments of 28 semi-pro soccer players.  For an objective measure, they captured heart rate history and tracked their distance travelled with GPS devices.  Then, after each training session, they asked the players for their RPE using the Borg CR-10 scale.  They found a very high correlation between the HR data, the distance travelled and the players’ RPE ratings.

“Being easy to perform and inexpensive compared with HR-based methods, sRPE should be regarded as a viable way to track internal load in training setup in soccer,” concluded David Casamichana, sport scientist at the University of the Basque Country and lead author of the study published in the Journal of Strength and Conditioning Research.

But what if young athletes report a “less than truthful” RPE in an attempt to either impress or fool their coach?  In the same way, what if the coach’s interpretation of a hard workout does not match with a player’s reaction to it?
Renato Barosso, of the School of Physical Education and Sport at the University of São Paulo, Brazil, gathered together 160 swimmers of different age-groups and different competitive swimming experience, and nine of their coaches.  Looking at their training plan for the day, the coaches were asked to rate the workout using the CR-10 RPE scale, prior to the session.  Then, 30 minutes after the training, the swimmers were asked for their RPE to see how well it matched the coaches’ estimates.  Athletes were divided into three age groups, 11-12, 13-14 and 15-16, while the workouts were classified as easy (RPE less than 3), moderate (3-5) or difficult (greater than 5).

As might be expected, the agreement between coach and swimmer was higher for older swimmers and lower for younger swimmers.  While the coach’s estimate of intensity was assumed correct, the researchers found that the swimmers aged 11-14 ratings differed across all three categories, easy-moderate-difficult.  The oldest swimmers only disagreed with their coaches at the difficult level.

So, while RPE can be trusted for an accurate estimate of training difficulty, it would benefit both athlete and coach to gather all available data in one online training system for comparison and analysis.  Being able to chart RPE over time against more objective measures like HR, repetitions or activities would enable better training plans.

How To Train The Batter's Brain To Reduce Strikeouts

It’s not getting any easier being a big league hitter.  Consider that in 2003, only three pitchers lit up the radar gun at 95 mph or more on at least 700 of their pitches, according to the Wall Street Journal’s Matthew Futterman.  Last season, 17 pitchers were able to bring that speed consistently.  In 2003, only Billy Wagner threw at least 25 pitches at or above 100 mph compared to seven pitchers last year.
Has the added heat affected the hitters? You bet.  Strikeouts in the MLB totalled 36,426 last season, an 18.3% increase over 2003.  To see the rise over the last 100 seasons, look at this interactive NY Times graphic.  "It's pretty simple," said Rick Peterson, director of pitching development for the Baltimore Orioles, in the WSJ article. "The harder you throw, the less time the batter has to swing and the harder it is to make contact.
Let’s crunch some numbers on the hitter’s dilemma.  At 100 mph, the ball will leave the pitcher’s hand and travel the 60’ 6” to the plate in under a half second (.412 to be exact).  For those facing a pitcher throwing “only” 80 mph, you get an additional 1/10 of a second.  Now, factor in that it takes 100 milliseconds for the image of the ball hitting your eyes to be delivered to and acknowledged by your brain.  Again at 100 mph, that lag means your brain is contemplating a ball’s location that has already travelled an additional 12.5 feet.
How then are players able to get around on a pitch at that speed, let alone make contact?  According to vision scientists at UC Berkeley, our brains make guesses.  Using the perceived speed and path of the ball actually seen, our visual cortex fast forwards it to a future location.  It is at that estimated point that we direct our muscles to make contact with the bat.
“For the first time, we can see this sophisticated prediction mechanism at work in the human brain,” said Gerrit Maus, postdoctoral psychology fellow and lead author of new research published this week in the journal, Neuron.
Maus and his fellow UC Berkeley researchers, Jason Fischer and David Whitney, were able to discover this prediction ability by actually fooling the brains of volunteers.  They asked six volunteers to watch a computer screen showing an optical illusion while their brains were being watched by an fMRI machine, which records and displays brain activity.
Called the “flash-drag effect”, the illusion (see video below) flashes stationary objects on the screen against a moving background.  The objects seem to move in the direction of the background motion, even though their location is fixed.  “The brain interprets the flashes as part of the moving background, and therefore engages its prediction mechanism to compensate for processing delays,” Maus said.

From the fMRI images, they observed activity in the V5 region of the visual cortex, pinpointing where this prediction model gets built in our brain.  “The image that hits the eye and then is processed by the brain is not in sync with the real world, but the brain is clever enough to compensate for that,” Maus said. “What we perceive doesn’t necessarily have that much to do with the real world, but it is what we need to know to interact with the real world.”
So, what can a hitter do to fine tune this predictive mechanism?  In a talk at last year’s Sloan Sports Analytics Conference, Peter Fadde, professor at Southern Illinois University, presented what he calls the “sixth tool”, aka pitch recognition.  By watching videos of a pitcher’s windup and release, but occluding the flight of the ball at different points in its path, a batter can exercise his or her visual cortex to make better models of ball flight and speed.

Strikeouts still matter at the next level.  Keith Hernandez, the former MVP and batting champ, told the WSJ, "Guys don't seem to care about striking out anymore, but when you strike out, you're not putting the ball in play, and when you don't do that, nothing can happen."