Soccer Robots Getting Smarter At RoboCup

Anyone who has ever bravely volunteered to coach a youth soccer team is familiar with the blank stares that ensue when trying to explain the offsides rule. The logic that combines moving players, the position of the ball and the timing of a pass is always a challenge for 10-year-old brains to grasp (let alone 40-year-old brains.) Imagine trying to teach this rule to an inanimate, soccer-playing robot, along with all of the other rules, movements and strategies of the game.

Now researchers have developed an automated method of robot training by observing and copying human behavior.

Why are scientists teaching robots to play soccer? The short-term motivation is to win the annual RoboCup competition, the "World Cup" of robotic development. International teams build real robots that go head to head with no human control during the game. This year's competition is in Graz, Austria in June.

Here's the final match from the 2008 RoboCup:



The long-term goal is to develop the underlying technologies to build more practical robots, including an offshoot called RoboCup Rescue that develops disaster search and rescue robotics.

In a study released in the March 2009 online edition of Expert Systems with Applications, titled "Programming Robosoccer agents by modeling human behavior", a team from Carlos III University of Madrid used a technique known as machine-learning to teach a software agent several low-level basic reactions to visual stimuli. "The objective of this research is to program a player, currently a virtual one, by observing the actions of a person playing in the simulated RoboCup league," said Ricardo Aler, lead author of the study.

In addition to actual robots, RoboCup also has a simulation software league that is more like a video game. In the study, human players were presented with simple game situations and were given a limited set of actions they could take. Their responses were recorded and used to program a "clone" agent with many if-then scenarios based on the human's behavior. By automating this learning process, the agent can build its own knowledge collection by observing many different game scenarios.

The team has seen early success at learning rudimentary actions like moving towards the ball and choosing when to shoot, but the goal is to advance to higher-level cognition, including the dreaded offsides rule. Implanting the physical robots with this knowledge set will give them a richer set of actions to choose from when they are exposed to visual stimuli from the playing field.

Previous attempts at machine learning relied on the robot/software to learn rules and reactions entirely on their own, similar to neural networks. Aler's team hopes to jump start the process by seeding the knowledge base with human players’ choices. While current video soccer games like FIFA 2009 already use a detailed simulation engine, transferring this to the physical world of robots is the key to future research.

RoboCup organizers are not shy about their ultimate tournament in the year 2050. According to their website, "By mid-21st century, a team of fully autonomous humanoid robot soccer players shall win the soccer game, comply with the official rules of the FIFA, against the winner of the most recent World Cup."

That's right; they plan on the robots beating the current, human World Cup champions. "It's like what happened with the Deep Blue computer when it managed to beat Kasparov at chess in 1997," says Aler.

Maybe they can also build a robot linesman who can always get the offsides call correct!

Rotate It Like Ronaldo?

"Rotate it like Ronaldo" just doesn't have the same ring to it as "Bend it like Beckham", but the curving free kick is still one of the most exciting plays in soccer/football. Starting with Rivelino in the 1970 World Cup and on to the specialists of today, more players know how to do it and understand the basic physics behind it, but very few can perfect it. But, when it does happen, by chance or skill, it is the highlight of the game.



But let's take a look at this from the other side, through the eyes of the goalkeeper. Obviously, its their job to anticipate where the free kick is going and get to the spot before the ball crosses the line. He sets up his wall to, hopefully, narrow the width of the target, but he knows some players are capable of bending the ball around or over the wall towards the near post. If you watch highlights of free kick goals, you often see keepers flat-footed, just watching the ball go into the top corner. Did they guess wrong and then were not able to react? Did they guess right but misjudged the flight trajectory of the ball. How much did the sidespin or "bend" affect their perception of the exact spot where the ball will cross the line? To get an idea of the effect of spin, here's a compilation of Beckham's best free kick goals (there's a 15 second intro, then the highlights) :







Researchers at Queen's University Belfast and the University of the Mediterranean in France tried to figure this out in this paper. They wanted to compare the abilities of expert field players and expert goalkeepers to accurately predict if a free kick would result in an on-target goal or off-target non-goal. First, a bit about why the ball "bends". We can thank what's called the "Magnus Force" named after the 19th-century German physicist Gustav Magnus. As seen in the diagram below, as the ball spins counter clockwise (for a right-footed player using his instep and kicking the ball on the right side), the air pressure on the left side of the ball is lower as the spin is in the same direction as the oncoming air flow. On the right side of the ball, the spin is in the opposite direction of the air flow, building higher pressure. The ball will follow the path of least resistance, or pressure, and "bend" or curve from right to left. The speed of the spin and the velocity of the shot will determine the amount of bend. For a clockwise spin, the ball bends from left to right.







The researchers showed the players three different types of simulated kicks, a kick bent to the right, a kick bent to the left and a kick with no spin at all. They showed the players these simulations with virtual reality headsets and computer controlled "kicks" and "balls" which they could vary in flight with different programming. The balls would disappear from view at distances of 10 and 12.5 meters from the goal. The reasoning is that this cutoff would correspond with the deadline for reaction time to make a save on the ball. In other words, if the keeper does not correctly guess the final trajectory and position of the ball by this point, he most likely will not be able to physically get to the ball and make the save.







The results showed that both the players and the keepers, (all 20 were expert players from elite clubs like AC Milan, Marseille, Bayer Leverkusen, Schalke 04), were able to correctly predict the result of the kicks with no spin added. However, as 600 RPM spin, either clockwise or counter-clockwise, was added to the ball, the players success declined significantly. Interestingly, the keepers did no better, statistically, then the field players. The researchers conclusion was that the players used the "current heading direction" of the ball to predict the final result, rather than factoring the future affect of the acceleration and change in trajectory caused by the spin.



Just as we saw in the Baseball Hitting post, our human perception skill in tracking flying objects, especially those that are spinning and changing direction, are not perfect. If we understand the physics of the spinning ball, we can better guess at its path, but the pitcher or the free kick taker doesn't usually offer this information beforehand!



Craig, C.M., Berton, E., Rao, G., Fernandez, L., Bootsma, R.J. (2006). Judging where a ball will go: the case of curved free kicks in football. Naturwissenschaften, 93(2), 97-101. DOI: 10.1007/s00114-005-0071-0

Baseball Brains - Hitting Into The World Series

Ted Williams, arguably the greatest baseball hitter of all-time, once said, "I think without question the hardest single thing to do in sport is to hit a baseball". Williams was the last major league player to hit .400 for an entire season and that was back in 1941, 67 years ago! In the 2008 Major League Baseball season that just ended, the league batting average for all players was .264, while the strikeout percentage was just under 20%. So, in ten average at-bats, a professional ballplayer, paid millions of dollars per year, gets a hit less than 3 times but fails to even put the ball in play 2 times. So, why is hitting a baseball so difficult? What visual, cognitive and motor skills do we need to make contact with an object moving at 70-100 mph?

In the second of three posts in the Baseball Brains series, we'll take a quick look at some of the theory behind this complicated skill. Once again, we turn to Professor Mike Stadler and his book "The Psychology of Baseball" for the answers.  First, here's the "Splendid Splinter" in action:

A key concept of pitching and hitting in baseball was summed up long ago by Hall of Fame pitcher Warren Spahn, when he said, “Hitting is timing. Pitching is upsetting timing.” To sync up the swing of the bat with the exact time and location of the ball's arrival is the challenge that each hitter faces. If the intersection is off by even tenths of a second, the ball will be missed. Just as pitchers need to manage their targeting, the hitter must master the same two dimensions, horizontal and vertical. The aim of the pitch will affect the horizontal dimension while the speed of the pitch will affect the vertical dimension. The hitter's job is to time the arrival of the pitch based on the estimated speed of the ball while determining where, horizontally, it will cross the plate. The shape of the bat helps the batter in the horizontal space as its length compensates for more error, right to left. However, the narrow 3-4" barrel does not cover alot of vertical ground, forcing the hitter to be more accurate judging the vertical height of a pitch than the horizontal location. So, if a pitcher can vary the speed of his pitches, the hitter will have a harder time judging the vertical distance that the ball will drop as it arrives, and swing either over the top or under the ball.

A common coach's tip to hitters is to "keep your eye on the ball" or "watch the ball hit the bat". As Stadler points out, doing both of these things is nearly impossible due to the concept known as "angular velocity". Imagine you are standing on the side of freeway with cars coming towards you. Off in the distance, you are able to watch the cars approaching your position with relative ease, as they seem to be moving at a slower speed. As the cars come closer and pass about a 45 degree angle and then zoom past your position, they seem to "speed up" and you have to turn your eyes/head quickly to watch them. While the car is going at a constant speed, its angular velocity increases making it difficult to track.

This same concept applies to the hitter. As the graphic above shows (click to enlarge), the first few feet that a baseball travels when it leaves a pitcher's hand is the most important to the hitter, as the ball can be tracked by the hitter's eyes. As the ball approaches past a 45 degree angle, it is more difficult to "keep your eye on the ball" as your eyes need to shift through many more degrees of movement. Research reported by Stadler shows that hitters cannot watch the entire flight of the ball, so they employ two tactics.

First, they might follow the path of the ball for 70-80% of its flight, but then their eyes can't keep up and they estimate or extrapolate the remaining path and make a guess as to where they need to swing to have the bat meet the ball. In this case, they don't actually "see" the bat hit the ball. Second, they might follow the initial flight of the ball, estimate its path, then shift their eyes to the anticipated point where the ball crosses the plate to, hopefully, see their bat hit the ball. This inability to see the entire flight of the ball to contact point is what gives the pitcher the opportunity to fool the batter with the speed of the pitch. If a hitter is thinking "fast ball", their brain will be biased towards completing the estimated path across the plate at a higher elevation and they will aim their swing there. If the pitcher actually throws a curve or change-up, the speed will be slower and the path of the ball will result in a lower elevation when it crosses the plate, thus fooling the hitter.

To demonstrate the effect of reaction time for the batter, FSN Sport Science compared hitting a 95 mph baseball at 60' 6" versus a 70 mph softball pitched from 43' away.  The reaction time for the hitter went from .395 seconds to .350 seconds, making it actually harder to hit.  That's not all that makes it difficult.  Take a look:

As in pitching, the eyes and brain determine much of the success for hitters. The same concepts apply to hitting any moving object in sports; tennis, hockey, soccer, etc. Over time, repeated practice may be the only way to achieve the type of reaction speed that is necessary, but even for athletes who have spent their whole lives swinging a bat, there seems to be human limitation to success. Tracking a moving object through space also applies to catching a ball, which we'll look at next time.

Baseball Brains - Pitching Into The World Series

With the MLB League Championship Series' beginning this week, Twenty-six teams are wondering what it takes to reach the "final four" of baseball which leads to the World Series. The Red Sox, Rays, Phillies and Dodgers understand its not just money and luck. Over 162 games, it usually comes down to the fundamentals of baseball: pitching, hitting and catching. That sounds simple enough. So, why can't everyone execute those skills consistently? Why do pitchers struggle with their control? Why do batters strike out? Why do fielders commit errors? It turns out Yogi Berra was right when he said, "Baseball is 90% mental, and the other half is physical." In this three part series, each skill will be broken down into its cognitive sub-tasks and you may be surprised at the complexity that such a simple game requires of our brains.

First up, pitching or even throwing a baseball seems effortless until the pressure is on and the aim goes awry. Pitching a 3" diameter baseball 60 feet, 6 inches over a target that is 8 inches wide requires an accuracy of 1/2 to 1 degree. Throwing it fast, with the pressure of a game situation makes this task one of the hardest in sports. In addition, a fielder throwing to another fielder from 40, 60 or 150 feet away, sometimes off balance or on the run, tests the brain-body connection for accuracy. So, how do we do it? And how can we learn to do it more consistently? In his book, The Psychology of Baseball , Mike Stadler, professor of psychology at the University of Missouri, addresses each of these questions.

There are two dimensions to think about when throwing an object at a target: vertical and horizontal. The vertical dimension is a function of the distance of the throw and the effect of gravity on the object. So the thrower's estimate of distance between himself and the target will determine the accuracy of the throw vertically. Basically, if the distance is underestimated, the required strength of the throw will be underestimated and will lose the battle with gravity, resulting in a throw that will be either too low or will bounce before reaching the target. An example of this is a fast ball which is thrown with more velocity, so will reach its target before gravity has a path-changing effect on it. On the other hand, a curve ball or change-up may seem to curve downward, partly because of the spin put on the ball affecting its aerodynamics, but also because these pitches are thrown with less force, allowing gravity to pull the ball down. In the horizontal dimension, the "right-left" accuracy is related to more to the "aim" of the throw and the ability of the thrower to adjust hand-eye coordination along with finger, arm, shoulder angles and the release of the ball to send the ball in the intended direction.

So, how do we improve accuracy in both dimensions? Prof. Stadler points out that research shows that skill in the vertical/distance estimating dimension is more genetically determined, while skill horizontally can be better improved with practice. Remember those spatial organization tests that we took that show a set of connected blocks in a certain shape and then show you four more sets of conected blocks? The question is which of the four sets could result from rotating the first set of blocks. Research has shown that athletes that are good at these spatial relations tests are also accurate throwers in the vertical dimension. Why? The thought is that those athletes are better able to judge the movement of objects through space and can better estimate distance in 3D space. Pitchers are able to improve this to an extent as the distance to the target is fixed. A fielder, however, starts his throw from many different positions on the field and has more targets (bases and cut-off men) to choose from, making his learning curve a bit longer.

If a throw or pitch is off-target, then what went wrong? Research has shown that despite all of the combinations of fingers, hand, arm, shoulder and body movements, it seems to all boil down to the timing of the finger release of the ball. In other words, when the pitcher's hand comes forward and the fingers start opening to allow the ball to leave. The timing of this release can vary by hundredths of a second but has significant impact on the accuracy of the throw. But, its also been shown that the throwing action happens so fast, that the brain could not consciously adjust or control that release in real-time. This points to the throwing action being controlled by what psychologists call an automated "motor program" that is created through many repeated practice throws. But, if a "release point" is incorrect, how does a pitcher correct that if they can't do so in real-time? It seems they need to change the embedded program by more practice.

Another component of "off-target" pitching or throwing is the psychological side of a player's mental state/attitude. Stadler identifies research that these motor programs can be called up by the brain by current thoughts. There seems to be "good" programs and "bad" programs, meaning the brain has learned how to throw a strike and learned many programs that will not throw a strike. By "seeding" the recall with positive or negative thoughts, the "strike" program may be run, but so to can the "ball" program. So, if a pitcher thinks to himself, "don't walk this guy", he may be subconsciously calling up the "ball" program and it will result in a pitch called as a ball. So, this is why sports pscyhologists stress the need to "think positively", not just for warm and fuzzy feelings, but the brain may be listening and will instruct your body what to do.


So, assuming Josh Beckett of the Red Sox is getting the ball across the plate, will the Rays hit it? That is the topic for next time when we look at hitting an object that is moving at 97 MPH and reaches you in less than half a second.

Putt With Your Brain - Part 1

If Mark Twain thinks golf is "a good walk spoiled", then putting must be a brief pause to make you reconsider ever walking again. With about 50% of our score being determined on the green, we are constantly in search of the "secret" to getting the little white ball to disappear into the cup. Lucky for us, there is no shortage of really smart people also looking for the answer. The first 8 months of 2008 have been no exception, with a golf cart full of research papers on just the topic of putting. 

Is the secret in the mechanics of the putt stroke or maybe the cognitive set-up to the putt or even the golfer's psyche when stepping up to the ball? This first post will focus on the mechanical side and then we'll follow-up next time with a look inside the golfer's mind.

Let's start with a tip that most golf instructors would give, "Keep your head still when you putt". Jack Nicklaus said it in 1974, "the premier technical cause of missed putts is head movement" (from "Golf My Way") and Tiger Woods said it in 2001, "Every good putter keeps the head absolutely still from start to finish" (from "How I Play Golf"). Who would argue with the two greatest golfers of all time? His name is Professor Timothy Lee, from McMaster University, and he wanted to test that observation. So, he gathered two groups of golfers, amateurs with handicaps of 12-40, and professionals with scratch handicaps. Using an infrared tracking system, his team tracked the motion of the putter head and the golfer's head during sixty putts.

As predicted, the amateurs' head moved back in unison with their putter head, something Lee calls an "allocentric" movement, which agrees with the advice that novice golfers move their head. However, the expert golfers did not keep their head still, but rather moved their heads slightly in the opposite direction of the putter head. On the backswing, the golfer's head moved slightly forward; on the forward stroke, the head moved slightly backward. This "egocentric" movement may be the more natural response to maintain a centered, balanced stance throughout the stroke.

"The exact reasons for the opposite coordination patterns are not entirely clear," explains Lee. "However, we suspect that the duffers tend to just sway their body with the motions of the putter. In contrast, the good golfers probably are trying to maintain a stable, central body position by counteracting the destabilization caused by the putter backswing with a forward motion of the head. The direction of head motion is then reversed when the putter moves forward to strike the ball." Does that mean that pro golfers like Tiger are not keeping their heads still? No, just that you may not have to keep your head perfectly still to putt effectively.

So, what if you do have the bad habit of moving your head? Just teach yourself to change your putting motion and you will be cutting strokes off of your score, right? Well, not so fast. Simon Jenkins of Leeds Metropolitan University tested 15 members of the PGA European Tour to see if they could break old physical habits during putting. His team found that players who usually use shoulder movement in their putting action were not able to change their ways even when instructed to use a different motion. Old habits die hard.

Let's say you do keep your head still (nice job!), but you still 3-putt most greens? What's the next step on the road to birdie putts? Of the three main components of a putt, (angle of the face of the putter head on contact, putting stroke path and the impact point on the putter), which has the greatest effect on success? Back in February, Jon Karlsen of the Norwegian School of Sport Sciences in Oslo, asked 71 elite golfers (mean handicap of 1.8) to make a total of 1301 putts (why not just 1300?) from about 12 feet to find out. His results showed that face angle was the most important (80%), followed by putter path (17%) and impact point (3%).

OK, forget the moving head thing and work on your putter blade angle at contact and you will be taking honors at every tee. Wait, Jon Karlsen came back in July with an update. This time he compared green reading, putting technique and green surface inconsistencies to see which of those variables we should discuss with our golf pro. Forty-three expert golfers putted 50 times from varying distances. Results showed that green reading (60%) was the most dominant factor for success with technique (34%) and green inconsistency (6%) trailing significantly.

So, after reading all of this, all you really need is something like the BreakMaster, which will help you read the breaks and the slope to the hole! Then, keep the putter blade square to the ball and don't move your head, at least not in an allocentric way, that is if you can break your bad habit of doing it. No problem, right? Well, next time we'll talk about your brain's attitude towards putting and all the ways your putt could go wrong before you even hit it!



Timothy D. Lee, Tadao Ishikura, Stefan Kegel, Dave Gonzalez, Steven Passmore (2008). Head–Putter Coordination Patterns in Expert and Less Skilled Golfers Journal of Motor Behavior, 40 (4), 267-272 DOI: 10.3200/JMBR.40.4.267-272


Jenkins, Simon (2008). Can Elite Tournament Professional Golfers Prevent Habitual Actions in Their Putting Actions? International Journal of Sports Science & Coaching, 3 (1), 117-127


Jon Karlsen, Gerald Smith, Johnny Nilsson (2007). The stroke has only a minor influence on direction consistency in golf putting among elite players Journal of Sports Sciences, 26 (3), 243-250 DOI: 10.1080/02640410701530902

Inside An Olympian's Brain

Michael Phelps, Nastia Liukin, Misty May-Treanor and Lin Dan are four Olympic athletes who have each spent most of their life learning the skills needed to reach the top of their respective sports, swimming, gymnastics, beach volleyball and badminton (you were wondering about Lin, weren't you...) Their physical skills are obvious and amazing to watch. For just a few minutes, instead of being a spectator, try to step inside the heads of each of them and try to imagine what their brains must accomplish when they are competing and how different the mental tasks are for each of their sports.


On a continuum from repetitive motion to reactive motion, these four sports each require a different level of brain signal to muscle movement. Think of Phelps finishing off one more gold medal race in the last 50 meters. His brain has one goal; repeat the same stroke cycle as quickly and as efficiently as possible until he touches the wall. There isn't alot of strategy or novel movement based on his opponent's movements. Its simply to be the first one to finish. 

What is he consciously thinking about during a race? In his post-race interviews, he says he notices the relative positions of other swimmers, his energy level and the overall effort required to win (and in at least one race, the level of water in his goggles.) At his level, the concept of automaticity (as discussed in a previous post) has certainly been reached, where he doesn't have to consciously "think" about the components of his stroke. In fact, research has shown that those who do start analyzing their body movements during competition are prone to errors as they take themselves out of their mental flow.


Moving up the continuum, think about gymnastics. Certainly, the skills to perform a balance beam routine are practiced to the point of fluency, but the skills themselves are not as strictly repetitive as swimming. There are finer points of each movement being judged so gymnasts keep several mental "notes" about the current performance so that they can "remember" to keep their head up or their toes pointed or to gather speed on the dismount. There also is an order of skills or routine that needs to be remembered and activated.

While swimming and gymnastics are battles against yourself and previously rehearsed movements, sports like beach volleyball and badminton require reactionary moves directly based on your opponents' movements. Rather than being "locked-in" to a stroke or practised routine, athletes in direct competition with their opponents must either anticipate or react to be successful.

So, what is the brain's role in learning each of these varied sets of skills and what commands do our individual neurons control? Whether we are doing a strictly repetitive movement like a swim stroke or a unique, "on the fly" move like a return of a serve, what instructions are sent from our brain to our muscles? Do the neurons of the primary motor cortex (where movement is controlled in the brain) send out signals of both what to do and how to do it?

Researchers at the McGovern Institute for Brain Research at MIT led by Robert Ajemian designed an experiment to solve this "muscles or movement" question. They trained adult monkeys to move a video game joystick so that a cursor on a screen would move towards a target. While the monkeys learned the task, they measured brain activity with functional magnetic resonance imaging (fMRI) to compare the actual movements of the joystick with the firing patterns of neurons. 

The researchers then developed a model that allowed them to test hypotheses about the relationship between neuronal activity that they measured in the monkey's motor cortex and the resulting actions. They concluded that neurons do send both the specific signals to the muscles to make the movement and a goal-oriented instruction set to monitor the success of the movement towards the goal. Here is a video synopsis of a very similar experiment by Miguel Nicolelis, Professor of Neurobiology at Duke University:


To back this up, Andrew Schwartz, professor of neurobiology at the McGowan Institute for Regenerative Medicine at the University of Pittsburgh School of Medicine, and his team of researchers wanted to isolate the brain signals from the actual muscles and see if the neuron impulses on their own could produce both intent to move and the movement itself. They taught adult monkeys to feed themselves using a robotic arm while the monkey's own arms were restrained. Instead, tiny probes the width of a human hair were placed in the monkey's motor cortex to pick up the electrical impulses created by the monkey's neurons. These signals were then evaluated by software controlling the robotic arm and the resulting movement instructions were carried out. The monkeys were able to control the arm with their "thoughts" and feed themselves food. Here is a video sample of the experiment:

"In our research, we've demonstrated a higher level of precision, skill and learning," explained Dr. Schwartz. "The monkey learns by first observing the movement, which activates his brain cells as if he were doing it. It's a lot like sports training, where trainers have athletes first imagine that they are performing the movements they desire."


It seems these "mental maps" of neurons in the motor cortex are the end goal for athletes to achieve the automaticity required to either repeat the same rehearsed motions (like Phelps and Liukin) or to react instantly to a new situation (like May-Treanor and Dan). Luckily, we can just practice our own automaticity of sitting on the couch and watching in a mesemerized state.



R AJEMIAN, A GREEN, D BULLOCK, L SERGIO, J KALASKA, S GROSSBERG (2008). Assessing the Function of Motor Cortex: Single-Neuron Models of How Neural Response Is Modulated by Limb Biomechanics Neuron, 58 (3), 414-428 DOI: 10.1016/j.neuron.2008.02.033

Meel Velliste, Sagi Perel, M. Chance Spalding, Andrew S. Whitford, Andrew B. Schwartz (2008). Cortical control of a prosthetic arm for self-feeding Nature, 453 (7198), 1098-1101 DOI: 10.1038/nature06996

HGH - Human Growth Hoax?

Athletes, both professional and amateur, as well as the general public are convinced that human growth hormone (HGH), Erythropoietin (EPO) and anabolic-androgenic steroids (AAS) are all artificial and controversial paths to improved performance in sports.  The recent headlines that have included Barry Bonds, Marion Jones, Floyd Landis, Dwayne Chambers, Jose Canseco, Jason Giambi, Roger Clemens and many lesser known names (see the amazingly long list of doping cases in sport) have referred to these three substances interchangeably leaving the public confused about who took what from whom.  With so many athletes willing to gamble with their futures, they must be confident that they will see significant short-term results.  

So, is it worth the risk?  Two very interesting recent studies provide some answers on at least one of the substances, HGH.


A team at the Stanford University School of Medicine, led by Hau Liu MD, recently reviewed 27 historical studies on the effects of HGH on athletic performance, dating back to 1966 (see reference below).  They wanted to see if there were any definitive links between HGH use and improved results.  In some of the studies, test volunteers who received HGH did develop more lean body mass, but also developed more lactate during aerobic testing which inhibited rather than helped performance.  While their muscle mass increased, other markers of athletic fitness, such as VO2max remained unchanged.  “The key takeaway is that we don’t have any good scientific evidence that growth hormone improves athletic performance,” said senior author Andrew Hoffman, MD, professor of endocrinology, gerontology and metabolism.



Both Liu and Hoffman cautioned that the amounts of HGH given to these test subjects may be much lower than the the purported levels claimed to be taken by professional athletes.  They also pointed out that at a professional level, a very slight improvement might be all that is necessary to get an edge of your opponent.  Hoffman also added an insightful comment, “So much of athletic performance at the professional level is psychological.”  If an athlete takes HGH, sees some muscle mass growth and isn't 100% sure of its performance capabilities, might he assume he now has other "Superman" powers?



That is exactly the premise that a research team from Garvan Institute of Medical Research in Sydney, Australia used to find out if HGH users simply relied on a placebo effect.  Sixty-four participants, young adult recreational athletes, were divided into two groups of 32 and tested for a baseline of athletic ability in endurance, strength, power and sprinting.  One group received growth hormone and the other group received a simple placebo.  It was a "double-blind" study in that neither the participants nor the researchers knew during the testing which substance each group received.



At the end of the 8 week treatment, the athletes were asked if they thought they were in the HGH group or the placebo group.  Half of the group that had received the placebo incorrectly guessed that they were on HGH.  Not too surprisingly, the majority of the "incorrect guessers" were men.  Here's where it gets interesting.  The incorrect guessers also thought that their athletic abilities had improved over the 8 week period.  The team retested all of the placebo group and actually did find improvement across all of the tests, but only significantly in the high-jump test.


Jennifer Hansen, a nurse researcher and Dr. Ken Ho, head of the pituitary research unit at Garvan have not released the data on the group that did receive the HGH, but they will in their final report coming soon.



So, let's recap.  On the one hand, we have a research review that claims there is not yet any scientific evidence that HGH actually improves sports performance.  Yet, we have hundreds, if not thousands, of athletes illegally using HGH for performance gain.  Showing the effect of the "if its good enough for them, its good enough for me" beliefs of the public regarding professional athlete use of HGH, we now have research that shows even those who received a placebo, but believed they were taking HGH not only thought they were improving but actually did improve a little.  Once again, we see the power of our own natural, non-supplemented brain to convince (or fool) ourselves to perform at higher levels than we thought possible.








Liu, H., Bravata, D.M., Olkin, I., Friedlander, A., Liu, V., Roberts, B., Bendavid, E., Saynina, O., Salpeter, S.R., Garber, A.M. (2008). Systematic review: the effects of growth hormone on athletic performance.. Annals of Internal Medicine, 148(10), 747-758.

Does Practice Make Perfect?

For years, sport science and motor control research has added support to the fundamental assertions that "practice makes perfect" and "repetition is the mother of habit".  Shooting 100 free throws, kicking 100 balls on goal or fielding 100 ground balls must certainly build the type of motor programs in the brain that will only help make the 101st play during the game.  K. Anders Ericsson, the "expert on experts", has defined the minimum amount of "deliberate practice" necessary to raise any novice to the level of expert as 10 years or 10,000 hours.

However, many questions still exist as to exactly how we learn these skills.  What changes happen in our brains when we teach ourselves a new task?  What is the most effective and efficient way to master a skill?  Do we have to be actually performing the skill to learn it, or could we just watch and learn? 

Then, once we have learned a new skill and can repeat it with good consistency, why can't we perform it perfectly every time?  Why can't we make every free throw, score with every shot on goal, and field each ground ball with no errors?  We would expect our brain to just be able to repeat this learned motor program with the same level of accuracy.
To answer these questions, we look at two recent studies.  The first, by a team at Dartmouth's Department of Psychological and Brain Sciences, led by Emily Cross, who is now a post-doc at Max Planck Institute for Cognitive and Brain Sciences in Leipzig, Germany, wanted to know if we need to physically perform a new task to learn it, or if merely observing others doing it would be enough. 

The "task" they chose was to learn new dance steps from a video game eerily similar to "Dance, Dance Revolution".  If you (or your kids) have never seen this game, its a video game that you actually get up off the couch and participate in, kind of like the Nintendo Wii.  In this game, a computer screen (or TV) shows you the dance moves and you have to imitate them on a plastic mat on the floor connected to the game.  If you make the right steps, timed to the music, you score higher.

Cross and the team "taught" their subjects in three groups.  The first group was able to view and practice the new routine.  The second group only was allowed to watch the new routine, but not physically practice it.  The third group was a control group that did not get any training at all.  The subjects were later scanned using functional magnetic resonance imaging (fMRI) while they watched the same routine they had either learned (actively or passively) or not seen (the control group).

As predicted, they found that the two trained groups showed common activity in the Action Observance Network (AON) in the brain (see image on left), a group of neural regions found mostly in the inferior parietal and premotor cortices of the brain (near the top of the head) responsible for motor skills and some memory functions.  In other words, whether they physically practised the new steps or just watched the new steps, the same areas of the brain were activated and their performance of the new steps were significantly similar.  The team put together a great video summarizing the experiment.  

One of the themes from this study is that, indeed, learning a motor skill takes place in the brain.  This may seem like an obvious statement, but its important to accept that the movements that our limbs make when performing a skill are controlled by the instructions provided from the brain.  So, what happens when the skill breaks down?  Why did the quarterback throw behind the receiver when we have seen him make that same pass accurately many times?  

To stay true to our theme, we have to blame the brain.  It may be more logical to point to a mechanical breakdown in the player's form or body movements, but the "set-up" for those movements starts with the mental preparation performed by the brain.

In the second study, electrical engineers at Stanford University took a look at these questions to try to identify where the inconsistencies of movement start.  They chose to focus on the "mental preparation" stage which occurs just before the actual movement.  During this stage, the brain plans the coordination and goal for the movement prior to initiating it.  The team designed a test where monkeys would reach for a green dot or a red dot.  If green, they were trained to reach slowly for the dot; if red, to reach quickly.  By monitoring the areas of the monkeys' brains through fMRI, they observed activity in the AON prior to the move and during the move.  

Over repeated trials, changes in reach speed were associated with changes in pre-movement activity.  So, instead of perfectly consistent reach times by the monkeys, they saw variation, like we might see when trying to throw strikes with a baseball many times in a row.  Their conclusion was that this planning activity in the brain does have an effect on the outcome of the activity.  Previously, research had focused only on breakdowns during the actual move and in the mechanics of muscles.  This study shows that the origin of the error may start earlier.

As electrical engineering Assistant Professor Krishna Shenoy stated, "the main reason you can't move the same way each and every time, such as swinging a golf club, is that your brain can't plan the swing the same way each time."  
Postdoctoral researcher and co-author Mark Churchland added, "The nervous system was not designed to do the same thing over and over again.  The nervous system was designed to be flexible. You typically find yourself doing things you've never done before." 
The Stanford team also has made a nice short video synopsis of their study.

Does practice make perfect?  First, we must define "practice".  We saw that it could be either active or passive.  Second, we know sports skills are never "perfect" all the time, and need to understand where the error starts before we can begin to fix it.

Play Better Golf By Playing Bigger Holes

Here are some quotes we have all heard (or said ourselves) on the golf course or at the ball diamond.

On a good day:
"It was like putting into the Grand Canyon"
"The baseball looked like a beach ball up there today"

On a bad day:
"The hole was as small as a thimble"
"I don't know, it looked like he was throwing marbles"

The baseball and the golf hole are the same size every day, so are these comments meaningless or do we really perceive these objects differently depending on the day's performance? And, does our performance influence our perception or does our perception help our performance?

Jessica Witt, an assistant professor of psychological science at the University of Virginia has made two attempts at the answer. First, in a 2005 study, "See the Ball, Hit the Ball", her team studied softball players by designing an experiment that tried to correlate perceived softball size to performance. She interviewed players immediately after a game and asked them to estimate the size of the softball by picking a circle off of a board that contained several different sizes. She then found out how that player had done at the plate that day. 

As expected, the players that were hitting well chose the larger sized circles to represent the ball size, while the underperforming hitters chose the smaller circles. The team was not able to answer the question of causality, so they expanded the research to other sports.

Fast forward to July, 2008 and Witt and her team have just released a very similar study focused on golf, "Putting to a bigger hole: Golf performance relates to perceived size". Using the same experiment format, players who had just finished a round of golf were asked to pick out the perceived size of the hole from a collection of holes that varied in diameter by a few centimeters. Once again, the players who had scored well that day picked the larger holes and vice versa for that day's hackers. So, the team came to the same conclusion that there is some relationship between perception and performance, but could not figure out the direction of the effect. Ideally, a player could "imagine" a larger hole and then play better because of that visual cue.

Researchers at Vanderbilt University may have the answer. In a study, "The Functional Impact of Mental Imagery on Conscious Perception", the team led by Joel Pearson, wanted to see what influence our "Mind's Eye" has on our actual perception. In their experiment, they asked volunteers to imagine simple patterns of vertical or horizontal stripes. Then, they showed each person a pattern of green horizontal stripes in one eye and red vertical stripes in the other eye. This would induce what is known as the "binocular rivalry" condition where each image would fight for control of perception and would appear to alternate from one to the other. In this experiment, however, the subjects reported seeing the image they had first imagined more often. So, if they had imagined vertical stripes originally, they would report seeing the red vertical stripes predominantly.

The team concluded that mental imagery does have an influence over what is later seen. They also believe that the brain actually processes imagined mental images the same way it handles actual scenes. "More recently, with advances in human brain imaging, we now know that when you imagine something parts of the visual brain do light up and you see activity there," Pearson says. "So there's more and more evidence suggesting that there is a huge overlap between mental imagery and seeing the same thing. Our work shows that not only are imagery and vision related, but imagery directly influences what we see."

So, back to our sports example, if we were able to imagine a large golf hole or a huge baseball, this might affect our actual perception of the real thing and increase our performance. This link has not been tested, but its a step in the right direction. Another open question is the effect that our emotions and confidence have on our perceived task. That hole may look like the Grand Canyon, but the sand trap might look like the Sahara Desert!



Witt, J.K. (2008). Putting to a bigger hole: golf performance relates to perceived size. Psychonomic Bulletin & Review, 15

Brains Over Brawn In Sports

Sometimes, during my daily browsing of the Web for news and interesting angles on the sport science world, I get lucky and hit a home run.  I stumbled on this great May 2007 Wired article by Jennifer Kahn, Wayne Gretzky-Style 'Field Sense' May Be Teachable.  It ties together the people and themes of my last three posts, focusing on the concept of perception in sports.


Wayne Gretzky is often held up as the ultimate example of an athlete with average physical stature, who used his cognitive and perceptual skills to beat opponents.  Joining Gretzky in the "brains over brawn" Hall of Fame would be pitcher Greg Maddux, NBA guard Steve Nash and quarterback Joe Montana.  They were all told as teenagers that they didn't have the size to succeed in college or the pros, but they countered this by becoming master students of the game, constantly searching for visual cues that would give them the advantage of a fraction of second or the element of surprise.



Kahn's story focuses on two sport scientists that we have met before.  Peter Vint, sport technologist with the US Olympic team, who I highlighted in the post, Winning Olympic Gold With Sport Science,  comments on this, "In any sport, you come across these players.  They're not always the most physically talented, but they're by far the best. The way they see things that nobody else sees — it can seem almost supernatural. But I'm a scientist, so I want to know how the magic works."  So, Vint and his team continue to search not only for the secret to the magic, but how it can be taught.



Vint acknowledges the work of one of his fellow sport scientists, Damian Farrow, of the Australian Institute for Sport, who was part of the discussion roundtable mentioned in my post, Getting Sport Science Out Of The Lab And Onto The Field.


He is also fascinated with the perceptual abilities of elite athletes.  In his own sport, tennis, he wanted to know how expert players could return serves much better than novice players.  Similar to the research we looked at in an earlier post about tennis, Federer and Nadal Can See the Difference, Farrow designed an experiment that would try to identify the cues that players might need to instinctively estimate the speed and direction of a serve.  He had three groups of players, expert, non-expert but coached, and non-expert/non-coaced novices, wear ear plugs to block out the sound of the ball hitting the racquet as well as occlusion glasses that could block vision with the touch of an assistant's button.  

By changing the point of the serve at which the glasses would go black, and the players would be "blind", he could try to isolate the action of the server that the expert players might be tuned into that the novices were not.  The decisive point was immediately before impact between the racquet and the ball.  Arm and racquet position at that point seemed to let the expert players estimate the direction of the serve more accurately than the novices.


But Vint and Farrow are not satisfied just knowing what an expert knows.  They want to understand how to teach this skill to novices.  From his own competitive tennis playing days, Farrow remembers that if he consciously focused his mind on things like arm position, racquet angle, etc., he would be miss the serve as his reaction time would drop.  He understood that players need to not only learn the cues, but learn them to the point of "automaticity" through implicit learning.  

You may remember our discussion of implicit learning from the post, Teaching Tactics and Techniques in Sports.   Malcolm Gladwell, in his best-selling book, Blink, calls this implicit decision-making ability "thin slicing" and gives examples of how we can often make better decisions in the "blink" of an eye, rather than through long analysis.  Obviously, in sports, when only seconds or sub-seconds are allowed for decisions, this blink must be so well-trained that it is at the sub-conscious level.

For Vint and Farrow, the experiments continue, looking at each sport, but beyond the raw physical and technical skills that need to be taught but often times are the only skills that are taught.   

Understanding the cognitive side of the game will provide the edge when all else is equal.

Teaching Tactics and Techniques In Sports

You have probably seen both types of teams. Team A: players who are evenly spaced, calling out plays, staying in their positions only to watch them dribble the ball out of bounds, lose the pass, or shoot wildly at the goal. Team B: amazing ball control, skillful shooting and superior quickness, speed and agility but each player is a "do-it-yourselfer" since no one can remember a formation, strategy or position responsibility. Team A knows WHAT to do, but can't execute. Team B knows HOW to do it, but struggles with making good team play decisions. This is part of the ongoing balancing act of a coach. At the youth level, teaching technique first has been the tradition, followed by tactical training later and separately. More recently, there has been research on the efficiency of learning in sports and whether there is a third "mixed" option that yields better performance.


Earlier, we took an initial look at Dr. Joan Vickers' Decision Training model as an introduction to this discussion. In addition, Dr. Markus Raab of the Institute for Movement Sciences and Sport, University of Flensburg, Germany, (now of the Institute of Psychology, German Sport University in Cologne), took a look at four major models of teaching sports skills that agree that technical and tactical skills need to be combined for more effective long-term learning.Each of the four models vary in their treatment of learning along two different dimensions; implicit vs. explicit learning and domain-specific vs. domain-general environments. 


Types of Learning

Imagine two groups of boys playing baseball. The first group has gathered at the local ball diamond at the park with their bats, balls and gloves. No coaches, no parents, no umpires; just a group of friends playing an informal "pick-up" game of baseball. They may play by strict baseball rules, or they may improvise and make their own "home" rules, (no called strikes, no stealing, etc.). In the past, they may have had more formal coaching, but today is unstructured.


The second group is what we see much more often today. A team of players, wearing their practice uniforms are driven by their parents to team practice at a specific location and time to be handed off to the team coaches. The coaches have planned a 90 minute session that includes structured infield practice, then fly ball practice, then batting practice and finally some situational scrimmages. Rules are followed and coaching feedback is high. Both groups learn technical and tactical skills during their afternoon of baseball. They differ in the type of learning they experience.

The first group uses "implicit" learning while the second group uses "explicit" learning. Implicit learning is simply the lack of explicit teaching. It is "accidental" or "incidental" learning that soaks in during the course of our play. There is no coach teaching the first group, but they learn by their own trial and error and internalize the many if-then rules of technical and tactical skills. Explicit learning, on the other hand, is directed instruction from an expert who demonstrates proper technique or explains the tactic and the logic behind it.



An interesting test of whether a specific skill or piece of knowledge has been learned with implicit or explicit methods is to ask the athlete to describe or verbalize the details of the skill or sub-skill. If they cannot verbalize how they know what they know, it was most likely learned through implicit learning. However, if they can explain the team's attacking strategy for this game, for example, that most likely came from an explicit learning session with their coach.



Types of Domains

The other dimension that coaches could use in choosing the best teaching method is along the domain continuum. Some teaching methods work best to teach a skill that is specific to that sport's domain and the level of transferability to another sport is low. These methods are known as domain-specific. For more general skills that can be useful in several related sports, a method can be used known as domain-general.

Why would any coach choose a method that is not specific to their sport? There has been evidence that teaching at a more abstract level, using both implicit and explicit "play" can enhance future, more specific coaching. Also, remember our discussion about kids playing multiple sports.Based on these two dimensions, Dr. Raab looked at and summarized these four teaching models:

• Teaching Games for Understanding (TGFU)
• Decision Training (DT)
• Ball School (Ball)
• Situation Model of Anticipated Response consequences of Tactical training (SMART)

TGFU

The TGFU approach, (best described by Bunker, D.; Thorpe, R. (1982) A model for the teaching of games in the secondary school, Bulletin of Physical Education, 10, 9–16), is known for involving the athlete early in the "cognition" part of the game and combining it with the technical aspect of the game. Rather than learn "how-to" skills in a vacuum, TGFU argues that an athlete can tie the technical skill with the appropriate time and place to use it and in the context of a real game or a portion of the game.

This method falls into the explicit category of learning, as the purpose of the exercise is explained. However, the exercises themselves stress a more domain-general approach of more generic skills that can be transferred between related sports such as "invasion games" (soccer, football, rugby), "net games" (tennis, volleyball), "striking/fielding games" (baseball, cricket) and "target games" (golf, target shooting). 



Decision Training

The DT method, (best described by Vickers, J. N., Livingston, L. F., Umeris-Bohnert, S. & Holden, D. (1999) Decision training: the effects of complex instruction, variable practice and reduced delayed feedback on the acquisition and transfer of a motor skill, Journal of Sports Sciences, 17, 357–367), uses an explicit learning style but with a domain-specific approach. Please see my earlier post on Decision Training for details of the approach. 


Ball School

The Ball School approach, (best described by Kroger, C. & Roth, K. (1999) Ballschule: ein ABC fur Spielanfanger [Ball school: an ABC for game beginners] (Schorndorf, Hofmann), starts on the other end of both spectrums, in that it teaches generic domain-general skills using implicit learning. It emphasizes that training must be based on ability, playfullness, and skill-based. Matching the games to the group's abilities, while maintaining an unstructured "play" atmosphere will help teach generic skills like "hitting a target" or "avoiding defenders". 



SMART

Dr. Raab's own SMART model, (best described in Raab, M. (2003) Decision making in sports: implicit and explicit learning is affected by complexity of situation, International Journal of Sport and Exercise Psychology, 1, 406–433), blends implicit and explicit learning within a domain-specific environment. The idea is that different sports' environmental complexity may demand either an implicit or explicit learning method. Raab had previously shown that skills learned implicitly work best in sport enviroments with low complexity. Skills learned explicitly will work best in highly complex environments. Complexity is measured by the number of variables in the sport. So, a soccer field has many moving parts, each with its own variables. So, the bottom line is to use the learning strategy that fits the sport's inherent difficulty. So, learning how to choose from many different skill and tactical options would work best if matched with the right domain-specific environment.  



Bottom-Line for Coaches

What does all of this mean for the coach? That there are several different models of instruction and that one size does not fit all situations. Coaches need an arsenal of tools to use based on the specific goals of the training session. In reality, most sports demand both implicit and explicit learning, as well as skills that are specific to one domain, and some that can transfer across several sport domains. Flexibility in the approach taken goes back to the evidence based coaching example we gave last time. Keeping an open mind about coaching methods and options will produce better prepared athletes.






(2007). Discussion. Physical Education & Sport Pedagogy, 12(1), 1-22. DOI: 10.1080/17408980601060184

Single Sport Kids - When To Specialize

So, your grade school son or daughter is a good athlete, playing multiple sports and having fun at all of them. Then, you hear the usual warning, either from coaches or other parents; "If you want your daughter to go anywhere in this sport, then its time to let the other sports go and commit her full-time to this one." The logic sounds reasonable. The more time spent on one sport, the better she will be at that sport, right? Well, when we look at the three pillars of our Sports Cognition Framework, motor skill competence, decision making ability, and positive mental state, the question becomes whether any of these would benefit from playing multiple sports, at least in the early years of an athlete (ages 3-12)? It seems obvious that specific technical motor skills, (i.e. soccer free kicks, baseball bunting, basketball free throws) need plenty of practice and that learning the skill of shooting free throws will not directly make you a better bunter. On the other end, learning how to maintain confidence, increase your focus, and manage your emotions are skills that should easily transfer from one sport to another. That leaves the development of tactical decision making ability as the unknown variable. Will a young athlete learn more about field tactics, positional play and pattern recognition from playing only their chosen sport or from playing multiple related sports?

Researchers at the University of Queensland, Australia learned from previous studies that for national team caliber players there is a correlation between the breadth of sport experiences they had as a child and the level of expertise they now have in a single sport. In fact, these studies show that there is an inverse relation between the amount of multi-sport exposure time and the additional sport-specific training to reach expert status. In plain English, the athletes that played several different (but related) sports as a child, were able to reach national "expert" level status faster than those that focused only one sport in grade school . Bruce Abernethy, Joseph Baker and Jean Cote designed an experiment to observe and measure if there was indeed a transfer of pattern recognition ability between related sports (i.e. team sports based on putting an object in a goal; hockey, soccer, basketball, etc.)

They recruited two group of athletes; nationally recognized experts in each of three sports (netball, basketball and field hockey) who had broad sports experiences as children and experienced but not expert level players in the same sports whose grade school sports exposure was much more limited (single sport athletes). (For those unfamiliar with netball, it is basically basketball with no backboards and few different rules.) The experiment showed each group a video segment of an actual game in each of the sports. When the segment ended the groups were asked to map out the positions and directions of each of the players on the field, first offense and then defense, as best they could remember from the video clip. The non-expert players were the control group, while the expert players were the experimental groups. First, all players were shown a netball clip and asked to respond. Second, all were shown a basketball clip and finally the hockey clip. The expectation of the researchers was that the netball players would score the highest after watching the netball clip (no surprise there), but also that the expert players of the other two sports would score higher than the non-expert players. The reasoning behind their theory was that since the expert players were exposed to many different sports as a child, there might be a significant transfer effect between sports in pattern recognition, and that this extra ability would serve them well in their chosen sport.

The results were as predicted. For each sport's test, the experts in that sport scored the highest, followed by the experts in the other sports, with the non-experts scoring the poorest in each sport. Their conclusion was that there was some generic learning of pattern recognition in team sports that was transferable. The takeaway from this study is that there is benefit to having kids play multiple sports and that this may shorten the time and training needed to excel in a single sport in the future.

So, go ahead and let your kids play as many sports as they want. Resist the temptation to "overtrain" in one sport too soon. Playing several sports certainly will not hurt their future development and will most likely give them time to find their true talents and their favorite sport.


Source:
Abernethy, B., Baker, J., Côté, J. (2005). Transfer of pattern recall skills may contribute to the development of sport expertise. Applied Cognitive Psychology, 19(6), 705-718. DOI: 10.1002/acp.1102

Federer and Nadal Can See the Difference

Watching Roger Federer and Rafael Nadal battle it out in the French Open final and now again in the Wimbledon final, I started thinking more about the interceptive timing task requirements of each of their visuomotor systems... yeah, right. C'mon, I just needed a good opening line for this post.


However, other than a 120 mph tennis serve, take a second to think about all of the different sports that send an object flying at you at very high speeds that you not only have to see, but also estimate the speed of the object, the movement of the object and what you want to do with the object once it gets to you.



Some examples are:
- a hockey puck at a goalie (70-100 mph)
- a baseball pitch at a batter (70-100 mph)
- a soccer ball kicked at a keeper (60-90 mph)


Previously, we took a look at this in baseball and in soccer and also discussed the different types of visual skills in sports. There, we broke it down into three categories:

- Targeting tasks
- Interceptive timing tasks
- Tactical decision making tasks

The second category, interceptive timing tasks, deals with the examples above; stuff coming at you fast and you need to react. There are three levels of response that take an increasing level of brainpower.

First, there is a basic reaction, also known as optometric reaction. In other words, "see it and get out of the way". Next, there is a perceptual reaction, meaning you actually can identify the object coming at you and can put it in some context (i.e. that is a tennis ball coming at you and not a bird swooping out of the sky).

Finally, there is a cognitive reaction, meaning you know what is coming at you and you have a plan of what to do with it (i.e. return the ball with top-spin down the right line). This cognitive skill is usually sport-specific and learned over years of tactical training. Obviously, for professional tennis players, they are at the expert cognitive stage and have a plan for most shots. Federer's problem was that Nadal had better plans.

But, in order to reach that cognitive stage, they first need to have excellent optometric and perceptual skills. Can those skills be trained? Or are the best tennis players born with naturally better abilities? Did their training make them better tennis players or are they better players because of some natural skills?


Leila Overney and her team at the Brain Mind Institute of Ecole Polytechnique Federale de Lausanne (EPFL) recently studied whether expert tennis players have better visual perception abilities than other athletes and non-tennis players. Typically, motor skill research compares experts to non-experts and tries to deduce what the experts are doing differently to excel.

In this study, an additional category was added. Overney wanted to see if the perceptual skills of the tennis players were significantly more advanced than athletes of a similar fitness level, (in this case triathletes), to eliminate the variable of "fitness", and also more advanced than novice tennis players (the typical comparison). To eliminate the cognitive knowledge difference between the groups, she used seven non-sport specific visual tests. Please see the actual study for details of all the tests.

The bottom line of the results was that certain motion detection and speed discrimination skills were better in the tennis players (in other words, being able to track a ball coming at you and its movement side to side).


So, the expert tennis players were better at tracking balls coming at them than triathletes and non-tennis players.... seems pretty obvious(!) But, these results are a first step to answering the question of "can these skills be trained"? We see that there is, indeed, a difference in ability level between expert players and athletes that are in similar shape and competitive spirit. Now, the question becomes, "how did these tennis players acquire a higher level of perception skill"? Was it "nature or nurture", "genetically gifted or trained through practice"?


Source: Overney, L.S., Blanke, O., Herzog, M.H., Burr, D.C. (2008). Enhanced Temporal but Not Attentional Processing in Expert Tennis Players. PLoS ONE, 3(6), e2380. DOI: 10.1371/journal.pone.0002380

The Coach's Curse - Mental Mistakes

"Donadoni rues Italian 'mistakes' against Dutch"

"Mental errors cost Demons in regional quarterfinal"

"Mental mistakes doom Rays in loss to Cardinals"

 
Every day, there is always a new variety of stories linked to the phrase, "mental mistakes".  Either the writer recaps a game, calling out the mistakes or a coach or player claims that mistakes were made. It has become sort of a throwaway phrase, "...we made a lot of mental mistakes out there today, that we need to avoid if we want to get to the playoffs..." The million dollar question then is HOW to reduce these mental mistakes. And, to answer that, we need to define WHAT is a mental mistake?

In a previous post, I introduced the "Sports Cognition Framework", which is a trio of elements needed for success in sports. These three elements are:

- decision-making ability (knowing what to do)

- motor skill competence (being physically able to do it)

- positive mental state (being motivated and confident to do it)

Most of the time, a mental mistake is thought of as a breakdown of decision-making ability. The center fielder throws to the wrong base, the tight end runs the wrong route, or the defender forgets to mark his man, etc. These scenarios describe poor decisions or even memory lapses during the stress of the game. They are not necessarily the lack of skill to execute a play or the lack of confidence or motivation to want to do the right thing. It is a recognition, in hindsight, that the best option was not chosen. In addition to glaring negative plays, there are also missed opportunities on the field (i.e. taking a contested shot on goal, instead of passing to the open teammate).

So, back to the payoff question: HOW do we reduce mental mistakes and poor decisions? Just as we practice physical skills to improve our ability to throw, catch, shoot, run, etc., we need to practice making decisions using a a training system that directly exposes the athlete to these scenarios. Dr. Joan Vickers, who we met during our discussion of the Quiet Eye, has created a new system which she calls the "Decision-Training Model", and is the focus of the second half of her book, "Perception, Cognition, and Decision Training". As opposed to traditional training methods that separate skill training from tactical decision making training, the Decision-Training model (D-T) forces the athlete to couple her skill learning with the appropriate tactical awareness of when to use it.

So, instead of an "easy-first" breakdown of a skill, and then build it up step by step, D-T begins with a "hard-first" approach putting the "technique within tactics" demanding a higher cognitive effort right up front. The theory behind D-T is that the coach is not on the field with the player during competition, so the player must learn to rely on their own blended combination of skill and game awareness. Research from Vickers and others shows that D-T provides a more lasting retention of knowledge, while more traditional bottom-up training with heavy coach feedback delivers a stronger short-term performance gain, but that success in practice does not often translate later in games. Practice and training need to mirror game situations as often and as completely as the real thing.

There are three major steps to Decision-Training (p. 167):

1. Identify a decision the athlete has to make in a game, using one of the seven cognitive skills (anticipation, attention, focus/concentration, pattern recognition, memory, problem solving and decision making)

2. Create a drill(s) that trains that decision using one of the seven cognitive triggers (object cues, location cues, Quiet Eye, reaction-time cues, memory cues, kinesthetic cues, self-coaching cues)

3. Use one or more of the seven decision tools in the design of the drill (variable practice, random practice, bandwidth feedback, questioning, video feedback, hard-first instruction, external focus of instruction)

This post was just to serve as an introduction to D-T. Dr. Vickers and her team at University of Calgary offer full courses for coaches to learn D-T and apply it in their sport. Combined with the visual cues of the playing environment provided by the Quiet Eye gaze control, D-T seems to offer a better tactical training option for coaches and athletes. Coming up, we will continue the discussion of decision-making in sports with a look at some other current research. Please give me your thoughts on D-T and the whole topic of mental mistakes!

See The Ball, Be The Ball - Vision and Sports

The whistle blows and Shaq goes to the line again after being fouled on purpose for the fourth time. And, again, we watch as he takes that awkward stance, looks at the basket and then clanks one of the back of the rim. We wonder how hard this can be... just aim and shoot! Isn't it that simple? Well, not exactly. In our introduction to this series I mentioned the research of Dr. Joan Vickers and her concept of the "Quiet Eye". In her book, Perception, Cognition and Decision Training, she describes this visual targeting pathway:


"...the visual pathway begins when information is registered on the eye's retina by the focal and ambient systems, then travels to the back of the head along the optic nerve and radiates to the occipital cortex, where visual information is registered as billions of features. These then race in parallel fashion both to the top of the head to the parietal cortex (dorsal) and along the sides of the head to the temporal (ventral) areas. There is an integration of information in the somatosensory cortex as the information goes to the frontal cortex, where the goals and intentions reside and plans are formulated for the specific event that is occurring. The flow of information then goes to the premotor and motor cortex at the top of the head before going down the spinal cord to the effectors." P.26


This same process repeats constantly during any athletic event and it is the most critical determinant of the outcome of the game. Just think about the types of visual work that needs to be done by an athlete (as defined by Dr. Vickers):

1. Targeting Tasks - being able to fixate on a target, fixed or moving, to be able to throw, kick or send an object towards it. (i.e. Shooting or passing a baseball, football, basketball, soccer ball, hockey puck, etc.)

2. Interceptive Timing Tasks - being able to recognize, track and finally control an object as it comes at you (aka "catching")

3. Tactical Decision Making Tasks - being able to take in an environmental scan of the field/court and recognize patterns of all the moving objects (i.e. a quarterback scanning his receivers and choosing the best option for a pass).

All of these scenarios require the athlete to focus or "gaze" on the right points in the environment and ignore the rest of the scene. Dr. Vickers' work has been to observe athletes of different skill levels, expert and non-expert, and define the "best practices" of visual control so that the non-expert athletes can be coached to better performance. Her research lab uses "eye-trackers" (see photo) to monitor the focus and gaze of the athlete's pupils as they perform their skills.

For example, she has found that expert baseball hitters focus on the release point of the ball exclusively, rather than random fixations on the pitcher's arm, head, jersey, etc. She found that expert golf putters focus on a specific point on the cup, then a specific point on the back of the ball and remain fixated on the point on the ball after the ball has left the putter blade.

Novices allow their gaze to wander from the ball to the hole, without a very specific focal point on either the cup or the ball. The term "Quiet Eye" comes from these observations that expert performers have consciously chosen points in their space to focus on rather than allowing their eyes to wander and fixate on multiple points (i.e. a "noisy" eye).


So, why does the Quiet Eye work? When we fixate on key points in our field of vision, how does this help our neuromuscular systems perform better? The subconscious part of our brain may be recognizing a pattern that we have seen and experienced before and directing our movements based on this information. Some have called this "muscle memory", meaning our brain has learned through repetition and practice how to throw a ball to a moving receiver at that distance and speed, and so, when presented with a similar scenario, knows what to do. Think about when you shoot a jump shot and sometimes you get that sensation, as soon as it leaves your hand, that the ball is going in. Your brain may be telling you that, based on past experience, when you've executed the same aim and same muscle movement then the ball has gone in.

This takes us back to the discussion we had in our previous post on baseball fielding regarding theories of perception-action combinations. The Information Processing model claims that we perceive the environment first through our senses, primarily our vision. Then, we access our memory to find the rules, suggestions and knowledge that we have gained from past experiences and these memories guide our action in the moment.

The Ecological Psychology model removes the memory access step and claims that our perception of the environment leads directly to our actions, as there is not enough time to access our lessons. If that is true, then how does the Quiet Eye help us? It seems the Quiet Eye is what we need to connect the current scenario (standing on the free throw line looking at the basket) with our lessons learned from the past (how we made this shot hundreds of times before). Research continues on this question and I'm sure we'll come back to this in future posts.


Next time, I will take a look at Dr. Vickers' "Decision Training Model", which builds on the Quiet Eye theory to train athletes to improve their tactical in-game decision making. We will look at the athletes who are known as having good "vision of the field" and how to raise everyone's game to that level.

So Why Can't Shaq Make Free Throws?

The NBA league average for free throw shooting is about 75%. Shaquille O'Neal's career average is 52.4%. Even worse, Ben Wallace's career average is 41.9%. The average for the NCAA Division 1 teams is 69%. The obvious question is why can't Shaq or Ben or Memphis do any better, but the bigger question is why do most of the best basketball players in the world miss 2 or 3 free throws out of 10? Maybe they just haven't heard about Joan Vickers and the "Quiet Eye".

For me, the best science is applied science. The same goes for sports science. Theories, physics, psychology, etc. are only useful in sports if they can be used to improve in-game performance. That's why I have always been a fan of academic work that leads to useful techniques in the field. Professor Joan Vickers of the University of Calgary has been applying her research into the human visual system and its effects on sports performance for over 25 years. She is the discoverer of the "Quiet Eye" skill that has been shown to significantly improve accuracy in targeting and decision-making skills in many sports. In addition to this "gaze control" technique, she also has developed a 7-step teaching process to improve the in-game decision-making of athletes, based partly on their visual perception skills.

She has a new book out that condenses all of these ideas, called Perception, Cognition and Decision Training. Over the next few days, I will do my best to paraphrase and explain the most useful information and techniques, but of course the best source is this book.
For an opening primer on the Quiet Eye, please take a look at this episode and this online video of PBS' Scientific American with Hawkeye himself, Alan Alda, shooting free throws.

Cristiano Roboto - The Soccer Playing Robot

Back in April, 80 teams of researchers from 15 countries got together to compete in the 2008 RoboCup German Open, a soccer tournament where the "athletes" are all totally autonomous robots like the one pictured above. Four players and a goalkeeper per team play on a 20x14 meter field and are independent of any human remote control. They need to have sub-systems that "see" the field, opponents and the goal; have locomotion logic to move forward, sideways and back; some tactical logic to sense an opponent and avoid "it"; and targeting to kick the ball in the direction of the goal.

You can see some brief clips of the robots on the pitch here. Try the second video to see the most game highlights. The discussion is in German, if any of you speak it, but the game clips are what to focus on.

The more practical future applications of these sub-systems is to program robots to do more meaningful tasks like search and rescue operations in dangerous areas, (fire, earthquake, enemy zones), using the same visual, locomotion, search algorithms that guide the robot on the soccer field. In fact, there is a RoboRescue competition as well.

What struck me most about watching these robots was the complexity of the logic that needs to be programmed. The visual system that must learn the field, the sidelines, the dimensions of the goal, the difference between a teammate and an opponent. The tactical system that must be "goal" directed, (pun intended). It must learn that the object of the game is to put the ball into the opponent's goal and stop the ball from entering your own goal.

The constant motion sensor to understand where they are on the field, when to dribble, when to stop, when to aim and when to kick. The researchers/programmers in this competition are some of the brightest minds in the world, yet when you watch the video, you might have the same reaction that I did; that this is an impressive start, but they still look rather rudimentary.

Thinking about the topics we cover here, we often take for granted all of the logic and skills that human athletes demonstrate every day. I'm thinking especially of our kids that can easily surpass the performance of these robots, even as young as 3 years old. My fascination, and probably these researchers, is HOW we are able to do these tasks so easily. If we understand more about the "how", then we can also design better practice environments to advance those skills even faster.
Source: Fraunhofer-Gesellschaft (2008, April 4). Soccer Robots Compete For The Title. ScienceDaily. Retrieved May 29, 2008, from http://www.sciencedaily.com/releases/2008/04/080401110128.htm#

A Keeper's Nightmare - Beckham, Ronaldo or Juninho

Whether you bend it like Beckham or Ronaldo or Juninho or even Nakamura; the curving free kick is one of the most exciting plays in soccer/football. Starting with Rivelino in the 1970 World Cup and on to the specialists of today, more players know how to do it and understand the basic physics behind it, but very few can perfect it. But, when it does happen, by chance or skill, it is the highlight of the game.

But let's take a look at this from the other side, through the eyes of the goalkeeper. Obviously, its their job to anticipate where the free kick is going and get to the spot before the ball crosses the line. He sets up his wall to, hopefully, narrow the width of the target, but he knows some players are capable of bending the ball around or over the wall towards the near post. If you watch highlights of free kick goals, you often see keepers flat-footed, just watching the ball go into the top corner. Did they guess wrong and then were not able to react? Did they guess right but misjudged the flight trajectory of the ball. How much did the sidespin or "bend" affect their perception of the exact spot where the ball will cross the line?

Researchers at Queen's University Belfast and the University of the Mediterranean in France tried to figure this out in this paper. They wanted to compare the abilities of expert field players and expert goalkeepers to accurately predict if a free kick would result in an on-target goal or off-target non-goal. First, a bit about why the ball "bends". We can thank what's called the "Magnus Force" named after the 19th-century German physicist Gustav Magnus. As seen in the diagram below, as the ball spins counter clockwise (for a right-footed player using his instep and kicking the ball on the right side), the air pressure on the left side of the ball is lower as the spin is in the same direction as the oncoming air flow. On the right side of the ball, the spin is in the opposite direction of the air flow, building higher pressure. The ball will follow the path of least resistance, or pressure, and "bend" or curve from right to left. The speed of the spin and the velocity of the shot will determine the amount of bend. For a clockwise spin, the ball bends from left to right.



The researchers showed the players three different types of simulated kicks, a kick bent to the right, a kick bent to the left and a kick with no spin at all. They showed the players these simulations with virtual reality headsets and computer controlled "kicks" and "balls" which they could vary in flight with different programming. The balls would disappear from view at distances of 10 and 12.5 meters from the goal. The reasoning is that this cutoff would correspond with the deadline for reaction time to make a save on the ball. In other words, if the keeper does not correctly guess the final trajectory and position of the ball by this point, he most likely will not be able to physically get to the ball and make the save.

The results showed that both the players and the keepers, (all 20 were expert players from elite clubs like AC Milan, Marseille, Bayer LeverkusenSchalke 04), were able to correctly predict the result of the kicks with no spin added. However, as 600 RPM spin, either clockwise or counter-clockwise, was added to the ball, the players success declined significantly. Interestingly, the keepers did no better, statistically, then the field players. The researchers conclusion was that the players used the "current heading direction" of the ball to predict the final result, rather than factoring the future affect of the acceleration and change in trajectory caused by the spin.

Game Highlights
Just as we saw in the Baseball Hitting post, our human perception skill in tracking flying objects, especially those that are spinning and changing direction, are not perfect. If we understand the physics of the spinning ball and we can better guess at its path, but the pitcher or the free kick taker doesn't usually offer this information beforehand! In the next few posts, I'll be looking at a related topic in perception; a concept known as "Quiet Eye", developed by Prof. Joan Vickers. Check back as this is one of the best applications of cognitive science in sports that I have seen.

Source:
Craig, C.M., Berton, E., Rao, G., Fernandez, L., Bootsma, R.J. (2006). Judging where a ball will go: the case of curved free kicks in football. Naturwissenschaften, 93(2), 97-101. DOI: 10.1007/s00114-005-0071-0

Baseball and the Brain - Fielding

With the crack of the bat, the ball sails deep into the outfield. The left-fielder starts his run back and to the right, keeping his eyes on the ball through its flight path. His pace quickens initially, then slows down as the ball approaches. He arrives just in time to make the catch. What just happened? How did this fielder know where to run and at what speed so that he and the ball intersected at the same exact spot on the field. Why didn't he sprint to the landing spot and then wait for the ball to drop, instead of his controlled speed to arrive just when the ball did? What visual cues did he use to track the ball's flight (just the ball? the ball's movement against its background? other fielder's reaction to the ball?)

Just like we learned in pitching and hitting, fielding requires extensive mental abilities involving eyes, brain, and body movements to accomplish the task. Some physical skills, such as speed, do play a part in catching, but its the calculations and estimating that our brain has to compute that we often take for granted. The fact that fielders are not perfect in this skill, (there are dropped fly balls, or bad judgments of ball flight), begs the question of how to improve? As we saw with pitching and hitting (and most sports skills), practice does improve performance. But, if we understand what our brains are trying to accomplish, we can hopefully design more productive training routines to use in practice.

(Mike Stadler, associate professor of psychology at University of Missouri, provides a great overview of current research in his book, "The Psychology of Baseball". I highly recommend it for the complete look at this topic. I'll summarize the major points here.)

One organization that does not take this skill for granted is NASA. The interception of a ballistic object in mid-flight can describe a left fielder's job or an anti-missile defense system or how a pilot maneuvers a spacecraft through a three dimensional space. In fact, a postdoctoral fellow at the NASA Ames Research Center, Michael McBeath , has been studying fly ball catching since 1995. His team has developed a rocket-science like theory named Linear Optical Trajectory to describe the process that a fielder uses to follow the path of a batted ball. LOT says the fielder will adjust his movement towards the ball so that its trajectory follows a straight line through his field of vision. Rather than compute the landing point of the ball, racing to that spot and waiting, the fielder uses the information provided by the path of the ball to constantly adjust his path so that they intersect at the right time and place. The LOT theory is an evolution from an earlier theory called Optical Acceleration Cancellation (OAC) that had the same idea but only explained the fielder's tracking behavior in the vertical dimension. In other words, as the ball leaves the bat the fielder watches the ball rise in his field of vision. If he were to stand still and the ball was hit hard enough to land behind him, his eyes would track the ball up and over his head, or at a 90 degree angle. If the ball landed in front of him, he would see the ball rise and fall but his viewing angle may not rise above 45 degrees. LOT and OAC argue that the fielder repositions himself throughout the flight of the ball to keep this viewing angle between 0 and 90 degrees. If its rising too fast, he needs to turn and run backwards. If the viewing angle is low, then the fielder needs to move forward so that the ball doesn't land in front of him. He can't always make to the landing spot in time, but keeping the ball at about a 45 degree angle by moving will help ensure that he gets there in time. While OAC explained balls hit directly at a fielder, LOT helps add the side-to-side dimension, as in our example of above of a ball hit to the right of the fielder.

The OAC and LOT theories do agree on a fundamental cognitive science debate. There are two theories of how we perceive the world and then react to it. First, the Information Processing (IP) theory likens our brain to a computer in that we have inputs, our senses that gather information about the world, a memory system that stores all of our past experiences and lessons learned, and a "CPU" or main processor that combines our input with our memory and computes the best answer for the given problem. So,IP would say that the fielder sees the fly ball and offers it to the brain as input, the brain then pulls from memory all of the hundreds or thousands of fly ball flight paths that have been experienced, and then computes the best path to the ball's landing point based on what it has "learned" through practice. McBeath's research and observations of fielders has shown that the processing time to accomplish this task would be too great for the player to react. OAC and LOT subscribe to the alternate theory of human perception, Ecological Psychology (EP). EP eliminates the call to memory from the processing and argues that the fielder observes the flight path of the ball and can react using the angle monitoring system. This is still up for debate as the IPers would argue "learned facts" like what pitch was thrown, how a certain batter hits those pitches, how the prevailing wind will affect the ball, etc. And, with EP, how can the skill differences between a young ballplayer and an experienced major leaguer be accounted for? What is the point of practice, if the trials and errors are not stored/accessed in memory?

Of course, we haven't mentioned ground balls and their behavior, due to the lack of research out there. The reaction time for a third baseman to snare a hot one-hopper down the line is much shorter. This would also argue in favor of EP, but what other systems are involved?

Game Highlights
Again, I have just touched on this subject, see Prof. Stadler's book for a much better discussion. Arguing about which theory explains a fielder's actions is only productive if we can apply the research to create better drills and practices for our players. My own layman's view is that the LOT theory is getting there as an explanation, but I'm still undecided about EP vs. IP . So many sport skills rely on some of these foundations, hence my "search for the truth" continues! As with pitching and hitting, fielding seems to improve with practice. As we move forward, we'll look at the theories behind practice and what structure they should take.

Baseball and the Brain - Hitting

Ted Williams, arguably the greatest baseball hitter of all-time, once said, "I think without question the hardest single thing to do in sport is to hit a baseball". Certainly, at the major league level, where pitches can reach 100 miles per hour, this is believable, but even at Little League, High School and College/Minor leagues, the odds are against the hitter. Looking at batting averages, 3 hits out of 10 at-bats will earn a player millions of dollars in the bigs, while averaging 4 or 5 hits out of 10 at the lower leagues will earn you some attention at the next level. As most of you know, Williams was the last major league player to hit .400 for an entire season and that was back in 1941, almost 67 years ago! In my second of three posts of the Baseball and the Brain series, we'll take a quick look at some of the theory behind this complicated skill.

Again, my main reference for these ideas is "The Psychology of Baseball" by Mike Stadler.


Some questions that come to mind regarding hitting a pitched baseball:
- What makes this task so hard? Why can't players, who practice for years and have every training technique, coach and accumulated knowledge at their disposal, perform at a consistenly higher level?
- What can be improved? Hand-eye reaction time? Knowledge of situational tendencies (what pitch is likely to be thrown in a given game situation)?

A key concept of pitching and hitting in baseball was summed up long ago by Hall of Fame pitcher Warren Spahn, when he said, “Hitting is timing. Pitching is upsetting timing.” To sync up the swing of the bat with the exact time and location of the ball's arrival is the challenge that each hitter faces. If the intersection is off by even tenths of a second, the ball will be missed. As was discussed in the Pitching post, the hitter must master the same two dimensions, horizontal and vertical. The aim of the pitch will affect the horizontal dimension while the speed of the pitch will affect the vertical dimension. The hitter's job is to time the arrival of the pitch based on the estimated speed of the ball while determining where, horizontally, it will cross the plate. The shape of the bat helps the batter in the horizontal space as its length compensates for more error, right to left. However, the narrow 3-4" barrel does not cover alot of vertical ground. So, a hitter must be more accurate judging the vertical height of a pitch than the horizontal location. So, if a pitcher can vary the speed of his pitches, the hitter will have a harder time judging the vertical distance that the ball will drop as it arrives, and swing either over the top or under the ball.

A common coach's tip to hitters is to "keep your eye on the ball" or "watch the ball hit the bat". As Stadler points out in his book, doing both of these things is impossible due to the concept known as "angular velocity". Imagine you are standing on the side of freeway with cars coming towards you. Off in the distance, you are able to watch the cars approaching your position with relative ease, as they seem to be moving at a slower speed. As the cars come closer and pass about a 45 degree angle and then zoom past your position, they seem to "speed up" and you have to turn your eyes/head quickly to watch them. This perception is known as angular velocity. The car is going a constant speed, but appears to be "speeding up" as it passes you, because your eyes need to move more quickly to keep up. This same concept applies to the hitter. The first few feet that a baseball travels when it leaves a pitcher's hand is the most important to the hitter, as the ball can be tracked by the hitter's eyes. As the ball approaches past a 45 degree angle, it is more difficult to "keep your eye on the ball" as your eyes need to shift through many more degrees of movement. Research reported by Stadler shows that hitters cannot watch the entire flight of the ball, so they employ two tactics. First, they might follow the path of the ball for 70-80% of its flight, but then their eyes can't keep up and they estimate or extrapolate the remaining path and make a guess as to where they need to swing to have the bat meet the ball. In this case, they don't actually "see" the bat hit the ball. Second, they might follow the initial flight of the ball, estimate its path, then shift their eyes to the anticipated point where the ball crosses the plate to, hopefully, see their bat hit the ball. This inability to see the entire flight of the ball to contact point is what gives the pitcher the opportunity to fool the batter with the speed of the pitch. If a hitter is thinking "fast ball", their brain will be biased towards completing the estimated path across the plate at a higher elevation and they will aim their swing there. If the pitcher actually throws a curve or change-up, the speed will be slower and the path of the ball will result in a lower elevation when it crosses the plate, thus fooling the hitter.

Game Summary
As in pitching, our eyes and brain determine much of the success we have as hitters. We took a quick look as it relates to hitting a baseball, but the same concepts apply to hitting any moving object; tennis, hockey, soccer, etc. In future posts, we'll look at practical ways to improve this tracking skill and the hand/eye/brain connection. As usual, practice will improve performance, but we want to identify the unique practice techniques which will be most effective. Tracking a moving object also applies to catching, which we'll look at next.