Of all of the decisions parents face regarding
their children's future, choosing between shoulder pads or running
shoes for their Christmas present seems trivial. Well, according to
Kevin Reilly, president of Atlas Sports Genetics, this is a decision you should not take lightly.
"If
you wait until high school or college to find out if you have a good
athlete on your hands, by then it will be too late," he said in a
recent New York Times interview. "We need to identify these kids from 1 and up, so we can give the parents some guidelines on where to go from there."
In
December, Reilly's company began marketing a $149 saliva swab test for
kids, aged 1 to 8, to determine which variant of the gene ACTN3 is in their DNA. According to a 2003 Australian study,
ACTN3 was shown to be a marker for two different types of athletic
prowess, explosive power or long endurance. While everyone carries the
gene, the combination of variants inherited, one from each parent,
differs.
Science of success The R
variant of ACTN3 signals the body to produce a protein,
alpha-actinin-3, which is found exclusively in fast-twitch muscles. The
X variant prohibits this production. So, athletes inheriting two R
variants may have a genetic advantage in sports requiring quick,
powerful muscle contractions from their fast-twitch muscle fibers.
In the ACTN3 study, Dr. Kathryn North
and her lab at the Institute for Neuromuscular Research of the
University of Sydney looked at 429 internationally ranked Australian
athletes and found significant correlation between power sport athletes
and the presence of the R variant. All of the female sprint athletes
had at least one R variant, as did the male power-sport athletes. In
fact, 50 percent of the 107 sprinters had two copies of the R variant.
What about those aspiring athletes that were not fortunate enough to inherit the R variant and its protein producing qualities?
North's
team also noted that the elite endurance athletes seemed to be linked
to the XX variation, although only significantly in the female sample.
In 2007, her team pursued this link by developing a strain of mice that
was completely deficient in the alpha-actinin-3 protein similar to an
athlete with an XX allele. They found the muscle metabolism of the mice
without the protein was more efficient. Amazingly, the mice were able
to run 33 percent farther than mice with the normal ACTN3 gene.
Cloudy future Additional research is showing mixed results, however.
In
2007, South African researchers found no significant correlation
between 457 Ironman triathletes, known for their endurance, and the XX
combination. This year, Russian researchers at the St. Petersburg
Research Institute of Physical Culture also failed to establish the
XX-endurance performance link among 456 elite rowers but did find the
RR connection among a sample of Russian power sports athletes.
So, can we at least find the next Usain Bolt among our kids?
"Everybody
wants to predict future athletic success based on present achievement
or physical makeup. But predicting success is much more difficult than
most people think," Robert Singer, professor and chair of the
department of exercise and sport sciences at the University of Florida
warns in the book "Sports Talent" (Human Kinetics Publishers, 2001) by Jim Brown.
"There
are too many variables, even if certain athletes have a combination of
genes that favors long-range talent," Singer said. "A person's genetic
makeup can be expressed in many different ways, depending on
environmental and situational opportunities. Variables such as
motivation, coachability, and opportunity can't be predicted."
Destiny? Just
as we assume that kids that are at the 99 percent percentile in height
are destiny-bound for basketball or volleyball, having this peek into
their genome may tempt parents to limit the sports choices for their
son or daughter.
Even Mr. Reilly expressed his concern
in the Times article: "I'm nervous about people who get back results
that don't match their expectations," he said. "What will they do if
their son would not be good at football? How will they mentally and
emotionally deal with that?"
For those parents that are just not ready to discover the sports
destiny of their child, or just want to save the $150, there is a much
simpler alternative. Hold your son or daughter's hand, palm up. Measure
the lengths of their index finger and their ring finger. Divide the
former by the latter. According to John Manning, professor of
psychology at the University of Central Lancashire, if the ratio is
closer to .90 than 1.0, you may have a budding superstar.
Manning explains in his aptly named new book, "The Finger Book"
(Faber and Faber, 2008),that the amount of a fetus' exposure to
testosterone in the womb determines the length of the ring finger,
while estrogen levels are expressed in the length of the index finger.
According to Manning's theory, more testosterone means more physical
and motor skill ability.
The digit ratio theory, as it
is known, has been the subject of more than 120 studies to find its
effect on athletic, musical and even lovemaking aptitude.
Don't
worry if the ratio is closer to 1.0, which is by far the norm. Plus,
you will be able to relax, enjoy your kids' sports events and only
worry about their genetic disposition to being happy.
When
it comes to improving your golf game, you can spend thousands of
dollars buying the latest titanium-induced, Tiger-promoted golf clubs;
taking private lessons from the local "I used to be on the Tour" pro;
or trying every slice-correcting, swing-speed-estimating,
GPS-distance-guessing gadget. But, in the end, it’s about getting that
little white sphere to go where you intended it to go. Don't worry,
there are many very smart people trying to help you by designing the
ultimate golf ball. Of course, they are also after a slice of this
billion dollar industry, as any technological advancement that can grab
a few more market share points is worth the investment.
In
fact, the golf ball wars can get nasty. Earlier this month, Callaway
Golf won a court order permanently halting sales of the industry's
leading ball, Titleist's Pro V1, arguing patent infringements involving
its solid core technology which Callaway acquired when it bought
Spaulding/Top Flite in 2003. Titleist disagrees with the decision and
will appeal, but in the meantime has altered its manufacturing process
so that the patents in question are not used.
The
challenge for golf ball manufacturers is to design a better performing
ball within the constraints set by United States Golf Association. The
USGA enforces limits on the size, weight and initial performance
characteristics in an attempt to keep the playing field somewhat level.
Every "sanctioned" golf ball must weigh less than 1.62 ounces with a
diameter smaller than 1.68 inches. It also must have a similar initial
velocity when hit with a metal striker, and rebound at the same angle
and speed when hit against a metal block. So, what is left to tinker
with? Manufacturers have focused on the internal materials in the ball
and its cover design.
Today's
balls have 2, 3 or 4 layers of different internal polymer materials to
be able to respond differently when hit with a driver versus, say, a
wedge. When hit with a driver at much higher swing speed, the energy
transfer goes all the way to the core by compressing ball, reducing
backspin. During a slower swing with a club that has more angle loft,
the energy stays closer to the surface of the ball and allows the
grooves of the club to grab onto the ball's cover producing more spin.
When driving the ball off of the tee, the preference is more distance
and less loft, so a lower backspin is required. For closer shots, more
backspin and control are needed.
The Science of Dimples
Which
brings us to the cover of the ball and all of the design possibilities.
Two forces affect the flight and distance of flying spheres, gravity
and aerodynamics. Eventually, gravity wins once the momentum of the
ball is slowed by the aerodynamic drag. Since all golf clubs have some
angular loft to their clubface, the struck ball will have backspin. As
explained by the Magnus Force effect, the air pressure will be lower on
the top of the ball since that side is moving slower relative to the
air around it. This creates lift as the ball will go in the direction
of the lower air pressure. Counteracting this lift is the friction or
drag the ball experiences while flying through the air.
Think about a boat moving through
water. At the front of the boat, the water moves smoothly around the
sides of the boat, but eventually separates from the boat on the back
side. This leaves behind a turbulent wake where the water is agitated
and creates a lower pressure area. The larger the wake, the more drag
is created. A ball in flight has the same properties.
The
secret then is how to reduce this wake behind the ball. Enter the
infamous golf ball dimples. Dimples on a golf ball create a thin
turbulent boundary layer of air molecules that sticks to the ball's
contour longer than on a smooth ball. This allows the flowing air to
follow the ball's surface farther around the back of the ball, which
decreases the size of the wake. In fact, research has shown that a
dimpled ball travels about twice as far as a smooth ball.
So,
the design competition comes down to perfecting the dimple, since not
all dimples are created equal! The number, size and shape can have a
dramatic impact on performance. Typically, today's balls have 300-500
spherically shaped dimples, each with a depth of about .010 inch.
However, varying just the depth by .001 inch can have dramatic effects
on the ball's flight.
Regarding shape, these traditional round dimple patterns cover up to 86
percent of the surface of the golf ball. To create better coverage,
Callaway Golf's HX ball uses hexagon shaped dimples that can create a
denser lattice of dimples leaving fewer flat spots. Creating just the
right design has traditionally been a trial-and-error process of
creating a prototype then testing in a wind tunnel. This time-consuming
process does not allow for the extreme fine-tuning of the variables.
Simulation Solution
At
the 61st Meeting of the American Physical Society's Division of Fluid
Dynamics last month in San Antonio, a team of researchers from Arizona
State University and the University of Maryland is reporting new
findings that may soon give golf ball manufacturers a more efficient
method of testing their designs. Their research takes a different
approach, using mathematical equations that model the physics of a golf
ball in flight. ASU's Clinton Smith, a Ph.D. student and his advisor
Kyle Squires collaborated with Nikolaos Beratlis and Elias Balaras at
the University of Maryland and Masaya Tsunoda of Sumitomo Rubber
Industries, Ltd. The team has been developing highly efficient
algorithms and software to solve these equations on parallel
supercomputers, which can reduce the simulation time from years to
hours.
Now
that the model and process is in place, the next step is to begin the
quest for the ultimate dimple. In the meantime, when someone asks you,
"What's your handicap?" you can confidently tell them, "Well, my golf
ball's design does not optimize its drag coefficient which results in a
lower loft and spin rate from its poor aerodynamics."
It
sounds like a sales job from a 12 year old; "Actually, Dad, this is not
just another video game. Its a virtual, scenario-based microcosm of
real world experiences that will enhance my decision-making abilities
and my cognitive perceptions of the challenges of the sport's
environment." You respond with, "So, how much is Madden 09?"
With over 5 million copies of Madden 08 sold, the release of the latest
version two weeks ago is rocketing up the charts. Days and late nights
are being spent all over the world creating rosters, customizing plays
and playing entire seasons, all for pure entertainment purposes. Can
all of those hours spent with controller in hands actually be
beneficial to young athletes? Shouldn't they be outside in the fresh
air and sunshine playing real sports? Well, yes, to both questions.
Playing video games, (aka "gaming"), as a
form of learning has been receiving increased recent attention from
educational psychology researchers. At this month's American
Psychological Association annual convention, several groups of
researchers presented studies of the added benefits of playing video games,
from problem-solving and critical thinking to better scientific
reasoning. In one of the studies by Fordham University psychologist Fran C. Blumberg, PhD,
and Sabrina S. Ismailer, MSED, 122 fifth-, sixth- and seventh-graders'
problem-solving behavior was observed while playing a video game that
they had never seen before. As the children played the game, they were
asked to think aloud for 20 minutes. Researchers assessed their
problem-solving ability by listening to the statements they were making
while playing. The results showed that playing video games can improve cognitive and perceptual skills.
"Younger children seem more interested in setting short-term goals for
their learning in the game compared to older children who are more
interested in simply playing and the actions of playing," said
Blumberg. "Thus, younger children may show a greater need for focusing
on small aspects of a given problem than older children, even in a
leisure-based situation such as playing video games."
Also, in a recent article on video game learning, David Williamson Shaffer, professor of educational psychology at the University of Wisconsin-Madision and author of the book "How Computer Games Help Children Learn",
argues that if a game is realistically based on real-world scenarios
and rules, it can help the child learn. “The question though is,"
Shaffer said, "is what they are doing a good simulation of what is
happening in the real world?" Shaffer explains the research happening
on this topic at his UW lab, named Epistemic Games:
There are some words of caution out there. In a recent article, educational psychologist Jane M. Healy, author of "Failure to Connect: How Computers Affect our Children's Minds and What We Can Do About It," urges educators to proceed carefully. "The
main question is whether the activity, whatever it is, is educationally
valid and contributes significantly to whatever is being studied," she
says. "The point is not whether kids are
'playing' with learning, or what medium they are playing in — a ball
field or a Wii setup or a physics lab or art studio — but rather why
they are doing it. Just because it is electronic does not make it any
better, and it may turn out not to be as valuable."
If we accept that there is some validity
to teaching/learning with video game simulations, how can we move this
to the sports arena? Obviously, there is no substitute for playing the
real game with real players, opponents, pressure, etc., but more teams
and coaches are turning to simulation games for greater efficiency in
the learning process. If the objective is to expose players to plays,
tactics, field vision and critical thinking, then a gaming session can
begin to introduce these concepts that will be validated later on the
field during "real" practice. This homework can also be done at home,
not requiring teammates, fields, equipment, etc. As mentioned in the
videos above, another driving factor in the use of games is to reach
this young, Web 2.0 audience through a medium that they already know,
understand and enjoy. The motivation to learn is inherent with the use
of games. The "don't tell them its good for them" secret is key to
seeing progress with this type of training.
One of the best examples of video game adaptation for sports learning is from XOS Technologies
and their modified version of the Madden NFL game. In 2007, they
licensed the core development engine from EA Sports and created a
football simulation, called SportMotion,
that can be used for individual training. With the familiar Madden
user interface, coaches can first load their playbook into the game, as
well as their opponent's expected plays. Then, the athlete can "play"
the game but will now see their own team's plays being run by the
virtual players. Imagine the difference in learning style for a new
quarterback. Instead of studying static X's and O's on a
two-dimensional piece of paper, they can now watch and then play a
virtual simulation of the same play in motion against a variety of
different defenses. With a "first-person" view of the play unfolding,
they will see the options available in a "real-time" mode which will
force faster reaction and decision-making skills. To take the
simulation one step further, XOS has added a virtual reality option
that takes the game controller out of the player's hands and replaces
it with a VR suit and goggles allowing him to physically play the game,
throw the ball, etc. through his virtual eyes. Take a look at this
promotional video from XOS:
XOS is winning some high praise for its system, including none other than Phillip Fulmer, Head Coach of the University of Tennesee football team.
“We’re leading the nation by taking advantage of this cutting-edge
technology and we couldn’t be more pumped about it,” Fulmer said. “UT
football has a long and storied tradition of success and because we
look to pioneer groundbreaking concepts before anyone else, we’ll
proudly continue that history. The XOS PlayAction Simulator begins a
new chapter for UT and we’re pleased to add it to our football training
regiment.” Albert Tsai, vice president of advanced research at XOS
Technologies, says, “We’ve basically added functionality to popular EA
video games such as customizable playbooks, diagrams and testing
sequences to better prepare athletes for specific opponents.
Additionally, the software includes built-in teaching and reporting
tools so that coaches Fulmer, Cutcliffe and Cooter can analyze and
track the tactical-skill development of the team. At the same time, the
Volunteers can experience immediate benefits because the familiarity
with the EA SPORTS brand requires little to no learning curve for their
players.”
So, the next time your son (or daughter!)
is begging for 10 more minutes on the Xbox to make sure the Packers
destroy the Vikings once again (sorry, a little Wisconsin bias), you
may want to reconsider pulling the plug. Then, send them outside for
that fresh air.
Most baseball general managers live in obscurity most of their careers. Its
their first hire, the manager, that usually gets the red hot spotlight,
after every win and loss, second-guessed by reporters with recorders
and then later by fans. The GM puts the players on the field and lets
the manager and his coaches take it from there. Billy Beane
, Oakland A's general manager, could have also been an unknown, albeit
interesting, name to the baseball audience if it were not for author
Michael Lewis' 2003 book, Moneyball
. Moneyball was a runaway hit (even today, 5 years later, it is #19 on
Amazon's list of baseball books). It has morphed into a full-fledged
catchphrase philosophy used by everyone from Wall Street (where Beane
borrowed the concept) to business consulting. The general theme is to
find undervalued assets (ballplayers) by focusing on statistics that
your competition is ignoring. Of course, you have to believe in your
metrics and their predictive value for success (why has everyone else
ignored these stats?) The source of most of Beane's buried treasure of
stats was Bill James and his Sabrmetrics. Like picking undervalued
stocks of soon to explode companies, Beane looked for the diamond in
the dust (pun intended) and sign the player while no one was looking.
Constrained by his "small-market" team revenues, or maybe by his
owners' crowbar-proof wallets, he needed to make the most from every
dollar.
The combination of a GM's shrewd
player selection and a manager who can develop that talent should
reward the owner with the best of both worlds: an inexpensive team that
wins. This salary vs. performance metric is captured perfectly in this
"real-time" graphic at BenFry.com
. It connects the updated win-loss record for each MLB team with its
payroll to show the "bang for the buck" that the GMs/managers are
getting from their players. Compare the steep negative relationship
for the Mets, Yankees, Tigers and Mariners with the amazing results of
the Rays, Twins and Beane's own A's. While the critics of Moneyball
tactics would rightly point to the A's lack of a World Series win or
even appearance, the "wins to wages" ratio has not only kept Beane in a
job but given him part ownership in the A's and now the newly
resurrected San Jose Earthquakes of soccer's MLS. Beane believes the
same search for meaningful and undiscovered metrics in soccer can give
the Quakes the same arbitrage advantage. In fact, there are rumours
that he will focus full-time on conquering soccer as he knows there are
much bigger opportunities worldwide if he can prove his methods within
MLS.
In baseball, Beane relied on the
uber-stat guru, Bill James, for creative and more relevant statistical
slices of the game. In soccer, he is working with some top clubs
including his new favorite, Tottenham-Hotspur,
of the English Premier League. While he respects the history and
tradition of the game, he is confident that his search for a
competitive advantage will uncover hidden talents. Analytical tools
from companies such as Opta in Europe and Match Analysis
in the U.S. have combined video with detailed stat breakdowns of every
touch of the ball for every player in each game. Finding the right
pattern and determinant of success has become the key, according to
Match Analysis president Mark Brunkhart as quoted earlier this year, "You
don't need statistics to spot the real great players or the really bad
ones. The trick is to take the players between those two extremes and
identify which are the best ones. If all you do is buy the players
that everyone else wants to buy then you will end up paying top dollar.
But if you take Beane's approach - to use a disciplined statistical
process to influence the selection of players who will bring the most
value - then you are giving yourself the best chance of success. Who
would not want to do that?"
Not to feel left out (or safe from scrutiny), the NBA now has its own sport-specific zealots. The Association for Professional Basketball Research (APBR)
devotes its members time and research to finding the same type of
meaningful stats that have been ignored by players, coaches and fans.
They, too, have their own Moneyball-bible, "The Wages of Wins " by David Berri, Martin Schmidt, and Stacey Brook. David Berri's WoW journal/blog
regularly posts updates and stories related to the current NBA season
and some very intriguing analysis of its players and the value of their
contributions. None other than Malcolm Gladwell, of Tipping Point and
Blink fame, provided the review of Wages of Wins for the New Yorker.
One of the main stats used is something called a player's "Win Score"
which attempts to measure the complete player, not just points,
rebounds and assists.
WS is then adjusted for minutes played with the stat, WS48. Of course,
different player positions will have different responsibilities, so to
compare players of different positions the Position Adjusted Win Score
per 48 minutes or PAWS48 is calculated as: WS48 – Average WS48 at
primary position played. This allows an apples to apples comparison
between players at a position, and a reasonable comparison of players
value across positions. Berri's latest article looks at the fascination with Michael Beasley and some early comparisons in the Orlando Summer League.
Will
these statistics-based approaches to player evaluation be accepted by
the "establishment"? Judging by the growing number of young,
MBA-educated GMs in sports, there is a movement towards more efficient
and objective selection criteria. Just as we saw in previous evidence-based coaching articles , the evidence-based general manager is here to stay.
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(3), 581-585.
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.
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.
You are a coach, trying to juggle practice plans,
meetings, game prep and player issues while trying to stay focused on
the season's goals. At the end of another long day, you see this in
your inbox:
MEMO
To: All Head Coaches
From: Athletic Director
Subject: Monthly Reading List to Keep Up with Current Sport Science Research
- Neuromuscular Activation of Triceps Surae Using Muscle Functional MRI and EMG
- Positive effects of intermittent hypoxia (live high:train low) on
exercise performance are not mediated primarily by augmented red cell
volume - Physiologic Left Ventricular Cavity Dilatation in Elite Athletes - The Relationships of Perceived Motivational Climate to Cohesion and Collective Efficacy in Elite Female Teams
Just some light reading before bedtime... This is an obvious
exaggeration (and weak attempt at humor) of the gap between sport
science researchers and practitioners. While those are actual research
paper titles from the last few years under the heading of "sport
science", the intended audience was most likely not coaches or
athletes, but rather fellow academic peers. The real question is
whether the important conclusions and knowledge captured in all of this
research is ever actually used to improve athletic performance? How
can a coach or athlete understand, combine and transfer this
information into their game?
David Bishop of the Faculty of Exercise and Sport Science at the University of Verona
has been looking at this issue for several years. It started with a
roundtable discussion he had at the 2006 Congress of the Australian
Association for Exercise and Sports Science with several academic sport
scientists (see:Sports-Science Roundtable: Does Sports-Science Research Influence Practice?
) He asked very direct questions regarding the definition of sport
science and whether the research always needs to be "applied" versus
establishing a "basic" foundation. The most intriguing question was
whether there already is ample research that could be applied that needs a good translator/professional to interpret for and
communicate to the potential users - coaches and athletes. The panel
agreed that was the missing piece, as most academic researchers just
don't have the time to deliver all of their findings directly to the
field.
In a follow-up to this discussion, Bishop recently published his proposed solution titled, "An Applied Research Model for the Sport Sciences" in Sports Medicine
(see citation below). In it, he calls for a new framework for
researchers to follow when designing their studies so that there is
always a focus on how the results will directly improve athletic
performance. He calls for a greater partnership role between
researchers and coaches to map out a useful agenda of real world
problems to examine. He admits that this model, if implemented, will
only help increase the potential for applied sport science. The
"middleman" practitioner role is still needed to bring this information to the front
lines of sports.
The solution for this
"gathering place" community seems perfect for Web 2.0 technology. One
specific example is this online community, iStadia.com.
Keith Irving and Rob Robson, two practicing sport science consultants,
created this site two years ago to fill this gap. Today, with over 600
members, iStadia is approaching the type of critical mass that will be
necessary to bring all of the stakeholders together. Of course, as
with any online community, the conversations there are only as good as
the participants want to make it. But, with the pressure on coaches to win and the desire of sport scientists to produce relevant
knowledge, there is motivation to make the connection.
Another trend favoring more public awareness of sport science is the recent surge in media attention, especially related to the upcoming
Beijing Olympics. In an earlier post, Winning Olympic Gold With Sport Science, I highlighted a feature article from USA Today. This month's Fast Company also picks up on this theme with their cover article, Innovation of Olympic Proportions,
describing several high-tech equipment innovations that will be used at
the Games. Each article mentions the evolving trust and acceptance of
sport science research by coaches and athletes. When they see actual
products, techniques and, most importantly, results come from the
research, they cannot deny its value.
Bishop, D. (2008). An Applied Research Model for the Sport Sciences. Sports Medicine, 38(3), 253-263.
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), summarized the 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.
Of course, we are always interested in your thoughts and opinions! Please add your comments.
Its
something that every coach and every athlete of every sport is
searching for... the EDGE. That one training tip, equipment
improvement, mental preparation or tactical insight that will tip the
game towards them. The body of knowledge that exists today in each
sport is assumed, with each competitor expected to at least be aware of
the history, beliefs and traditions of their individual sport. But, if
each team is starting with the same set of information then the team
that takes the next step by applying new research and ideas will
capture the edge.
To me, that is what sport science is all
about. The goal is to improve sports performance by imagining,
analyzing, experimenting, testing, documenting and training new methods
to coaches and athletes.
You might have seen a great article in the 6/23 edition of USA Today; "In hunt for Olympic gold, techies are major players" by Jodi Upton. We meet Peter Vint, a "sport technologist" in the Performance Technology Division of the US Olympic Training Center
in Colorado Springs, CO, whose job it is to find ways to win more gold
medals. From the article; "The next revolution, Vint says, is breaking
down the last secrets of elite athletes: response time, how they read
the field and other players — everything that goes into the vision,
perception and split-second decision-making of an athlete. 'We've
always looked at that as mysterious, something that's unmeasurable and
innate,' Vint says. 'But we think it can be taught.'"
Interestingly,
Vint cites another pioneer in evidence-based sports coaching, Oakland
A's general manager, Billy Beane. "We're becoming progressively more
data-driven," Vint says of the center's training efforts. "We are
trying to pursue what Sabermetrics and Billy Beane did for baseball,
identifying factors that can truly influence performance." The radical
concept that Beane created, as documented in the bestseller, "Moneyball" by Michael Lewis,
is to stop searching for "the edge" in all the same places that
everyone else is looking. Instead, he started from scratch with new
logic about the objectives of the game of baseball itself and built
metrics that gave new insight into the types of players and skill sets
that he should acquire for his team.
If sport science is going
to thrive and be accepted, it faces the challenge of inertia. The ideas
and techniques that are the product of sport science can also be
captured in the phrase, "evidence based coaching". Just as evidence
based medicine has slowly found its place in the physician's exam room,
the coaching profession is just beginning to trust the research.
Traditionally, "belief based coaching" has been the philosophy favored
in the clubhouse. Training drills, tactical plans, player selection and
player development has been guided by ideas and concepts that have been
handed down from one generation of coaches to the next. Most of these
beliefs are valid and have been proven on the field through many years
of trial and error. Subjecting these beliefs to scientific research may
not produce conclusions any different than what coaching lore tells us.
But, today's coaches and athletes see the competition creeping closer
to them in all aspects, so they are now willing to at least listen to
the scientists. Beane likens it to financial analysis and the stock
market. The assumption is that all information is known by all. But, if
someone can find a ratio or a statistic or make an industry insight
that no one has considered, then they own the competitive advantage; at
least until this new information is made public.
It takes time,
though, to amass enough data to convince a head coach to change years
of habits for the unknown. Reputations and championships are on the
line, so the changes sometimes need to be implemented slowly. Vint
describes the gradual process of converting U.S. hurdler Terrence
Trammell and his coach to some of his ideas. "The relationship between
the athletes and sports scientist is critical," Vint says. "But (for
some), biomechanics has not yet provided useful enough suggestions."
There still is debate on evidence based coaching vs. belief based coaching. Here are two opposing opinions; evidence-based: "The Second Law of Thermodynamics" by Brent S. Rushall of San Diego State University
and belief-based: "Evidence Based vs. Belief Based Coaching" by Richard Todd of Webball.com. If you have a few minutes, please read each opinion and offer your take on this. After considering these opinions, Robert Robson,
sport psychologist and management consultant, stated, "Sports coaching
should absolutely be evidence-based, but any argument that places the
sole source of evidence in the realm of the scientific method is, I
would argue, naive and lacking in an understanding of the philosophical
underpinnings of science."
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.
"Mental errors cost Demons in regional quarterfinal"
"Mental mistakes doom Rays in loss to Cardinals"
If
you are a frequent visitor to my site, you may have noticed a
customized Google news feed on the right-hand side of the page. At the
top are different phrases to
select to get relevant news stories (i.e. the "Sports Science"
selection will list stories on just that.) Every day, there is always a
new variety of stories linked to the phrase, "mental mistakes" (the
list from 6/10/08 is displayed above). 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 wide receiver 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 the 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!
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.
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.
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.
As promised, we begin our look at the three most important technical skills of baseball: Pitching, Hitting and Catching.
Each of these skills apply to other sports as well, but I thought we'd
stick with the current season of baseball as the sport du jour. Again, my
focus for "80 Percent Mental" is to look at sports cognition in a
generic sense across all sports, occasionally digging deeper into
individual sport specialties. The practical side of this is to
understand how our brains and nervous system perform these skills that
we often take for granted, so that we can brainstorm (yuk-yuk) on new
ways to teach, practice and perfect these skills.
Pitching/Throwing
Pitching
a 3" diameter baseball 46 feet (for Little League) or 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?
Questions that come to my mind regarding pitching/throwing skills and baseball include:
- Why can't a pitcher control ALL of his/her pitches? Why do some not only miss the strike zone, but are wild?
- Is the breakdown physical in the muscle sequence of the throw or is it in the connection between eyes, brain and body?
Again, one the best references I have found on this is "The Psychology of Baseball"by Mike Stadler, published by Gotham
Books. Prof. Stadler digs into many of these topics and I will
paraphrase from his findings. I won't do it justice here, so please put
it on your reading list.
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,
looking at our first question, 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? Prof. Stadler collects
many different studies that review the possible
physiological/mechanical reasons for "bad throws". 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.
Game Summary
I've
only touched the surface for this topic. We'll see some of these themes
in the hitting and catching posts that are coming up. One useful
takeaway here for youth coaches is that some players will have a
genetic advantage in throwing and may be your "natural" pitchers. As we
dig deeper into these topics, we will be able pull out more practical
tips for players and coaches.
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Which
brings us to the cover of the ball and all of the design possibilities.
Two forces affect the flight and distance of flying spheres, gravity
and aerodynamics. Eventually, gravity wins once the momentum of the
ball is slowed by the aerodynamic drag. Since all golf clubs have some
angular loft to their clubface, the struck ball will have backspin. As
explained by the Magnus Force effect, the air pressure will be lower on
the top of the ball since that side is moving slower relative to the
air around it. This creates lift as the ball will go in the direction
of the lower air pressure. Counteracting this lift is the friction or
drag the ball experiences while flying through the air.
