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The
Trouble with Golf Technology:
Why
Technologists are not Teachers
By Geoff Mangum Golf
"science" is unfortunately anemic in terms
of the robustness of the exchange of views, the rigor
of the methodology, and the directedness of effort.
The role of technology in this situation, also unfortunately,
exacerbates rather than ameliorates the "science."
Science that shuns critique is pseudo-science
in the worst sense. Currently, technologists develop
golf training and monitoring devices without deep
insight into what the teacher of golf needs to know
or what the student of golf needs to learn. This creates
a false priesthood of golf technologists who create
justifications and rationales for patterns of data-gathering
determined almost exclusively by the technology itself
rather than developing technology to gather relevant
data. This state of affairs, albeit accompanied by
the seductiveness and the "aura" of hard
science, is mainly an unsound and wasteful injection
of technology that causes misdirection and confusion
in the golfing community and delays real progress
in teaching and learning. What follows is a critique
of golf technology and an analysis of why it gets
off track, along with suggestions for correcting the
problems. Lack of intelligent development of technology
In
golf, technologies for measuring various parameters
in the behaviors of the game are invariably developed
by technologists who are not skilled practitioners
of the game. There are a number of related reasons
for this, primary among them being that expert golfers
and golf instructors are not usually trained in science
and technologists are so specialized in their expertise
that they have no time to acquaint themselves with
the complex art and science of the skilled movements
in golf. What results is that technologists, seeking
applications for their expertise, turn their technical
skills to golf and unilaterally design technologies
that capture certain data about the behaviors. To
the extent the choices of the parameters to measure
are investigated and analyzed before selection and
design of the technology for capture, the technologists
have a skimpy budget of time and effort for mastering
the complexity of the skills at issue and so content
themselves with little more than a general level of
acquaintance derived from a superficial reading of
commonly acceptable representations of the movement
expertise and at most a loose partnership with anyone
credited by the game's hierarchical guardians with
passable knowledge. For
example, the Science
and Motion PuttLab is a measurement technology
designed by a neuroscience researcher skilled in hand
movement disorders but completely unacquainted with
golf, a mathematician and software designer also completely
unacquainted with golf, and an itinerant golf club
professional who happened to be handy. Given the ultrasonic
signalling technology that measures putter head position
during a putting stroke, the team then set itself
to the issue of what to measure. The choice of what
to measure is dictated by the capabilities of the
technology -- in this case, the position in space
and time of the putter head during the stroke. The
mathematical engineers at MIT have followed the same
pattern in the development of the iClub
technology for measuring club dynamics in the
full-swing and in putting strokes -- a non-golfers
seeking to apply his software and signal-capture technology
chose golf and then set about choosing parameters
to measure based upon the capability of the technology.
There are numerous other examples in golf technology
wherein the technology is available and then directed
at golf without careful consideration of the teaching-learning
context and its unique demands for information. What
is wrong with this picture? In the scientific method,
technology does not determine the quarry. Theory determines
the quarry (phenomena to investigate), although technology
may limit the capability of science to probe the phenomena
(think: Fermilab, SLAC, CERN, and accessible energy
regimes for probing high-energy phenomena). Fundamentally,
scientific technology is designed to go where theory
directs it. And theory in any field of expertise is
the constantly updated explanation of existing data
in a manner that accounts for all current data, predicts
future lines of investigation to verify or falsify
the theory, and convincingly contextualizes an understanding
of the data in a wider, interconnected collection
of related fields of expertise (think: Einstein and
the Michelson-Morley "ether" experiments
and the prediction of gravitational lensing of light;
think: Richard Feynman and the theory of QED as later
verified by CERN experimentalists). What
should this picture look like? The technologist must
first honestly acknowledge that he does not comprehend
the current state of the field of expertise with sufficient
breadth and depth to enable the intelligent choices
of what needs measuring and why. Rather than have
the technology itself dictate what may be measured,
with the rationale for choosing these parameters following
along wagging its tail appealingly behind, the technologists
needs to be informed about what to measure and why
before he designs the technology (think: Henry Cavendish
and the torsion bar measurement of G, in which the
torsion bar technology was designed to investigate
and measure G; think: Galileo and the telescope designed
for studying the heavens; think: all astrophysics
technologies for exploring different energy realms
of the universe from infrared to X-ray and gamma frequencies).
The priesthood of the technologists
Granted
that the scientific method may follow empirical inductive
pathways or theoretical deductive pathways at various
stages, this does not mean that one approach is superior
or more scientific than the other. The interplay between
the two approaches makes them mutually supporting.
Theoretical prediction isolates phenomena for study;
experimental empiricism validates or falsifies theory;
theoretically-informed reasoning explains discordant
or anomalous data and incorporates new information
into a revised theoretical comprehesion that generates
new predictions; and so forth. What does not happen,
however, is empirical data-gathering without theoretical
contextualizing. But
this is exactly what technologists find themselves
doing. Why? The false credo of the measurer is "To
measure is to know." This is unscientific. To
measure is to gather data or information. The information
may or may not be relevant to the phenomena under
investigation, and the measurement may or may not
be at a useable level of precision or in a format
that lends itself to comprehension. To comprehend
the relevance of information derived from measurement
in terms of cause-and-effect is to attain useful knowledge
that can be expressed in theoretical terms and that
can be applied and tested further by the scientific
community. Technologists in golf do not participate
in this scientific methodology, and instead simply
measure whatever the technology is capable of measuring. The
trouble with technology used in this fashion arises
as soon as the technologist is called upon to explain
the data and to teach golfers the practical use of
the knowledge rightly comprehended (think: square
peg must fit round hole). Almost without exception,
technologists are forced by this situation to retreat
to the formation of a data-determined "model"
of ideal behavior. A "model" is typically
distinct from a theory, in the technologists' world,
because a "model" is seen as an empirically
emergent "pattern" inherent in the behaviors
of golf when collectively measured and therefore "revealed"
with a sufficiently large data sample, whereas a "theory"
is a product of reasoning that unifies the comprehended
data of disparate fields of expertise in a manner
that resolves anomalies in the data. In other words,
the technologist distrusts and disavows reasoning
and the conceptual views of others in strict favor
of data. This "model" building, then, is
implicitly essential to the technologist's methodological
raison d'tre, and comes with its own justification
and badge of superiority: empirical data gathering
is a better scientific approach than the deductive
reasoning of the theorist, offering "real proof"
The
technologist has the advantage of wrapping himself in the white robes
of the lab and the mystical aura of so-called "hard science."
But if the "modeling" process is examined closely, the lab
coat begins to look much more like the gauze curtain concealing the
Wizard of Oz at his dials. Firstly, the only pattern that could possibly
emerge from collective data-gathering is that predetermined by the
parameters chosen to measure in the design of the technology. Good
choices, poor choices, informed choices, or default choices -- why
these parameters rather than others? Secondly, the only pattern that
could possibly emerge from this methodology is also predetermined
by the sample population. This second problem is inevitably "handled"
by the justification-hungry technologist by narrowing the population
whose behaviors are measured to a communally accepted "elite"
population of high-skilled golfers. The trouble here is that it is
manifestly illogical to proceed from the fact that a given subpopulation
of golfers is "highly skilled" in comparion to others to
any of the following conclusions: a) measurements of the subpopulation reveal the "best"
data pattern for the skills as there is implicitly no skill level
higher than that displayed by the elite subpopulation; b) mimicry
of the data pattern of the elite golfers is a sound and effective
way to improve golfers with lesser skills; c) members of the elite
subpopulation cannot really benefit from the "model" in
terms of improving current skills but can at best use the "model"
for maintenance purposes; and d) the data pattern itself is its own
"explanation" of the skills.
But
this is simply wrong-headed and unscientific. Science
needs both the empiricist and the theoretician, working
cooperatively to complement one another. Begging the
question of what is best and how to teach it with
a "model" of elite golfers is similar to
hunting elephants with a microscope. What happens
if the technologist is told by an expert golfer that
there are golfers beyond his "model" subpopulation
who display skills significantly different from and
superior to those revealed by the "model"?
In a word: "tilt". The forced teaching role for technologists
If
a technologist finds himself with a technology that
generates data out of context of what is known for
teaching and learning the skill, then he is either
forced to "teach" using a derived "model"
data set, or he frankly disavows teaching altogether
and merely posits the value of the technology to as
many and as diverse teaching approaches as possible
so that the technology has "value" to as
broad a "market" as possible. This response
is common to many technologies in golf: The TaylorMade "Motion
Analysis Technology by TaylorMade" (MATT system)
is presented by top technology chief Tom Olsavsky
as follows: "We're not here to teach putting,
to be sure," says TaylorMade's Tom Olsavsky.
"We fit the club to the swing, not the other
way around." Similarly, the Swedish
Putting Guide stroke training technology is explicitly
NOT designed to be used to train any one particular
stroke style, but is adaptable to use by teachers
using many diverse styles. And ALL commercial putter
fitting systems explicitly disavow any connection
with a preferred stroke style, as technologists in
this area have repeatedly learned from experience
that coupling instruction for improved stroke technique
BEFORE fitting the putter to the golfer invariably
reduces business (e.g., David
Edel fitting system per personal communication
January 2005; PING
fitting system geared to "your preferences
and goals"). Common sense rather convincingly
holds that the golfer's postures and motion pattern
should be improved before the equipment fitting or
training device cements him into his stroke pattern.
Yet in all these technological applications, the technologists
disavow any role in teaching for improvement other
than to have as many teachers as possible use their
technology. Disjuncture between learning and teaching
Thus,
instead of questioning and testing whether the elite
"model" represents the best display of skills
or the most seminal skills for teaching lesser golfers
or whether the use of the technology should be tied
to specific teachings, technologists default to the
"black box" approach to applying acquired
"data" (versus "knowledge") without
the necessity of understanding the cause-and-effect
processes. The technology gathers a collective "model"
from the data and then the same technology is used
to measure whether the learner can produce data that
closely mirrors the "model." The essence
of a "black box" approach is that the input
produces an output in a predictable enough manner
but without any comprehension of the cause-and-effect
processes by which input yields output. The technologist
does not attempt to explain WHY the elite golfer performs
the way he does, but simply uses trial and error to
settle on which inputs most reliably and consistently
generate "model"-like data outputs. This
situation sets up a war between technologists and
teachers. The technologist has defined the so-called
"scientific" ground with the technological
design, but the teacher frankly has other fish to
fry. Good teachers routinely do not use technology
precisely because it detracts from the learning process.
It is not uncommon for top golf teachers to use video
technology ONLY to show to the student that the student's
ideas of what is actually happening are not correct,
so the student will harken more assiduously to the
guidance of the teacher. Why
aren't the two on the same page? The short answer
is the hubris of the technologist lethally combines
with the poor definition of teaching science standard
in golf. The technologist assumes his choices of parameters
to measure are relevant to understanding and teaching
the phenomena, even though the parameters are chosen
without benefit of deep insight into the skills and
without a background in teaching the skills. Cloaked
with this protective armor of delusion, the technologist
further assumes he knows what is relevant and also
that he can teach a student how to perform so that
the student data looks more like elite data. The golf
instructor typically does not have a very solid focus
on the process of teaching in terms of skills evaluation,
diagnosis of strengths and weaknesses, teaching protocols
to advance with permanent and incremental improvements
in skills, and the role of measuring and recording
skills for evaluation, diagnosis, and progress monitoring.
This lack of teaching-learning science on the instructor's
part does not usually fare well in the golfing public's
perception of which is more potent: the gee-whiz technology
in the hands of the white labcoat "scientist"
or the golf teacher who is a good player perhaps but
only a so-so instructor not trained in the use of
science. This situation promotes a "scientific
priesthood" in favor of the mere technologist,
and this conferred status inexorably tempts the technologist
to adopt the role of teacher as well. That
technologists are NOT teachers immediately reveals itself whenever
a technologist is asked the disarmingly simple question: "Based
upon your data, what do you SAY to a student so that his effort at
the skill generates data more closely mirroring the "model"
data set?" The answer is always without exception a blank stare
from the technologist. The communication process that is the heart
of the student-teacher learning process is practically non-existent
and is at best stunted and nearly unintelligible (e.g., "you
should try to narrow your s.d. on this parameter.") The
most striking modern example of this delusional science
is seen in the application of EEG
and "gaze-tracking" technology to
putting. In an article in Golf Digest (December 2003) about the so-called "Quiet
Eye" in putting, Dr Joan Vickers measures
patterns of gaze change in putting. The data is that
"better" golfers have a more organized pattern
of saccadic gaze shifts plus a 2-3 second moment of
non-shifting just prior to initiating the stroke whereas
"poorer" golfers display a more "unorganized"
pattern of shifts and a stillness period of only 1-2
seconds. Dr
Vickers offers this "explanation" of why
the gaze pattern of "better" golfers results
in better performance: "Why is it essential that
you develop a Quiet Eye when you putt? It's simple
-- your hands are controlled by your brain, which
gets valuable information about what to do from your
eyes. As you putt, your brain needs to organize more
than 100 billion neurons. These neural networks are
informed by your gaze, and control your hands, arms
and body as the stroke is performed. These networks
will stay organized for only a short period of time;
a window of opportunity opens that must be used when
it is at its most optimal." That's extremely
vague and assumption-riddled language, and there is
NO science offered to support any of these explanatory
statements. Dr Debbie Crews in the same article, using
EEG technology, then "explains" what the
"brain is doing" that makes this "better"
pattern of gazing superior to the pattern used by
"poorer" golfers. Dr Crews claims: "Over
all, the good putter shows "harmonized"
activity throughout the brain. This is similar to
the keys on the piano. Certain combinations of notes
create greater harmony than other combinations. They
may not all sound the same but are beautiful when
put together in specific patterns." This highly
metaphor-laden language is also devoid of science. The
so-called "quiet eye" phenomenon may be
a real cause-and-effect contributor to improved performance
between the two subgroups measured (actually, college
students with high handicaps versus other college
students with not-so-high handicaps), but the phenomenon
as it stands is simply a by-product of what the technology
measures. Whether the visual processing contributes
(or not) or whether something else entirely underlies
the performance difference (muscle movements, internal
psychology of the more skilled golfers, etc.) is not
disassociated from the data. And these "putting
experts" have never in all their academic writing
displayed the sligtest familiarity with brain science,
as a detailed survey of their published writings shows
NO citations of brain science works throughout their
careers. Because there is at best a confused and superficial
theory (no more accurate than a "pop psychology"
version of neuroscience, if that) for what this gaze
pattern might contribute to improved performance,
there is no science to test and verify what are the
cause-and-effect processes at work in the golfer.
And in the same vein, there is no intelligible basis
for formulating what to SAY to the golfer about how
to benefit from the phenomena, other than somehow
do what better golfers appear to do. Empirical wanderings without theoretical pathfinding
Why
do technologists measure what they measure? The answer
is: Simply because they can, which is the least intelligent
reason. Technologists should measure what matters,
or at worst what is likely to shed light on the phenomena
under investigation for purposes of cause-and-effect
comprehension. In golf, measuring the club motion
is the least useful sort of measurement. Measuring
what the golfer DOES to generate the club motion is
a vastly superior choice, but even then the REAL quarry
is how does the golfer do what he does, why does he
do it that way and not another, and how might this
behavior by the golfer be molded by instruction into
a more efficacious pattern? This
approach cannot be undertaken without theory, and
a strictly empiricist approach commonly employed today
in golf science is doomed from the start because it
does not design the technology with these questions
firmly in mind. So what SHOULD Technologists do?
Technologists
have valuable skills in data gathering and manipulation,
but they are most emphatically NOT trained in what
counts or why about golf skills. Before designing
a technology, and front-loading its data-gathering
capacities in restrictive ways, the technologist must
learn to ask REAL experts what the relevant parameters
are for measuring and what is the underlying cause-and-effect
comprehension that justifies these choices. This would
avoid the "priesthood" effect and also avoid
a lot of after-the-fact justifying of the technology
and its square-peg forcing into a round-hole. For
example, modern learning theory as informed by recent
advances in neuroscience indicates that the old "rote"
or "muscle memory" approach of sports scientists
in the 1970s is seriously deficient, and needs to
be augmented with cognitive structures and a deeper
part-whole approach to skills. This means to technologists
that there is "feedback" and then there
is "feedback." Technologists uniformly rely
on the outdated notion that repetitive exposure to
"good" feedback information is sufficient
for sound learning, and this is simply not the case.
From the teacher's point of view, how the skill is
performed best, and how the golfer student best gets
this optimal performance accomplished, is what needs
to be taught and learned. Raw "feedback"
from the putter head, for example, has some relevance,
but it is at a level removed from the more seminal
"feedback" of HOW the golfer student moved
to produce the putter head action. The former sort
of feedback is typically called "knowledge of
result" or KR, whereas the latter sort is called
"knowledge of performance" or KP. But there
is an even more important level of feedback -- HOW
DID THE STUDENT GENERATE THE KP AND WHAT CAN THE TEACHER
DO TO STABILIZE EFFECTIVE PROCESSES AT THIS LEVEL? Frankly,
technologists know scarcely anything at all about
this level of teaching and learning. Not many teachers
do either. But theorists do. The bread and butter
of a good theorist for teaching golf skills is a sound
understanding of the performance data in terms of
cause-and-effect such that the theorists can communicate
with a wide variety of students to successfully instill
stable and efficacious performance strategies. The
theorist uses technology to validate and invalidate
teaching concepts and techniques, to assess the efficacy
of techniques, to develop novel techniques, and to
monitor progress. But the quarry is always "what
to say to the student" so the student learns
well. This
all means that the theorist cum teacher needs assistance
from the technologist in the defining of WHAT TO MEASURE
and WHY, calling upon the skills set of the technologist
to indicate what may possibly best suit the needs
of the teacher. The teacher doesn't so much want to
know whether the golfer student successfully moved
the putter head straight-back in the backstroke so
much as he wants to know whether the golfer KNOWS
not to use the muscles of his hands and arms to start
the putter head back from its static resting position,
due to the adverse effects caused by this flawed muscles
activation pattern. The teacher does not so much want
feedback about the putter face squareness coming forward
as he wants information about the role of stability
of the tempo and stroke pivot at the base of the neck
managing the forces of the stroke to promote consistent
and accurate re-squaring of the putter face with minimal
effort and attention. The teacher does not so much
need a "picture" of a golfer student's brain
during putting so much as he needs an understanding
of what the brain processes should be in terms of
cause and effect and how best to repeat these brain
processes with minimal effort and attention. No technologist
today looks in these quarters for parameters to measure,
so there is really no useful "science" being
generated. Instead, there is much smoke and little
to no light. Because
of this state of affairs, there is vast room for improving
the role of technology is promoting better golf, but
technology is currently headed down a dead-end path.
The good news is there is such potential in the near
future. The bad news is this message is not a welcome
message in the status quo. Technologists will need
to drink deeply from a new firehose in order to shift
to this more useful role. We'll see how it works itself
out. |
