Modeling the Swing  The Future
Dave Tutelman
 January 16, 2012
So
where do we go from here?
That is a reasonable question to ask. Modeling the golf swing is not
likely to stop after 2011 (unless the Mayans were right). Where do I
see this science going in the next few years, and why? I have spent
part of my engineering career as a technology
forecaster, and will try to apply those methods to the question,
What's next for mathematical models of the golf swing.
Demand Pull or Supply Push
In my article on technology forecasting, I started by insisting that we
must identify the force driving the evolution we are investigating. It
can be supplydriven or demanddriven. Let me repeat something I said
in
that article:
Think about the two adages:
 Build a better mousetrap and the world will beat a path to your
door.
 Necessity is the mother of invention.
These are diametrically opposed views of what drives the evolution of
technology. The views might be expressed as technologydriven vs market
driven. Another way of expressing the dichotomy is supplydriven vs
demanddriven.
 The first says that evolution is technology driven. If you come
up with a truly better technology, then people will buy it.
 The second says that evolution is marketdriven. If there is a
big enough need, the technology will (or must) be invented to
fill the need.
There are success stories for both views; they are not mutually
exclusive. Let's try to identify the supply and demand considerations
driving
the modeling of the golf swing.
Demand
(Let's hit this first, because it is where the need is. If the modeling
is purely supplydriven, then it probably is neither useful nor
interesting except to the modelers themselves.)
 Golf instructors want to know what science says they should be
teaching. (In my experience, most golf instructors don't want to know
this, but they should. Unfortunately, it can take over a decade for
established scientific fact to become accepted  along with its
implications  by the instruction community. This is especially true
if science contradicts the
traditional myths of golf. Sorry, PGA, but that's what I've seen. OTOH,
there are certainly a significant minority of instructors who really do
want to understand the science behind what they teach. The best of them
will ask a lot of challenging questions, and they will accept answers
that can be backed up.)
 Golfers have the same needs  and often the same questions  as
the instructors. This is true at all levels, from the
technicallyminded beginner to the tour pro looking for an edge.
 Golf
club manufacturers want to know the science behind the
swing, so they can design and build betterperforming clubs. (Again,
the community contains both the scientifically curious and the Luddites
and mythperpetuators. Club companies are salesdriven. Some actually
improve performance and then convince the customers of the fact.
Unfortunately, many of them find it easier to sell their wares by
pandering to the myths the customers already believe.)
Supply
 Technology is improving all the time, including the technology of
mathematically modeling the golf swing. Computers are bigger, faster,
and cheaper than last year, and will be more so this year  so
computingintensive models that are infeasible now might well be
routine with the faster computers of the future. And the
instrumentation to do the kinematic models has been proliferating for
the past few years. The precursors are in a lot of hands now. It should only be a
matter of time before it produces some new, interesting models.
 The skilled labor supply is there. Our institutions of higher
learning have a bumper crop of technically savvy graduate students,
many looking for thesis topics. In departments of kinesiology,
biomechanics, and even engineering, mathematical models of the golf
swing have been a ticket to a cap and gown. I see this trend
continuing, and even growing.
We will look at the trends in more detail starting with supply, so we
know what is possible. Then we will examine demand, in the form of
interesting questions that a model might help to answer.
Supply Push
Most of our supply side discussion deals with technology. But not all.
We mentioned "skilled labor" as a supply factor. I'd like to say a few
things relating the skill level to the sort of model you might expect
as a result.
Computing
Since it was postulated around 1970, Moore's
Law
has described the cost of computing remarkably accurately. Roughly
paraphrased, it says that computer bang for the buck doubles every two
years. This has tracked reality for four decades now, and through a
trillionfold increase in computing power per dollar. That is not just
a difference in degree; it is a difference in kind.
What do I mean by that? In 1970, it was important for the programmer to
be clever. Organize the program to compute only what really needs to be
computed. Go to considerable effort to write bugfree programs, because
you didn't have a computer right at hand; you submitted your job to a
big comp center machine (well, big by the standards of the day), and
didn't see the output for half a working day. Today, by contrast, there is so much
storage and compute power right on your desktop (and even in your
pocket) that it's OK to throw computing at a problem without thinking
much about it. Compute everything
first and select the important results after you see the output.
Unthinkable in 1970!
What does this mean to golf swing modeling? Consider a statement in
MacKenzie's paper: "Due
to the length of the closed form solution of the dynamical equations
representing the model, the number of shaft segments was limited to
four. The inclusion of additional shaft segments resulted in the
computer program becoming too large to compile into an executable file."
This suggests that, even in 2009, there were problems too large to be
conveniently handled by the existing computer solutions (both the
computers themselves and the program packages that support modeling).
As the modelers get access to increased computing power, the complexity
of the models can increase. More segments to a flexible shaft. More
elements in a model. More iterative convergence algorithms applied to
larger multivariate models.
This has special impact on inverse dynamics kinetic models, because
they require convergence algorithms (to match the kinetics to the
kinematics) for a large number of variables (over 40 timevarying functions had to be synthesized in Nesbit's work).
The march of evercheaper computing will encourage studies with:
 More complex models, meaning more variables in the model.
 Models of more golfers (because it takes less time and money to
model each golfer).
 Computer comparison of golfers. The eyeball is still the way most
lessons are learned from a swing model. Graphs and the researchers'
insight picks up the important differences between golfers. But cheap
computing and storage has made possible "data mining"  programs
looking for interesting trends in complex data. I foresee data mining
programs making suggestions to the researchers on what to examine in
the model output.
 More portable data capture. Computers the size of cell phones
(perhaps the computers in
cell phones), and even computers in golf clubs, will allow data capture
to leave the laboratory and go out on the golf course. I don't know
where that will lead, but the possibilties are exciting.
I'm confident there are other things that swing modelers will figure
out to do with all their computing power, but I'm pretty sure the above
list will happen.
Motion Capture Systems
Every model needs to be validated by measurements on real golfers. This
is especially true with kinematic studies, which are all about
capturing and correlating the motions of real golfers. (And, by
extension, inverse dynamics kinetic studies, whose first step is a
kinematic model.) Fortunately, the technology is moving rapidly in
motion capture systems.
Most such systems are camerabased. That is not surprising, given how
technology has put digital photography on a trend curve that rivals
computing. (I have been serious about photography since 1950. I had a
Rolleiflex as a teenager, and several good 35mm SLRs since. None of
them can match the performance nor features of my current camera, a
Canon for which I paid just over $100 two years ago. The successor to
that camera has a few features I covet, and it sells for under
$100.) So the ability to do 3D motion capture with multiple highspeed
digital cameras is definitely "there", and is getting more capable and
cheaper as you read this.
A newer technology with fewer entrants so far is motion capture by
sensors. (Think accelerometers, gyroscopes, strain gauges, etc.) The
one that pops immediately to mind is the KVest.
The existing product is being used only for teaching, and does not
instrument nearly enough for research and modeling. But it is a
credible forerunner for implementations that could support modeling.
So far, these motion capture systems  photographic and sensor based  are being used mostly for
instruction. At least one is being used for shaft fitting (the
Fujikura/Vicon Enso). Not many are there for research, much less
for the specialized research of swing modeling.
Let me expand a bit on the "creative tension" between teaching and
swing modeling. Instructors teach what they believe to be true. In most
cases, they believe because of what they have been taught (tradition)
and their own personal experience (observation). Fairly little of their
"knowledge" comes from understanding swing models. That means that what
they are teaching might be right  or not  but it is not based on
science.
But teaching is a highnumbers game. There are thousands of golf
teachers^{[1]}
and millions of golf students (or potential students). If my business were technology applicable to sports, it is worth
competing in a market that sells to golf instructors. The
market for researching swing models is smaller by several orders of
magnitude; I would be much less motivated to invent a product for that market. Fortunately, the needs are not all that different.
Much of the hardware is basically the
same for swing instruction and swing modeling. Clearly,
some of the software is different. But our technological
society has a history of graduate students writing sophisticated
software for which the market was not clearly large. (Those familiar
with the history of the Internet know exactly what I'm talking about.)
And graduate schools are the center of activity in swing modeling.
So we have the technology, or we almost certainly will soon.
Insight, imagination, and forward dynamics
This is a supply problem!
Forward dynamics kinetic models, especially simple ones, are a great
way to learn what matters. Granted, more parameters in the model allow
a more faithful representation of the swing. But they also tend to
obscure the essential points of what is happening, what we want to
accomplish. It takes insight to figure out what matters when looking at
all the data from a fullbody model  or any model with a lot of
parameters. And it takes both insight and imagination to devise a
model with a small number of parameters, that is still complex enough to
deal accurately with interesting questions about the golf swing. Once
again, let me invoke Einstein: "Everything
should be as simple as possible, but not simpler."
The real problem is that it takes insight and imagination to do useful
modeling. The army of grad students getting into the picture have the
training and the skills, but only the rare individual has the insight
and imagination to come up with a simple model that works. That is why
we still only have two relatively simple models: the double pendulum
and MacKenzie's model.
What path will swing modeling follow for the next few years? Absent
someone with great talent or a flash of imagination and insight, we can
still make progress. It will be the progress of looking for patterns in
a sea of multivariate data from toocomplex models. Given the
advances in technology, we can still learn new and interesing things
from the models. But there may be a way to get a kinetic model with
more detail than MacKenzie's but without the multivariate clutter of a
40plusparameter fullbody model. Some approaches that may bear fruit
include:
 We learned from Nesbit & Serrano's work that the big
providers of power are the hips, lumbar and thoracic regions, and the
right elbow. The most advanced "simple" model we have currently is
MacKenzie's. That model accounts for the right elbow as left shoulder
torque, and lumps the other major providers into torso torque. The
addition of three to six parameters (roughly double the complexity)
might handle all these knowntobeimportant factors. That is still far
less than a fullbody model, and manageable for whatif questions.
 Examination of the fullbody model may show that the
behavior of
some joints are not independent; that changes in one parameter are
always accompanied by a predictable corresponding change in another
parameter  or perhaps more than one other parameter. Each such strong
correlation is an opportunity to combine
two or more parameters into one. In that way, we reduce the
dimensionality of the model.^{[2]}
Do that enough, and even a fullbody model may become a manageable
whatif tool.
 So far, my assumption about whatif questions assumed that
they are questions that involve tweaking parameters of a
forwarddynamics kinetic model. (E.g. change the time profile of wrist
torque.) But suppose the whatif question is a kinematic question?
(E.g. what is the effect of more hip slide  or more hip rotation 
early in the downswing?) If that can be expressed by tweaking the
kinematic measurements, then a fullbody inverse dynamics model can be
exactly the way to answer the question. But be careful; expressing
those changes may be harder than you think. Changing the way the hips
behave is very likely to affect other kinematics of the swing: the
knees, the shoulders, etc. So it may be very difficult to set up a
kinematic whatif situation that reflects what a real human can do.

Demand Pull
Here are some kinds of
questions that could be answered by the proper model, if it were
available  and rather difficult to answer any other way.
Validate Existing Swing "Folklore"
There
is a lot of existing "folklore" about the golf swing. Undoubtedly much
of it is true. Almost equally undoubtedly, some of it is not. And then
there is contradictory folklore, assertions that have both pro and con
adherents. It would be
very nice if we had golf swing models that could address such questions.
As an example, take the matter of centrifugal release vs wrist torque.
The folklore was to hit with the hands. No less an expert than Ben
Hogan said so. But the double pendulum model
says otherwise, emphatically. And each successive model has
confirmed it. So not all of the common wisdom about the golf swing is
true wisdom.
Which brings us to the sort of questions that we would want physics to
answer. Which means, of course, we want a mathematical model that
answers them. For instance:
 It has become generally accepted that the "Xfactor" is
associated with clubhead speed.^{[3]}
The Xfactor is the establishment of a
big angle between the hips and the shoulders, and holding that angle
through the downswing. It would be nice to have a model where you can
play with that angle, and quantify the effect.^{[4]} Such research would either go a long way towards
telling us why the Xfactor provides more distance  or perhaps let us know that
the Xfactor was not the primary factor at work in distinguishing the
longhitting from shorthitting golfers.
 The notion of an actual swing plane
has been demolished by data from Coleman and Rankin, and the modeling
work of Mackenzie and Sprigings. But the MacKenzie model could be used
to study questions like, "What does a flatter/more upright 'plane'
actually do?" and, "What would be the result of actually keeping the
club on a plane?" Such questions are actually being addressed as
this is written.^{[4]}
This barely scratches the surface of interesting questions. You
can make a game of it while you watch golf on TV. Any time Johnny Miller or Peter Kostis
or one of the other swing commentators analyzes a swing, just ask
yourself, "Is what he is saying actually true?" It's shocking how often
the answer is sufficiently nonobvious (or even blatantly wrong) that a look at the physics is
appropriate.
Accuracy
Almost every modeling study to date has focused on clubhead speed at
impact. There are a few reasons for this:
 Distance sells! It is
dramatic, and most people who play golf want to hit the ball farther.
 It is measurable. It's temptingly easy to look at the output of a
model as a single number. And clubhead speed is a very easily
interpreted number.
But accuracy is just as important in scoring well, and it's hard to
find a single physical model (either mathematical or mechanical) that
addresses accuracy. The only one that comes to mind involves putting;
Dave Pelz and the "Perfy" robot have given rise to a number of
mechanical putting simulators and putter testers that might be
considered mechanical models of the putting stroke. But there are lots
of questions of accuracy that could and should be addressed by a swing
model. A few examples:
 What are the muscle moves that lead to impact with a closed face,
an open face, and a square face? That probably means closed or open
compared with the position of the clubface at address, since the
absolute position can be dealt with by strengthening or weakening the
grip.^{[4]}
 What are the muscle moves that lead to an outtoin clubhead
path, and intoout path, and a downtheline path? With the loss of
the swing plane myth, we need something to replace the concept in a
practical, teachable way.
 Consider things we do to increase distance, such as increasing
wrist cock, holding the lag, and increasing the Xfactor^{[3]}. What does
each of these do to directional accuracy?
In addition to control of direction, "accuracy" can be taken to
mean repeatability of result. Repeatability is closely related to the
notion of a solid impact. Both are definitely desirable. Is there any
way to quantify that in the
output of a model, with that sort of criterion for accuracy? I can
think of a few.
 The most direct way to determine repeatability is to run the
model a statistically significant number of times, with random
variation of the parameters around their design value. That is called
by simulation people a "Monte Carlo study".
 Less direct, but
undoubtedly more efficient and probably almost as effective, is to look
at how fast the outputs (clubhead direction, clubface position, clubhead speed) are changing in the vicinity of impact. The first derivatives of these
quantities are good indicators of how repeatable they are; the larger
the absolute value of the derivative, the harder it is to replicate on
the golf course.
 The problem with looking at output derivatives is that things
producing a high clubhead speed are likely to produce high derivatives of
most measures in the vicinity of impact. (For instance, look at the
total power curves compared to total work in the NesbitSerrano paper.
Or look at the various graphs that can be output from SwingPerfect.)
That implies the notnecessarilycorrect conclusion that clubhead speed
and accuracy work against one another. So let us look at the
sensitivity of the outputs to changes in input. This can be quantified
as the partial derivatives of the output at impact, with respect to the
various inputs.
Two of these approaches (#1 and #3) require a model that can be run
repeatedly with slightly changed inputs, to perform numerical
differentiation or to get a statistically significant sample. Any of the models we have looked at fills that bill,
because we are not running iterative convergence on them. Even a model
with 40plus parameters should be able to run pretty quickly on today's
desktop computers. The real problem with more parameters means there
are more partial derivatives to consider, or more inputs to Monte Carlo.
The approaches where we vary the inputs probably should
have the input variations scaled, according to which muscles are
involved and how much control we have in repeating that force or
torque. That requires a lot of input from biomechanics experts. That's
not a bad thing; I'm just pointing out that it is more than a physics
or engineering problem.
Popular Science
No, I don't mean the magazine. Let's reword that as "participatory
science". An important characteristic of science
is the reproducibility of results, and the invitation to others
to reproduce the results. This calls for programs that simulate the
models, so other researchers can verify the lessons for themselves,
then go on to learn more lessons from the model.
I can cite an admirable example for the double pendulum: Max Dupilka's SwingPerfect
program. I said admirable, not perfect. There are some controls that I
and other researchers wish had been incorporated. But such shortcomings
are few and far between. The program does a truly remarkable job of
answering questions that the model can deal with. And it's free.
What we need now is a similar program for other models. The
MacKenzieSprigings model just cries out for such a program. Does
anybody know if one is available? It would seem that an opensource
project for this would be appropriate.
 It would leave the model itself open to inspection. SwingPerfect
is closed source. I am pretty sure that it is a correct representation
of the double pendulum, but I am not 100% certain. There might have
been errors in implementation, errors so small and subtle that the
answers are close enough to be reasonable  but are wrong. (In fact, I
have worked with such a program for driver
trajectories: plausible but wrong. Such programs can teach
incorrect lessons. I had to rewrite one of my web articles
after realizing it and switching to a correct program.)
 It would allow "anybody" to make enhancements to the program as
researchers desire them. SwingPerfect can only be enhanced if Max
decides he wants to make the enhancement.
It would be difficult to opensource Nesbit's fullbody model for
several reasons. One is legal; the GeBOD model and the ADAMS software
are proprietary and substantial. Another is complexity; today's typical
desktop machine may not have enough horsepower to run both the model
and the
matcher (optimizer).
Summary
We have reviewed a few important models of the golf swing, specifically:
Each has its own lessons to teach. And each type of model answers
questions in a different way. How we use and extend these models going
forward will be interesting to watch.
Acknowledgements
I'd like to thank Jim McLean, Sasho MacKenzie, and Paul Wilson for constructive comments that resulted in a significant improvement in this article.
Notes:
 According to the information I
could glean in February 2012, the PGA of America has 28,000 members and
the US Golf Teachers Federation has 25,000. While not all the PGA
professionals are active teachers and while there may be some overlap of
membership, I'm pretty sure that those numbers include 30,00040,000
golf instructors.
 For an example of reducing the
dimensionality of a model based on a correlation discovered in the
data, see my article
on gear effect. I observed a strong, nearly linear correlation
between I
and c,
and was able
to combine them into a straight line (so the single parameter was the
position along the straight line). In fact, I was able to reduce
dimensionality even further. The straight line passed through the
origin with a slope I/c. Everywhere I or c appeared in my
model, it was always the ratio I/c. That ratio
is the constant slope of the straight line, not a parameter at all. We
managed to lose two parameters,
I and c, and replace both with a known
constant. Even though I and c did vary,
their ratio did not, and the ratio was always the same in the real world.
 The XFactor is an observation made by Jim McLean
and first described in Golf Magazine in 1992. It holds that a prime
determinant of clubhead speed, and thus distance, is the size of the
angle between the hips and the shoulders from the top of the backswing
through impact. In his book "The XFactor Swing" (Collins, 1997),
McLean provides more details on how he came to that conclusion, and how
to incorporate it into your swing.
 Some of the questions listed as "future
work" above are already being addressed. For instance:
 Sasho
MackKenzie has made me aware of a recent paper of his
that looks at the effect of swing plane and the club's position above
or below it. In the paper, he investigates how this affects clubhead
speed, clubface
squaring, and other results of the swing.
 MacKenzie has also experimented
with incrementally more complex models. In private communications, he
discussed his model with the added parameter of a pelvis section, or a
bending left elbow. As questions come up that need such models, it is
clearly not prohibitive to build and use those models.
Last
modified  March 7, 2012
