Hello, I'm Louis Passfield, I'm Professor
of Sports Science here at the University
of Kent and I'm also a member of the
Endurance Research Group. Over the last
25 years I've been working in sports
science and I came into sports science
with a particular plan. My aim was to win
the Tour de France and I thought by
studying the sports science and applying
it to my own training I'd be able to be
successful in this way. Instead I learnt
two important lessons from studying
sport science, the first was that science
doesn't yet have all the answers and the
second was that unfortunately I lacked
the talent to win the Tour de France.
So my ambition had been to be able to
stand on top of the podium but
unfortunately I realized that was never
going to be the case, not as a cyclist
anyway. However, I've been very privileged
over the course of my time as a
scientist to work in a number of elite
environments and in particular I was
able I was part of the team that
prepared for the Barcelona Olympics, the
Atlanta Olympics and also the Beijing
Olympics too. And more recently instead of
working with the athletes directly I've
been mentoring members that other
scientists or members of those teams
working for the London Olympics and now
for Rio, working with the English
Institute of Sport. Cycling is a
wonderful sport for scientists because
we can take measurements and calculate
things. These particular devices are a
power meter, which measures exactly how
hard the cyclist is working and we can
attach this directly to the cyclist's
bicycle so we can see how hard she he or
she is working during their training or
their races. We first used this device to
conduct a study looking at the hour
record and this is a record conducted on
the track and what we do is we see how
far someone can cycle in one hour and
the harder you work, the further you'll
go in that hour. If you look at this
graph here you'll see records set from
the late 1870s right through till the 2000s and in particular notice how
the record increases rapidly in
those last few years. Now we were
intrigued by this rapid increase in
performance and what the cause for that
might be and so what we tried to do was
use data from the power metres to model
how the record was broken and what the
reasons behind this were. One of the
complicating factors for this was that
the records were broken using
different cycling positions. So here
you'll see the traditional racing
position that was used for many of the
records up until the beginning of that
increase and then the position evolved
into this one where more aerodynamic
position and aerodynamic helmets and
clothing was adopted. And then along came
the Scotsman Graeme Obree who innovated
even further, first by adopting a
position which sawed his arms off and he
bent forward and cyled like this. That
position was subsequently outlawed by
the International Cycling Federation and
so he innovated again and developed a
Superman position with his arms out in
front of him instead. Subsequently, we
were to calculate that both of these
positions were much more aerodynamic
than anything that had preceded it. So
when we came to look at the hour record
and do the calculations what we could do
what we could see was how hard it each
of the riders had worked in order to
break the hour record. The records
coloured in red here show where the hour
record was broken with a lower power
output, in other words the rider was not
working quite as hard and the record was
that was developed by innovation rather
than by changes in training or fitness.
So you can see every other record in the
run through that period was developed by
increases in cycling position,
aerodynamics and equipment, rather than
through changes in fitness. Next it went
on to look at pacing or the way in which
you ride during a race and the strategy
you adopt. Here we see a course where we
have a hill, or two hills I should say,
and also periods of headwind or tailwind,
so the arrows mark the direction in
which the wind is blowing. The challenge
for the cyclists then is to work out how
to distribute his effort during that
race, if the course were flat and the
wind changed the wind didn't change
direction then a uniform effort all the
way through would be this the most
successful strategy. But, when you have
hills, uphill and down, and head winds
and tail winds, what then do you do? Do
you work harder into some sections,
easier into others and how much should
you vary your effort. Again, we were able
to use mathematics in this situation to
model what happens to the rider during
this race and how that effort should be
distributed. In this case, we can use this
relatively straightforward equation, a
linear equation of motion for the
cyclists, to calculate the impact of
different strategies and by comparing
those different strategies we
were able to show that over the course
of a 40 kilometer race it was possible
for a cyclist to save 30 seconds by
varying the effort, working harder in the
slower sections of the race, so by going
uphill and working harder or into the
headwind and working harder, you actually
reduce the amount of time you lose there
and then you can make it up by going
more easily into the downhill sections
and into tailwind sections and overall
then the race time is improved.
Subsequently, I worked with Patrick Cangley
at the University of Brighton to develop
a more complicated model. So the last one
was simply the equation you saw in the
slide, this one, what we did was we took
the whole bicycle and the rider and put
them into the computer completely. With
this model we can change any aspect of
the of the bicycle or the rider, we can
change the wheels, the tyres, we can make
the rider large or small, change the
weight, and we can run the model over any
course that can be that can be captured
from, for example, Google Earth. Using this
model, you can pedal it so that if you
don't ride correctly it will even fall
over and what we'd ended with,
was then use an actual race to feedback
that model to the riders in real time.
Here on this graph you can see the
profile in the dotted line here of the
course. So we've got a big hill in the
middle of the course and a descent
towards the end and then we also showed
the power profile or how hard the rider
should work during the race. What we then
did was tell the rider, during the ride
itself,
how hard to pedal. So that you can see
that as they as the hill
goes up, as a rider ascends, the power
output is increased and then, as the
rider hits the descent, power
output decreases during the easier section of the race. In this actual
scenario what happened was the rider was able to save 12 seconds over his normal
performance by following the model,
rather than trying to adopt a constant
pace throughout. So now we know not only
that this model works in
theory but it works in practice as well.
In future, what we imagine is that
something like this can happen.
These glasses are commercially available
now and they will give you feedback in
real time on how hard you're working
and so it would be possible for us to provide
the strategy for the rider
on their glasses in real-time as they
race. Now, clever is that model may be and
the technology that's gone into it, one
of the key things is that we still don't
know how to train the model, we still
can't that tell it what the most
effective exercise it should be doing
to enhance its performance and that's
the bit that I'm really excited or
really passionate about. So in fact we
can calculate or measure the training
input and we can look at the
consequences of that training in
terms of the output or the performance,
but what we don't know is how those two
are linked. This bit in the middle, the
training, is still very much a black box.
So anybody who's working with athletes
or even people who are exercising for
maximum health benefits are really
relying on the scientists and their
intuition or experience in order to
prescribe training, it can't be done from
a scientific basis yet because we simply
don't have the information. Now one of
the things that I'm particularly
interested in is whether we can use the
data that we're gathering from training
to learn more about the process of
training itself. So devices like this are
able to capture huge amounts of training
data and perhaps we can then interrogate
that data to learn more about the
training process. So this is the
challenge that I'm working on currently.
The difficulty here is that training is
not easy to model. This is an example of
what you get if you detach a power metre
to a bicycle
of a reasonably elite rider, this is a
four hour training session and you can
see how the effort varies rapidly
throughout the ride and you can see
there are periods of high-intensity work
here and here,
big blocks where the power outputs are
quite high, but overall the pattern is
what we'd call stochastic or hugely
variable, virtually impossible to use for
mathematical modeling. The other thing
that we don't know is which bits of this
training are the most important. So, for
example, some scientists have suggested
that just the most high-intensity
training, conducted for only short
periods of time is enough, so just what's
in the yellow band here. Other scientists
and quite a number of coaches suggest
that polarized training, where you
combine high intensities, so this yellow
band at the top, with low intensities is
the best way of training effectively.
But these bands and where those lines
are drawn are entirely arbitrary, they're
not scientifically derived. So maybe they
should be here, or perhaps a little
higher, or maybe a lot lower, we really
don't know the answer to these questions
at all from a scientific perspective at
the moment. So perhaps what we can do is
turn this around and study the training
of athletes in order to learn more about
that process. In this particular study,
what we did was look at the training of
well-trained endurance runners. We took a
large dataset gathered by GPS and simply
put watches on the wrists of
runners in order to get that data. And
what we were trying to do is to identify
which periods of the training were
effective or ineffective and essentially
to try and learn as much from that data
as possible. We had lots of data to learn
from, so the first thing we had to do was
to take an individual training session,
such as this one, and try and work out a
way of modelling one session. Once we've
got one session then we could think
about what we would do with a week's
worth of training or a month's worth of
training or even a year's worth of
training and capture all of that and try
and interrogate that too. And then of
course we had more than one runner, we
had 13 runners in this study, so we had
to do it for all 13 runners too.  The major
challenge we had was trying to make
sense of those individual sessions and
we were very pleased that we were able
to come up with a solution for that,
which we've called the training
distribution profile. These two squiggly
lines at the top here show two
different training sessions for a runner.
These are very difficult to model
mathematically because of the huge
variability, the stochastic nature of them that I
talked about earlier. Beneath them though
is exactly the same data, all of the
information still present, but now
transformed into a training distribution
profile. This is something that's very
easy to describe mathematically and now
we can chop that up mathematically and
ask the question, which bits of that
training are the most effective. And by
using these training distribution
profiles what we were able to do is to
take that group of runners analyze the
whole of their training for a year and
here's an example of one runner's
training shown for the whole of the
study on one plot, which now it looks a
lot simpler, a lot cleaner than the
previous graphs we were looking at
and by analysing that data we were able
to show that using the training profiles
we could both visualise the data but
more importantly we could calculate from
their data which speeds mattered most.
And for the cohort as a whole what we
found was that their training between
five point three and five point seven
meters per second was the training that
was associated with increases in
performance, so, if you like, this was the
magic bullet for those runners.
We also found that their laboratory
tests that we conducted told us nothing
useful in terms of predicting which
training intensity they should do. So by
analysing their training rather than
their laboratory measures we were able
to learn more about what enhanced their
performance. And finally, we could use the
model we developed to actually predict
how much their performance would improve.
So we could link how much training they
did to how much their performance would
benefit too. So, in the future, we can
imagine a situation where athletes
compete not by seeing who crosses a line
first but rather riding or running next
to each other side by side and then at
the conclusion of the race turning
tapping each other on the shoulder to
download the data and then comparing
their performances through the data
itself. Thank you
