So really, when it comes to strategizing, it
could be an instance of, okay, let's open you
at this number. We know what you're capable of.
Let's open you at this. This will give you the
best opportunity to get 300 or more, which might
lead to a medal. Naturally, when you take that
information. to a competition, as you see the
board start to change, that's when you can make
a little bit more of a freer choice of what load
to put on because this information is there to
just provide some form of strategy moving into
competition. Once you're in competition, it's
then down to intuition and how to read the board.
So I think that's one of the steps that would
be nice to look at in the future. Before I kind
of get onto the performance zones, I think it's
important to understand this started from something.
quite simple so the paper is quite complex even
when we developed it i had the help of a data
scientist who happens to be one of my weightlifters
you know an incredible help in providing information
on how to make this sort of bus setup Welcome
to Evidence Strong Show. It's my pleasure to
have you the second time. The previous time I
had you, we talked about your paper on stable
and variable components in Olympic weightlifting
technique. And today we'll be talking about predicting
performance. But for three people who haven't
watched the previous one, could you briefly introduce
yourself? Firstly, thank you for having me on
again. I appreciate it. I feel quite privileged.
So my name is Sean Chavda. I'm the program lead
for Strength and Conditioning. conditioning,
distance education, a master's program at Middlesex
University. I'm also head of weightlifting coach
at Middlesex University where I coach Silver
Chachit for Great Britain and Jessica De Silva
for Portugal. And alongside my academic endeavors,
I'm also a senior performance scientist at British
Weightlifting. Awesome. So these are your qualifications.
You also do a PhD and this study we'll be talking
about today is a part of your PhD. This is the
first part of the PhD. Yes, almost. I forgot.
to mention that I'm doing that. Awesome. It's
important. So why would we be interested in predicting
performance? I mean, ultimately, if we can predict
anything, it's always quite useful to be able
to do that, right? And, you know, let's not beat
around the bush around predictions. If there's
anything we could predict in life, it'd be lottery
numbers because it's far more lucrative. But
predicting weightlifting performance actually
came about because of a real life performance
problem, which I encountered in the build up
to Tokyo. And, you know, we weren't coming through
this traditional route, having qualified, but
instead through the International Olympic Committee's
refugee Olympic team. So not many people knew
about Sil's abilities, as he didn't have the
privilege to compete at. international events
for like five years, but we knew his capabilities.
But what we didn't know is whether those capabilities
would be worthy of this unspoken target, which
we'd kind of set for him, which was finishing
within top 10. So with this ranking in mind,
in the buildup, I figured it was really important
to try and find out what total would be required
to achieve that top 10 ranking. And that's where
I ended up questioning whether I could actually
try and predict this. So this is the why. Now,
you use something, a concept previously not discussed
that widely. It's called performance zones. So
could you explain what it's all about? Yeah.
So before I kind of get onto the performance
zones, I think it's important to understand this
started from something quite simple. So the paper
is quite complex. Even when we developed it,
I had the help of a data scientist who happens
to be one of my weightlifters, you know, an incredible
help. in providing information on how to make
this sort of a bus setup. But essentially everything
was done in Excel at the beginning with scatter
plots and regression lines. And what I did for
each of the competitors in the men's 96 group
is I predicted what they might do at the games.
And if we fast forward a little bit, then it's
something I'll go into more depth on later. I
then predicted what total is likely to be achieved
between the places of ninth and 10th. And that
ninth and 10th can be termed a performance zone.
So to answer your original question, what is
a performance zone? It's a cluster of ranks.
So in the paper, I take the rank from one to
15, but I break them down into a medal zone.
So that would be a performance zone. Fourth to
fifth, which was a performance zone two. Essentially,
that's like the outside shot at achieving a medal.
Sixth to eighth, which I believe top eight is
the standard. you need to achieve to qualify
for the Games, but also you achieve an Olympic
diploma. as well which i didn't find out until
the after the games um and then ninth to tenth
is again your outside shot to achieve a top eight
and then 11th to 15th which was the maximum number
of lifters within um the olympic categories so
that's how i split the performance zones but
what's important is i consulted with the performance
manager at british weightlifting to ensure that
these performance zones were highly contextual
to what we need them for but it's important to
note that in other Other sports or other governing
bodies, they might deem performance zones to
be slightly different. So, for example, you might
have first and second as a performance zone,
third and fifth as a performance zone. And whatever
you decide to use as a performance zone just
needs to be justified and worked within your
own practice. But these are the ones that we
decided were the most logical and the most useful.
So anytime where the placing matters for qualification
for other events or making a team or getting
a scholarship. or whatever, that would be the
zone qualification you would or classification
you would use. Right, yeah. Okay, so you tried
to predict what is the likely total required
for each zone. How did you go about it? Yeah,
so the weight class change that happened prior
to November 2018, I think it was July 2018, might
have been the last competition at the old weight
categories. This was kind of the foundation of
the paper because we have loads of historic information
on these old weight categories but we have nothing
on the new ones so one of the objectives of the
paper was to see if we could predict what totals
would be achieved at specific performance zones
in each weight category or well in each new weight
category which had been introduced by the IWF
using the old weight category data so essentially
the new category data didn't exist and when they
did exist there's far too small of a sample to
make a prediction so we had to use historic data
and then once we developed these predictions
we could then check if those predictions were
close or not to what actually happened at Tokyo,
European Champs, and the World Champs, for example.
So what competitions did you take into account?
So just the major three for us in Great Britain,
which would be the Olympic Games, World Championships,
and European Championships, so continental. Okay,
and which years? So this was part of the methods,
which I think was quite important. So if I go
through the methods, that might help answer the
question. what years we used and kind of came
about it. So the data was scraped from the IWF
website from 1998 all the way through to 2021.
I took all the totals of all competitors. Then
I reduced the data down to the top 15 at each
competition. So World Challenge, European Challenge
and Olympic Games. With regards to the years,
what I did is I ran some effect sizes between
each year within each competition type. So European
Championships, 1998, 99, so on. And I compared
those years. So I just took the average achieved
and just compared those years to ensure that
in general, there wasn't a large difference between
each of these years. And this essentially helps
us identify it. like an outlier year where performances
might have just completely dropped or just gone
up by you know a significant amount so once we're
happy that there are either small or moderate
changes between them just decide okay that's
acceptable let's pull the data together for each
competition so it helps increase the sample that
we're using for the prediction okay i have two
questions about the data so one would be how
did you decide to transform the previous weight
classes into the new ones if you did or how did
you kind of group them together and then the
other would be some athletes will reappear from
one competition to the other so some data come
from the same athlete so how how did you handle
that yeah excellent questions actually questions
that came up in the review process when prior
to this being published so the The first question,
correct me if I misinterpret the question, was
what was the first question? No, no, the first
one was white glasses because you mentioned before.
July 2018, the weight classes changed. We have
that data and you included data in this analysis
from 1998, which means you included also the
data from the previous weight classes scheme.
So how did you manage? So it was all the data
from 1998 to 2018. Every single competition that
had this, they would have the same weight categories,
right? They were all put in to, they were all
aggregated for each performance zone, right?
Now with that, we split them into rank zones.
So you've got your aggregate for each rank zone.
We then ran something called a K -fold cross
validation, which essentially just evaluates
the model's ability to predict when given unseen
data. which is what we're going to be putting
into it at some point. But also what it does,
it stops us overfitting this regression line.
So the regression line we fit in was a polynomial
regression, basically curves through. Similar
to weightlifting totals and body weight, it's
polynomial. It goes up, it'll plateau and it'll
start to come down. Same as strength in relation
to body weight, it's curve linear, not linear.
So anyway, we run this polynomial regression.
Because we're using the old data, that's all.
plotted and the curve means at any body weight
category on the bottom axis, we can just go at
this point. which is a new weight category, go
up, find the intercept, and then we can predict
what the total would be for that new weight category
in that specific performance zone within that
specific competition type. So you took the actual
data of the body weight data of each athlete
in each weight class, and when you predicted
the total, then you were able to then go back
and kind of you had a linear. relationship polynomial
but kind of not by classes so not by categories
more continuous yes no it was by category so
if you look at the paper there's some supplemental
supplementary information which are graphs and
in those graphs show um on the bottom axis it
has each olympic weight category or each new
weight category i should say and on the vertical
axis it's got the total and it's a simple it's
a simple case of just intercepting vertically
and then horizontally to that regression line
to then find what the predicted total would be
for that weight category and the new weight category
we use instead of just putting in the number
89 or putting in 102 what we did is we took the
new weight category information that had been
competed in which was i think only two european
champs maybe one or two world championships we
just took the average weight as opposed to just
taking that body weight the reason we took the
average body weight is because on the top end
of the spectrum where you have no end cap to
be as heavy as you want we felt we felt like
that would influence the predictability so to
keep things fair and logical we just went take
the average of each new weight category because
we know that we have that information and let's
put that in because It represents the mean of
the group of their body weight. So we're not
just going to put 89 in if most of them fall
around 88 .7 or 88 .6, for example. So that's
how we predict it. So old weight category data
plotted and new weight category data used as
the intercept. Okay. I'm looking through them
because I thought maybe we could just put a graph
on top of the video. The version I have is before
publication. publisher shouldn't but it's like
every zone will have a separate i can share my
screen if you like yeah i've got so i've got
a graph here for like the olympic medal zone
which i'm i'm happy to to show you awesome so
this is part of the supplementary materials.
For example, let's say this regression line that
runs through the middle of these squares were
developed using the old weight category data.
We've then got some confidence intervals around
it, 95 % confidence intervals. So if we were
to run this on, say, a homogeneous population,
run a regression, 100 times 95 of those regression
lines will likely fall within this range. We
have a predictive interval on the outside. Predictive
intervals are a little bit wider. but these essentially
account for any future individual cases which
might come in. And that's why they're extremely
wide. So for example, in the future, if one person
lifts in the 96, they're probably going to be
between this bound and this bound here, the dash
line. So we can see for the men's Olympic medal
zone, so first and third, the prediction is this
value here. So approximately, let's just say
400 kilos, if you want the actual value. values
that are available in the tables above paralyzed
people with loads of numbers. So let's just take
it as 400. So that's our prediction. And then
after the Olympics, I took an average of the
medal zone and I took a standard deviation and
I simply plotted it on top. So this shows that
actually it was an overprediction compared to
what actually happened. However, the top end
almost reaches that 95%. confidence range so
while you might look at this and think or somebody
may look at this and think well that's an over
prediction it's it's rubbish in actual fact you'd
rather have an over prediction than an under
prediction because it means you're trying to
gear gear up to lift like that weight which is
you know far heavier than what was actually achieved
so it might mean on game day if you're expecting
this to happen and you lift close to it and this
actually happens, you end up overachieving and
potentially getting a medal. Whereas if we look
at the 73, the prediction's almost spot on in
terms of the average. So second place was really,
really close. But if you achieve anything between
these red lines, you're likely to then have got
a medal. In the same breath with predictive intervals
here, if you were to achieve this value, it would
have put you outside the middle zone. So with
these confidence intervals that we have, we're
just trying to account for as much error as possible.
But being at a competition is extremely dynamic
and anything can happen, especially at highly
competitive competitions. But with the ability
to predict, at least we have some opportunity
to strategize longitudinally or even just going
into competition. So I think it certainly has
a utility in sport. Should we have a look at
the 9th to 10th place zone or to 11th to 15th?
How in comparison will it be wider? How it will
look? yeah so as we expected the lower down the
ranks you get the wider the interval becomes
and that's for pretty much most competitions
more so for olympic than the world champs i believe
and the reason being is olympics is about equality
and inclusion and representation so you know
while you are getting very very good weightlifters
your top eight are the ones that are really world
leading because they're the ones that have qualified
by being the best in the world. The rest then
get allocated to continents. Now, they might
be the best in the continent. However, in the
overall world ranking, they may not be so high,
which means naturally from maybe ninth down,
you're going to start to see these confidence
intervals spread a lot more than you would at
the higher echelons of performance, which is
quite evident when you get to 11th to 15th. In
terms of prediction and And the strength of the
predictions, would you say that they are a little
bit less accurate for these places too, the prediction
model? It depends how you view it, because in
theory, if you were to say, how good is your
prediction into how good is your predictability?
Everything that's happened has fallen within
the predictive intervals. I see. So actually
it's a hundred percent. Yes, we can predict.
The problem is, is the actual value itself. The
range is far too large that we're predicting.
So, you know, it's... I suppose it'd be the equivalent
of saying, think of a number between 1 and 3.
I'm more likely to get the number correct, right?
But if I open up, think of a number between 1
and 100, I've made my chance a lot smaller to
predict that. So it's similar to there. Whereas
if I just scoot up to, I mean, the medal zone
here, you can see the 11th to 15th zone is really,
really wide. And a lot of the actual, actually
what happened was an underprediction. in comparison,
sorry, what actually happened was less than what
was predicted. Whereas when we look at the world
champs. first and third, a lot of them are very
close, or the standard deviations cross the prediction,
right, with the exception of the 89 and the 102.
And you can see the prediction interval is smaller,
confidence interval is smaller, and what was
predicted as a point estimate and what actually
happened were very close. Would you say it's
kind of a ceiling effect with the metal zone
versus 11s to 15s? Yeah, the margins up top are
a lot smaller, yeah, for sure. sure one of the
things we have discussed from a practical and
an applied perspective is looking at first second
and third and looking at how they may cross over
using similar models but as soon as you do that
your sample just gets a lot smaller anyway so
the predict the ability to predict and generalize
might be reduced but if that's what you have
available at least again you have something that
enables you to strategize as opposed to going
well it's not robust statistically so let's not
use it at all i think that's where practice and
application and statistics and academia need
to kind of come together and go right this is
what we should do and this is how we should be
as robust as possible these are the confinements
we have or the barriers that we have what's the
next best thing that we can do so also as we
will progress there will be more data because
there will be more competitions with the new
weight classes so yes exactly and that That's
one of the things that we're looking to do for
the women's weight categories. So this was just
done for the men. But what we'll do this time
is because these new weight category totals exist,
we're going to add them to the old data. So you
then have an entire spectrum of old data and
new data in. So you'll have a range of body weights
and then see if our predictability gets better
that way, because there still won't be enough
data of the new weight categories. Because, you
know, we're confining it down to weight categories
and we're further confining it down to competition
type. So every time, and then performance zone.
So every time you're getting smaller and smaller
and smaller. So that's the next step that we're
planning to do over the next few months. That's
exciting. I forgot to get the answer to the question.
How did you handle the reappearance of the athletes?
Did you reduce the data set because it took maybe
the best one or did you... as it was? How did
you handle that? Yeah, really good question.
I feel like you might have been the one that
reviewed my paper because it began with a question.
And it's a really valid point, especially as
it affects the generalizability of the models.
But what we did is we treat each total as an
individual case and not each athlete. And we
did that because performance changes over time.
So it either go up or down and therefore so will
the rank that. the total achieves or that person
achieves and given we're trying to predict totals
in specific performance zones it's important
to capture this information if we want to ensure
our model is representative of what might actually
happen but also like you mentioned if we use
if we select the athlete and what competition
and what total we use to analyze then we run
into selection bias you know which one did we
choose and why would we have chosen that And
as a result of that, you also significantly reduce
the sample. So not only have you got selection
bias, you've got sample reduction as well. And
with that in turn, you then reduce your ability
to predict, which is our point of the paper.
So using athlete reappearance was just lesser
of the two evils, I suppose. And we felt like
it was justified enough to be able to do that.
So statistically speaking, we would love each
data point. So each total be from a separate...
separate different person each time different
person that's not how the competitions run though
so probably leaving the total as it appeared
in the competition was more close to the reality
than selecting one or the other yeah and i'm
certainly no statistician so my depth of understanding
of statistics wouldn't be as definitely nowhere
near as good as someone who's a data scientist
but this is where as i mentioned before like
the robustness of this but making sure we're
getting enough information that can be utilised
within our field is. It's really important to
kind of come to an intermediary conclusion where
we're like, okay, it's robust enough. It's not
statistically, it might not be perfect, but because
of what the data is and how the data is collected.
Because you could have somebody that medals at
Europeans and then comes 10th in a world championships,
for example. So which one are you now taking?
Are you taking their best one? Are you taking
their worst one? I think it pulls out more problems
than it does. solved to be honest yeah fair enough
i think it was statistics you always are if everything
is perfect then the prediction is 100 but it
is far away from reality because reality is never
perfect so you have to i guess find this balance
so you can find that exactly and if if if data
was always so perfect and so easy to use especially
for predictions like i said before time and cheap
you know i wouldn't be predicting weightlifting
i'd be predicting lottery numbers But it gives
us a good starting point at the very least. And
I'm sure somebody far smarter than me will come
up with a model that's... even better and you
know is more maybe more malleable so it can change
as the dynamics of weightlifting changes as well
who knows ai is developing quickly maybe you
will know definitely yeah very quickly is there
anything from the methods and setup of the study
i didn't ask about but you think it's important
really no i feel like i'm i i've covered feel
like i've covered it to be honest okay so are
we ready for the for the results i would like
us to go on more you to tell us what were the
prediction across the competitions and across
the weight classes okay so that's quite a there's
going to be a quite a big one to to answer because
there's so many sub categories of of it because
you know We're predicting category. We're predicting
competition type, category. And then under those
categories, we've got subcategories of performance
zones. Yes. So if I were to answer it in the
most simplistic way as an overarching answer,
yes, the predictive models were pretty good at
predicting weightlifting performance at different
performance zones. However, these will vary depending
on the competition type and depending on the
performance zone and the weight category that
you're looking at. What we did see, like we mentioned
before, is as you come down the performance zones,
so to the lower ranked lifters, so 11th to 15th,
the predictability there and the fit of the data
for the regression line was reduced quite a bit.
So the R -squared value for the four performance
zones at the top were all pretty much above 0
.9. Some were close to 1, like 0 .99, 0 .96,
0 .97. As soon as you get to zone 4. five, 11th
to 15th, there's a significant drop, you know,
0 .6, 0 .89, 0 .77 for Olympics, World and European
champs. I mean, if we were to just pick one at...
At random, let's say 67 kilo men's category.
Performance zone two, for example, had a prediction
of, so between fourth and fifth, the prediction
was 321 kilos. What actually happened was 321
kilos plus or minus one kilo. So that's less
than 0 .05%, 0 .04 % of the difference. And the
absolute difference was zero. Keep in mind when
I present the standard DVR, in this paper, it's
not to any decimal. It's always rounded up because
in weightlifting, we lift to a kilo or more.
So actually, that was pretty much on the nose.
Yeah, it was a bullseye. Whereas if we, let's
say, move to the 109 plus for zone two, fourth
to fifth at the Olympic Games, the prediction
was 442. What actually happened was 414. So the
difference is just under 6 .5%, which is... 28
kilo difference that's quite large so in that
instance if you've got an athlete that's trying
to achieve fourth to fifth and you're using that
as a predictive model it's certainly over achieving
or over predicting so if they go in lifting like
420 at the very minimum and they're like oh i'm
never gonna get fourth or fifth you'd be surprised
because in this instance 414 was the total which
kind of brings me on to something else which
i think would like to to look into in more depth
is how these performance zones cross over. I
think it's really important. For example, we
have our standard deviation. We have our average.
We have our standard deviation. And let's say
this is our medal zone. Then we'll have our fourth
to fifth and so on and so forth. So we'll keep
that down. But they cross over at some point.
So it just changes the conversation you have.
like look if you achieve a very random number
if you achieve 300 kilos you're going to be above
the average of fourth to fifth but it also means
you might meddle so really when it comes to strategizing
it could be an instance of okay let's open you
at this number we know what you're capable of
let's open you at this this will give you the
best opportunity to get 300 or more which might
lead to a medal naturally when you take that
information to a competition, as you see the
board start to change, that's when you can make
a little bit more of a freer choice of what load
to put on because this information is there to
just provide some form of strategy moving into
competition. Once you're in competition, it's
then down to intuition and how to read the board.
So I think that's one of the steps that would
be nice to look at in the future. So the zone
will have the number and then confidence and
then standard deviation. so it's the plus minus
however many kilos so that's the number in theory
you would need to total to be in that zone to
so to place in that zone but it can be less or
more depending who is coming to the competition
and exactly it and it all depends on the day
and i think That's really important. And to come
back round to the original reason why this idea
kind of came up is we wanted to know the realities
of Finnish. finishing within the top 10. But
we also know that on the day, anything can happen.
So while we'll have a strategy of, look, if you're
going to attempt 200 kilo clean and jerk, we
need to start you at this. Then we look back
at that individual's information. Okay, the last
time you hit 200, you started at this. This is
how training has been going. And it becomes a
conversation and it has to be very much a relationship
-based conversation to have because it's a big
decision to make. I'm not going to go off, completely
go off of it. predictive model. But like I said,
it provides a starting point, provides us an
ability to strategize. And also from a funding
perspective, models like this might help identify
trajectories of individuals and their development.
And if you have a relatively robust predictive
model and their trajectory is moving to a performance
zone, which is aligned to funding, then you can
say, look, this is what we're predicting is going
to be the middle zone. This is the trajectory
of the athlete. In the next four years, it's
very likely they're able to achieve this. So
it's got multiple applications. Okay, now in
terms of how the predictions you made were true
or accurate or close to. accurate in terms of
type of the competition was it easier to predict
the totals for olympics awards or for europeans
it was all easy to predict it's just how strong
Well, how good were those predictions? For the
first four performance zones, pretty decent.
For the fifth performance zone, 11th to 15th,
they weren't good. But generally speaking, there
were more overestimations than underestimations.
A potential reason for that could be the impact
of COVID and people now just returning back into
competition. So I'm very, very much excited to
look at. what's happened this year and comparing
it to our prediction model but also as i mentioned
for the women's one where we developed that adding
the current existing new category data and seeing
if that has an even better ability to predict
so was it better for 11 to 15 for the words because
i i would assume that words would be the most
stable in terms of yes yeah i'm in on the same
opinion of that and i'm looking at it now it
was the confidence intervals smaller okay but
you still have wide predictive intervals but
if we forget the predictive intervals for a second
they were still narrower than they were for the
olympic one but you're right in saying that they
in theory should be more stable for worlds because
you actually have the top 15 in the world so
the variation will be far less in comparison
to like i mentioned the olympics where you're
not necessarily getting the top 15 in the world
and i say top 15 is because that's the sample
that we extracted for analysis i had played around
with commonwealth championships and i mean that
was incredibly varied but you expect commonwealth
championships was extremely varied it's not something
i i ended up pursuing because after doing it
a couple of times I realized there's probably
not a huge amount of value in doing it for this
because the variations were so large, the confidence
intervals and predictive intervals. So the only
other way of maybe looking at that one would
be just the medal zone, possibly. Certainly for
England, where we are eyeing up medals at that
specific competition. So possibly it becomes
a bit more useful. Yeah. The sample is way smaller,
even comparing to Europeans, less countries.
it makes sense but you would hope that it's at
least if you could only predict the metal zone
it would still be worth it yeah i think so i
think it would be and again it's only to open
up the conversation of if first second and third
will cross over you know it's like okay if you
hit a 200 total that's a potential bronze medal
but it also is the lower bound of the silver
medal so hit 200 and you should be in focus out
for a medal so you know it drops conversations
and i think that's that becomes really important
what a motivation yeah Yeah, exactly. And I think
sometimes it also helps, potentially can help
reaffirm what a coach is thinking in terms of
what they should be achieving going into a comp
or what they'd like to achieve going into a comp
or what they think the medal zone will look like.
Can we talk a little bit about the weight classes?
Because weight classes are not every five kilos.
They are differently organized in weightlifting
because the population is spread. different like
curves so were there any differences in prediction
and strength of the prediction in terms of weight
classes? Yes. So one of the issues we considered
that we would have is on the two ends of the
weight spectrum, the lightest and the heaviest.
But then we realized the lightest probably doesn't
matter as much because you can be as light as
you want, but nobody is as light as they want.
They're as close to the, let's say, 55 kilo threshold
as possible. Whereas your 109 plus is you're
going to have people weighing 115 kilos. and
170 kilos. There's a huge span. That's why we
opted to take the average weight of those that
competed in the heavier top -end weight classes,
because it's the best representation of that
group average. So what we did see, I mean, I've
got the tables in front of me only because I'm
not going to remember these numbers off the top
of my head, but the World Championships 109s,
within that category, the performance zone prediction
became... slightly worse. When you look at the
same performance zone between different categories,
it just varies. So for example, the medal zone
for the 89 kilos, the difference between the
prediction and the actual was three points. 40%.
The prediction for the 109 plus was, I mean,
just over 0 .05%, 0 .06. So it's pretty much,
again, on the money. And remember, these are
averages. So if anything, it tells you silver
medal, not gold or bronze. So yeah, it does vary.
There's no pattern I could see between the different
weight categories at different performance zones.
But there are less lifters in the 55 from what
I remember. remember the lighter categories which
means the data is kind of a bit more bit more
spread oh could you could you repeat um i didn't
understand the point in the 55 there wasn't in
the world champs so what i didn't notice so in
the world champs the 55s had the prediction to
the actual the percentages were all above five
percent difference which is quite big but the
reason being it's not an olympic weight category
right so and neither was 89 and neither was was
102 at the time the data was collected. And then
because people are changing weight category,
naturally these values will end up changing.
And that's why when you look at the paper, there's
no difference between the predicted and the actual
for the new weight categories at the Olympics.
Because the 55, the 89 and the 102 will be contested
in the up and coming Olympics, but they weren't
in the last Olympics. I don't know. If you're
a fan of weightlifting, like your head is spinning
because every Olympic cycle we have changed.
changes, how others qualify, what the weight
classes are and so on. So strap in for the ride.
No, I think it's just important to highlight
that the predictive model paper is hopefully
a starting point that can be developed further.
It's certainly... a good opportunity to help
strategize going into major competitions opens
up the doors to try and apply this to other areas
other sports but also you know masters competitions
youth juniors and look at how that changes longitudinally
as well and yeah i think the more the more data
that we we end up getting hopefully the predictability
ends up being a lot more refined and a lot more
accurate and it's also really important to note
that those had sanctions or were banned were
extracted from the data when it was known that
they had a ban in some cases there might be athletes
in there who haven't yet been sanctioned or might
be in the future but taking that one person out
if it's in a medal zone for example could influence
predictability but there's no controlling that
yes so like i like i said earlier hopefully some
you can develop something it's a little bit more
malleable and as things change within the field
the data changes in the process of the prediction
ends up updating as uh as results update but
i hope i hope people enjoyed the read and found
it if not useful then at least interesting i
can't ask you about your favorite color because
i did it last time but i will ask you about your
favorite lift and why oh that's a good question
my favorite lift to do myself or to yes no you
um i've got to say back squat because i i don't
weight lift anymore i think i like back squat
you know it's a good good exercise for for low
body strength and you can you know add a bit
of weight weight to it but it also humbles you
very quickly as well might be a day where you
don't feel so good and you'll find out very quickly
so it's got to be a back squat a boring answer
i'm afraid but no no in terms of coaching do
you prefer coaching snatch or clean and jerk
I suppose. Whatever the problem is in front of
me, both are fun to coach and they both have
their similarities, but it always depends on
the person in front, for sure. So politically
correct. I know, I know. If I were to choose,
I'd say snatch, if I were to choose, because
of the speed of movement and the intricacy. And
I also think, Once someone understands the snatch,
the clean tends to be developed a little bit
easier. And that's, I'm talking about a complete
novice here. Okay, I'm satisfied. We can move
on to the next question. Okay, where people can
find you if they want to connect or learn more
about what you do? Yeah, so I'm contactable via
email. So my email is s .chavda, that's spelled
C -H -A -V -D -A, Violet Delta Alpha, at mdx
.ac .uk. I'm also available on most social media
platforms or academic platforms, so LinkedIn,
ResearchGate. just stick my name in i'm sure
i'll come up twitter my twitter is shy the number
two tweet underscore to tweet was it shy underscore
to tweet yep shy underscore to tweet and instagram
is coach underscore awesome thank you so much
for today you're welcome thanks for having me
on a second time as soon as you publish the next
one there will be a third one so be prepared
I'm glad somebody's finding the research interesting.
Yeah, of course. I live for research.