You see these AI apps and stuff like that, that
people use. And I think the programs that they
build on some of the better ones are like perfectly
fine. You know what I mean? But you know, you're
never, you're not going to get like, it's not
going to help you on a competition day. It's
not going to critique your technique. It's not
going to help you with your mindset and stuff
like that as well when it comes to approaching
competition. And I think that that is something
very similar that I would highlight with respect
to our model in the sense that this will never
plan a competition strategy for you. It will
just maybe give you some information that is
useful in your coach's book. it alongside everything
else to help you make the application of the
art of coaching a little more informed and perhaps
a little more systematic if possible you know
that that's kind of the intent behind it at least
Hi Ian, it's my pleasure to have you on Evidence
Strong Show. If you could briefly introduce yourself.
Hi Alex, thank you very much for having me on.
It's my pleasure. My name is Ian Darrah. I guess
I have a bit of a mixed background. So I have
a PhD in exercise physiology. My PhD was mostly
focused kind of around the domain of trying to
identify almost different biomarkers of different
training status. So resistance trained, endurance
trained individuals. Before kind of getting into
sports science, I was an athlete. So I was a
reasonably decent. national level Olympic weightlifter
in Ireland. I did a couple of international competitions
as a junior as well. A couple of junior national
medals, a couple of senior national medals. So
like an OK strength athlete, which got my interest
in sports science. When I was an undergrad, up
until still kind of now, I did a lot of coaching
as well. So a lot of Olympic weightlifting coaching
for probably about 10 years, a small bit of strength
and conditioning stuff as well. And I now currently
work for a large hospital network here in northeast
Pennsylvania. I work as a researcher in a sports
medicine department. kind of essentially working
with large scale electronic health record data
to do looking at the influence at the moment
now, mostly of malnutrition on surgical outcomes.
So again, kind of integrating some of that physiology
stuff and nutrition stuff a little bit with some
data science too. And the paper is about powerlifting.
So how did it come to be? Open Powerlifting is
an extremely well -maintained powerlifting database.
I'm sure many of your lifters, particularly anyone
who competes in powerlifting are familiar with
it. But whoever runs that website, I don't actually
know the individuals by name or anything like
that. They do an extremely good job of kind of
curating the database and maintaining it. If
I recall correctly, I think it's updated potentially
daily. If it's not updated daily, it's updated
weekly. And they make that data free and open
source and available for download as a CSV file.
So that was how I kind of came up with the idea
of working with this database. And I kind of
iterated through a few different ideas, trying
to come up with something, partially looking
at the literature, partially just kind of playing
around with different ideas. for a couple of
months. And then believe it or not, I actually
came across an Instagram post. It was just a
spot. I can't remember who posted it, but it
was just a small little infographic that was
talking about how much you jump between lifts
and basically strategizing, you know, when you're
in a powerlifting meet, how much should you jump
between first attempt, second attempt, so on
and so forth. And I was like, OK, this is actually
something we could potentially look at here.
So the idea that I kind of crudely came up with
was I have this this database. I kind of diluted
it down into some specific. specific contexts.
And I was like, let's see if we can potentially
quantify or at least explore with some degree
of detail the relationship between how much you
decide to jump between two subsequent attempts.
So if my opening attempt is 100 kilograms and
I take my second attempt at 110 kilograms, I've
jumped 10 kilograms. Can we look at how that
influences the probability that I will be successful
on the attempt that I'm jumping to? So if I make
that 10 kilo jump, what is the probability that
that attempt will be successful? This is perfect
because they're predicting or they're trying
to come up with reasonable attempts and jumps
for the competition. This is part of the art
of coaching. So the coach develops relationship
with the athletes so that in part they can predict
when the athlete is more likely to make the next
attempt and what the jumps should be. And here
you are taking a big data set from many, many
athletes, thousands of athletes and trying to
see. whether we can actually statistically calculate
what the jumps are. And if we do a specific jump,
what is the probability of hitting the attempt
for females and for males? Yeah, and that's probably
something to touch on in that one of the things
that's really important to mention is that for
predictive models to be useful, they often need
a really large amount of data. One of the things
I've commented on as well is the fact that we
filtered it down to IPF, International Powerlifting
Federation. competitions for a couple of reasons
but one of them is that they have very strict
technical rulings so you have i think in the
study we had around 96 or 93 000 lifters total
you know which is an insane number of competitors
but because of the rule set we can also say within
a reason like within reason that they the lifts
were judged under somewhat consistent standards
you know could you explain what building a model
is i can talk in detail about particular type
of model we did maybe later down the line but
what you essentially see as a model and you'll
see them in research studies they are in essence
something kind of like a generalizable equation
so what i mean by that is the sense that if we
have built this model today and we've reported
the equation of it there are a bunch of factors
in that equation such as the body weight of the
lifter the age of the lifter the sex of the lifter
and so on as well as some constant values and
the idea there is that it's kind of like like
a calculator essentially in that if you took
that equation and you You had values for yourself.
You know, I know my age. I know my sex. I know
my body weight. I know my jump attempt size.
You can plug them in and you'll get a result,
you know, just by using the constants. So a model
is essentially like a pre -built calculation.
You know, it has inputs and you again, like it
has inputs and it has an output. And obviously
the output is generally what you're kind of trying
to, you know, the desirable thing you're trying
to predict if it's a predictive model. And then
models also have essentially quality. You know,
you have good model. Good models and bad models.
There's a very famous statistics quote. I can't
remember who it's attributed to, but it's the
idea that all models are terrible, but some models
are useful. So something to kind of note is that
there will always be a detraction from reality
in any statistical model that you build. So even
if you see something that is, and there are a
number of parameters, essentially, you'll build
a statistical model, you'll assess its quality
based on a number of parameters. So you can tell,
is this model good at predicting the outcome
or bad at predicting the outcome in essence?
But even models that are very good at predicting
the outcome. predicting outcomes are not perfect
you'll never have a model that particularly in
this circumstance let's say i wouldn't call it
a criticism but one of the skepticisms we've
received from kind of friends of mine who are
practitioners and stuff like that is the idea
that oh so you think that like this will make
a decision for coaches and the idea that is like
absolutely not because there is still a black
box component here this is more about understanding
the relationships and giving some maybe quantifiable
benchmarks that you can come in with some baseline
information and then add that to what you intuitively
know about your athlete through working with
them or just, again, through your coach's intuition.
It's about informing decisions instead of making
them for you. So I've kind of gone on a bit of
a tangent there, but maybe to bring it back in
together, a model is essentially, it's a machine.
It has a number of inputs and it's built and
you'll build a model sequentially. So you'll
start with one input. So for example, when we
developed the model for this study, we had the
output decided, which was... whether a lift would
be successful or not and then we had our first
input which was the jump size between attempts
so we just started with two things the jump size
whether it's 10 kilograms 20 kilograms whatever
and the probability of a successful attempt and
we looked at that and we go okay how well is
how good is this alone at predicting our outcome
and then we added something in well what about
whether it's different between squat bench and
deadlifts because the amount you will jump is
different between whether the lift is a squat
bench or deadlift so then we added in the lift
type and that was a factor then we added in What
we've termed the attempt transition, which is
not a great term, but that's essentially whether
you're jumping between the first and second attempt
or the second and third attempt. So how far you
are within each given lift, you add that in and
you can see if nuance kind of is extracted further
on down the lines. That's essentially how the
model was built by sequentially adding in different
factors that we had data on that we thought were
relevant, evaluating the model's parameters,
but then also trying to balance it off how easy
it would be to interpret the model. So there
are other factors we could have added to our
model. make it a better predictor, but it becomes
too confusing to understand how it's producing
results at that point, which means it's more
difficult to use those results in practice. So
again, that's kind of that trade -off between
complexity and interpretability that I was talking
about at the beginning. My understanding for
now is you found this data set, you decided to
look at the jumps and the probability of success,
and then you tried to include different variables
one by one, adding them to the model and seeing
what... you can, that the results will be different
and whether the model is a good fit. How do you
call it? Yeah, essentially, like, does it become
better at predicting whether a lift will be successful
or not? And I mean, I think contextualizing that
probably makes it a little bit easier to understand
in the sense of, if we think about just considering
how much you jump alone, you know, that can be
useful. But the amount you jump, it doesn't matter
if you're a six foot eight, 200 kilogram male
or a four foot nine, 48 kilo. female the amount
you jump will always be bigger on your squat
and your deadlift than it will be on your bench
press you know like a you know for some people
a 50 kilogram jump on the squat may be achievable
but most people will not make a 50 kilogram jump
on the bench press so if you go oh well this
model only considers how much you jump but the
amount i jump is very very different between
my bench press let's say than my deadlift you
know or the amount i jump is very very different
between my last opening or my last warm -up weight
and my opening weight you know Or my third attempt
is so on and so forth. So it's just like the
best way to think about it is like because like
the way you kind of think about it when you're
building the model is, oh, but what about this
situation? And do I have a variable that I can
contextualize that to? You know what I mean?
But what about older lifters versus younger lifters?
Does the effect stay for older lifters? OK, I
should include age as a variable in the equation
to see if that changes the prediction. What about,
you know, males versus females? What about squats
versus bench press and so on and so forth? So
that. The main predictor is this jump size, but
then we have all these other variables. And the
statistical term is a covariate, but it's essentially
like caveat, a contextualizing factor, you know.
But obviously, the more you contextualize, the
more difficult it can become to just generalize.
You know what I mean? The more nuanced you make
your model, the more difficult it is to say how
it applies to everybody, you know, to some degree.
And so there is, as I've kind of mentioned, a
balancing act between, you know, making sure
that you explain enough that the information
is useful, but you don't explain so much that.
You can't say, like, you can't give a statement
that most people can then take information from
to use in their, in their training or their,
their competing practices. Essentially what we
used is a, what's known as a non -linear logistic
regression approach. And again, you'll see this
with statistics all the time and that like you
get all these fancy terms, but it is actually
something that can be simplified. So regression
is a term that you see thrown around a lot. Anytime
you see the word regression in relation to a
statistical model, it's simple. simply means
a model that's looking to predict something.
Logistic then simply refers to the fact that
it's using a logic function, which basically
is designed to look at binary variables. So yes,
no. So in our case, was the lift successful or
not? You know, a lift cannot be kind of successful.
You know, the judges either give you two white
lights or three white lights or they don't. You
know, so when we say logistic regression, it's
a model that's designed to predict a yes, no
outcome. Good lift, bad lift. That's kind of
the logistic element of it. Non -linear, how
regression works in its most simple form is that
you have your data already collected or provided
to you or whatever, and you come in with a preconceived
relationship between your variables. So probably
the best example to think about is linear regression,
which even if you've never heard that term before,
you've probably actually seen it. If you've ever
seen a scatter plot with a bunch of different
points and exactly a slanted line that's either
going upwards or downwards, that's linear regression.
And how linear regression works essentially is
I say, I think there is a straight line relationship
between my two variables that I'm interested
in. Let's say the amount I jump in a lift and
the probability of that lift being successful.
I think the more you jump, the more likely you
are to be successful or the more you jump, the
less likely you are to be successful. And you
draw that line the best you can based on how
the data is shaped. And the quality of the model
is essentially assessed based on how well your
data orbits around that straight line. So if
I drew a straight line for that example and all
the data was just very, very close to the line,
we'd be like, oh, this is a great model because
this shape that I've drawn, which is explaining
a relationship, a very linear relationship, more
is better, less or less is worse. My data conforms
to that shape. Therefore, it's a good model.
We used a nonlinear approach. And the reason
for that is a lot of relationships are nonlinear.
And all nonlinear means is the line is not straight.
And there are actually plenty of examples in
sports and exercise science. of nonlinear relationships
that a lot of people might be familiar with.
Anyone who's ever seen the results of a VO2 max
test before, so essentially an aerobic fitness
test where you run on a treadmill, you ride a
bike, usually for 12 to 15 minutes, the test
gets progressively harder minute on minute and
you track oxygen consumption. What you'll see
is oxygen consumption increases in a straight
line and then it reaches a plateau and it stops.
So it's almost kind of like a truncated S shape
or something like that. That is an example of
a nonlinear relationship. Oxygen consumption
increases, increases. increases when you reach
302 max it gets stuck so that that's an example
of one we see in sports and exercise science
and we did the same thing here so we said there
is a we're building a model to predict this outcome
of good lift or bad lift and we're assuming that
this relationship is non -linear basically meaning
that there will be some kind of curve in the
relationship and we can kind of get in when we
get into the results it's a better way to kind
of explain it but like contextualizing it the
best way you could describe it is increasing
your jump size to some extent will potentially
increase your probability of a successful lift
up into a point. But obviously you can't jump
forever. You know what I mean? So if you say,
oh, if I jump two kilos versus five kilos, I
might have a better chance of making the lift
with five kilos. But you can't do that into infinity
where you go, well, then, well, if I jump eight
kilos, well, if I jump 18 kilos, well, if I jump
25 kilos, anyone who's ever lifted before is
going to go, okay, that doesn't make sense. You
know, there comes a point. I add 200 kilos, you
know, it eventually levels off. That's essentially
what the non -linearity aspect of our model is
kind of meant to describe. The fact that like
you may see an increase up into a point in benefit,
but through logic, there has to be a ceiling
where the relationship begins to kind of change.
And so in its most simple form, think of the
relationship between jump size and the probability
of lift being successful is a squiggly line.
of some description. It's not a straight line
relationship. And again, that just conveys a
little bit of complexity there, less so to do
with the model and more so to do with the phenomenon
itself. But I think anyone who's lifted before
will have some intuitive feeling of that. Anyone
who's actually loaded a bar up to a one rep max
or a three rep max or competed in some kind of
way will understand that feeling to some extent.
So there is some logic behind it, which I think
makes it a small bit easier to understand. But
I do realize that element of the study is core.
quite complex and difficult to digest for sure.
No, I think the practicality of the study is
at some point it makes no sense to make this
jump. So the jump is too big or it can be also
too small, but let's focus on too big. So if
you are coaching through the competition an athlete
you do not know personally or your relationship
with this athlete is very short so that you don't
have experience in jumps, there is probably a
number for this particular. athlete or because
they are a male in this weight class and it's
a back squat there is a cut of number that you
shouldn't cross in this generic let's say situation
only because you don't have this experience and
relationship developed one of the things i will
say is like i think definitely take anything
when we talk about specific numbers here as an
approximation i think that the things that are
important to note are what is the shape of the
relationship in terms of like in the case of
i guess kind of spoiler alert in the case of
all of the lifts we found that like increasing
the amount you jump up into a point will increase
the probability of your lift being successful
but there is an inflection point where that begins
to change you know and and your probability of
lifting so in in other words you can jump too
much which i obviously is i think something that's
not going to blow anybody's mind but the shape
of the relationship is approximately similar
across lifts but the the ranges are different
which again so you need to know things like the
range with which I can jump between squat attempts
is larger than the range within which I can jump
safely between bench press attempts. When it
comes to individual lifters, then I think that
is probably the biggest part where like knowing
your athletes comes in because like, I mean,
anyone who's been around lifters before, I knew
people, people of similar weight classes to me
or maybe marginally heavier who could make enormous
jumps that I just, you know what I mean? Like
for me, if I was doing squats, say, like anything
more than 10 kilos would be very, very considerable
for me to jump, even from warmup weights. I just,
you know, it, it, it messed with the way my squats
felt and, you know, my, I would misgrew things
and stuff like that. So I had to be very meticulous
in my warmups for squatting. Whereas I knew people
who could come in 70 kilos on the bar, two reps,
140 kilos on the bar, two reps, 180 kilos on
the bar, five sets of eight reps. And he was
just like, right. Yeah, I can't, I can't do that.
You know, that kind of work. Or, you know, like
getting to their work sets as quickly as possible,
you know. So and, you know, those might be and
you might see that across lifters with very similar
profiles in terms of similar one rep maxes, so
on and so forth, similar body weights. So that
is obviously where the nuance of that coming
in and knowing that that relationship will be
consistent across lifters. But like the ranges
might vary by lifter by lifter. And some of the
factors we explore is meant to assist in understanding
factors that may influence those ranges, essentially.
I know we've probably kind of jumped ahead a
little bit by talking about that, but yeah, that
is in essence kind of what we were trying to
do. And I think we've accomplished it to some
extent. I guess the very boring result that I
will mention at the beginning is that our model,
so we separated again, just to reemphasize, we
did a male and a female model. And again, that
was partially for interpretability and partially
just to avoid the male data overly influencing
the female data from a predictive standpoint.
But our two models had, there's a model parameter,
it's the only one I'll talk about, which is called...
sensitivity, which is essentially an indication
of how good the model is at predicting a successful
lift attempt. And they had a sensitivity for
the male model. It was 87 % and the female model,
I think it was 88%. So that's, you know, if you're
rounding it up, I'll be, I'll be nice to myself
and round it up. Nine out of 10. successful lifts
in both models were detected by the model correct
were predicted by the model correctly you know
what i mean so nine out of ten times if you make
a lift this model will will have predicted that
you were going to make it you know whether you're
a male or female before we kind of maybe talk
about the jump um effect size stuff i think there's
there's two things on the descriptive side that
i thought when we were discussing the results
as a research team we were kind of working together
on it i thought were very very interesting some
of my colleagues didn't so much but i I thought
they were useful to go into. The first thing
we did is we did on the descriptive level, which
lifts had the highest overall success rates.
So squat bench and deadlift for males and females.
They're slightly different between sexes, but
I thought it would just be interesting to note.
So I think for both males and females, the bench
press and the bench press had the lowest success
rate. So I think it's around low 60%, like 62
to 65 kind of percent, give or take for males
and females. I think it was slightly lower for
females. So irrespective of attempt or anything,
you've only got about a 60 to 65 % chance of
making a bench press lift. In comparison, the
squats and the deadlifts were both in the mid
to high 70%. So people tend to, you know, miss.
The rate of bench press success is about 10 to
12 % lower for both males and females and then
either of the other lifts. And it's something
I think that comes across consistently throughout
our results is that the bench press is a bit
of a bizarre animal in terms of some... some
of the results we see. But I think that is anecdotally
known in the powerlifting community. I think
there's a term people use, poverty bench, which
is basically you've got someone who's a good
squatter and a good deadlifter, but just for
whatever reason, an absolutely terrible bench
presser. And, you know, I squat 250 kilos and
I can't bench 110 kilos, you know, kind of situation.
But I thought that was interesting. The other
thing that I think is worth commenting on is
that the irrespective of anything else, what
lift you're doing, your age, anything like that,
the probability of a successful third attempt
was around 10 to 12 % lower than the probability
of a successful second attempt. So all of the
situations being equal, people tend to miss more
on third attempts with that. The probability
of a successful third attempt is around 50%.
And the probability of a second attempt is a
low 62, 63%, you know, kind of. And those numbers
will vary marginally between males and females,
but generally speaking. All other factors being
equal, you know, you have a one in two chance
of making a third attempt in comparison to a
second attempt. I thought that was interesting.
Now, and there is some other literature, might
be a study by Kyle Travis. I think it might be
2019 study. We cited it within our article, but
they looked at, if I remember rightly, I think
they looked at world championship, like IPF world
championship play, I think top 10 or something
like that. And they did estimate that you will
see from first attempt to second attempt to third
attempt, like a progressive decrease. in the
rates of a successful lift across all three lifts.
But I might be misremembering the authors of
that study, but it is cited in our article. I
just thought it was interesting. There are factors
at play there in the sense that obviously people
might take chances on their third attempt more
and therefore, you know, are more likely to miss.
But it is, I think that is something that's useful
to know for coaches in that, like all other factors
being equal before you start thinking about how
you're handling your lifter and, you know, planning
their lifting day strategy. There is kind of...
of a natural progressive decline in success rates
across attempts. And that's useful to know because
I think it is useful for informing. It is information
that can be used to inform attempt strategies,
you know, but that's all just some descriptive
stuff that we found before kind of getting into
the nitty gritty of the model that I personally
thought was actually useful findings from the
study, but it could just be me. No, it makes
sense. If coaches are coaching for a long time,
they have this feeling that whatever jumps you
can make between the first and second. the second
and third the jump should be smaller or that
you have to accept that the probability for lifter
to make this lift happen will be lower yeah and
it's potentially something where if you're going
to make an ambitious jump it might be worth doing
it after your first attempt but before your third
attempt you know and I think that's something
that people might intuitively do anyway particularly
when I was competing but we had like there was
a lot of these phrases that got kind of thrown
around but I think it was like first attempt
is to stay in it so you don't bomb out second
attempt is to is to win it and then the third
attempt is kind of in your back pocket if you
need it you know so like whatever your best score
of the day you thought was going to be try to
get it on your second attempt so that you know
you you have a conservative opener You're going
to stay in the competition. You're going to register
a total. Take your chance on your second attempt.
If you miss it, you've got another chance at
it. Great. If you make it, hey, there's a bit
of wiggle room here. You know, we can get a few
extra kilos on the board for placing, PBs, whatever,
you know, that sort of stuff. And I guess I think
that kind of that anecdotal strategy does also
seem to line up a little bit with what we've
asked, like with that result from our study.
So in some sense, probably validating what at
least some coaches are doing, but also. helps
to emphasize that point and that you can quantify
it with numbers to say that this is a real thing.
You know, it helps, maybe helps emphasize that
it is important to consider those sort of factors
when it comes to planning competition strategies.
Maybe, I guess, we could talk about the jump
size effects, which are the main aspect of the
model. So I guess kind of talking about it, I
guess we have to kind of partition this by sex
a little bit. But what we found, generally speaking
for both sexes, without some minor, minor detail,
generally the squat and the deadlift kind of
responded the same in terms of jump attempt size
in the sense that for males for example you can
see like anywhere between 5 to 20 kilograms on
both the squat and the deadlift you will actually
see an increase in your probability of a successful
attempt if you jump anywhere between 5 and anywhere
under 20 and again that's going to be where you
know and I think those are quite reasonable jumps
that people tend to make on those lifts And obviously
the individual variability is what decides because
there's still quite a big range between five
and 20 kilos. But that's where individual circumstances
kind of come into play in that middle zone. Generally
speaking, you will see declines in the probability
of success in squats or deadlifts if you are
a male with jumps over 20 kilos. And that effect
was a little bit stronger on third attempts in
comparison to second attempts, which I think
is, you know, by the time you reach your third
deadlift, you're pretty tired. Jumping 20 kilos
is a tall order. For males on the... Bench press,
jumping less than 10 kilos, generally speaking,
between attempts will improve or you'll have
higher success if you jump less than 10 kilos.
But you will see that like there's a really sharp
drop off with larger jumps on the third attempt
again in comparison to the second attempt. And
that's really just a more granular manifestation
of what I was talking about previously when we
talked about third attempt success rates in comparison
to second attempt success rates. For females,
you'll see kind of something similar. on the
squat and deadlift in that like you can see,
you know, increasing success rates, but the range
is smaller. So generally for females based on
our model between nine and 11 kilos or eight
and 11 kilos in and around is where you will
see kind of you're likely to see an increased
probability of success in comparison to males
who were five to 20. So that range is tighter,
but the principle is the same. Something we saw
with females. And again, this is probably something
that you would see a little bit of an anecdote
in that. like the extent to which females see
an increase in the probability of bench press
success is very, very small. So I think we said
anywhere, it's like something like between two
and four kilos or something like that as estimated
by the model. And I mean, yeah. And again, I
think we saw like a marginally lower probability
of success rate on the bench press for females
as well. And particularly on third attempt size,
like any kind of moderate, you'll see it in one
of the figures in the study. If I was to pull
it up just for any. who wants to kind of read
the study, all two of the people who want to
read this study, in figure 4C on the right -hand
side, you'll see kind of the female bench press
result is like a complete, it's like a waterfall.
You know what I mean? It comes up within a very
narrow range and then drops off very, very hard.
It's difficult. You know, it is in theory a similar
relationship to what the males see, but it's
considerably more pronounced. And it's difficult
to know why that is. There's, I mean, it's consistent.
in the literature that females display lower
estimates of upper body strength relative to
males, even in comparison to like lower body
strength differences. I think Tommy Lundberg
from the Karolinska Institute has a really nice
large scale study where they just compared different
athletic kind of traits between males and females.
I think you see things like very large differences
between males and females in terms of things
like punching power and so on and so forth. So
I only hide that to say that that difference
is pronounced across other like upper. body strength
and power related kind of movements between males
and females. But I actually, from what I understand,
the mechanisms behind that are not well understood.
I think there's some stuff around muscle mass
distribution and stuff like that, but I find
it difficult to believe that that is the whole
story. But it is something that's identified
in our study as well. It is in somewhat, in some
ways consistent with other similar athletic style
events, but it is difficult to understand why
that relationship is much more profound. in females
compared to males, but it is there. So I guess
kind of wrapping that up, that section of it,
it's to say that squats and deadlifts, generally
speaking, have similar relationships for both
males and females in the sense that larger jumps
can be tolerated and larger jumps will, within
certain ranges, increase the probability of successful
lifts. So you can actually, to some extent, jump
too small. Like when we talk about the upper
limits of the jump size in this study, like,
you know, I think the upper limits are more represented.
of a physical limits there and that like you
can literally only lift so much. Like I'm not
gonna, I don't think anyone ever missed a 25
kilo jump because they just didn't believe hard
enough. You know, that kind of thing. It's probably
just 25 kilos is really, really heavy. But when
we talk more within those nuanced small ranges,
like the difference between, let's say on a squat
or a deadlift, the difference between making
a two and a half kilo jump on a squat versus
like eight kilo jump, six kilo jump, five kilo
jump, something like that. That is probably where
that kind of comes into play. And again, like.
That is purely speculation by me. I don't fully
know, but I think that is something that could
be a factor of just like giving people an appropriate
challenge. That would be my guess. Yeah. And
it's probably a good time to point out in that,
like, I think because you see this, I mean, it's
a slight detraction from us in terms of we're
building a model that's like more around competition
strategy, but like, you know, a machine can build
you a decent training program, but a machine
can never coach you. You know what I mean? Like
you see these AI apps and stuff like that, that
people use. And I think the programs that they
build on some of the better. ones are like perfectly
fine you know what i mean but you know you're
never you're not going to get like it's not going
to help you on a competition day it's not going
to critique your technique it's not going to
help you with your mindset and stuff like that
as well when it comes to approaching competition
and i think that that is something very similar
that i would highlight with respect to our model
in the sense that this cop this this will never
plan a competition strategy for you it will just
maybe give you some information that is useful
in your coach's bucket alongside everything else
to help you make the application of the of coaching
a little more informed and perhaps a little more
systematic if possible. You know, that's kind
of the intent behind it, at least. I think it's
also useful to have you build models separately
for females and males. And anecdotally, coaching
females and males is slightly different. So if
you can see it in the model too, then you can
take it in and keep it in mind in terms of how
do I even talk about the possible numbers? And
if I have a lifter who is studying, how should
I gauge this? standard way to coach the jumps
in the warm up or in the approaching PR and so
on because if you do things for the first time
you don't have preferences you don't know so
you have to start somewhere yeah and I mean this
isn't something we looked at but something I
did and it's more applicable again to weightlifting
than powerlifting so we're really getting tangential
now but one thing I noticed in weightlifting
and a little bit I coach mostly males throughout
my career just through happenstance I worked
with some females but mostly males but something
I did notice from like because the club I coach
that was different from the club that I trained
at. So some of the stuff I noticed with the girls
that I trained with was they would always open
the competitions like way closer to their max
than I would. You know, like I would train with
girls who might have like, say, 82, 83 kilos
snatch and they'd be opening at like 78, 79 kilos.
Whereas I might have like 125 kilos snatch. I'm
going to open at like 115 kilos. You know, I
mean, like way, way further away. But they were
much more comfortable like opening really heavy
and then making those more precise jumps where
I was. like I need to open really light and make
bigger jumps you know and that was something
that I mean again it's pure anecdote and it's
weightlifting instead of powerlifting but that
was that was one of the factors that did inform
separating out models that those models was just
me being like well I have this experience of
like whether it's physiological or psychological
it's like just seeing girls approach it differently
to how guys do you know and being like okay that
you know you because whether it is physiological
or psychological it may still manifest in the
data if it's something that's kind of very consistent
obviously that's pure anecdote but but i i do
i do think there is something to it you know
and to some degree you know we did some linear
fixed effects and essentially again fancy statistical
terms we included some things in the model that
we did not make non -linear just because again
just makes things more complicated makes things
harder to interpret so we were like we'll just
keep these as linear because it's much easier
it's it's a much more straightforward interpretation
but the caveat there is that the like take it
with more of a grain of salt. Essentially, it's
not as a precise finding as some of the stuff
we've talked about already. So the three things
we included in there were the first attempt weight.
So the opening attempt weight, essentially. So
what your opener is, body weight, your exact
body weight, you know, as opposed to your weight
class and age. And we included those as linear
effects just to see how they also interacted
with the probability of a successful attempt.
We found opening weight had a negative association
with the probability of lift success, which I
guess, kind of make heavier you open, the less
likely you are to make subsequent lifts. So again,
there's probably a sweet spot there, but that's
potentially not surprising. Body weight had a
positive association with lift probability success.
So heavier lifters tend to make more lifts. Again,
that's within reason, probably something that
makes sense. And likely these relationships,
if we were to do very granulated analysis of
them, would have some kind of a nonlinear relationship,
but how I would interpret them within the context
of... lifters here would be like, it's probably
a good idea to fill out your weight class. So
what I mean by that is if I lift in the 105 kilo
weight class, it's not really valuable for me
to be 100 kilos. I would probably have a better
chance of making lifts if I weigh 106 kilos,
don't have breakfast, weigh in at 105, happy
days, you know, that kind of situation. So that
would be the way I would be interpreting those
relationships for now. Similarly, you know, it's
useful to probably have a conservative goal putting
weight. And very interestingly, we didn't. find
that age meaningfully influenced successful lift
probability I actually kind of thought you would
either see something on either end of the spectrum
where younger lifters would miss more lifts because
they're perhaps overly enthusiastic or older
lifters would miss more lifts because they're
old you know but if you look at that figure in
the study I think it might be figure five it's
essentially like a horizontal line so there really
wasn't a relationship there now that could also
just say that even if younger lifters are more,
and being anecdotal here, say chaotic in their
lifting attempt strategies, they might be young
enough to do that. Whereas older lifters may
kind of learn to become conservative as they
get older. And therefore, you know, that might
be why that happens. Something that's probably
very late in the game to mention, we did account
for individual lifter variability in our model.
So all that means is essentially, to some extent,
we tried to capture the fact that like, if I'm
in open powerlifting and I have three competitions,
there's a relationship between those three competitions
and there is a relationship between the attempt
strategies that were applied by me within those
competitions so I might be different from people
who through all of the circumstances look similar
to me you know in terms of guys who similar height
similar weight similar lift numbers all that
sort of stuff there's still going to be differences
and I think we estimated that effect to be somewhere
between 12 to 14 percent and what what that kind
of just means is that like not it doesn't strictly
mean that like the values range that I've given
you can be estimated to vary between 12 to 14%.
Instead, it just simply says that like the individual
lifter is accounting for like differences between
lifters is accounting for like kind of. 12 %
of the error in our model, essentially. So difficult
to interpret, but it is accounted for, which
again, just adds some precision to our estimates
when we generalize it. We're knowing which competitions
are paired to the same lifter over and over and
over again. But I think those are the last findings
of the study, at least. Awesome. Huge study.
If anyone is brave enough, they should go and
at least look at the figures because I think
that's helpful. You have lots of beautiful figures
there. So that is... something I would recommend
now I have two questions to finish the first
one is what is your favorite lift my favorite
lift so when I was I'm retired from weightlifting
now but when I was weightlifting my favorite
lift was the split jerk believe it or not I was
a bit of a peculiar athlete in that I was I very
rarely missed jerks I would very often miss cleans
I wasn't particularly strong so I generally knew
if I completed a clean I was going to make the
jerk so I was you know I would finish the clean
very difficult clean I get to the top of it I'm
like oh yeah here we go here's the fun part so
I used to I know a lot of people like the split
jerk but I really enjoyed it now it's probably
something like a back squat or something like
that you know it makes me feel like I'm still
vaguely athletic but those are my if I can give
you two answers those are my two answers split
jerk and back squat that will work and last question
is where people can find you if they want to
look at your work or see what you're up to yeah
so I'm on Research Case Ian Darrah D -A -R -R
-A -G -H And I am on LinkedIn as well under the
same name. I have an Instagram. Instagram is
probably the best place to contact me if you're
looking to, say, get a copy of the study or if
you are looking to just reach out with a question
or anything like that. That is the best place
to find me. Bean Swole is my Instagram. Bean
as in the legume and Swole as in I once was Swole.
So if you're trying to find me there, that's
probably the best place to contact me. Otherwise,
LinkedIn or ResearchGate. I think the article
is available for free on ResearchGate. If not,
you can request it. then I'll provide it to you.
If people are looking for full texts of the article,
I'm happy to provide that. Awesome. Thank you
so much, Ian. Pleasure. Thank you. Yeah, no,
the pleasure was all mine. I really enjoyed it.