Be critical. Think about what you are measuring
and why you're measuring it. And does the metric
actually represent what you think it does? And
so even if you don't use the domains that I've
presented in this study, I would love it for
you to go out and be very critical and understand
the reasoning behind the selection of the test
and the selection of the metric. Hi, Mary. It's
my pleasure to have you on Evidence Strong Show.
If you could briefly introduce yourself. My name
is Mary Claire Jeannot. I typically go by MC
in the sport environment. And I just completed
my PhD at La Trobe University under the supervision
of Dr. Lachlan James, Dr. David Carey, Professor
Sam Robertson, and Professor Paul Gaston. I'm
now working with the Canadian Sport Institute
as a lead S &C coach and data analyst. And I've
just started a new position as the IST lead for
the Canadian women's volleyball team. And for
the Australian listeners, an IST lead is kind
of like a high performance manager type position,
slipping along those lines. So high performance
sport, data analysis, these are four topics that
lead to the topic of today's conversation, which
is strength, measurement and testing. So let's
start with why. Why it is important. Why did
you, because that was the topic of your PhD,
isn't it? Yeah, correct. Why did you go there?
This is the first. Yeah, great. So as you kind
of mentioned, this was the part of my PhD. and
this review that has just been published kind
of kick -started this whole concept that led
to my PhD. And it is well accepted in the strength
and conditioning space that maximal lower body
strength can be expressed in a number of different
ways. This can look like... a rapid foot strike
in a sprint, a vertical jump to block a volleyball,
or a ruck in rugby, where an athlete is moving,
you know, not only their own mass, but an opponent
and maybe two opponents' mass at the same time.
Now, these are all expressions of lower body
strength, but as we know, they represent something
different, and we use different training strategies
to be able to target each of these different
strength expressions. And it is important to
monitor strength qualities that are expressed
in sport. We're not just going to necessarily
measure one rep max back squat. and assume that
it's going to translate to all the different
expressions of strength that I kind of briefly
introduced. And so S &C coaches and sports scientists
have gone down the route of looking to measure
more unique or more specific qualities of strength
that are more specific to different force expressions
experienced by the athlete. This can be, this
is really good, but it can be a slippery slope
sometimes as we try to refine, refine, refine
down to very specific and very like discrete
metrics. Because it's not necessarily telling
us more information than what a bigger, more
inclusive metric would. And this is particularly
true when we start measuring strength looking
at or using force platforms. They've been used
a lot in the literature to measure very unique,
very small, different discrete types of strength
expression. And instead of creating more clarity
on how strength is expressed in sport, it's actually
clouded our understanding and created some confusion
because we don't know which metrics are important,
which metrics. may be redundant. And so that
kind of underlined the basis of my PhD, which
was what are these strength qualities that underlie
these strength metrics that we regularly collect
in the strength conditioning environment? And
how can we best identify them using the wide
body of metrics that are available in the literature?
And that's kind of under the assumption that
there are physical characteristics that underlie
performance of a task. Metrics related to a specific
physical characteristic, such as the ability
to produce really, really high forces or the
ability to produce force really, really quickly
will likely be highly correlated. So if we have,
for instance, peak force in a squat jump and
peak force in a countermovement jump, you know,
assuming that those are very similar tasks, they
likely will be highly correlated because someone's
ability to produce high force dynamically underlies
both of those metrics. Now, because of this,
it may be advantageous to group metrics that
are highly correlated and partition those who
are not very highly correlated as they are likely
underpinned by similar or different characteristics.
And this kind of led us to this strength classification
process, which can be used by practitioners to
help guide test and metric selection, as well
as the metric interpretation within the strength
diagnosis process with the understanding that
there may be a simpler or a more straightforward
understanding of what different types of strength
characteristics exist and finding which metric
best represents that particular strength characteristic.
All right. So just to please let me know whether
I understood correctly. So strength is this big
term we all... about strength and getting our
athletes stronger but now you can measure it
in different ways and strength can be expressed
in different ways too it can be huge loads moved
slowly or it can be lower loads but moved very
explosively so what we measure so the tests we
use may measure different things the things that
are important for certain athlete for example
so that would mean that from all the tests that
are possible in strength testing we have to choose
the ones that are important for this athlete,
for this sport, for this position. And now we
have to ensure that the ones we choose will be
highly correlated with the underpinning ability
or capability that is important for the sport.
Yeah, correct. And just on top of that, you know,
we can have a bunch of metrics that are highly
correlated to a sport -specific skill. And that
may, you know, it may be viewed by a practitioner
that that's really good, that all of these metrics
are highly correlated to, let's say, sprinting.
And if all of those metrics are highly correlated
to sprinting, it's likely that they may all tell
you the same thing. And we don't necessarily
want to just keep comparing an individual strength
metric to a sport specific quality all the time,
because that may just be very, very redundant.
And, you know, if we're distracting, if we're
trying to look at the holistic picture of an
athlete and the holistic understanding of strength
expression, that may be important for sports.
So the first step is understanding what exists
in an athlete. And this may differ from athlete
to athlete. athlete, but what range and what,
yeah, what range and force expressions or physical
characteristics underlie performance of that
athlete. And then we can go in and start relating
each of those important pieces to sport performance.
And then that's when the individual can, or the
practitioner can determine which one of those
characteristics are more important than the other.
So would the goal be, this is the idea profile
of the athlete in this sport, in this position,
this is this athlete, something is missing or
is lower or is... extremely high. Now we want
to know what, then we want to relate it to capabilities.
Then we want to choose the best test for this
capability. Then we want to train and then measure
again. That's how we would use it. Yeah, pretty
much. So you would figure out, first you would
figure out what range of strength qualities exist.
And this can be looking at tests of a variety
of different constraints, which we will go into
later today. And then you'll be able to reduce
them down to metrics from those tests. that tell
you something unique. Then you can see where
a potential gap is in that athlete. For instance,
the athlete jumps high in a catamaran jump, lifts
a lot of weight in a squat, has a high peak force
in an isometric test, but has really poor reactive
strength in a drop jump. We can go in and say,
okay, we want to then measure that or, sorry,
train that particular quality in the reactive
strength because that's lower than everything
else. But that may not be as important for a
swimmer as it is for a field sport athlete, for
instance. And so we can look at these domains
or... range of strength qualities and first determine
which ones are important for sport performance.
And then we go in and say, okay, where are the
gaps and where do they matter? All right. So
we covered the why. We have a clarity on why
we should even care. Now, how did you go about
classifying what strength capabilities they are
and then the metrics, which one would be paired
with which? Yeah, great question. This was the
main basis of this review was to observe the
literature within the context of of kind of a
threshold -based approach of classification and
use that to discuss what potential strength qualities
exist. So what we did was we looked at, we did
a literature search and looked for all of the
studies that compared two or more different metrics
of strength. So there were lots of studies that,
for instance, had a kind of move and jump and
a back squat, and they had a correlation between
kind of move and jump, jump height and back squat
load. So that's a good example of what exists
in the literature. And there are more comprehensive
ones as well. that looked at more of the relationship.
So the, you know, using regression models to
see how changes in counter -movement jump height
relate to changes in one rep fast squat. All
of those studies we looked at and tried to summarize
and we've summarized in tables in this review.
And what we did was interpreted them in the context
of more similar or more different. So we used
a cutoff threshold of 50 % shared variance and
used that to interpret whether or not metrics
were very... similar or whether or not metrics
were more different than similar. And so a 50
% shared variance cutoff is an R squared of 0
.5 or an R of 0 .71. So we use those thresholds
throughout and looked at all of the relationships
that exist in the literature between metrics.
Now, in order to effectively compare metrics,
because there are too many to just go one for
one throughout the whole body of literature.
So we kind of made it a two -step approach. And
what we first did was grouped metrics based off
of the... task that they were derived from. So
we use different task constraints to classify
or categorize the different groups of tests.
We grouped them based off of the external constraint,
and that would look like, sorry, an external
load. And what that would be is either acceleration
due to gravity on the center of mass. So that
would be a test such as a drop jump or rebound
jump, where you're adding that pre -loading of
acceleration on the center of mass prior to the
test performed. Could be an external load. load,
such as a barbell. And then there were time constraints.
So a task that required a task to be performed
within a certain period of time. Now, a good
example of this would be the drop jump, where
the instructions are given to move as fast and
as high as possible. So that's a temporal or
timing constraint. And the test needs to be performed
ground contact times of less than 250 milliseconds.
And so that's a constraint that would then group
a test and how that would differ from something
like a counter -moving jump, where there are
no... tiny constraints. And then we also grouped
them based off of the muscle action. So that's
dynamic or isometric primarily. And under the
dynamic category, we had non -ballistic and ballistic
tasks. A ballistic task would be a jump, whereas
a non -ballistic would be something like a one
rep max deadlift or squat. And so in the first
round of comparisons, we use that threshold approach
to compare metrics that existed in the same test
category. So for instance, within the counter
movement jump, we looked at jump time and the
core. correlation between jump time and jump
height and all of those different metrics that
exist, all from what's available from the literature.
And from that, we determined whether or not different
potential classifications or groups of metrics
tended to emerge from within a test category.
And then we looked across test categories. So
we would look at, for instance, within the reactive
strength test type, we would compare metrics
from there to an isometric task and see what
exists in the literature with different relationships.
between those different task types. And by doing
so, we were able to fully look at what's available
in the literature in a somewhat systematic approach
to be able to understand what types of strength
qualities based on these metrics exist. So did
you just go whatever is in the literature with
the threshold in mind, whatever was tested or
investigated, that's what you used to make classification?
Yes, exactly. So there were some tests that had
a lot of literature on it, such as the... movement
jump and one or a back squat, for instance, there's
a decent amount of literature out there for that,
whereas other ones there were not. And so we
discussed the potential gaps that existed in
the literature, which were pretty evident. There
were some pretty gray areas, particularly with
the loaded jump and relating that variable to
any other strength test. Where do you want to
see the differences and where you are all right
with having a more global measure, I guess? Yeah,
exactly. And as part of this research too, there's
kind of levels of strength. information you can
say so if we're just looking at dynamic outcomes
so we're just looking at how high you know how
much impulse can you create dynamically in an
unloaded condition then you likely only need
one test but if you want to dig a little bit
deeper and understand how that impulse is generated
or your strategy behind that then you can kind
of look at another level and at one point in
those layers depending on your ahu that you're
interested in then you can dig a little bit deeper
and include that squat jump in addition to a
counterweight jump well this is really important
because what it means for practitioners who working
with the team is they don't have to go deep enough
with all the athletes. They may just need the
deeper measurements for some athletes, not the
others, which is time -saving and so on. So this
is really good. Yeah, exactly. And there's a
tendency to go deep. pretty quick without kind
of zooming out and looking at the big picture.
And so there's a lot of, there are a lot of options
out there to, you know, to go big picture without
missing out too much. And so that's, that's definitely
the purpose of this work is, okay, can we get
90 % of the information by doing 1 % of the tests
or 1 % of the metrics? And the answer is probably,
yeah. And sometimes you will want to dig into
that extra 10%, but that's not always the case.
And it's frequently not, not, not usually the
case. So you touched the very important point
so as the technology makes its way to sports
science and athlete monitoring we are tempted
to measure everything because we can but then
we end up with huge data sets that are very hard
to process and make sense of and then used to
meaningfully impact the decision making I guess
on either selection or training management and
so on so having structure of metrics what is
measuring what and to what and how important
global or how in -depth the measurement is, is
actually super useful for that reason also. Yeah,
you nailed it. What did you use and how? Yeah,
so for this review, we used a threshold approach
to determine similarity or uniqueness between
metrics. And for the review, what we did was
just look at all of the literature that exists
with comparing different metrics under different
tests and use the reported either association.
or a correlation. So that's using a correlation
analysis and just a single R. We also looked
at studies that had relationships between metrics.
So that would be after a regression model, you
would get an R squared. And then we also looked
at, and mostly included in the discussion of
the findings, factor analyses or principal component
analyses. Now these existed mostly in comparisons
of the counter -movement jump, but they also
existed in other spaces as well, where they were
looking at the component loading of different
metrics from different tests to determine you
know how many different types of strength expression
existed essentially essentially what we're doing
here and so although those are hard to directly
compare to correlation analyses or regression
analyses they provided extra strength essentially
to our findings with those correlation regression
analyses and so we use those values to cluster
anything that was in r squared of 0 .5 or greater
into the more similar than different category
and everything that was 0 .49 or less R squared
in the more different than similar category.
And that equated to a cutoff of an R of 0 .71.
So we use those parameters to help discuss the
findings. There were some that kind of crossed
that gray area and those were discussed in the
context of, you know, kind of the crossing zero.
Some of them are more similar, some of them are
different, but a lot of them that were pretty
certain. about kind of really fell into the one
camp. So we're either a lot similar or a lot
different growth literature. Awesome. Could we
go step back and could you simply explain what
did you use correlation for and what did you
use regression for? So all we did was look at
the results from the recent studies and some
had used correlation analyses and some had used
regression analyses to compare the relationships
between different metrics of strength. And so
for the purpose of the study, we kind of took
them as a very similar result. So if there was
a correlation of, let's say, 0 .88, we would
know that that would be in the more similar than
different category. So metrics such as jump height
and takeoff velocity of a countermovement jump,
because they're derived pretty much from the
same variable, they were like a 0 .99 correlation.
And so there were some of the metrics that really
existed far in that end. And then with the regression
analyses, the same process was gone through.
but they would be interpreted as an R squared.
So a 0 .5 would be more similar than different,
whereas a 0 .49 would be more different than
similar. So all we did with that was just interpret
the findings in that context or in that systematic
approach. So whatever the authors of the original
study used, you would take that and interpret
it according to your cutoff points. Yes, yes,
exactly. Which was a bit difficult sometimes
because, well, a lot of the time, because...
If we're just looking at a single measure, we
don't, we all know as S &C coaches that the way
a test is performed can greatly affect the metric.
And so we're just looking at that single outcome
metric, the correlation between two measures.
You know, there's a lot of assumption behind
that, but that was part of the review process.
And I think we did an okay job to under, to explain
the limitation of this approach, but it just
helped guide discussion. And like I said, there
were some that were very end ranges and we could
be pretty certain about those. those things the
stuff we just said hey we don't know and we need
to improve our research to to be able to find
out how to sift through that very not a tricky
question maybe too detailed question but i had
a person here on the evidence sanctuary talking
about how the cues for a drop jump will influence
the outcome so whether the athlete is instructed
to jump as high as possible or to rebound as
fast as possible will influence what metrics
will be elevated versus the So was it enough
studies to go into that level? So did you separate
what the cue was used and then what the correlations
were between the metrics? I'm really happy you
asked that because that actually was one of the
main discussion points at the end of our paper.
And we didn't necessarily, because the cues were
kind of not always reported, we didn't necessarily
go to the cues, but we did compare the contact
times. So when it was reported, we could see
that the metrics from the drop jump that were
derived from a test that had less than... 250
milliseconds of contact time were unique from
a countermovement jump. So if we're talking about
jump height and jump height, they were unique
if the constraints of the drop jump were, like
you said, jump high and jump fast and they had
a contact time of less than 250 milliseconds.
But if in some cases where the contact times
were elongated past the 250 milliseconds, then
the relationship between countermovement jump
and drop jump metrics were much closer. And so
they would actually be grouped into the same
category. So this was a really important finding
that, you know, the task constraints greatly
influence the discovery of these strength domains
or strength qualities. And so we have to be very
specific when we are conducting these tests so
that we understand how the test is performed,
what the outcome of the test is trying to be,
what we're trying to gather from that test. And
if for some reason a person can't do a drop jump
with a contact time of 250 milliseconds, then
it likely isn't the right test for them. They're
not measuring it. reactive strength quality.
So we have to consider all of those factors when
we are implementing this testing. I like finding
things out. I thought that it may be a little
bit too detailed question, but actually looked
into it. So are we ready for the results? Yeah,
sounds good. Awesome. So what are the results?
Okay, so at the end of this whole systematic
approach, we found that there were five different
domains of strength. So we termed them domains
of strength, and those were reactive, unloaded
dynamic, loaded dynamic, maximal isometric. and
early isometric. Yes. So these group of strength
metrics were discovered by first comparing within
the task category and then again across task
categories, as we mentioned. But there were some
considerations. So like I mentioned previously,
the reactive strength category is highly dependent
on how the test is performed. So as we mentioned,
if you get a contact time greater than 250 milliseconds,
then it actually, the metrics derived from that
test actually group in the unloaded dynamic category.
And so in order for... For it to be a distinct
domain of strength, the task has to be performed
a certain way. And so that was a main kind of
outcome of our study. Another notable finding
was within the unloaded dynamic strength category.
As there's a decent amount of literature to suggest
that there are actually different strength qualities
that can be discovered within the unloaded dynamic
strength category. So for instance, the timing
of the test. So we're talking about a counter
-movement jump here. The timing of the counter
-movement, the forks input of the... counter
movement. So we're talking about like mean concentric
or mean propulsive force, the outcome. So we're
talking about jump height there. And then there
are also some other movement strategy characteristics,
such as the speed of the depth of the counter
movement. So those all kind of emerged in this
study and some other studies as likely unique
characteristics. But when those metrics were
compared across task categories, it got a little
gray. So we couldn't necessarily find distinctions
when measuring outside of the task category.
So because of that, we decided to collapse the
unloaded dynamic strength to a single quality.
But there's a lot of more work that can be done
in that area. And then another important finding,
sorry, the loaded ballistic tasks. So we're talking
about a loaded countermovement jump or squat
jump. There is very few studies out there that
actually looked at a correlation between a non
-ballistic task such as a one rep max back squat
or an unloaded countermovement or any other.
comparisons, really. There were very few studies
that looked at the comparison between a ballistic,
a loaded ballistic task and an isometric or reactive
strength tasks. So those studies don't necessarily
exist when we're looking at it that way. And
so because of that, we weren't really able to
determine its distinction. So where a loaded
jump may be more similar or different than an
unloaded jump or a one rep MACBAT squat. So then
we know there's a spectrum here. We don't know
where the difference starts to occur. And so
there's more research to be done on where that
loaded ballistic task becomes more different
than an unloaded ballistic task and vice versa
working the other way around. So that was a big
gap identified in this study. And then lastly,
another important finding was the distinction
between early and maximal isometric strength.
So this is something that has been theoretically
discovered in individual studies suggesting that
early isometric strength produced, so force produced
kind of within the first 100 milliseconds or
so, may be... distinct from your maximal force
capability. And we found in the study that it
does, in fact, consistently demonstrate a distinction.
So yes, there's a distinction identified between
early and maximal isometric strength, where the
early isometric strength metric was likely around
100 milliseconds. So force produced at 100 milliseconds
is usually the cutoff. So anything past that,
so past 150 milliseconds. So if you're measuring
force in an isometric task for around 200 or
200... 50 milliseconds. It is more similar to
maximal. isometric strength than it is to the
early. So when we are looking at isometric tasks
and want to look at a distinction, it's recommended
that about 100 or 150 milliseconds is where you
measure that strength expression. And anything
later, it's very, very similar to maximal. But
this is only possible if the task is done with
a rapid intent. And what I mean by rapid intent
is that it's been instructed to push or pull
as fast and as hard as possible. So those two
characteristics critical if we do want to measure
rapid strength or this early isometric strength.
And that's really important. So it's another
example of ensuring that the task constraints
are upheld or else the whole representation of
a strength domain. won't exist. And so, you know,
with some of the force plate technology, you
can make a flag or a tag that says if it's done
rapidly or not. And I'd recommend doing that
because it does influence the interpretation
of that metric. Okay. So now let's go back to
practitioners. How would they go about choosing,
I guess? Yeah. So the first step is determining
what your athlete is used to performing. So before
we even go down the road of what exists, it's
what can the athlete perform? athlete has never
done an isometric test before, brain them up,
show them how to do it, make sure they're used
to do it, or else you won't get a maximal expression
of force. So that would be my number one advice
is make sure that the tests that you are doing
allow us for the athlete to express force maximally.
Second, figure out what, you know, out of the
range of tests, which ones matter the most for
your athlete group. So if you're looking at novice
athletes who, you know, everyone just needs to
get stronger and get exposure to strength conditioning,
I would I wouldn't worry too much about getting
the wide spectrum of strength, but I would measure
countermovement jump and I would get jump height.
I would get kind of the big rocks measured early.
And then when you are working with an athlete
group that is well -trained and you are looking
at kind of a wide range of strength expressions,
the tests I would recommend are a drop jump or
a rebound jump. So one of those two to get a
reactive strength quality, a countermovement
jump. And from there, you can get the big, you
know, the big strength. domain of just unloaded
dynamic but then you can also get all of those
other kind of second layer metrics as well i
would then do either a one rep max or a loaded
jump squat now these can be these are sometimes
avoided by strength strength trainers or or snc
coaches or you know the coaches just say no or
the athletes are like nah no way and so if that's
the case and what i currently do is find ways
to do heavy non -ballistic movements and measure
the velocity of the bar So I gain information
on that quality by doing assessments of their
strength training practices rather than doing
a distinct test. And I find that that works really
well. But measuring that heavy expression of
dynamic strength is a unique quality. And so
we need to measure it somehow if you do want
to fill out that spectrum. On that, do you cue
athletes to, I don't know, they do heavy squats
in a set of five reps. Do you cue them to do
them as quickly as they can? That's a really
good question. I program to have kind of heavy
3Ds. And so they would be moving them fast. It
would be in a maximal intent day. So it's like,
hey, these are important. We're trying to get
a max velocity on these. And they wouldn't necessarily,
like they wouldn't jump, but they would move
the bar with high intent, I tend to call it.
And they, you know, the athletes, when they do
have the accelerometer or the linear position
transducer on the bar, they tend to push for
that higher velocity. And so it works well in
my context. I know that's not always the case,
but just that's a really good point. If you are
using that option, that it needs to be a maximal
intent or else you're not going to measure the
quality that you want. And so if you can't get
that in training and you need to do a specific
testing, then know that that is your option,
that you should do the specific testing. And
then in addition to the dynamic test, doing isometric
test two does seem to measure a unique quality
of kind of that, you know, that underlying ability
to produce force irrespective of a movement strategy.
And so getting a maximal isometric. strength,
and then also a rapid or a strength test, or
sorry, a strength metric at 100 milliseconds
is also what I recommend. Now, if you're trying
to avoid the rapid movement in, well, it's not
movement, but the rapid intent in an isometric
task, as some practitioners are, I would say
do a slow gradual and just know that you're likely
missing that extra bit of information that maybe
you want to incorporate later on because it does
tell you something unique. But overall, those
are the tests I recommend and you can get most
information. from those four tests. Awesome.
I have a tricky question now. So are you familiar
with Olympic weightlifting? Yeah, that's a sport.
So if you would assist a skill coach, a weightlifting
coach, and you would propose a battery of tests
for a weightlifter, what would be your approach?
Good question. I would first, if I were to do
this the really complicated way, I would first
measure a series of tests using a force platform
and then do some data reduction myself to understand
which ones are different. which ones are similar.
Now with weightlifting, with Olympic weightlifting,
because these movements, particularly like a
mid -thigh pull, is so specific to their sport,
it was essentially designed for the sport, you
might actually see very unique emerging elements
of strength domains than if you were to measure
a field sport athlete, for instance. So what
I would do is I would do an unloaded catamaran
jump. I likely wouldn't do a reactive strength
test due to the nature of their sport. So because
they don't necessarily have any cutting or a...
preload, I wouldn't kind of tap into that. that
realm as it's not specifically evident to be
important for their task. And I think there might
be subtle differences in the concentric only
aspect of weightlifting that might emerge rather
than a reactive strength test. Yeah, I would
do a counter movement jump and an isometric mid
-bike pull. And then since like that sport is
so dependent on just obviously it's about how
much you lift, you kind of always have a really,
really close and really clean understanding of
what they can move. dynamically. And so the two
ends of the spectrum might be where you'd want
to measure because you're not necessarily training
those two ends of the spectrum. And so gaining
some insight on what they absolutely can produce
and what they can do under the lightest load
might give you some insight of what they need
to improve in order to get that loaded dynamic
task better. All right. Awesome. Would you have,
if you would have to give advice for the coaches,
SNC coaches or other coaches that may use this
study, how... they should go about it? Would
I take home messages? Yeah. The biggest one is
be critical. Think about what you are measuring
and why you're measuring it. And does the metric
actually represent what you think it does? And
so even if you don't use the domains that I've
presented in this study, I would love it for
you to go out and be very critical and understand
the reasoning behind the selection of the test
and the selection of the metric. The more that
we can understand, even just the math behind
it, the more... that we can be intentional with
selecting different tests. Now, if you are wanting
to use the results of this study, and this is
particularly helpful for like a field sport athlete
type of cohort, I'd recommend starting with those
big rocks that I recommend in those five domains,
and then moving on to the second layer, which
may be presented in my subsequent studies and
other authors' subsequent studies that look into
the details of each test. But start with the
big rocks, do them well, do them consistently,
have a grasp of the understanding. of what you're
measuring and then go from there. Do a few things
and do them well. Do them consistently. Know
what they are. Those will go a longer way. It's
easier to do, too, because you can focus on just
these things and have the data. Athletes can
do them really well. The data is clearer. The
variance is smaller. The data is better quality.
Therefore, your reasoning will be closer to what
is, hopefully, closer to what is going on. Yeah,
exactly. You just limit as many of those potential...
human errors as possible and and then you you
get to the real thing right okay so two questions
to finish the first one would be what is your
favorite exercise yeah so i grew up playing like
varsity ice hockey and so uh we loved our bulgarian
split squats and so as much as that's everyone's
least favorite exercise that's still my favorite
i can still bulgarian you know 70 kilos all right
then the last question is where people can find
you online if they want to follow your work and
be up to date or maybe even ask a question. Where
should they go? Yeah, great. My research is always
updated on ResearchGate and you can find me,
I think it's MCGino. And then also on Twitter
and Instagram, MCGino. Those are most of the
platforms that I'll be on. And everything, because
I'm the only MCGino around, is just at MCGino
and you'll find me in all those places. Awesome.
Thank you so much, Mary. It was a pleasure. Yeah,
thank you.