July 7, 2025 • 43 mins
In Episode 61 of the OGX Podcast, the team explores the layered role of data in coaching and player development. From system-wide training models to individual athlete assessments, they break down how data can elevate performance when paired with experience and context. The conversation addresses the realities coaches face in adapting to the evolving sports science landscape and why data collection is just the beginning.
Topics include:
• The three layers of performance data
• The art of decision-making in coaching
• Why context and application matter
• Training systems that empower athletes
• Finding the balance between data and feel
This episode is a must-listen for coaches, trainers, and performance professionals who want to make sense of the metrics and create real outcomes for athletes.
#ogxpodcast #playerdevelopment #sportsanalytics #athletedevelopment #coachingstrategies #biomechanics
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:04):
All right, back for another episode and it's July, which is
crazy. I don't know if anyone else
feels like that's crazy, but I think it's crazy this summer is.
It's also wild that it's July and that it's only July.
Yeah, at the same time. Yeah, that's life I guess.
Both too fast and too slow. So GX motto also we're moving
(00:27):
too fast and too slow all at once.
Yes. All right, today we are going to
talk about data. That sounds very vague, but this
idea of I think, you know, we kind of use terms like
data-driven data forms. There's a lot of different words
that get thrown around around the type of training approach or
(00:49):
player development approach thatwe take.
And I think it's interesting to think about it a little more
nuanced and to kind of talk through the different ways that
we use data within that and sortof why we believe in data and
the ways that we incorporate it into training.
I think a lot of times when you say it's like data-driven or
(01:10):
data informed or all the different words that we use,
people get very hyper focused onlike one way of using data.
And maybe they don't like that way of using data or they've
seen that way not working. And our use of data kind of goes
across a lot of different levels.
And so I think talking through that when you're thinking about
player development and how even some of our like college
(01:32):
partners use data in the same way where they're using it kind
of in different levels. So I think of the way we use
data at OGX in terms 3 categories and kind of you guys
can tell me if you have others or disagree or I'm not
categorizing this, right. But there's sort of like 3
layers. The first is our programs and
systems are built off of the data we get fed.
(01:54):
So we've talked even in our bio mechanics assessments about
collecting a lot of data and having normative ranges and
letting that sort of tell us thestory.
That's one way that you use dataand sort of big picture our
training programs and sort of what we've kind of got to as far
as systems have been fed by data.
Meaning we saw athlete with X, we tried this data or this
(02:17):
program, we saw it have this sort of results, whether that
was in changes to bio mechanics or changes to performance.
And we did that on a grand scaleand that built our system.
So each one of our systems, while they look like just like a
bunch of drill sets, they're sort of data-driven and that
they, we got to them with the data that we were informed.
(02:38):
And so that's this like big picture of how we look at data
and it feeds us. Then there's the individual
athlete. So the individual athlete comes,
we get data on them in a varietyof ways.
This is her performance, this isher bio mechanics.
And we sort of give her that data program and we watch in
ways because it's not the same for every athlete.
(02:58):
Even if an athlete has differentvery similar patterns and maybe
we're going after similar things, what we expect to see as
a result in the data might be different.
So ex athlete had I just had an athlete yesterday.
She's a very high level athlete.She had she has very high exit
velocities. And one of the things we saw
from her patterns is that her launch angle is actually rather
low, given her ability to kind of get the ball even more in the
(03:21):
air and hit her extra base hit. So her patterns might look
similar to it. You know, athlete A and athlete
B's patterns might be similar, but we're chasing this data
results in athlete a higher launch angle, whatever that is.
And that's how we're watching tosee if her program is working.
And then there's the third use of data, which is very on the
spot, which is what you sort of see online and looks a little
(03:43):
maybe sexier, which is pitch design or you know, you're
targeting this amount of hard hit balls and hitting or
whatever it is. You're looking at the data on
the spot to try to give it as a source of feedback in that
training session. So those are the kind of three
different ways that we use data and training.
What I will say is that there's also the game versions of each
(04:04):
of those, which is I am making Xlineup decisions based on all of
this data that I have. That's the big picture.
This individual athlete, I see this data.
So, you know, this is where I think she fits in and there's
the on the spot of I don't know how much we get on the spot
unfortunately, but on the spot of Oshu in this.
(04:25):
And you know, I see her Velos going down or whatever.
So in the on the spot I'm using data to import that.
So that's kind of how we think about data.
I don't know, did I miss anything there?
Miss any characterizations of the different ways we use data.
No. And I think that generally this
is kind of an extension of last week's conversation, which was
centered on bio mechanics, right?
It was basically like, yeah, data collect.
(04:47):
There's there's data collection,then there's ways to tell you
one little piece of it. Does that help your actual
story? Is it applicable?
Like basically what we really tried to talk about last week
and and advocate for is a much more comprehensive look at how
what that athletes data is in comparison to others, how it's
(05:10):
then connected to what she does.Continue to monitor that and
it's the basically what you're describing now is the ball
flight version of our discussionlast week in bio mechanics,
right. So like anyone could come on the
scene and be like, we use data cue the like thumbs up and like
sparkle in my tooth, you know, like yay, but and that's great
(05:32):
and that's that's like, no, you don't.
But for it to have an impact on what that athlete is doing
either on the spot or generally in the background as she is
trying to make a change, it has to be used in systems, right?
And which is what we were describing are the three
different systems in which we use data.
(05:53):
And I don't know if you could, Idon't know if it's that clean.
There's three systems, right? Those are generally good, good
categories, I'd say. And it's where HQ really came to
life. This is our hub to manage that
entire world, right? Because you're right, you get
all this data and it's informingso many different pieces that
(06:15):
that you then have to be able tosee all of it together to make
the best, you know, most informed decision for an
athlete. So I see, you know, I think this
is definitely linked to the conversation of HQ, why that was
born, why we have to be able to see everything in one spot.
And it's I think I see a lot of similarities to our conversation
from last week. It's just the shift from bio
(06:35):
mechanics and to ball play data.Yeah, I was going to say too, I
think it also when you really understand the way that you
would have a data informed system, player development
system, this idea too that like,oh, that's just they just use
data they don't like coach also,it makes no sense when you
understand the sort of bridge between all of those, which is I
(06:56):
have athlete A that walks in thedoor, you know, she does an
assessment. I see that this is her story.
We and there's in reality, are there these like really one off
examples of something super unique?
Sure, when you get to the very highest level, you might find
this very unique story. But the large majority of
athletes and especially in our current system where it goes
(07:19):
through college, you know, it's starting to expand and it's
going to continue to expand. And that's amazing.
But we're really talking about kids and it's like slightly
going into the adults years is that for the most part, they
start getting kind of bucketed. It's a speed story, It's a
posture story story. It's, it's these things that we
see these, you know, some version of this breakdown.
(07:41):
And so we see that it tells us this sort of like, OK, we know
based on years of data that the way that the most efficient way
generally to attack that is X. So that it's this program, it's
this base of program and we should if, if it's working for
this athlete, see this change, right?
(08:02):
So you put them kind of in that system to start.
And then it's where it actually allows a massive art of
coaching. And you, you need to be really
informed on those, which is it'snot just like then it's like,
well, well, this, you know, thisshould work because that's what
the data tells us. It's it's watching and making
sure that sort of her own experience of that matches that.
(08:24):
And it really gives you, I think, and we've said this in a
variety of ways, much more spaceto actually have the art of
coaching within with some parameters of what you think
that it's going to work. Art of coaching just so we're
clear is not guessing. It's like sometimes we think
it's more coaching when you're just standing behind and
guessing and for. On the flip side, yes.
(08:45):
On the flip side, when in the world of consulting, when, you
know, I start to realize that certain coaches, whether it's
the maybe the pigeon coach that I'm actually on the call with or
it's the head coach doing it, it's a very like panic on the
spot. Make a decision.
That's also not coaching. That's that is just like flying
(09:07):
by the seat with your pants. You know what I mean?
Like that's not coaching. Coaching is being very like
poised, informed, educated and making the best decisions based
on what you've already know, what you the knowledge you
brought coming in and the knowledge that's in front of you
right on the spot. That's coaching.
Is that easy? Hell no, right?
Everyone knows that. But this whole just like
(09:29):
abandoned any information we have in the background just for
the spot or there are moments maybe that might happen, yes,
but when you live in that world also not coaching, we can't call
that the art of coaching. I would argue it's not even
coaching, right? So like it's not good coaching.
I guess that's what you can say.And what we're really talking
about here is then in the background, the other extreme of
(09:50):
that is having this information X + Y = Z.
And no matter what you're looking at here, don't
incorporate that information, right?
Like this athlete's boss light makes her a great closer.
OK, Well, Sally like craps her pants every time she goes out
into a stressful situation. Might want to, right?
(10:11):
Might want to account for that, you know, like there is this
world where what you're seeing in front of you, Nate, needs to
be taken into consideration. And so when people are like, oh,
you're just data nerds when theyhave that type of complaint,
it's when someone sides on the other side of that, right?
All I do is like, well, these are what the numbers say and
you're not factoring into like what's in front of you, what's
(10:32):
the human experience, etcetera. So you know, our systems are
designed to take into account all of it and that's what you're
describing right now. You're describing this like make
the educated decisions based on what we know.
Bring in the data, informed training systems, protocols, oh,
we need to get rid of this in her pattern.
Then target this because we've seen that work, like bring in
(10:53):
these data, informed systems andprotocols, know what our goals
are and then see what's actuallyin front of you to be like, you
know what, I applied every training approach and protocol
that we typically do. We are missing something.
We're not getting the response. We thought that happens
sometimes it happens less and less since obviously so much of
(11:13):
our approaches, our data to inform, but it does happen.
And so there is this in the moment, keep working it, keep
working it. That's an entire year of a
consulting relationship. We start off with like, here's
what we know about this athlete,here's what we should target and
just all year long we're on the like, is it working, is it
working, is it working? Let's keep, you know, changing
our our suggestions and how we're going to attack it,
(11:35):
etcetera. So I think like to me, that
world is exactly what coaching is.
That is exactly what coaching actually is.
So if you if you are a coach andyou're listening to this podcast
episode and you find yourself living on just one side of the
fence on the other, rather than straddling both sides of it,
probably time to check yourself right?
Of just like, is that the best approach?
(11:56):
I would argue no. Yeah, well, and it's because you
can't. We can't yet measure.
We can't measure everything. You know, there's the reality in
coaching that's like as we get tools to measure more and more
and to quantify more and more than you get to take that into
consideration. When we played, we could
quantify basically nothing besides stats, you know, like we
(12:18):
could quantify so little. And so you would take into
consideration stats like that's what you could do.
And then, you know, by the time we were like, literally, we
talked about right view pro thatcame out like their senior year
college, I think. And so by the time we were
there, I was like, oh, now we can take into consideration
video and maybe that was good. Maybe it was bad.
At least we could see video for the first time.
(12:39):
And we've we've continued to have these steps along the way,
but things that we can't quite quantify are like
competitiveness, you know, it's like mental toughness, whatever
that means. And that's a whole host of worm
rabbit hole there. But sort of like, why does this
athlete with lesser tools have more success?
(13:00):
You know, and when it starts to get into this, like some of the
times it's just that we literally can't measure whether
she whether she actually has lesser tools.
So maybe her ball fight looks lesser, but now we're starting
to understand things like relaterelease height and different
release angle. And maybe actually she doesn't
have lesser tools. It's just that we haven't
learned the nuance of why her tools work the way that they do.
(13:23):
And it's just like we keep learning layers of that.
And so you're still able to takemore and more into
consideration, but they're stillat this point because we can't
measure everything. And you know, we we can measure
even less than they can. And baseball right now, there's
still this point where you're going to have to make a little
bit of a non data jump and you have to go into these this world
(13:46):
where you live in both. But if you know the thing
meaning it's something that we can measure and you're refusing
to, to incorporate that into anysystem that you have, like that
just makes no sense. Like the reason we build these
tools and we measure things and we and especially the things
that we've had for years at thispoint, like ways to measure ball
flight and and different things like that.
(14:07):
Not considering that at this point, that's just silly.
Like that, doesn't. It's like information is never
bad. It's like, would you like this
medication that we have extensive research on that we
know it's likely to help you, orwould you like the medication
that we don't know anything about?
Like who would raise your hand? Be like, I'll take the one that
no one, no one knows anything about because like we all know
(14:28):
no, tell me as much as you know,I want to make this like
informed decision, right? And so I think it's so we've
talked about this. We know anytime people are like
data SMEDA, you know, it's really that makes no sense
because data is everything. Data is information.
Laura, you've you've had talks on this many a times of like
data does not just mean the amount of breakable has like
(14:48):
data is everything. It's every bit of information.
So to say like, I don't use information to make decisions
legitimately doesn't make any sense.
And we know anyone that's sort of like, oh, when the data world
comes in, this happens in baseball too.
It's when they think people are living on too far on like the
one side of the fence, right? I mean, that's really what we're
talking about. But like information, you want
(15:10):
to arm yourself with as much information as possible.
And then obviously take a look at the humans that are in front
of you that are going to carry out those decisions, you know,
that sort of like hold the weight of those decisions and
take that in as information as well.
I would think, go ahead, Laura. I always think like the some of
the conversations I have early on with coaches who maybe and I,
(15:32):
I, I respect coaches that are inthis scenario or they've been in
the game a very long time without data and are seeing this
world evolve, seeing, you know, a business like ours evolve in
player development with data, informed decisions.
And they see it as, yes, this isthis is what's coming next in
the game. And I have to catch up.
(15:54):
I respect the hell out of that because first of all, that's
hard even to admit in terms of, you know, being in the game so
long and feeling like you probably could, you know, sort
of rest on those coaching laurels a little bit.
And I always appreciate when coaches, you know, veteran
coaches are like, I've got to get on board with the data.
And I, what I would say right now to any coach that is, is
maybe sitting on the extreme side of, you know, no data never
(16:17):
is. We've the, the train of collect
the data is kind of already leftthe station, meaning like your
competitors are at least collecting data.
Most likely they may not know what to do with it yet.
So they're collecting information.
I always think like in this likecontinuum, they're collecting
information. They might have, you know, some
dots on the graph. They might have some ideas of
some of the places they're looking for information,
(16:39):
probability of relationships inside their team or their
systems, whatever. But they're at least collecting
it. That's to me, that is the
standard at this point. And if you're at least
collecting it, then the next step is to figure out, OK, what
how do I organize this? And I will say that's typically
where most coaches, I'll say kind of like a college player
(17:00):
development positions, they get a little stuck, right?
Is the, I've got the pieces, I have the tech, I can get you CS
VS I can, I can pull this data somewhere, but I don't know what
to do with it. And that, you know, I feel like
HQ sells itself. Then in this next kind of step
of information, it's like, all right, well, then I got to put
it into some sort of context. So you're talking about these
different levels of data we haveand how you may utilize it on
(17:23):
the spot from an assessment standpoint about our programs.
It's just contextual. I think about, you know, what
we, how we utilize Hitrex data inside of HQ.
It's not just exit velocity for every swing you take.
It's contextual. It's the type of ball, the type
of bat, the environment. So when I, you know, I think of
coaches that are like, I'm nevergoing to use data.
(17:44):
My like, kind of plea to you is like, listen, just collecting
it, most of your competitors arealready doing that.
The ones that are even more evolved are starting to make
sense of it. They're using a system like HQ,
they're developing their own internal systems, or they're at
least popping it into a spreadsheet and starting to
explore it. And that's, that's becoming the
(18:04):
standard. And so I think like, you know,
the art of coaching, plus this data plus understanding your
athletes, they're, you know, base mental state, where they
are on their menstrual cycle, all these things that affect
them as humans. Well, that's just bonus that
given gives you all this clarityof like, all right, the data
tells me what to expect. There's some probability to it.
(18:25):
It gives me some maybe clarity on how I could bucket my
athletes or be more efficient inmy decision making.
And then there's my art of coaching, right?
There's my game decisions or my training decisions, and then
there's the athlete. And those three pieces to me are
the entire coaching, you know, Venn diagram of where they all
overlap is to me what the heart of coaching is.
(18:47):
And to say that that, you know, the data is irrelevant.
I just think you're, you're missing a huge piece of
information that with some work,some support in a system like HQ
can just, I mean, accelerate your ability to make decisions,
to make better recruiting decisions, game decisions,
training decisions, how your team spends their time.
(19:07):
I just think like no one has time to sit and guess anymore.
It's, it's too, there's too muchcoming at us and you've got to
have it all together at that point.
I was going to say the concept of like collect, organize, and
then basically to go back into the the concept that Christa
like the buckets that she came out with, it's like it's
(19:28):
organized. And then do you want to know how
to influence that data? Right?
Like most athletes, we don't just we collect it, we organize
it. We now know what it means, we
interpret it, we understand HQ is built to show you what
understands. There will be a layer to HQ as
well that's going to give you some baseline like resources to
know how to influence it. But obviously we can come in and
help with that process even more.
(19:48):
But like most athletes are not just looking at their data,
you're like, sounds good, they're perfect where they are
never want to change. And so you want to influence it.
And then here's that bucket, Krista, right?
It's kind of like reverse engineering those categories
that you said of like now we have these like strategies,
protocols that we have developedthat our data informed to now go
(20:08):
in and influence. And now here comes that feedback
loop. Because we haven't organized, we
can influence it. Rewatch it in, you know how that
changed it, and keep going in that loop.
I have a couple a couple things.The first is I think the idea of
like, I don't think any. I actually don't think there are
many people saying data's irrelevant.
I think what happens is you have, you have two sides.
(20:30):
You have, I'm going to, I'm going to make a data informed
decision and they're like, all right, let's see what happens.
And I think what actually happens is we don't understand
data. It's like as soon as it, it's
like not to get controversial, but it's like back to like
someone who gets a vaccine and they have X, they get sick.
And so we're like, see, you know, it's it's like a one
(20:52):
thing. So the example I was thinking is
yesterday I'm a Cardinals fan and yesterday they played
Pittsburgh and Paul schemes was pitching and like, the guy's a
freak. You know, his ERA is 2 years,
like one point something like this total freak.
But the Cardinals have never. Lost to him.
Which is totally random, like just in for some reason, the
(21:13):
Cardinals going into the game yesterday were four.
No. And I was picturing my dear mom
at home being like, this guy's not even good.
What's our problem? Because in her experience, she's
like, we beat him every time. And so yesterday we scored no
runs off of him and lost. And as we should probably give
him what is actually true about him.
But I think it's like when you just rely on what you see in
(21:36):
this one glimmer of time and youdon't understand how data plays
out. Of course you're you could bat a
kid 2nd and that could make sense in every world and she
could go in a slump O and eight.It's like that doesn't mean she
shouldn't be there. Now you might wiggle some things
for a second, but your goal probably is to get that kid back
into that spot because that's generally what everything is
(21:59):
telling you. And so I think what actually
happens in those scenarios is not necessarily it's as clear as
someone's like Dave is stupid, but they're like, you're telling
me X and that's not happening. And the the glimmer of time
they're giving it to happen or the way that they're trying to
do it, or they're keeping the pitcher in longer than she
should. So like the the truth about what
would work starts to actually not be true anymore.
(22:20):
Like there's different scenarios.
They sort of prove themselves right by using the data wrong.
And I think that's often what happens.
So that was something I was thinking.
The second thing is I had this call yesterday with a dad whose
daughter is coming for an assessment.
And he was telling me it was really interesting to me.
He played I think amateur or like Junior Golf.
(22:43):
And he went to college and played Junior Golf.
And he was saying at the time heplayed, there was no data, which
in golf is crazy because now golf is like the most data
forward thing. And so his he said his son
picked up golf really late and got really good really fast.
And part of the the reason he got interested in data for his
daughter in softball is because he said, like, I don't think my
son could have gotten that good that fast, like had he played
(23:07):
when I did. He uses data in everything.
And he's like, he really gets itbecause of the world he's grown
up in. So he's interested in it.
And and he had such a fast feedback loop.
And I thought it was such an interesting take.
Like he just basically saw take the own experience of he got.
So he was able to start late andget and because the feedback was
(23:29):
so good. So just that was a random
thought. My last thing and this is more.
I like that guy. I don't know who he is, but I
like. That.
Couple weeks. So yeah.
So my last thing, this is more open-ended.
I had a college coach reach out and maybe I'll cook this for him
because I never answered him. And he asked, do your pitching
(23:50):
people think that you can changerelease height?
And I actually think it's an interesting question because I
think there's a lot of layers tothe answer and I just hadn't
gotten around to answering. And I think it goes to all the
things we've talked about with data lately, which is first, I
think the actual first question that you want to ask is do you
want to change release height? Is release height so important
(24:14):
of a metric that we want to go around trying to change people's
release site? And I think probably and, and we
could maybe there's someone out there that has more information.
But I think for us right now we would say we don't know the
answer to that yet. This is such a new metric and
the like. We can probably we're probably
going to get there pretty fast as we've been able to collect a
lot more in game data and see release sites.
(24:36):
But that's something we've just started looking at because
that's been a metric at a volumelevel that we've just gotten
access to because it is more of an in game.
Yes, like Rev Soto has released height, but you don't get
results. So you would be making a lot of
jump there. And so this is something new.
So I think this goes to the first thing is that first you
have to understand about the data is like, are you, do you
(24:58):
want to just mess around with that?
Is that something that you want to do?
And then I do think it's interesting because once you
kind of get that data, you do get in this stage of like, let
me mess around for a minute and it's like there's athlete XI,
see her patterns. The risk reward is probably
(25:18):
there like she's not having. Success and maybe like a basic
scientific process. Right.
And and you do it on athletes where you're like, if we don't
do something different like thismight be it for you.
So you, you, you've used those athletes, you know, with their
consent and understanding that that's what they're doing.
So like, I don't have a system around this yet, but if we could
get your release height from A to B, we think you could have
(25:40):
for success. We haven't done it on a grand
scale yet, so let's just try some different things and see
what influence it has. That's how you start that
process. And eventually that happens at a
frequency where you're like A = B in most cases.
I know if I do this, I can influence X.
And so my actual answer to that coach is not sure, both.
(26:00):
Not sure if you want to and not sure if you can because we
haven't tried yet. I think you, we're probably
going to find you do want to. We that's our gut just based on
some of the things we've seen and but who knows if you can and
who knows how you can yet. I don't think we've, we've
messed with that before, but I think that's the data process.
It's not just we have this data,you know, let me grab this.
(26:25):
This kid has this weird release height.
So let's all go try to give everyone that release height.
It's not that simple. You have to look at it on a
bigger scale and understand whatall that means.
And then you go into this. Can I influence it?
Let me try with some athletes and then they build the scale
that I do that and then the loopcomes all the way, you know,
back to the ends. But I think that's a good like
(26:47):
data my. Brain is now my brain is now
going back to like last week's discussion of like we got off
the podcast and I was like the phrase I was going to use and I
thought maybe wouldn't land well, but was that like.
Be. Wary of a of a chocolate covered
toast where it's like it looks so great, right?
(27:08):
And this is what social media is.
This is the idea of something being like super sexy.
And so I'm thinking about this world of like, you know what we
could do Krista? I could make an Instagram real
and be like release height matters with music in the
background, Boom, boom, boom andrelease height matters and you
want to release height of this and this is how you're going to
(27:28):
do it and and put that out thereand like boom, sexy as hell,
nothing but cover to her. You know, like like seriously,
like it's just that is the reality.
And so I think there's when you're talking about data, when
you're like in the trenches of avery like data informed systems
(27:48):
that like that's the the sexiness of the process is like
how informed, how educated, how methodical that is going on and.
This is I someone used this in amuch different way, which I'm
not even going to say what this analogy was used for to really
turn this clock. As a dude, Paul is not.
Going to know what the hell to believe.
They're like episode, you know? It's like a toilet.
(28:10):
It goes slow at 1st and then allat once.
And I'm not going to tell you why that was used for me, but it
wasn't related to this. But that is data.
So we are at a place now where we're like pitch designs easy,
like we could just influence things right away because, but
that took years to get our placewhere we thought that.
(28:31):
And so right. It's it's the same thing with
like, can you influence release height maybe even in a year?
Because we actually have been building like this.
We're in a different place. And when we started collecting
data and we have a lot fast and we have it's all in the place
where we can ask questions of itfaster.
And so maybe the process of how long it took us to get to pitch
design being easy was, you know,four years.
(28:52):
And maybe to this question of release height is a year, but it
still is going to go so slow at fast first, which is like, let's
just get all the what are we trying to look at?
We're trying to look at if release gets release heights get
swings and misses. OK, To do that, we have to see
all the pictures with the same ball flight but different
release sites, you know, like what questions are we asking?
So first you have to do that. It's very slow.
(29:14):
And then if the answer to that is like, yes, it matters.
OK, Now we start to see athletesthat come through our doors
where it's like you have you look more like athlete B where
your ball flight is good, but itwould be elite if we could
change your release site to looklike, you know, athlete A.
And so now we try to do that andthat's still very it's slow
(29:34):
process because we're kind of guessing, you know, it's it's
informed guessing maybe just because we understand emotion so
much, but we're kind of guessingfor what it is.
And then that kind of works and it works on another athlete or
it doesn't work on another athlete and we get and then it's
the toilet, right, all at once. And now it's like, yeah, we can
change release site. It's easy.
You know, you get to this place where you get there or you can't
(29:55):
or the answer is you can't chasethe release because you said
most patterns so much. Well, you said Ash about a reel
like that. One obviously made me laugh
because you would never put out a reel like that in that way.
But that's why. But two, that's why.
And also because you couldn't figure it out how to do.
That's true. Too bold the.
(30:16):
Music to the video. Listen, I'm the last person that
should that should criticize anybody's use of Instagram.
But but my, the thought that came to my brain when, you know,
you were talking about that, it was like the, the whole like,
and I'll just say social media, but the, you know, the Instagram
influencer that, you know, thesebig sweeping claims of every
pitcher needs to do insert new fancy sexy thing.
(30:40):
You have to be really thoughtful.
Is the person consuming that information.
That and I'll just use the phrase influencer, not, you
know, labeling anybody specific,but just the phrase influencer
with sweeping generalizations like that they are praying on
your lack of knowledge. They're praying on the fact that
you do not know what you need. So be very cautious.
This is just like my word of like advice is like if someone
(31:02):
is making a sweeping generalization about your
position and what it needs and training and every pitcher has
to do X blah, blah, blah, you need to know if that even works
for you. And so if you go and pursue that
training modality that, you know, change in your mechanics,
you've got a measure that did anything of value for you.
(31:24):
Because the flip side of all of this, we have talked about the
ways that data, you know, has allowed us to be more efficient
and, and all these positive things.
The flip side of of sort of the the dark side of not having the
data is also when you don't havethese buckets that you identify,
which is just organization of your information, but these
buckets of athletes, It's how weget to protocols.
(31:46):
And it's also how we know when it is inappropriate to apply a
protocol to a particular athlete.
They may match all of the criteria, but there's one thing
about them that makes that protocol dangerous and can put
them at an increased injury risk.
And so the flip side of all of this is not just performance,
it's better is also what not to do.
(32:07):
And to me that is like, you know, from a healthcare
standpoint, I'm like, oh, that'sa big one for me because without
your health performance is not going to come easily.
But I'd be really cautious aboutlistening to any, any authority
in big air quotes. Make general sweeping, you know,
over over generalizations about what everybody should be doing,
(32:30):
particularly as a pitcher. That's.
Just I think you you have to askyourself what you're on toward.
This is also the bio mechanics piece is what you're being drawn
toward, the picture of the sensors and the on the forehead
and the force plate and the likemarketing And hey, we obviously
put out marketing things as wellbecause like we don't want it to
look boring here. It's not freaking boring, you
(32:53):
know, So like, I get that, but it is what's drawing you to a
place or drawing you to something like the flash of it,
the chocolate part of it, right,Sir?
This is why the entry to get into something with us is a
sales call because we're like, give us the opportunity to
explain the substance. You you really can't.
It's why we obviously value thispodcast so much because we can
get into a little bit of a little bit of depth of what
(33:15):
actually goes on behind OGX walls instead of just like, Oh,
that looks fun. Oh, that looks cool.
Oh, that looks like this. There's just like you that that
little snapshot, it has no connection whatsoever to the
substance of what a product is. And that means bio mechanics,
that means ball flight. Like I'm thinking about first.
(33:35):
We kept talking about the like, you know, people who don't like
data, but the same would be like, oh, data, data, data.
I put a rap Soto down in my pitching lessons.
I'm a pitching coach and I'm like, look at how we can change
your data, But there's no the substance of that.
There's no place to organize that data.
There's no place for a feedback loop.
(33:56):
There's no so protocols maybe that are like, OK, because your
data says this, we think we needto do this.
So now let's apply this trainingprotocol.
There's none of that. That's the substance, right?
And This is why I'm using that phrase like chocolate covered
turd because it's like it made it looks so enticing of like,
oh, there's data there with thatpitching coach X whatever.
(34:16):
So that's what we want. That's what we need.
But there's nothing behind that.You want to throw down a machine
and stare at numbers. No one knows what the hell is
good, not good, what that means in the grand scheme of who you
are. You can't set goals off of that,
let alone influence what you do.That's a bunch of garbage.
So I think that it it goes on. This is what you know, how to
use data is so complicated because there are tears of it.
(34:40):
For it to be as comprehensive aswe have it here.
And that's why I keep going backto that phrase, although it's
kind of cringy, is because it really is about if it were to
really land, for it to matter toan athlete or a coach, it has to
have all of those layers. It has to have all of that
substance. I was thinking.
Pretend it just it's nothing actually there.
(35:01):
Yeah. I was thinking of like the
where, you know, we referenced last week force plates and kind
of where we are in that that process.
And I think that there's kind ofthere's a force plate like
ground, you know, where you're putting energy into the grounds
and understanding that even in the motions, kind of the top
layer. And then there is this like,
where does this athlete produce power from?
(35:23):
And we are a little bit more in the like slower part of that
right now. That's just the reality of like
we're starting to understand when an athlete gets power from,
you know, X, it might make more sense for us to try to, you
know, really maximize that in the swing or in the the pitch.
And we're kind of starting to make some suggestions and see
(35:44):
how that. And to your point though, Laura,
doing that while looking at whatthe influence of it is, is so
important because if you're justtinkering with someone, which is
not to say you can't do that, let's make sure it's informed
first, but that is going to be apart you, you are going to be a
part in data collection where you are experimenting a bit.
That is just the reality of it. If you are experience
(36:07):
experimenting with it, but you better be measuring what you say
that is going to influence and not just saying you get power
from the ground. So let's get more squatty and
then see how that goes. And the athletes like, I feel
crappy and the data saying nothing's influencing, but
you're like, Nope, but that's what it says.
So we're going to keep doing it.That is not what we're talking
(36:27):
about here. If it's like that's not
experimenting and that's not relying on what the data is
telling you back and that's not using the loop that you're
trying to use for your benefit. And so it's hey, you know, it
looks like generally you get more power from the upper body,
from anterior core, and that's where you get it from and the
swing. So it probably doesn't make
(36:48):
sense for us to be in this super.
You know, we're trying to get itfrom the ground in a very wide
stance and we're just trying to go from our legs or put force
into the ground. That's not where we get it from.
So let's try XYZ, let's play around with it a little bit and
then let's watch both in how youfeel, not like right away that
felt bad. Obviously that's not how it
works. But do you can you get
(37:08):
comfortable with whatever we're doing?
So how is the athletes sort of subjectively feeling?
And then B, is the data getting better?
We said that's going to give youmore power.
We said that's going to give youwhatever your exit velocity
should go up, your bad speeds, it should go up.
Is that happening? And if the athlete after two
weeks is like, I feel not great and I'm not getting more power,
(37:32):
you know, the thing you said would happen isn't happening.
What is not data is saying, well, just keep doing it because
that's what the data says is supposed to happen.
And so that there's. Something wrong with you?
Either or you make the kid internalize it.
Yes. And so I think that's also where
we see some things is when we'rein this like interim period
where we maybe start to see there is like a piece of data
(37:53):
that's interesting and we can get this nuanced and we might be
able to tackle something that wehaven't been able to tackle on
player development. And so we're experimenting.
But then all of a sudden you're like, but I'm going to pull this
experiment away from the resultsand I'm just going to like say
I'm right. And that that doesn't work.
Like that is not what we're talking about here.
And so it has to be this loop where it's you, you, you
(38:15):
understand what you want to influence.
That's first and foremost what'simportant.
You got to have a big enough data set that that what you are
trying to influence makes sense and has been proven as something
that you should be trying to influence.
That's the sort of like that's the foundation of a data-driven
anything. And then it's OK, you hit this
stage where you're trying to build systems on how do I
(38:36):
influence that thing, right? How do I get velocity to go up
given the this athletes input? And then the the final piece of
that is, is that happening? And you just cycle through these
things over and over and over. And, and for us, an athlete that
has stayed kind of in our systemthe whole time, we keep learning
more nuances of what we can go after.
And so we can keep kind of tackling different things
(38:59):
throughout that process. But that's what a data informed
system is. It's closing the loop all the
way back. It's not just the bottom, it's
not just the top, it's this whole, you know, cycle.
And to all coaches attempting todo that system without HQ,
godspeed. The other thing, the other thing
I was going to say was I think, you know, yes, in totality that
type of training system, but even take a step before that.
(39:20):
If you are a parent, if you are an athlete yourself who is about
to open your wallet to someone who's going to collect the data
on you, Be very be very informedand very clear that collecting
the data is the easy part. That's the easy part, meaning
taking the data from you, whether you are volunteering,
whether you are paying for it, just know and be informed that
(39:40):
that the data collection is the easy part.
If you are paying for a data collection and you are not
getting that data back and givenback to you in some way, even if
it's not in the most perfect actionable way or with a
recorded debrief, but it's not given back to you with some
context, what are you paying for?
(40:01):
That's what I always ask that question is what are you paying
for? If there is not some end result,
the exception I would make for this is if you are participating
in a research study and it's, you know, at a university, but
that's a separate scenario. You are going to go to a camp,
you are going to go to an event,you are going to pay someone to
collect data. That's how you going to get the
(40:21):
data back. That's always the question I ask
when, when parents ask should I do this particular event or
camp? What are you going to get out of
it? And if the answer is I don't
even get a report, what are you paying for, right?
You're just paying basically to give that data away.
So if you're going to give the data away, at least get
something out of it and incite areport of something that you're
going to have something tangibleafter that.
(40:44):
And that's, that's very generousof you, Laura, because I think a
whole other podcast episode is when people give reports,
because most of the time, as I mentioned before, they're like,
I went to such and such. This is a report I I got and I
just write like it's garbage. That's garbage.
So I'm like, be better, be better in what you're showing
your athletes because they thinkit's gold, because it's a report
and APDF with numbers on it and you just hand it back garbage.
(41:06):
Talk about praying off of someone's lack of knowledge.
So I think of just like that kind of stuff pisses me off
because I'm like, no, this isn'tput in the context of this.
This doesn't mean this doesn't mean this.
And so obviously those are athletes that are connected to
OGX. They're connected to the like
information data hub of the softball world, right?
So they have a source to be like, what does this tell me?
I'm like rebillionths of what you actually need to know in
(41:29):
your process, you know, so very generous of you to say at least
give something back. And that's better than nothing.
But some of this, some of the shit that I see actually come
out of whether it's camps, whether at the university level,
private level, what's handed back to these athletes, I'm
like, wow, wow. That was literally highway
robbery right there of what you just handed them.
(41:50):
And we're like data camp, you know, it's freaking brutal.
So that's probably an episode all in of itself and maybe feels
like a little bit on the attack,but I don't care.
It really bothers me because I think these are athletes who are
hungry for information. These are parents hungry for
information. They're shelling out a lot of
money to be able to get it. And so, you know, I at least
(42:10):
feel like as people with the utmost level of like knowledge
and integrity, we know that no matter what you spend to come
through our doors, you're going to walk out with information
that's like exactly what you wanted, exactly what you needed
and exactly geared toward, you know, what the individuality of
of your athlete. And so I think when families do
that and they go somewhere for information, they're left with
(42:31):
just it's a piece of paper that looks like numbers and they're
like woo fancy. But in reality it's nothing.
I think like shame on you. That's that's brutal.
So anyway, bring that topic up later for about another week,
because that'd be a good. One yeah, bookmark.
All right, we'll end it there. We could talk about this for a
while. I think next week we're going to
kind of bridge off of this and talk through like how did the
(42:55):
side to influence patterns on someone and when to do that and
what the process should look like.
And maybe some like old adages of trust the process you got to
get worse before you get better and how maybe we shouldn't does
the process. Know that we're trusting it.
That's what I want to know. Does the process know?
Yes, a good one. That's a T-shirt.
(43:16):
All right, until next week. Happy 4th of July everyone.
Or hope, I guess by the time youlisten to this, hope you had a
good 4th of July. Till next time.
OGX Nation, we love you, we appreciate you, but we'd love
you even more if you would like subscribe to this podcast, give
us reviews, send us comments on what you want to hear, then we'd
really, really love you. Got to do it.
(43:37):
Do it now.
All right, back for another episode and it's July, which is
crazy. I don't know if anyone else
feels like that's crazy, but I think it's crazy this summer is.
It's also wild that it's July and that it's only July.
Yeah, at the same time. Yeah, that's life I guess.
Both too fast and too slow. So GX motto also we're moving
(00:27):
too fast and too slow all at once.
Yes. All right, today we are going to
talk about data. That sounds very vague, but this
idea of I think, you know, we kind of use terms like
data-driven data forms. There's a lot of different words
that get thrown around around the type of training approach or
(00:49):
player development approach thatwe take.
And I think it's interesting to think about it a little more
nuanced and to kind of talk through the different ways that
we use data within that and sortof why we believe in data and
the ways that we incorporate it into training.
I think a lot of times when you say it's like data-driven or
(01:10):
data informed or all the different words that we use,
people get very hyper focused onlike one way of using data.
And maybe they don't like that way of using data or they've
seen that way not working. And our use of data kind of goes
across a lot of different levels.
And so I think talking through that when you're thinking about
player development and how even some of our like college
(01:32):
partners use data in the same way where they're using it kind
of in different levels. So I think of the way we use
data at OGX in terms 3 categories and kind of you guys
can tell me if you have others or disagree or I'm not
categorizing this, right. But there's sort of like 3
layers. The first is our programs and
systems are built off of the data we get fed.
(01:54):
So we've talked even in our bio mechanics assessments about
collecting a lot of data and having normative ranges and
letting that sort of tell us thestory.
That's one way that you use dataand sort of big picture our
training programs and sort of what we've kind of got to as far
as systems have been fed by data.
Meaning we saw athlete with X, we tried this data or this
(02:17):
program, we saw it have this sort of results, whether that
was in changes to bio mechanics or changes to performance.
And we did that on a grand scaleand that built our system.
So each one of our systems, while they look like just like a
bunch of drill sets, they're sort of data-driven and that
they, we got to them with the data that we were informed.
(02:38):
And so that's this like big picture of how we look at data
and it feeds us. Then there's the individual
athlete. So the individual athlete comes,
we get data on them in a varietyof ways.
This is her performance, this isher bio mechanics.
And we sort of give her that data program and we watch in
ways because it's not the same for every athlete.
(02:58):
Even if an athlete has differentvery similar patterns and maybe
we're going after similar things, what we expect to see as
a result in the data might be different.
So ex athlete had I just had an athlete yesterday.
She's a very high level athlete.She had she has very high exit
velocities. And one of the things we saw
from her patterns is that her launch angle is actually rather
low, given her ability to kind of get the ball even more in the
(03:21):
air and hit her extra base hit. So her patterns might look
similar to it. You know, athlete A and athlete
B's patterns might be similar, but we're chasing this data
results in athlete a higher launch angle, whatever that is.
And that's how we're watching tosee if her program is working.
And then there's the third use of data, which is very on the
spot, which is what you sort of see online and looks a little
(03:43):
maybe sexier, which is pitch design or you know, you're
targeting this amount of hard hit balls and hitting or
whatever it is. You're looking at the data on
the spot to try to give it as a source of feedback in that
training session. So those are the kind of three
different ways that we use data and training.
What I will say is that there's also the game versions of each
(04:04):
of those, which is I am making Xlineup decisions based on all of
this data that I have. That's the big picture.
This individual athlete, I see this data.
So, you know, this is where I think she fits in and there's
the on the spot of I don't know how much we get on the spot
unfortunately, but on the spot of Oshu in this.
(04:25):
And you know, I see her Velos going down or whatever.
So in the on the spot I'm using data to import that.
So that's kind of how we think about data.
I don't know, did I miss anything there?
Miss any characterizations of the different ways we use data.
No. And I think that generally this
is kind of an extension of last week's conversation, which was
centered on bio mechanics, right?
It was basically like, yeah, data collect.
(04:47):
There's there's data collection,then there's ways to tell you
one little piece of it. Does that help your actual
story? Is it applicable?
Like basically what we really tried to talk about last week
and and advocate for is a much more comprehensive look at how
what that athletes data is in comparison to others, how it's
(05:10):
then connected to what she does.Continue to monitor that and
it's the basically what you're describing now is the ball
flight version of our discussionlast week in bio mechanics,
right. So like anyone could come on the
scene and be like, we use data cue the like thumbs up and like
sparkle in my tooth, you know, like yay, but and that's great
(05:32):
and that's that's like, no, you don't.
But for it to have an impact on what that athlete is doing
either on the spot or generally in the background as she is
trying to make a change, it has to be used in systems, right?
And which is what we were describing are the three
different systems in which we use data.
(05:53):
And I don't know if you could, Idon't know if it's that clean.
There's three systems, right? Those are generally good, good
categories, I'd say. And it's where HQ really came to
life. This is our hub to manage that
entire world, right? Because you're right, you get
all this data and it's informingso many different pieces that
(06:15):
that you then have to be able tosee all of it together to make
the best, you know, most informed decision for an
athlete. So I see, you know, I think this
is definitely linked to the conversation of HQ, why that was
born, why we have to be able to see everything in one spot.
And it's I think I see a lot of similarities to our conversation
from last week. It's just the shift from bio
(06:35):
mechanics and to ball play data.Yeah, I was going to say too, I
think it also when you really understand the way that you
would have a data informed system, player development
system, this idea too that like,oh, that's just they just use
data they don't like coach also,it makes no sense when you
understand the sort of bridge between all of those, which is I
(06:56):
have athlete A that walks in thedoor, you know, she does an
assessment. I see that this is her story.
We and there's in reality, are there these like really one off
examples of something super unique?
Sure, when you get to the very highest level, you might find
this very unique story. But the large majority of
athletes and especially in our current system where it goes
(07:19):
through college, you know, it's starting to expand and it's
going to continue to expand. And that's amazing.
But we're really talking about kids and it's like slightly
going into the adults years is that for the most part, they
start getting kind of bucketed. It's a speed story, It's a
posture story story. It's, it's these things that we
see these, you know, some version of this breakdown.
(07:41):
And so we see that it tells us this sort of like, OK, we know
based on years of data that the way that the most efficient way
generally to attack that is X. So that it's this program, it's
this base of program and we should if, if it's working for
this athlete, see this change, right?
(08:02):
So you put them kind of in that system to start.
And then it's where it actually allows a massive art of
coaching. And you, you need to be really
informed on those, which is it'snot just like then it's like,
well, well, this, you know, thisshould work because that's what
the data tells us. It's it's watching and making
sure that sort of her own experience of that matches that.
(08:24):
And it really gives you, I think, and we've said this in a
variety of ways, much more spaceto actually have the art of
coaching within with some parameters of what you think
that it's going to work. Art of coaching just so we're
clear is not guessing. It's like sometimes we think
it's more coaching when you're just standing behind and
guessing and for. On the flip side, yes.
(08:45):
On the flip side, when in the world of consulting, when, you
know, I start to realize that certain coaches, whether it's
the maybe the pigeon coach that I'm actually on the call with or
it's the head coach doing it, it's a very like panic on the
spot. Make a decision.
That's also not coaching. That's that is just like flying
(09:07):
by the seat with your pants. You know what I mean?
Like that's not coaching. Coaching is being very like
poised, informed, educated and making the best decisions based
on what you've already know, what you the knowledge you
brought coming in and the knowledge that's in front of you
right on the spot. That's coaching.
Is that easy? Hell no, right?
Everyone knows that. But this whole just like
(09:29):
abandoned any information we have in the background just for
the spot or there are moments maybe that might happen, yes,
but when you live in that world also not coaching, we can't call
that the art of coaching. I would argue it's not even
coaching, right? So like it's not good coaching.
I guess that's what you can say.And what we're really talking
about here is then in the background, the other extreme of
(09:50):
that is having this information X + Y = Z.
And no matter what you're looking at here, don't
incorporate that information, right?
Like this athlete's boss light makes her a great closer.
OK, Well, Sally like craps her pants every time she goes out
into a stressful situation. Might want to, right?
(10:11):
Might want to account for that, you know, like there is this
world where what you're seeing in front of you, Nate, needs to
be taken into consideration. And so when people are like, oh,
you're just data nerds when theyhave that type of complaint,
it's when someone sides on the other side of that, right?
All I do is like, well, these are what the numbers say and
you're not factoring into like what's in front of you, what's
(10:32):
the human experience, etcetera. So you know, our systems are
designed to take into account all of it and that's what you're
describing right now. You're describing this like make
the educated decisions based on what we know.
Bring in the data, informed training systems, protocols, oh,
we need to get rid of this in her pattern.
Then target this because we've seen that work, like bring in
(10:53):
these data, informed systems andprotocols, know what our goals
are and then see what's actuallyin front of you to be like, you
know what, I applied every training approach and protocol
that we typically do. We are missing something.
We're not getting the response. We thought that happens
sometimes it happens less and less since obviously so much of
(11:13):
our approaches, our data to inform, but it does happen.
And so there is this in the moment, keep working it, keep
working it. That's an entire year of a
consulting relationship. We start off with like, here's
what we know about this athlete,here's what we should target and
just all year long we're on the like, is it working, is it
working, is it working? Let's keep, you know, changing
our our suggestions and how we're going to attack it,
(11:35):
etcetera. So I think like to me, that
world is exactly what coaching is.
That is exactly what coaching actually is.
So if you if you are a coach andyou're listening to this podcast
episode and you find yourself living on just one side of the
fence on the other, rather than straddling both sides of it,
probably time to check yourself right?
Of just like, is that the best approach?
(11:56):
I would argue no. Yeah, well, and it's because you
can't. We can't yet measure.
We can't measure everything. You know, there's the reality in
coaching that's like as we get tools to measure more and more
and to quantify more and more than you get to take that into
consideration. When we played, we could
quantify basically nothing besides stats, you know, like we
(12:18):
could quantify so little. And so you would take into
consideration stats like that's what you could do.
And then, you know, by the time we were like, literally, we
talked about right view pro thatcame out like their senior year
college, I think. And so by the time we were
there, I was like, oh, now we can take into consideration
video and maybe that was good. Maybe it was bad.
At least we could see video for the first time.
(12:39):
And we've we've continued to have these steps along the way,
but things that we can't quite quantify are like
competitiveness, you know, it's like mental toughness, whatever
that means. And that's a whole host of worm
rabbit hole there. But sort of like, why does this
athlete with lesser tools have more success?
(13:00):
You know, and when it starts to get into this, like some of the
times it's just that we literally can't measure whether
she whether she actually has lesser tools.
So maybe her ball fight looks lesser, but now we're starting
to understand things like relaterelease height and different
release angle. And maybe actually she doesn't
have lesser tools. It's just that we haven't
learned the nuance of why her tools work the way that they do.
(13:23):
And it's just like we keep learning layers of that.
And so you're still able to takemore and more into
consideration, but they're stillat this point because we can't
measure everything. And you know, we we can measure
even less than they can. And baseball right now, there's
still this point where you're going to have to make a little
bit of a non data jump and you have to go into these this world
(13:46):
where you live in both. But if you know the thing
meaning it's something that we can measure and you're refusing
to, to incorporate that into anysystem that you have, like that
just makes no sense. Like the reason we build these
tools and we measure things and we and especially the things
that we've had for years at thispoint, like ways to measure ball
flight and and different things like that.
(14:07):
Not considering that at this point, that's just silly.
Like that, doesn't. It's like information is never
bad. It's like, would you like this
medication that we have extensive research on that we
know it's likely to help you, orwould you like the medication
that we don't know anything about?
Like who would raise your hand? Be like, I'll take the one that
no one, no one knows anything about because like we all know
(14:28):
no, tell me as much as you know,I want to make this like
informed decision, right? And so I think it's so we've
talked about this. We know anytime people are like
data SMEDA, you know, it's really that makes no sense
because data is everything. Data is information.
Laura, you've you've had talks on this many a times of like
data does not just mean the amount of breakable has like
(14:48):
data is everything. It's every bit of information.
So to say like, I don't use information to make decisions
legitimately doesn't make any sense.
And we know anyone that's sort of like, oh, when the data world
comes in, this happens in baseball too.
It's when they think people are living on too far on like the
one side of the fence, right? I mean, that's really what we're
talking about. But like information, you want
(15:10):
to arm yourself with as much information as possible.
And then obviously take a look at the humans that are in front
of you that are going to carry out those decisions, you know,
that sort of like hold the weight of those decisions and
take that in as information as well.
I would think, go ahead, Laura. I always think like the some of
the conversations I have early on with coaches who maybe and I,
(15:32):
I, I respect coaches that are inthis scenario or they've been in
the game a very long time without data and are seeing this
world evolve, seeing, you know, a business like ours evolve in
player development with data, informed decisions.
And they see it as, yes, this isthis is what's coming next in
the game. And I have to catch up.
(15:54):
I respect the hell out of that because first of all, that's
hard even to admit in terms of, you know, being in the game so
long and feeling like you probably could, you know, sort
of rest on those coaching laurels a little bit.
And I always appreciate when coaches, you know, veteran
coaches are like, I've got to get on board with the data.
And I, what I would say right now to any coach that is, is
maybe sitting on the extreme side of, you know, no data never
(16:17):
is. We've the, the train of collect
the data is kind of already leftthe station, meaning like your
competitors are at least collecting data.
Most likely they may not know what to do with it yet.
So they're collecting information.
I always think like in this likecontinuum, they're collecting
information. They might have, you know, some
dots on the graph. They might have some ideas of
some of the places they're looking for information,
(16:39):
probability of relationships inside their team or their
systems, whatever. But they're at least collecting
it. That's to me, that is the
standard at this point. And if you're at least
collecting it, then the next step is to figure out, OK, what
how do I organize this? And I will say that's typically
where most coaches, I'll say kind of like a college player
(17:00):
development positions, they get a little stuck, right?
Is the, I've got the pieces, I have the tech, I can get you CS
VS I can, I can pull this data somewhere, but I don't know what
to do with it. And that, you know, I feel like
HQ sells itself. Then in this next kind of step
of information, it's like, all right, well, then I got to put
it into some sort of context. So you're talking about these
different levels of data we haveand how you may utilize it on
(17:23):
the spot from an assessment standpoint about our programs.
It's just contextual. I think about, you know, what
we, how we utilize Hitrex data inside of HQ.
It's not just exit velocity for every swing you take.
It's contextual. It's the type of ball, the type
of bat, the environment. So when I, you know, I think of
coaches that are like, I'm nevergoing to use data.
(17:44):
My like, kind of plea to you is like, listen, just collecting
it, most of your competitors arealready doing that.
The ones that are even more evolved are starting to make
sense of it. They're using a system like HQ,
they're developing their own internal systems, or they're at
least popping it into a spreadsheet and starting to
explore it. And that's, that's becoming the
(18:04):
standard. And so I think like, you know,
the art of coaching, plus this data plus understanding your
athletes, they're, you know, base mental state, where they
are on their menstrual cycle, all these things that affect
them as humans. Well, that's just bonus that
given gives you all this clarityof like, all right, the data
tells me what to expect. There's some probability to it.
(18:25):
It gives me some maybe clarity on how I could bucket my
athletes or be more efficient inmy decision making.
And then there's my art of coaching, right?
There's my game decisions or my training decisions, and then
there's the athlete. And those three pieces to me are
the entire coaching, you know, Venn diagram of where they all
overlap is to me what the heart of coaching is.
(18:47):
And to say that that, you know, the data is irrelevant.
I just think you're, you're missing a huge piece of
information that with some work,some support in a system like HQ
can just, I mean, accelerate your ability to make decisions,
to make better recruiting decisions, game decisions,
training decisions, how your team spends their time.
(19:07):
I just think like no one has time to sit and guess anymore.
It's, it's too, there's too muchcoming at us and you've got to
have it all together at that point.
I was going to say the concept of like collect, organize, and
then basically to go back into the the concept that Christa
like the buckets that she came out with, it's like it's
(19:28):
organized. And then do you want to know how
to influence that data? Right?
Like most athletes, we don't just we collect it, we organize
it. We now know what it means, we
interpret it, we understand HQ is built to show you what
understands. There will be a layer to HQ as
well that's going to give you some baseline like resources to
know how to influence it. But obviously we can come in and
help with that process even more.
(19:48):
But like most athletes are not just looking at their data,
you're like, sounds good, they're perfect where they are
never want to change. And so you want to influence it.
And then here's that bucket, Krista, right?
It's kind of like reverse engineering those categories
that you said of like now we have these like strategies,
protocols that we have developedthat our data informed to now go
(20:08):
in and influence. And now here comes that feedback
loop. Because we haven't organized, we
can influence it. Rewatch it in, you know how that
changed it, and keep going in that loop.
I have a couple a couple things.The first is I think the idea of
like, I don't think any. I actually don't think there are
many people saying data's irrelevant.
I think what happens is you have, you have two sides.
(20:30):
You have, I'm going to, I'm going to make a data informed
decision and they're like, all right, let's see what happens.
And I think what actually happens is we don't understand
data. It's like as soon as it, it's
like not to get controversial, but it's like back to like
someone who gets a vaccine and they have X, they get sick.
And so we're like, see, you know, it's it's like a one
(20:52):
thing. So the example I was thinking is
yesterday I'm a Cardinals fan and yesterday they played
Pittsburgh and Paul schemes was pitching and like, the guy's a
freak. You know, his ERA is 2 years,
like one point something like this total freak.
But the Cardinals have never. Lost to him.
Which is totally random, like just in for some reason, the
(21:13):
Cardinals going into the game yesterday were four.
No. And I was picturing my dear mom
at home being like, this guy's not even good.
What's our problem? Because in her experience, she's
like, we beat him every time. And so yesterday we scored no
runs off of him and lost. And as we should probably give
him what is actually true about him.
But I think it's like when you just rely on what you see in
(21:36):
this one glimmer of time and youdon't understand how data plays
out. Of course you're you could bat a
kid 2nd and that could make sense in every world and she
could go in a slump O and eight.It's like that doesn't mean she
shouldn't be there. Now you might wiggle some things
for a second, but your goal probably is to get that kid back
into that spot because that's generally what everything is
(21:59):
telling you. And so I think what actually
happens in those scenarios is not necessarily it's as clear as
someone's like Dave is stupid, but they're like, you're telling
me X and that's not happening. And the the glimmer of time
they're giving it to happen or the way that they're trying to
do it, or they're keeping the pitcher in longer than she
should. So like the the truth about what
would work starts to actually not be true anymore.
(22:20):
Like there's different scenarios.
They sort of prove themselves right by using the data wrong.
And I think that's often what happens.
So that was something I was thinking.
The second thing is I had this call yesterday with a dad whose
daughter is coming for an assessment.
And he was telling me it was really interesting to me.
He played I think amateur or like Junior Golf.
(22:43):
And he went to college and played Junior Golf.
And he was saying at the time heplayed, there was no data, which
in golf is crazy because now golf is like the most data
forward thing. And so his he said his son
picked up golf really late and got really good really fast.
And part of the the reason he got interested in data for his
daughter in softball is because he said, like, I don't think my
son could have gotten that good that fast, like had he played
(23:07):
when I did. He uses data in everything.
And he's like, he really gets itbecause of the world he's grown
up in. So he's interested in it.
And and he had such a fast feedback loop.
And I thought it was such an interesting take.
Like he just basically saw take the own experience of he got.
So he was able to start late andget and because the feedback was
(23:29):
so good. So just that was a random
thought. My last thing and this is more.
I like that guy. I don't know who he is, but I
like. That.
Couple weeks. So yeah.
So my last thing, this is more open-ended.
I had a college coach reach out and maybe I'll cook this for him
because I never answered him. And he asked, do your pitching
(23:50):
people think that you can changerelease height?
And I actually think it's an interesting question because I
think there's a lot of layers tothe answer and I just hadn't
gotten around to answering. And I think it goes to all the
things we've talked about with data lately, which is first, I
think the actual first question that you want to ask is do you
want to change release height? Is release height so important
(24:14):
of a metric that we want to go around trying to change people's
release site? And I think probably and, and we
could maybe there's someone out there that has more information.
But I think for us right now we would say we don't know the
answer to that yet. This is such a new metric and
the like. We can probably we're probably
going to get there pretty fast as we've been able to collect a
lot more in game data and see release sites.
(24:36):
But that's something we've just started looking at because
that's been a metric at a volumelevel that we've just gotten
access to because it is more of an in game.
Yes, like Rev Soto has released height, but you don't get
results. So you would be making a lot of
jump there. And so this is something new.
So I think this goes to the first thing is that first you
have to understand about the data is like, are you, do you
(24:58):
want to just mess around with that?
Is that something that you want to do?
And then I do think it's interesting because once you
kind of get that data, you do get in this stage of like, let
me mess around for a minute and it's like there's athlete XI,
see her patterns. The risk reward is probably
(25:18):
there like she's not having. Success and maybe like a basic
scientific process. Right.
And and you do it on athletes where you're like, if we don't
do something different like thismight be it for you.
So you, you, you've used those athletes, you know, with their
consent and understanding that that's what they're doing.
So like, I don't have a system around this yet, but if we could
get your release height from A to B, we think you could have
(25:40):
for success. We haven't done it on a grand
scale yet, so let's just try some different things and see
what influence it has. That's how you start that
process. And eventually that happens at a
frequency where you're like A = B in most cases.
I know if I do this, I can influence X.
And so my actual answer to that coach is not sure, both.
(26:00):
Not sure if you want to and not sure if you can because we
haven't tried yet. I think you, we're probably
going to find you do want to. We that's our gut just based on
some of the things we've seen and but who knows if you can and
who knows how you can yet. I don't think we've, we've
messed with that before, but I think that's the data process.
It's not just we have this data,you know, let me grab this.
(26:25):
This kid has this weird release height.
So let's all go try to give everyone that release height.
It's not that simple. You have to look at it on a
bigger scale and understand whatall that means.
And then you go into this. Can I influence it?
Let me try with some athletes and then they build the scale
that I do that and then the loopcomes all the way, you know,
back to the ends. But I think that's a good like
(26:47):
data my. Brain is now my brain is now
going back to like last week's discussion of like we got off
the podcast and I was like the phrase I was going to use and I
thought maybe wouldn't land well, but was that like.
Be. Wary of a of a chocolate covered
toast where it's like it looks so great, right?
(27:08):
And this is what social media is.
This is the idea of something being like super sexy.
And so I'm thinking about this world of like, you know what we
could do Krista? I could make an Instagram real
and be like release height matters with music in the
background, Boom, boom, boom andrelease height matters and you
want to release height of this and this is how you're going to
(27:28):
do it and and put that out thereand like boom, sexy as hell,
nothing but cover to her. You know, like like seriously,
like it's just that is the reality.
And so I think there's when you're talking about data, when
you're like in the trenches of avery like data informed systems
(27:48):
that like that's the the sexiness of the process is like
how informed, how educated, how methodical that is going on and.
This is I someone used this in amuch different way, which I'm
not even going to say what this analogy was used for to really
turn this clock. As a dude, Paul is not.
Going to know what the hell to believe.
They're like episode, you know? It's like a toilet.
(28:10):
It goes slow at 1st and then allat once.
And I'm not going to tell you why that was used for me, but it
wasn't related to this. But that is data.
So we are at a place now where we're like pitch designs easy,
like we could just influence things right away because, but
that took years to get our placewhere we thought that.
(28:31):
And so right. It's it's the same thing with
like, can you influence release height maybe even in a year?
Because we actually have been building like this.
We're in a different place. And when we started collecting
data and we have a lot fast and we have it's all in the place
where we can ask questions of itfaster.
And so maybe the process of how long it took us to get to pitch
design being easy was, you know,four years.
(28:52):
And maybe to this question of release height is a year, but it
still is going to go so slow at fast first, which is like, let's
just get all the what are we trying to look at?
We're trying to look at if release gets release heights get
swings and misses. OK, To do that, we have to see
all the pictures with the same ball flight but different
release sites, you know, like what questions are we asking?
So first you have to do that. It's very slow.
(29:14):
And then if the answer to that is like, yes, it matters.
OK, Now we start to see athletesthat come through our doors
where it's like you have you look more like athlete B where
your ball flight is good, but itwould be elite if we could
change your release site to looklike, you know, athlete A.
And so now we try to do that andthat's still very it's slow
(29:34):
process because we're kind of guessing, you know, it's it's
informed guessing maybe just because we understand emotion so
much, but we're kind of guessingfor what it is.
And then that kind of works and it works on another athlete or
it doesn't work on another athlete and we get and then it's
the toilet, right, all at once. And now it's like, yeah, we can
change release site. It's easy.
You know, you get to this place where you get there or you can't
(29:55):
or the answer is you can't chasethe release because you said
most patterns so much. Well, you said Ash about a reel
like that. One obviously made me laugh
because you would never put out a reel like that in that way.
But that's why. But two, that's why.
And also because you couldn't figure it out how to do.
That's true. Too bold the.
(30:16):
Music to the video. Listen, I'm the last person that
should that should criticize anybody's use of Instagram.
But but my, the thought that came to my brain when, you know,
you were talking about that, it was like the, the whole like,
and I'll just say social media, but the, you know, the Instagram
influencer that, you know, thesebig sweeping claims of every
pitcher needs to do insert new fancy sexy thing.
(30:40):
You have to be really thoughtful.
Is the person consuming that information.
That and I'll just use the phrase influencer, not, you
know, labeling anybody specific,but just the phrase influencer
with sweeping generalizations like that they are praying on
your lack of knowledge. They're praying on the fact that
you do not know what you need. So be very cautious.
This is just like my word of like advice is like if someone
(31:02):
is making a sweeping generalization about your
position and what it needs and training and every pitcher has
to do X blah, blah, blah, you need to know if that even works
for you. And so if you go and pursue that
training modality that, you know, change in your mechanics,
you've got a measure that did anything of value for you.
(31:24):
Because the flip side of all of this, we have talked about the
ways that data, you know, has allowed us to be more efficient
and, and all these positive things.
The flip side of of sort of the the dark side of not having the
data is also when you don't havethese buckets that you identify,
which is just organization of your information, but these
buckets of athletes, It's how weget to protocols.
(31:46):
And it's also how we know when it is inappropriate to apply a
protocol to a particular athlete.
They may match all of the criteria, but there's one thing
about them that makes that protocol dangerous and can put
them at an increased injury risk.
And so the flip side of all of this is not just performance,
it's better is also what not to do.
(32:07):
And to me that is like, you know, from a healthcare
standpoint, I'm like, oh, that'sa big one for me because without
your health performance is not going to come easily.
But I'd be really cautious aboutlistening to any, any authority
in big air quotes. Make general sweeping, you know,
over over generalizations about what everybody should be doing,
(32:30):
particularly as a pitcher. That's.
Just I think you you have to askyourself what you're on toward.
This is also the bio mechanics piece is what you're being drawn
toward, the picture of the sensors and the on the forehead
and the force plate and the likemarketing And hey, we obviously
put out marketing things as wellbecause like we don't want it to
look boring here. It's not freaking boring, you
(32:53):
know, So like, I get that, but it is what's drawing you to a
place or drawing you to something like the flash of it,
the chocolate part of it, right,Sir?
This is why the entry to get into something with us is a
sales call because we're like, give us the opportunity to
explain the substance. You you really can't.
It's why we obviously value thispodcast so much because we can
get into a little bit of a little bit of depth of what
(33:15):
actually goes on behind OGX walls instead of just like, Oh,
that looks fun. Oh, that looks cool.
Oh, that looks like this. There's just like you that that
little snapshot, it has no connection whatsoever to the
substance of what a product is. And that means bio mechanics,
that means ball flight. Like I'm thinking about first.
(33:35):
We kept talking about the like, you know, people who don't like
data, but the same would be like, oh, data, data, data.
I put a rap Soto down in my pitching lessons.
I'm a pitching coach and I'm like, look at how we can change
your data, But there's no the substance of that.
There's no place to organize that data.
There's no place for a feedback loop.
(33:56):
There's no so protocols maybe that are like, OK, because your
data says this, we think we needto do this.
So now let's apply this trainingprotocol.
There's none of that. That's the substance, right?
And This is why I'm using that phrase like chocolate covered
turd because it's like it made it looks so enticing of like,
oh, there's data there with thatpitching coach X whatever.
(34:16):
So that's what we want. That's what we need.
But there's nothing behind that.You want to throw down a machine
and stare at numbers. No one knows what the hell is
good, not good, what that means in the grand scheme of who you
are. You can't set goals off of that,
let alone influence what you do.That's a bunch of garbage.
So I think that it it goes on. This is what you know, how to
use data is so complicated because there are tears of it.
(34:40):
For it to be as comprehensive aswe have it here.
And that's why I keep going backto that phrase, although it's
kind of cringy, is because it really is about if it were to
really land, for it to matter toan athlete or a coach, it has to
have all of those layers. It has to have all of that
substance. I was thinking.
Pretend it just it's nothing actually there.
(35:01):
Yeah. I was thinking of like the
where, you know, we referenced last week force plates and kind
of where we are in that that process.
And I think that there's kind ofthere's a force plate like
ground, you know, where you're putting energy into the grounds
and understanding that even in the motions, kind of the top
layer. And then there is this like,
where does this athlete produce power from?
(35:23):
And we are a little bit more in the like slower part of that
right now. That's just the reality of like
we're starting to understand when an athlete gets power from,
you know, X, it might make more sense for us to try to, you
know, really maximize that in the swing or in the the pitch.
And we're kind of starting to make some suggestions and see
(35:44):
how that. And to your point though, Laura,
doing that while looking at whatthe influence of it is, is so
important because if you're justtinkering with someone, which is
not to say you can't do that, let's make sure it's informed
first, but that is going to be apart you, you are going to be a
part in data collection where you are experimenting a bit.
That is just the reality of it. If you are experience
(36:07):
experimenting with it, but you better be measuring what you say
that is going to influence and not just saying you get power
from the ground. So let's get more squatty and
then see how that goes. And the athletes like, I feel
crappy and the data saying nothing's influencing, but
you're like, Nope, but that's what it says.
So we're going to keep doing it.That is not what we're talking
(36:27):
about here. If it's like that's not
experimenting and that's not relying on what the data is
telling you back and that's not using the loop that you're
trying to use for your benefit. And so it's hey, you know, it
looks like generally you get more power from the upper body,
from anterior core, and that's where you get it from and the
swing. So it probably doesn't make
(36:48):
sense for us to be in this super.
You know, we're trying to get itfrom the ground in a very wide
stance and we're just trying to go from our legs or put force
into the ground. That's not where we get it from.
So let's try XYZ, let's play around with it a little bit and
then let's watch both in how youfeel, not like right away that
felt bad. Obviously that's not how it
works. But do you can you get
(37:08):
comfortable with whatever we're doing?
So how is the athletes sort of subjectively feeling?
And then B, is the data getting better?
We said that's going to give youmore power.
We said that's going to give youwhatever your exit velocity
should go up, your bad speeds, it should go up.
Is that happening? And if the athlete after two
weeks is like, I feel not great and I'm not getting more power,
(37:32):
you know, the thing you said would happen isn't happening.
What is not data is saying, well, just keep doing it because
that's what the data says is supposed to happen.
And so that there's. Something wrong with you?
Either or you make the kid internalize it.
Yes. And so I think that's also where
we see some things is when we'rein this like interim period
where we maybe start to see there is like a piece of data
(37:53):
that's interesting and we can get this nuanced and we might be
able to tackle something that wehaven't been able to tackle on
player development. And so we're experimenting.
But then all of a sudden you're like, but I'm going to pull this
experiment away from the resultsand I'm just going to like say
I'm right. And that that doesn't work.
Like that is not what we're talking about here.
And so it has to be this loop where it's you, you, you
(38:15):
understand what you want to influence.
That's first and foremost what'simportant.
You got to have a big enough data set that that what you are
trying to influence makes sense and has been proven as something
that you should be trying to influence.
That's the sort of like that's the foundation of a data-driven
anything. And then it's OK, you hit this
stage where you're trying to build systems on how do I
(38:36):
influence that thing, right? How do I get velocity to go up
given the this athletes input? And then the the final piece of
that is, is that happening? And you just cycle through these
things over and over and over. And, and for us, an athlete that
has stayed kind of in our systemthe whole time, we keep learning
more nuances of what we can go after.
And so we can keep kind of tackling different things
(38:59):
throughout that process. But that's what a data informed
system is. It's closing the loop all the
way back. It's not just the bottom, it's
not just the top, it's this whole, you know, cycle.
And to all coaches attempting todo that system without HQ,
godspeed. The other thing, the other thing
I was going to say was I think, you know, yes, in totality that
type of training system, but even take a step before that.
(39:20):
If you are a parent, if you are an athlete yourself who is about
to open your wallet to someone who's going to collect the data
on you, Be very be very informedand very clear that collecting
the data is the easy part. That's the easy part, meaning
taking the data from you, whether you are volunteering,
whether you are paying for it, just know and be informed that
(39:40):
that the data collection is the easy part.
If you are paying for a data collection and you are not
getting that data back and givenback to you in some way, even if
it's not in the most perfect actionable way or with a
recorded debrief, but it's not given back to you with some
context, what are you paying for?
(40:01):
That's what I always ask that question is what are you paying
for? If there is not some end result,
the exception I would make for this is if you are participating
in a research study and it's, you know, at a university, but
that's a separate scenario. You are going to go to a camp,
you are going to go to an event,you are going to pay someone to
collect data. That's how you going to get the
(40:21):
data back. That's always the question I ask
when, when parents ask should I do this particular event or
camp? What are you going to get out of
it? And if the answer is I don't
even get a report, what are you paying for, right?
You're just paying basically to give that data away.
So if you're going to give the data away, at least get
something out of it and incite areport of something that you're
going to have something tangibleafter that.
(40:44):
And that's, that's very generousof you, Laura, because I think a
whole other podcast episode is when people give reports,
because most of the time, as I mentioned before, they're like,
I went to such and such. This is a report I I got and I
just write like it's garbage. That's garbage.
So I'm like, be better, be better in what you're showing
your athletes because they thinkit's gold, because it's a report
and APDF with numbers on it and you just hand it back garbage.
(41:06):
Talk about praying off of someone's lack of knowledge.
So I think of just like that kind of stuff pisses me off
because I'm like, no, this isn'tput in the context of this.
This doesn't mean this doesn't mean this.
And so obviously those are athletes that are connected to
OGX. They're connected to the like
information data hub of the softball world, right?
So they have a source to be like, what does this tell me?
I'm like rebillionths of what you actually need to know in
(41:29):
your process, you know, so very generous of you to say at least
give something back. And that's better than nothing.
But some of this, some of the shit that I see actually come
out of whether it's camps, whether at the university level,
private level, what's handed back to these athletes, I'm
like, wow, wow. That was literally highway
robbery right there of what you just handed them.
(41:50):
And we're like data camp, you know, it's freaking brutal.
So that's probably an episode all in of itself and maybe feels
like a little bit on the attack,but I don't care.
It really bothers me because I think these are athletes who are
hungry for information. These are parents hungry for
information. They're shelling out a lot of
money to be able to get it. And so, you know, I at least
(42:10):
feel like as people with the utmost level of like knowledge
and integrity, we know that no matter what you spend to come
through our doors, you're going to walk out with information
that's like exactly what you wanted, exactly what you needed
and exactly geared toward, you know, what the individuality of
of your athlete. And so I think when families do
that and they go somewhere for information, they're left with
(42:31):
just it's a piece of paper that looks like numbers and they're
like woo fancy. But in reality it's nothing.
I think like shame on you. That's that's brutal.
So anyway, bring that topic up later for about another week,
because that'd be a good. One yeah, bookmark.
All right, we'll end it there. We could talk about this for a
while. I think next week we're going to
kind of bridge off of this and talk through like how did the
(42:55):
side to influence patterns on someone and when to do that and
what the process should look like.
And maybe some like old adages of trust the process you got to
get worse before you get better and how maybe we shouldn't does
the process. Know that we're trusting it.
That's what I want to know. Does the process know?
Yes, a good one. That's a T-shirt.
(43:16):
All right, until next week. Happy 4th of July everyone.
Or hope, I guess by the time youlisten to this, hope you had a
good 4th of July. Till next time.
OGX Nation, we love you, we appreciate you, but we'd love
you even more if you would like subscribe to this podcast, give
us reviews, send us comments on what you want to hear, then we'd
really, really love you. Got to do it.
(43:37):
Do it now.
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