April 30, 2025 • 50 mins
In this episode, the OGX team dives into the evolution and purpose of the HQ platform—OGX’s innovative data visualization and athlete management system. They explore how HQ empowers both coaches and athletes through improved data clarity, customized training insights, and transparent performance metrics. The team also discusses how HQ bridges the gap between biomechanics and real-world development, giving youth athletes and college recruits the tools to better understand their training outcomes and long-term trajectory. This episode highlights the critical role data plays in athlete development, recruitment, and the future of coaching in youth sports.
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Episode Transcript
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(00:04):
Back for another episode with all of us.
And today we are going to talk about our HQ platform and
specifically kind of what we've been building to do some data
visualization, how we got there,the importance of it, everything
around it. So I'll maybe just give a little
back story and then I'll kick itto some to you guys to talk a
(00:25):
little bit about your experience.
But we have been working internally for a long time now.
It's just sort of like slow and steady to make sure that all of
the sort of information that we gather on our athletes gets out
in a way that is digestible and actionable for our coaches.
That's kind of how it always started internally for us is
(00:46):
that we had a way to bring that in and make it actionable for
our coaches. And HQ platform, which is a
web-based platform to get that information out now gives our
athletes access to that same information and sort of make
sure that everyone has the same information in front of them
and, and see sort of their journey.
I think this is like really important for us.
(01:06):
It's taken us a long time of sort of ciphering through all of
the different things that we input.
I remember there were times where we had, I don't know, I
think athletes almost felt like Guinea pigs with us because we
were just always inputting, you know, our sort of like gathering
information and, and then they never maybe saw it or we would
(01:28):
randomly tell them about it or randomly get and put it.
And so this really closes that loophole and, and holds us
accountable. And, and we've been working
through this for a few years of if we're going to gather
information, it doesn't mean every single thing needs to sort
of come out in this report, because sometimes you're just
seeing what matters and, and that type of thing.
But that for the most part, anytime we're asking athletes to
(01:50):
do things, movement assessments,things like that, that they see
the results and they see how that that impacts sort of their
side of things. So that's really where this came
from. It's been quite a work in
progress. So we're really excited about
it. And all of our athletes now,
right now have access to it. The college athletes or the
(02:10):
college coaches and that we workwith have had access to this
platform for a while and have been good testers for us of
what's important. And so, yeah, we're just really
excited about it. And they kind of want to talk
through the importance, I think,of putting this in the hands of
athletes and coaches and that type of thing.
So Ash, I think I'm going to kick it to you because you've
obviously had your hands in H Cube for a while because so the
(02:33):
first people that were really using it in addition to our
internal coaches were, you know,your work with a lot of the
colleges. So maybe walk through what that
experience has been like, how you guys kind of deciphered what
you were looking at, what was important, their experience with
it? How has that been?
Yeah, I'm, I'm going to start with like a little intro before
(02:53):
I get into the college work specifically.
But I was going to say that I think HQ is just, it's such a
good representation of the growth and a journey of OGX.
Like we started with, let's invest in buff light monitors
and sensors and what the hell isthis even telling us?
But let's just gather it and let's try to look at it on like
(03:17):
paper and figure it all out. And then we revolved into this
like, OK, let's build. And Laura, you'll get into this,
but like a homemade Sheets version of some data
visualization. I don't mean to undermine it.
It's a very robust system. It's just build off of some
like, you know, Laura's Quarantine project 2020 of
(03:40):
building data visualization, youknow, models through Google
Sheets. And so we used that for years
and that was huge for us. We were not only we were out of
the world of just collecting data and on the spot being able
to look at it. Now we were taking the data,
putting it into a system and thesystem was showing us, you know,
(04:00):
being able to compare day by day, week by week, month by
month, looking at trends. No longer am I going up in
velocity or am I not? Like we literally can see it.
That was so powerful. So we basically built the
backbone of everything, trainingand programming off of that.
Like, OK, now we can see where an athlete, what they're
actually doing. OK, if I train her this way, if
(04:23):
I program her this way, if we goafter this, is it working?
So this like feedback loop emerged, which is the center of
who OGX is. It's using data as feedback of
whether or not we're on the right track, which is not the
traditional like instructor model.
(04:43):
Like we're not the traditional instructor model, but you go to
lessons that instructor claims to be, you know, the Messiah of
pitching, I guess, right. And to know this is my way of
doing it. This is the way of doing it.
In 1994, I had an athlete that pitched wherever.
And so now I'm the Messiah of pitching.
That's kind of offensive, but that's basically the extreme
(05:05):
version of what we see in the like instructor world.
Obviously not every instructor is like that, but, and now
they're just like, do as I say and you'll get, you'll be good,
right? And So what OGX is based on is
like, we're going to collect a shit ton of data from a body
standpoint, from a ball flight standpoint, health and Wellness
standpoint, movement quality, power.
And then we're going to really make an educated guess, a highly
(05:28):
educated guess on how to train based on what we're trying to
achieve goal wise. And then we're going to see if
it's working. We're going to use that system
to like measure it and see if it's working.
That has been the center of who we are.
And so I think that the next phase of this, like we've been
operating into like we're based in data.
Everything we do is based in data.
And that's true. But until we turn our screen
(05:52):
around and show it to our athletes, it really was only
facing us, which has been driving us freaking insane the
last whatever, however many years.
So when you say Crystal like this has been a really long
journey, it's because it's been like driving us nuts.
We want our athletes to see everything, not just when the
trends are good, not just when the trend is from last month to
(06:12):
this month, but everything that we collect here because that is
full transparency. That is what we want our
athletes have access to. But the process of building that
platform we joked about like we you read like an ex post or
something from a business owner that was like, I'm currently
three years into a 5 month like update or something.
(06:33):
And we laughed at the time and like, yeah, we're still
laughing. But actually it's not funny at
all because that has been the process.
It's like, you know, it's so slow, but it's so powerful.
So now we're at this point wherethe athletes, if we collect it,
you can see it. You can take a look at your
trends, you can look at your comparisons, you can take a look
(06:54):
at all the data that's being collected.
You can see it all. That means you're holding
yourself accountable. You're holding the program
you're under and your coaches accountable.
You're able to share with college coaches from a
recruiting standpoint. If you're at the youth level,
we'll get into it, but there's just so incredibly powerful.
So at the college front, that's exactly how we're utilizing it,
right? We are utilizing it early in the
(07:17):
year to see we're setting these goals.
OK, let's assess them early in the year.
What do we want to really go after with this pitcher between
now and to preseason? Are we getting there?
Are we on the right track? Let's watch.
And this that is like almost apples to apples about how we
use it at the high school level from an OGX standpoint.
Now that we've been in season inthe college level, it's a little
(07:40):
different. It's very in game management.
We haven't gotten there yet withour high school athletes because
they don't typically have accessto data as frequently and we're
not, you know, our athletes are just getting HQ access now and
when they're in high school season.
So it'll be interesting is we can help them with that a little
bit more. But in the college front, you
know, a lot of the schools we work with, they had in game
(08:02):
data. So our, you know, our platform,
we've worked for it to be able to take in all sorts of, you
know, from a lot of different text sources.
It's not just oh, you can only put in if it's Rep Sodo, it
could only do it if it's track man or yak and tech it it'll
take in all of those. And we're seeing the in game
data. We are able sometimes to look OK
(08:22):
on the right. Here's the end game on the left.
Here is the here's what we're seeing from practice earlier
that day in the bullpen the day before or whatever that looks
like. Are the issues we're having
command based? Are they that this athlete is
throwing, you know, 64 in training and 69 in the game and
(08:45):
with that, that down pitch is disappearing.
You know, like we're now able toreally see those types of
things. We're watching trends, we're
watching velocity trends, we're watching spin rate trends, and
we're advising throughout the week workload.
Should we give that athlete an extra day off?
Should we really be focused on going after sync here?
(09:06):
How do we prep her at a higher intensity since Vilo's going up
so high in game? It's informing our consulting
decisions on an A weekly basis. It's incredibly powerful just
for the coaches to be able to see like we're on the same side
of the table. This isn't like they're telling
me and they're just like if I'm in a consulting call, they're
(09:28):
taking my advice and like hopingfor the best.
This is like we're sitting down together, We're looking at the
data together, We're coming up with what needs to be different
for the following week and beyond, talking about a plan and
then sitting back together like are we on the right track?
It's like so much more of a collaborative environment
because of what HQ provides. So it's been an incredibly
(09:51):
powerful tool. Are there things like you get an
error early on, like an error message when we click on an
athlete's name? I'm like, damn it, you know, so
that I'm like right in Laura andshout out as I'm sure we'll give
a ton of shout outs during this episode to Ryan, who is our
developer, who probably who thisday I've never met, probably is
like a terrified of me because I'm like immediately wherever
(10:11):
you are fits the following, you know, because there's like all
those bugs we were working through early on.
It's obviously in a really good place now, which is why it's at
a place where we're like turningit around to our athletes.
And I can't even imagine going into consulting this college
season without it. I mean, you think my notebook
looks crazy now keeping track ofall this stuff because, you
(10:32):
know, I'm like a beautiful mind scribbling like nuts.
And without a you Can you imagine?
I would literally be like tapingthings all over my.
I was thinking, I was thinking you're talking, I think some of
the important things about why we needed to build it the way we
did and and why maybe some of the like existing platforms
don't just kind of work as is. Because obviously there's data
(10:55):
visualization that comes along with a lot of the tech.
And I think one of the things that we realized was it's both
not going to work for coaches and also probably isn't a fair
ask. Ask for the tech companies to
also be the people who are determining what is important,
(11:17):
what should visualize, what are the things that are going to
influence decision making. And so we couldn't ever use
those data visualization things alone.
And it's taken US time to kind of figure out.
And it's evolving because, yeah,as we are able to import this
data to build a platform like this, we have to make all of our
data speak to each other. And as now our data, you know,
(11:39):
anything we input from an an athlete, whether it's, you know,
Wellness questionnaires or movement assessments or body
weight, you know, ball flight, all of that stuff, as it starts
to speak together, we can keep refining the questions we're
asking of it. How we're having to influence
data. The in game data piece is huge.
We haven't had in game data on this sort of like level before,
(12:02):
just the game hasn't. And and certainly we haven't.
And so we can start to kind of ask different questions and make
decisions about that. And so, you know, our coaches
use it to influence training, which then helps us to ask what
matters in training, ask what matters in game.
You know, start to to see those things and to keep refining what
(12:23):
is being shown. Why is it being shown?
Why does it matter? You know, I think are hitting
performance expert has gone through a few iterations and
probably has like 1 more. And even that it's like when
athletes ask, hey, you know, what does this mean?
And if we can't answer it and tie it directly to like their
performance, then it either doesn't belong there.
(12:44):
We haven't refined it enough. And so that's really how we've
been working on that. But I think that's what's been
so important because I think up until this point, our game just
tried to rely on whatever dashboard that tech created and
that tech company created. And a lot of times that just it
wasn't thoughtful. And again, this isn't really,
we're all for knocking people that don't invest enough in the
(13:06):
game, but this isn't really a knock because I just don't.
Why would the product developer know what matters for training
like that? You know, there is a level
that's just not a real ask and so you can't use the data like
that. And so we all kind of ran into
where it was like, well, you know, we saw a lot of things
people were just using on the spot too much or they just
(13:28):
weren't using data in the way that is really going to help
performance. And so that to me is the most
exciting thing in addition to which we already referenced.
We keep saying giving it back toathletes, but yes, a lot of them
have access like if they have used that tech, they can log in
in some way. But what we've seen about the
way that the tech companies kindof developed the data and
different platforms do that is that it's not actually the
(13:49):
athletes. And so a lot of times I go to
camps or something with data, I go to like different places and
I get all this data. And there's no sort of like
singular place that I, it's not that even like own the data, but
that I can access my data in a way that makes sense for me.
And so that's been really important to us.
(14:09):
Again, it's kind of through our sources and it's not like people
get access to it forever. But when they're in our systems
that they just, they get it, it's theirs, they get to see it.
We're not withholding anything, we're not hiding anything.
We're not pulling back what theycan see.
We're not sort of doing any of that.
But it's really like anything inputted for that athlete goes
right back out to the athlete. And that's been important to us
to kind of, you know, visualize the data in those ways because
(14:33):
we hadn't always seen that. And it felt a little bit like we
were kind of in that ball game when we weren't doing that of
like, you're getting better, trust me.
Like, you know, here, here's what I see, but you don't.
And that they're able to kind ofsee that and ask questions for
it about it now has really been an important journey for us.
Did I miss anything, Laura? Did we miss anything from sort
of like your standpoint of kind of data input, data
(14:56):
visualization from the standpoint of like, you know,
the scientists on staff here? I think.
The. The accuracy of being three
years into a 5 month app launch is so accurate, so accurate.
You know, this our systems, I think are it's so important, I
think to keep in mind that like.This.
(15:18):
Athlete management system, whichit primarily is for us
internally and part of that athlete management is data
visualization. It's being able to put that
information back in front of thecoaches, internal or external is
really irrelevant, but back to the coaches and then the
athletes of course, where it canbecome actionable.
And I think it's so important. We didn't start, we didn't start
with the data visualization. We started with the
(15:40):
fragmentation of all of this technology and built the systems
kind of piece by piece around it.
So then when it came time and you know, actually referenced
that we built this all in Sheetsand, and there was a lot of
limitations to that. You know, I am sometimes I feel
cursed and blessed to have gone through that, but I learned a
ton just about what it means to scale systems like this.
(16:03):
And obviously, you know, Google Sheets have its limitations as,
as it, as it does as a visualization platform, but it
served us so well for so long because it didn't really cost us
much, right? We just really used and leverage
the systems that we had internally based on what we
already had access to, built thethe training platform, built the
principles, the theories, the applications, and then said, all
(16:26):
right, here is our, our second round of this.
Let's go into this athlete management concept system
visualization, we're going to call it with a different and
updated lens because we had experience being in Sheets.
And I I still remember some of the transitions just in talking
with Ryan of things that we could never do in Sheets just
simply because of its limitations that now are so much
(16:48):
more scalable and, and availableto us.
Something as simple as, you know, there are are places where
our coaches and our admin can basically go in and, you know,
take a custom pitch for a particular pitcher that Rap Soto
calls custom 1 and relabel that a custom pitch for her and those
types of things that make the data so personal to them.
It's not just like, you know, oh, here's this picture over
(17:09):
here that is no, a label. It's not relevant to me.
It can be so unique and individual to them and that lens
that we kind of came through of this, like we need to have it
facing the athletes. It's why it's taken as long as
it has. You know, we've had bits and
pieces of it, but the scalability of deploying sheets
based dashboards to 100 plus athletes is a lot and
(17:29):
unsustainable and you know, rifewith errors and issues.
But the the giving it back to the athlete has taken us a long
time because we have had to really internally work.
This has pushed our business practices, it's pushed our
training protocols. It's forced us into in a very
positive way. It's forced us into me being
very clear about some of the logic of our decisions because
(17:51):
it's got to get programmed right.
It's got to go into A10 concept,so to speak.
And so it's helped us, I think to kind of get out of this, you
know, yes, we still absolutely make changes and evolve our
processes and do tons of qualityimprovement.
But we're at this point now withthe training systems and seeing
with our eyes the impact that we're making that now we can
(18:12):
start to pair. Okay, here's how we show that
and show that story. And it's helped us, like I said,
refine our business practices, refine our athlete management
systems and how we, you know, want to approach this data.
It's been amazing to support ourcoaches in in their data
literacy and starting to understand, like when you see
this stuff in the wild, because it's going to start coming for a
(18:33):
sport here, you know, more frequently than it has, you want
to be educated. You want to make sure that
you're, you know, you're going into that with a good lens.
So then working with it every day.
It just it separates them so much as coaches being able to
make those decisions. And obviously the power for our,
you know, our college partners, particularly right now that, you
know, that information is at their fingertips and it's it's
all in one place. And I think that's the most
(18:55):
important thing is that it's it's a centralized place that we
are trying to, you know, make sure is literally a headquarters
for our athletes and coaches. Yeah.
I was thinking like we were at aplace where we were like, we
have so much data, you know, we have so much data.
We have all this, this, you know, inputs coming in about
each of our athletes. And again, that ranges from, you
(19:16):
know, objective ball flight monitors to, you know, like I
said, Wellness questionnaires, first periods.
Like there's a, there's a whole host of, of things that we can
draw from. And so in preparation for this,
like what, what Google Sheets and our sort of manual systems
didn't require in the way that we have had to sort of revamp
(19:37):
things and really prep for this was to put the data in a way, in
a relational way where we're able now to ask those things.
And so when we get stopped or stuck, you know, and we've
started as we've been in the system, I think that's been some
of the most exciting parts of it, which, you know, we're just
kind of hitting the surface of. And it's going to continue to,
to grow for us is when we, you know, sometimes we get to places
(19:59):
where we're like, we don't. We don't have the answer like we
would assume based on the level of information we've had up
until this point that this pitcher would be having success
with this pitch or this hitter, you know, would be having
success in the games based on what we're seeing.
And we're able now to dig a little deeper because of the way
that the data is sort of speaking to each other to dig to
(20:20):
see what is the nuances here that maybe we're missing.
Is there something correlating that we haven't considered
before because we weren't able to pull that data in in this way
and that that isn't yet the things that were necessarily
visualizing. But it gives us that point
where, you know, when we're hitting a roadblock with our
athletes, we're able to dig a little deeper and not just in
(20:42):
sort of the the small amounts ofdata that we had, you know,
deemed relevant, I guess up until that point.
But really like the whole systemof inputs that are coming in to
start to ask some some bigger questions on that.
And so I'm really excited for that because I think, you know,
I think now at this point, like two pitch talks in a row of like
the in game data, the in game data.
(21:02):
And I think it it's like in gamedata mixed with this data being
in a place that is relational, that you can ask a bunch of
questions that, you know, does this break, you know, get this
result, does this wait, get these belows, does this, you
know, like whatever those questions are you're asking.
And I think that's really exciting because it just hasn't
(21:24):
been done before in that way. And so I think, you know, we're
going to be able to continue to grow and iterate on our
protocols like so much faster and so much better by having
access to that. I feel like you're getting ready
to jump in there, Ash. I was just thinking about like,
you know, I think outside of thethe consulting piece, which is
(21:45):
huge, which is not just limited to the college side.
Like I mean facilities who have this, travel organizations who
have this, which we have had a few who entered into our like
beta phase to do this. Their ability to now do like
consulting and make decisions oftheir athletes based on someone
helping them understand that data.
There's no doubt about it. This is huge for that, but I
(22:06):
would argue that the number one thing that we just haven't
gotten to yet, we're just now like starting to give our own
internal OGX athletes access to this.
The number one thing that I like, can't wait to see how it
like influence. I'm saying influences because
that's professional. But what I want to say is like
blows the hell up, you know, is this like recruiting world?
(22:28):
So that has just, I think been really challenging for me
individually this year is spending the majority of the
time in college, in game data, seeing what works, what doesn't
at all different levels, workingwith the coaches to help their
athletes get the, you know, achieve things XYZ.
(22:52):
And then switching over into thehigh school world where I'm
like, is this the same sport? It is a huge disconnect often
times outside of when I feel like OG athletes are being
advised by us in OGX because we're operating under this
(23:14):
umbrella of being educated ourselves by the in game data.
The in game college data is whatfilters down for us to know what
then an athlete should be striving for when she's 12
versus 14 versus 16 versus 18, right?
So it influences the training journey and how you're advising
(23:34):
goals. It influences if an athlete is
like someone that matches the standards of your program
recruiting wise. Like it's just it's everything.
And So what it often feels like is that the college game, at
least in the bubble of OGX and our college.
Partners. Is shifting so much into being
(23:55):
educated around data. The in game data is now
dictating to us where we need togo, what works, what doesn't,
what's good, what's not. And then over on the high school
level, it is still feels so archaic.
It feels like not, it feels likeit is.
It's just based on whether or not you're getting results in
(24:18):
your travel ball tournaments, which is an absolute circus.
It's just athletes who are exhausted, just flailing, doing
whatever. And so that you know, that might
be a little bit harsh, but it, it is not a great show of like,
if you have success in travel ball, you're definitely going to
scale up the college level. It's not that simple.
And so we just base results off of, of that.
(24:41):
And then at the instructional level, we just are like, whether
or not our instructor like tellsus that's good.
And then we're hiring agents to help us with recruiting who
don't know anything maybe about the data of that level.
That's what Staffs look like, you know, like they don't maybe
know that that staff is really just looking for a drop ball
(25:03):
pitcher. Is your athlete actually a drop
ball pitcher? Not does she have a pitch she
calls a drop? Does she legitimately have a
drop ball that would scale at that level?
So it's like this combination ofjust taking instructors,
subjective thoughts on if you'regetting better, you're not
paired with the results of travel ball, paired with like
(25:24):
recruiting services. It's just a mess.
And I can't wait for us. And I think it'll take a little
bit of time, but for us to startinfluencing this world and
starting to connect, use data toconnect the youth level to
reality. And that that's really the path
to getting into college and playing at that level.
(25:47):
Yeah, I think at the end of the day, it's there's not enough
people right now at the youth level actually in systems that
are designed to get them better.Like it's just, and it's all
about the, it's just about grind.
It's about I work hard. I put in the time, I put in the
hours. Like look how hard I'm working,
Look at all the extra work I do.Look at the all the lessons I go
(26:08):
to, look at all the practices I go to.
Look at this 5:00 AM workout I did like it's.
Just the money my parents and the money I spend.
It's just. Input, input, input, input.
And I think that it's so it's just so random.
And each of those inputs and listen, we're a business.
So like, you know, there's no shame in this.
(26:29):
This is not like, and, and we'rea nonprofit where no one pays to
work with us. So I mean, and we're all
businesses here, but each of those inputs is a separate
business and they have their separate incentives for keeping
you in that loop. And it's not like everyone's out
there maliciously. I mean, there certainly are
people doing that, but it's thisis not like, and we're all out
(26:49):
here malicious, actually trying to take advantage of kids, but
sort of unknowingly we think like, yeah, this kid works so
hard, they're going to keep getting better.
And there's never been a measurement of what that even
means except that I continue to play the game, you know, and I
continue to walk on the travel ball stage and have some level
(27:11):
of success or do OK. And then, you know, I think what
often happens and what we see onthe flip side of that is in the
sort of worst case scenario, they then don't get, it's early
and they don't get recruited theway they thought they were going
to do. They get hurt.
You know, there's there's something that happens to them
in that high school journey thatreally surprises them because
they felt like up until that point I was doing all the things
(27:34):
I was supposed to be to, to go on that path.
Or. It doesn't even, you know, for a
variety of reasons. They do get recruited.
They have enough to get over that hump.
It's a college program that doesn't know about data or what
works or whatever. And they get to the other side
and they are surprised by the sort of lack of success they
have or, or whatever gets, you know, on the other side.
(27:56):
And I think there's just such a a better way to approach that to
make sure that you're actually developing in a way that makes
sense that you're going after things that are going to
actually input, you know, what you are able to do.
And we can tie that gap so much closer now because we have in
game data at these levels. Like it's just this is not, you
(28:16):
know, we're still making some inferences because most of the
in game data right now is reallytargeted at the highest level of
our collegiate game. And so of course, as you're
thinking about what does that look like at each of the tiers
and things like what's going to have success, there's some
nuance to that. We're where we're going to have
to make inferences, but we have such a tighter gap of that.
It's just not like it's not a question mark.
So this idea of like I just am cranking, I'm I'm going for it.
(28:40):
I one time on the pocket radar popped 70.
So shouldn't I be getting recruited and having success at
this level? Like all of those things that
you think you're doing, you can just relieve some of that as you
really start to understand your story and you track it along the
way that's tied to something that relates to the scale you're
trying to go AT and that you're able to keep doing that.
(29:01):
And I think that's really what we are built on and what we've
been trying to do and really putthat information into the hands
of the athletes and their and their families.
And I think that's what's so important.
But we see it all the time. It's just, you know, it's hard
not to just get into that cycle when you're in at the youth game
because that's what you think success is.
(29:23):
And a lot of times the kids thatare grinding in that way are,
are the ones who are having success on the field because
they're the ones tough or resilient enough, I guess.
And so survivorship bias way. Yeah, right.
Survivorship bias, right? Yeah, I'm thinking about.
Some of the like, like the categories of athletes that we
(29:45):
see at the youth level. And a big category is the like,
I work so hard, but I'm not getting recruited.
And then they come for an assessment, they get access to
see what they're and we can say to them like this is where this
stacks up. And it's often times it's just
average. And so the reason why you're not
really getting recruited, it's often.
Times it's like everyone keeps pitching it's like and this is
(30:06):
same for hitting. To be honest, I don't think
these are different. It's slightly above average
below. And so when you were 14, yeah,
everyone was like you throw 63 or you hit the ball 70 mph.
Like we've talked about this before, the like 70s kind of as
people are starting to get comfortable with exit below,
(30:28):
people reference as 7 like you like.
And so in their minds, one, theywere, they, they are because of
that getting recruited by at that stage, they're getting
followed by high because that isabove average at that stage.
And then it's like, but then that's it.
And one, you can't just assume that's going to keep going up.
And two, how you've got access to that because of this like
(30:51):
grind mentality and all of thosethings like for sustaining often
you don't have any brake. You know, there's a bunch of
things that come with that. You don't brake, you might not
have change of speeds and hitting it's similar.
You don't have adjustability with that.
And so it's like it doesn't one,it's not going to be that easy
for us to keep getting your exitbelow to go up unless now we are
thoughtful about it. And then two, sure, if you run
(31:13):
into a ball, you're going to hitit really hard, but like you, I
could throw you in 42 spots and you wouldn't touch it.
So that's not going to work. And so there's these, you know,
I think that happens all the time when you say they come and
they're like not it's not like people that come to us and they
have extremely low velocity or something like that are like, I
really thought it's, you know, it's the large majority.
(31:36):
And I think you have pitchers that come to us throw high 50s
into, you know, 64. They throw in that range.
And so they if they threw that at 14, they're like, I'm right,
yeah, throw at 64. I'm smoking percent.
And a lot of times in those conversations, I'll say to them,
like if you were, if I was in the admissions director at
Harvard and you were like, I have a 24ACT.
(32:01):
I don't even know if they do ACTanymore, but like there's an
example, right? And like my GPA is the 3.1 that
is maybe like, you know, nothingwrong with that, but you can't
get into Harvard. And everyone's like, yeah,
obviously everyone knows there'slike these qualifications.
Now, of course, once you get in,once you hit that threshold, how
(32:21):
hard you work, how much of A competitor you are, like all
these things are going to weigh into your success when you get
there. But there are thresholds because
now that we have the data, this isn't like anyone who works hard
has success at these levels. It's very clear.
Objectively, the combinations ofit's not just if you have high V
low, we see low V low successfulwhen the break profile looks
(32:43):
like X&Y, when you're right-handed versus left-handed,
your change of speed does this versus that your break profile,
it looks like this and that there's a combination of all of
these factors, but they're there.
We're not seeing it's very like mediocre, you know, like, you
know, we're not seeing examples of that where it's just like
very average data and we're likethis should not be scaling.
(33:03):
And it is it might have that picture, might have a good game,
they might have a good outing, you know, but but to like this
ongoing success. And so everyone seems to get
those conversations when we're talking about like the academic
and we're like, it's the same. The challenge is that you don't
have access to like those thresholds and that criteria at
the youth world. So what you think it looks like
(33:24):
to actually get recruited acrossthat threshold is to show up at
camp and hustle hard. You know, like hustle, have a
good attitude. I'm not.
Saying, well, you don't have access to those.
You don't have access to those thresholds, right?
Many, many programs didn't have access to that thresholds, and
still don't. And they.
Don't or they don't. We hadn't around stuff like
that, right? Right.
(33:45):
And we and we hadn't trains based on the influence of data.
And so I also think what the what the game has had to rely on
leading up until these past few years is, as you said, like, OK,
I have the 14 year old who throws 64 is the assumption that
that kid is going to be on the linear path into my program.
(34:06):
And so I'm going to by the time I get her her freshman year of
college, she's going to throw 6870.
And that's what scales. And as we've been able to have
this really, you know, gathered so much more information about
the bio mechanics behind hittingand pitching, the how different
programs influence, you know, wecould see what works.
(34:27):
And the data is, you know, what is leading the show for us of is
it working or not? You know, performance data
specifically is leading the showof if it's working or not.
Then we can also now the collegecoaches even have a more in the
in the highest level of college coach that has access to this
information has it even more sort of like there's a, you
(34:48):
know, difference between what I have as a high school and what I
have as college. Again, this is not every college
program of understanding of like, yeah, you're not going to
scale like this. And so you can't come to me
versus all of a sudden a kid where you're like, wait, how'd
that kid that kid was, you know,under me and performance until
this point. But now that kid gets recruited
(35:09):
because we have this better ideaof what are the things that we
can influence? Is the person in the right
system for us to be able to influence it?
Are they going to keep developing?
And that is is why it's important for it to be in the
athletes hands too. Is like, let's not just know the
thresholds, but how can I influence where I'm sitting
realistically, not just like some random go put a bunch of
(35:30):
work in and and hope it sticks. Christy, you mentioned like, you
know, the, the bio mechanics data that were, you know,
obviously that's one of our major databases and different
forms and different types of collection.
And, and I, let me preface this,I'm just as guilty as it is very
much a, it's a research thing towant to group people together
(35:50):
and makes it easy to compare. It makes it, you know, easy to
draw conclusions ideally. And so this, this concept, when
I see, you know, like, you know,normative values or here's
everyone you know, here's ball flight ranges and it's broken up
by playing level. I just think like, no, no, no,
no, no, we've missed the boat because there is a we can no
longer assume and we should not assume that the, the, you know,
(36:15):
projection of high school hitting or pitching patterns
improves organically to the college level that somehow the
college level is this comparisonideally pro, of course, but that
we should be comparing between playing levels.
I mean, with, with we've been, you know, revamping our reports
and, and with our improved technology.
(36:36):
Like it's not about playing level.
Let's be really clear here, right?
You when you're bucketing, particularly bio mechanics
patterns, you're looking at things like what velocity bucket
does she fit in? There's something she's doing
that is unique to her that allows her to throw 65 plus.
What does she look like comparedto someone else who throws 65
plus? That then helps us to understand
(36:56):
the next Velo tier below that. What does this person not have
because she's not throwing 65 plus?
Or is that just what her Max is for how she's built?
And so going by playing level. And I see these like comparisons
of like, you know, 14 years, 16,just like they're garbage.
And then comparing high school to college is even worse.
And look, and I'm just as guiltyof it.
(37:17):
There are published, you know, pieces of information with my
name on it comparing playing levels.
But in this in this scenario where we have this data now, you
can't, you cannot just assume that high school to college
players get better. You can't assume that anymore.
And so when I think of it from the coaching lens too, I think
of what you just said, Krista, it's like all of a sudden, you
(37:39):
know, she taps into some, she's a hidden gem.
And it's like if you just make the assumption everyone gets
better, you're going to be really disappointed.
Because the reality is they don't.
They don't. Yeah.
Well, and it's I was going to say, and that's really specific
to bio mechanics and we see thisa lot.
I actually think we just saw this last week, I saw this with
some of the, the hitting assessments we did down in Tampa
(38:01):
is that the, the, the patterns look exactly the same sometimes
as a 22 year old, like there's no difference between their
patterns and it's, it's actuallynot their patterns.
So this what's holding them back.
It's that when we measure their strength, it's look, it's very
low because they are young. And so there is like, OK, there
(38:21):
it, there might be some linear path we can take for you to hit
the ball harder or throw the ball faster.
That's just related to whatever mass you might need to put on
some, you know, power numbers need to go up.
We need to fuel the fire of those patterns, you know, and
that happens very often. And there is often, you know,
the an older player who has really poor patterns and that's
(38:45):
blocking them. And it's just this the the
pattern specific data is, is really unique to that.
And you have to have which is again, all back to the point of
why you want your data to speak to each other is like you can't.
You know, we've said this beforeand we were guilty of this
before our data was in this and we're still putting more data
sources and still getting betterat this.
But like, you know, you look at some just like I collect a bunch
(39:09):
of bio mechanics data. It's not in visualization, it's
not speaking to each other. It's just line, you know,
numbers on the line. And all of a sudden I see like
this kid throw 70 and there's one number on that line that
looks different than everyone else's.
And I'm like, that's it, you know, that's the thing, you
know, or is that someone's doingbad and you see something that
seems out of range and you go after that.
(39:30):
And it's like, that can be really, that's really where data
did live for a long time, even for us.
And that can be so dangerous because that might have, that
might be different than someone else's, but that might have no
correlation to the thing you're chasing.
And so the putting the data intoa database where it's constantly
speaking to each other and you can and really ask like, does
that matter? Does that matter to whatever
(39:52):
you're trying to influence is soimportant?
Because then data, if you don't do that, data becomes exactly
what we're talking about. It's just like, you know, you're
just throwing things at grab this data number, grab this data
number and I'm going to go chaseyour arm didn't move as fast as
that kid. And so obviously that's the
thing. It's like, is it that the thing
does that correlate to what we're trying to go after?
(40:13):
And you really, you know, you have to have your data in the
system like this to be able to ask those questions.
And it's why it's so important, because.
Then, and I think we, we see this often and, and probably
we're pretty guilty of it at thebeginning of our journey, then
data, you know, training with data becomes no different than
let's try this random drill thisweek and, and see what happens.
It can. It can really become random if
(40:34):
that's the kind of way you're inputting and outputting the
data, which can get really dangerous.
I think it's important to differentiate the like ball
flight piece from the the bio mechanics piece because I think
from a recruiting standpoint, I think the bio mechanics like the
bio mechanics piece really informs training a lot where it
can inform the recruiting piece.And I think we're just a ways
(40:56):
away from this is understanding like efficiency.
So if you're like she has the ball flight to be my like 3
times through a lineup kid, but her patterns show to be so poor
that her efficiency is so low that I don't know if she can
sustain the workload of an ace even though she has the ball
flight of an ace or I at least have to know that I'm going to
have to manage her. So.
(41:18):
Hard, like really put a lot of energy and capacity.
I think we're a couple years away from that.
We're we're doing that like main, you know, like really
helping coaches understand that we have these direct
relationships with them. But for coaches to just see that
and to know how to do that. I think we're a little bit away
from that. Bio mechanics and and athletes
being able to see that data really informs the like their,
(41:38):
their training and their trends,their their progress.
The recruiting world is kind of X's and O's.
And I think the challenge right now is like, you'll go to a
single camp and just like hand that data and that's just like a
snapshot of a single day. I'm picturing this world where
obviously in HQ you can just youcan click multiple dates, you
see trends over time. You can show a coach, I've been
(42:00):
working on this. Look at my, I'm on the up like
you can just show them that I'm picturing this world where our
athletes, and this is not a likefuturistic world.
This is the world now, now that we've built this platform where
coaches now see in athletes their their story with their
buff light data. They determine if they meet that
threshold and then they can go see them play and see how they
(42:23):
compete, how they handle adversity, what type of athlete,
what kind of a kid they might be, you know, of course, all
those things that also matter. But this idea of just like them
being able to see a snapshot of your like ongoing, not just
data, but progress and evolutionof your data because you're a
kid, you're a child. You are not just going to be
stuck in something. You're not recruiting an athlete
(42:46):
at 16 with like, she has the skills right this minute to
GAIL, you have to see that it's going to keep growing into what
works at your level. And now this is allowing us to
see that. I also just had this snapshot,
took everything not to just likecrack up laughing while one of
you was talking because I was inmy own head thinking about this.
Our VHS tapes when we were recruiting, what we did was we
(43:06):
had like someone with like a Rep.
It was like we're. Grand and yeah.
We we spliced only the good ones, you know what I'm saying?
Like just like to show people swinging and missing.
But the reality is like we have athletes and This is why I'm
like HQ is amazing. I'm using it on a daily basis in
college consulting, but can we please hurry the hell up with
(43:28):
getting it to our athletes? Because if one more athlete
sends us a A very similar, it's like going back to 1996 or 1990,
seven, 1998 where we were like showing videos of us striking
people out, handing that to colleges and being like C.
And we still have athletes that are like, I was going to send
this to coaches. I'm going to post this on on X.
(43:48):
Is that good? I'm like, no, it is not good.
The reason it is not good is because we have no idea the the
kids that are swinging at those pitches are probably just
average high schoolers. If that pitch is 6263 with like
a little bit of horizontal run or just like happens to have
just like it's bullet and so that bullets hard at the high
(44:10):
school level, you know, but it'ssomething that's really not like
that tells us nothing. It tells us nothing.
So stop sending videos of yourself striking random people
out with pitches that show nothing.
It's just like, I think things like that are so frustrating to
me. I'm like, Oh my God, this is
what I did 800 years ago when I was trying to get recruited.
That's how like the needle has moved very far in our game.
(44:33):
But at the youth level, it freaking hasn't.
It hasn't. And I think it's just like, I
don't know, it's been a tough world.
I think to see the youth world operating in some dinosaur age
travel ball dictating who you play for, what's good and what's
not. And I'm not saying that
especially some of the like big time organizations, they have
experience working with athletesthat then scale.
(44:55):
They know what it looks like. There's this top tier of
athlete. We're just using our eyes,
telling coaches it's probably always going to work for.
Right, their exceptions, their exceptions, not the rule.
Top 3% of athletes we're like coaches are like she's got
something you want her but there's a giant pool out there
of athletes that have some like unique tools things that don't
(45:18):
look typical and that we now know scale we.
I have to tell, I have to tell the Lawrence Gurman story and
then we're going to wrap it up because it's just so good.
And so like by me using her name.
But I think it, this is this, this can and should happen more.
I mean, I, I do think, you know,Lauren is a pitcher at Iowa
(45:40):
State right now and played on myteam and trained with us when I
was still coaching and wasn't like the recruiting process just
it wasn't going her way. And I, you know, she came to us
with not, you know, her ball fight changed significantly
while she was with us and and started to get really outlier
with what she was able to throw.We don't have to get into all of
(46:00):
that or what she threw, but it just didn't always show in
trouble ball. Now when once you this is not
taking credit, but I knew what her ball fight was.
So once she played for me, I will say it did show she struck
out a lot of people and we were able to throw the pitches that
were the outlier pitches in thathad success.
But up until that point, she didn't play for me until her
senior year. They were throwing the wrong
pitches. They weren't sort of doing that.
(46:22):
She's not. Throwing whatever he throws
curves right. She's not a.
She's not a very high V low kid.Like she's not over the top V
low. And so like that's not, no one's
going to hold up their pocket radar and be like, Yep, sign
this kid up. And we were frustrated because
we were like, I just, I mean, I remember being just like, I
would send people like 2 performance summaries like
nasty, like which of these kids would you want?
(46:43):
And one of them was like, currently in the, you know, a
Power 4 school and what was her?And they were like, well,
obviously her. I'm like, yeah, no kidding.
What is happening? You know, so frustrated about it
because it was like she didn't fit the mole for what people
wanted at the time. And then this was a few years
ago before we really had access to that.
And so eventually, kudos to IowaState is that they they lost a
kid with a similar pitch profileto her because they did use
(47:05):
data. And so they recruited her not
having seen her like she did not.
They never came to watch her play before they signed her.
They just went purely on data. Now, I will say, and I've said
this to everyone that Lauren is also like a very big gamer.
So in addition to the data, you know, if she backs it up, she
(47:25):
shows up for games and competes her ass off and is a gamer too.
So that's great that she was able to do that.
She's a major contributor there now.
And it's purely based off the pitch profile works at that
level. And and that is that can happen.
And I think, you know, to me, asyou're describing this pool, she
was in the sort of like giant pool about their kids.
And often because they just called random pitches that for
(47:48):
her weren't her exceptional pitches.
It was like 62 and a ball outside like, Nah, you know,
like this is not what I'm going for.
And and until we were able to really lead with the data and
say, like, this is actually the pitch that she throws that is
off the chart. This is the.
Show to compare with that and show it time and time again.
It's not just, you know, actually rough.
(48:09):
It's not just a snapshot. These are stable tools.
It's not just one piece in time.And what's so powerful about
this is that at that time, Krista, when you were like
asking coaches, it's because coaches were also not informed
in in game data. The exciting thing now and the
reason why I'm like, let's just blow up the recruiting space in
a positive way, meaning blow up what's not.
(48:29):
What's archaic about it is because college coaches are now
they're able to see the in game data, more schools have access
to it. Obviously we're showing it more.
Schools are starting to understand it more.
So now they get it way more thanthey did 345 years ago,
obviously. So now they get it.
They know what they should be looking for, but the athletes
(48:50):
are in the dark. They don't get it.
The coaches don't get it. You know, their parents don't
get it. That's the gap that I think I'm
trying to identify. And so it's like we're at this
place now where the college coaches are are in a much
different world now. We need to connect that to the
athletes so that that they can see whether or not they're on
path for a. Conclusion The thing with Warren
(49:11):
should happen all the time. Absolutely.
The fact that I have to go back a few years for that story and
it was a fluke. It was a fluke.
You know, I mean, in all honesty, like there was we, you
know, like all the stars alignedfor that sort of story to
happen. But that should be happening all
the time. The College.
Know that it can now. Yeah, it can.
The high schoolers can. They can see their data, they
(49:31):
could train to the data, they can understand it and we can all
speak the same language. And so we're excited for HQ to
keep like pushing that forward that, you know, the top of our
game, that our athletes, we all have access to this.
Our coaches are being held accountable from a training
standpoint of what we're doing. And that's really the world.
We can close this loop and gap and everyone can be informed and
making those decisions. So we chat a lot.
(49:52):
I'm going to wrap this up OK, But this is and this.
Is a good one your data. This is like game changing.
Big. Powerful piece to the next
evolution of our game. Yeah, so excited to see where it
continues to grow. I'm sure we'll talk about where
it continues to grow as it grows.
Yep, on here, I'm sure. And until next time, like and
subscribe the OGX podcast anywhere you listen to your
(50:14):
podcast. It helps us and we appreciate
it.
Back for another episode with all of us.
And today we are going to talk about our HQ platform and
specifically kind of what we've been building to do some data
visualization, how we got there,the importance of it, everything
around it. So I'll maybe just give a little
back story and then I'll kick itto some to you guys to talk a
(00:25):
little bit about your experience.
But we have been working internally for a long time now.
It's just sort of like slow and steady to make sure that all of
the sort of information that we gather on our athletes gets out
in a way that is digestible and actionable for our coaches.
That's kind of how it always started internally for us is
(00:46):
that we had a way to bring that in and make it actionable for
our coaches. And HQ platform, which is a
web-based platform to get that information out now gives our
athletes access to that same information and sort of make
sure that everyone has the same information in front of them
and, and see sort of their journey.
I think this is like really important for us.
(01:06):
It's taken us a long time of sort of ciphering through all of
the different things that we input.
I remember there were times where we had, I don't know, I
think athletes almost felt like Guinea pigs with us because we
were just always inputting, you know, our sort of like gathering
information and, and then they never maybe saw it or we would
(01:28):
randomly tell them about it or randomly get and put it.
And so this really closes that loophole and, and holds us
accountable. And, and we've been working
through this for a few years of if we're going to gather
information, it doesn't mean every single thing needs to sort
of come out in this report, because sometimes you're just
seeing what matters and, and that type of thing.
But that for the most part, anytime we're asking athletes to
(01:50):
do things, movement assessments,things like that, that they see
the results and they see how that that impacts sort of their
side of things. So that's really where this came
from. It's been quite a work in
progress. So we're really excited about
it. And all of our athletes now,
right now have access to it. The college athletes or the
(02:10):
college coaches and that we workwith have had access to this
platform for a while and have been good testers for us of
what's important. And so, yeah, we're just really
excited about it. And they kind of want to talk
through the importance, I think,of putting this in the hands of
athletes and coaches and that type of thing.
So Ash, I think I'm going to kick it to you because you've
obviously had your hands in H Cube for a while because so the
(02:33):
first people that were really using it in addition to our
internal coaches were, you know,your work with a lot of the
colleges. So maybe walk through what that
experience has been like, how you guys kind of deciphered what
you were looking at, what was important, their experience with
it? How has that been?
Yeah, I'm, I'm going to start with like a little intro before
(02:53):
I get into the college work specifically.
But I was going to say that I think HQ is just, it's such a
good representation of the growth and a journey of OGX.
Like we started with, let's invest in buff light monitors
and sensors and what the hell isthis even telling us?
But let's just gather it and let's try to look at it on like
(03:17):
paper and figure it all out. And then we revolved into this
like, OK, let's build. And Laura, you'll get into this,
but like a homemade Sheets version of some data
visualization. I don't mean to undermine it.
It's a very robust system. It's just build off of some
like, you know, Laura's Quarantine project 2020 of
(03:40):
building data visualization, youknow, models through Google
Sheets. And so we used that for years
and that was huge for us. We were not only we were out of
the world of just collecting data and on the spot being able
to look at it. Now we were taking the data,
putting it into a system and thesystem was showing us, you know,
(04:00):
being able to compare day by day, week by week, month by
month, looking at trends. No longer am I going up in
velocity or am I not? Like we literally can see it.
That was so powerful. So we basically built the
backbone of everything, trainingand programming off of that.
Like, OK, now we can see where an athlete, what they're
actually doing. OK, if I train her this way, if
(04:23):
I program her this way, if we goafter this, is it working?
So this like feedback loop emerged, which is the center of
who OGX is. It's using data as feedback of
whether or not we're on the right track, which is not the
traditional like instructor model.
(04:43):
Like we're not the traditional instructor model, but you go to
lessons that instructor claims to be, you know, the Messiah of
pitching, I guess, right. And to know this is my way of
doing it. This is the way of doing it.
In 1994, I had an athlete that pitched wherever.
And so now I'm the Messiah of pitching.
That's kind of offensive, but that's basically the extreme
(05:05):
version of what we see in the like instructor world.
Obviously not every instructor is like that, but, and now
they're just like, do as I say and you'll get, you'll be good,
right? And So what OGX is based on is
like, we're going to collect a shit ton of data from a body
standpoint, from a ball flight standpoint, health and Wellness
standpoint, movement quality, power.
And then we're going to really make an educated guess, a highly
(05:28):
educated guess on how to train based on what we're trying to
achieve goal wise. And then we're going to see if
it's working. We're going to use that system
to like measure it and see if it's working.
That has been the center of who we are.
And so I think that the next phase of this, like we've been
operating into like we're based in data.
Everything we do is based in data.
And that's true. But until we turn our screen
(05:52):
around and show it to our athletes, it really was only
facing us, which has been driving us freaking insane the
last whatever, however many years.
So when you say Crystal like this has been a really long
journey, it's because it's been like driving us nuts.
We want our athletes to see everything, not just when the
trends are good, not just when the trend is from last month to
(06:12):
this month, but everything that we collect here because that is
full transparency. That is what we want our
athletes have access to. But the process of building that
platform we joked about like we you read like an ex post or
something from a business owner that was like, I'm currently
three years into a 5 month like update or something.
(06:33):
And we laughed at the time and like, yeah, we're still
laughing. But actually it's not funny at
all because that has been the process.
It's like, you know, it's so slow, but it's so powerful.
So now we're at this point wherethe athletes, if we collect it,
you can see it. You can take a look at your
trends, you can look at your comparisons, you can take a look
(06:54):
at all the data that's being collected.
You can see it all. That means you're holding
yourself accountable. You're holding the program
you're under and your coaches accountable.
You're able to share with college coaches from a
recruiting standpoint. If you're at the youth level,
we'll get into it, but there's just so incredibly powerful.
So at the college front, that's exactly how we're utilizing it,
right? We are utilizing it early in the
(07:17):
year to see we're setting these goals.
OK, let's assess them early in the year.
What do we want to really go after with this pitcher between
now and to preseason? Are we getting there?
Are we on the right track? Let's watch.
And this that is like almost apples to apples about how we
use it at the high school level from an OGX standpoint.
Now that we've been in season inthe college level, it's a little
(07:40):
different. It's very in game management.
We haven't gotten there yet withour high school athletes because
they don't typically have accessto data as frequently and we're
not, you know, our athletes are just getting HQ access now and
when they're in high school season.
So it'll be interesting is we can help them with that a little
bit more. But in the college front, you
know, a lot of the schools we work with, they had in game
(08:02):
data. So our, you know, our platform,
we've worked for it to be able to take in all sorts of, you
know, from a lot of different text sources.
It's not just oh, you can only put in if it's Rep Sodo, it
could only do it if it's track man or yak and tech it it'll
take in all of those. And we're seeing the in game
data. We are able sometimes to look OK
(08:22):
on the right. Here's the end game on the left.
Here is the here's what we're seeing from practice earlier
that day in the bullpen the day before or whatever that looks
like. Are the issues we're having
command based? Are they that this athlete is
throwing, you know, 64 in training and 69 in the game and
(08:45):
with that, that down pitch is disappearing.
You know, like we're now able toreally see those types of
things. We're watching trends, we're
watching velocity trends, we're watching spin rate trends, and
we're advising throughout the week workload.
Should we give that athlete an extra day off?
Should we really be focused on going after sync here?
(09:06):
How do we prep her at a higher intensity since Vilo's going up
so high in game? It's informing our consulting
decisions on an A weekly basis. It's incredibly powerful just
for the coaches to be able to see like we're on the same side
of the table. This isn't like they're telling
me and they're just like if I'm in a consulting call, they're
(09:28):
taking my advice and like hopingfor the best.
This is like we're sitting down together, We're looking at the
data together, We're coming up with what needs to be different
for the following week and beyond, talking about a plan and
then sitting back together like are we on the right track?
It's like so much more of a collaborative environment
because of what HQ provides. So it's been an incredibly
(09:51):
powerful tool. Are there things like you get an
error early on, like an error message when we click on an
athlete's name? I'm like, damn it, you know, so
that I'm like right in Laura andshout out as I'm sure we'll give
a ton of shout outs during this episode to Ryan, who is our
developer, who probably who thisday I've never met, probably is
like a terrified of me because I'm like immediately wherever
(10:11):
you are fits the following, you know, because there's like all
those bugs we were working through early on.
It's obviously in a really good place now, which is why it's at
a place where we're like turningit around to our athletes.
And I can't even imagine going into consulting this college
season without it. I mean, you think my notebook
looks crazy now keeping track ofall this stuff because, you
(10:32):
know, I'm like a beautiful mind scribbling like nuts.
And without a you Can you imagine?
I would literally be like tapingthings all over my.
I was thinking, I was thinking you're talking, I think some of
the important things about why we needed to build it the way we
did and and why maybe some of the like existing platforms
don't just kind of work as is. Because obviously there's data
(10:55):
visualization that comes along with a lot of the tech.
And I think one of the things that we realized was it's both
not going to work for coaches and also probably isn't a fair
ask. Ask for the tech companies to
also be the people who are determining what is important,
(11:17):
what should visualize, what are the things that are going to
influence decision making. And so we couldn't ever use
those data visualization things alone.
And it's taken US time to kind of figure out.
And it's evolving because, yeah,as we are able to import this
data to build a platform like this, we have to make all of our
data speak to each other. And as now our data, you know,
(11:39):
anything we input from an an athlete, whether it's, you know,
Wellness questionnaires or movement assessments or body
weight, you know, ball flight, all of that stuff, as it starts
to speak together, we can keep refining the questions we're
asking of it. How we're having to influence
data. The in game data piece is huge.
We haven't had in game data on this sort of like level before,
(12:02):
just the game hasn't. And and certainly we haven't.
And so we can start to kind of ask different questions and make
decisions about that. And so, you know, our coaches
use it to influence training, which then helps us to ask what
matters in training, ask what matters in game.
You know, start to to see those things and to keep refining what
(12:23):
is being shown. Why is it being shown?
Why does it matter? You know, I think are hitting
performance expert has gone through a few iterations and
probably has like 1 more. And even that it's like when
athletes ask, hey, you know, what does this mean?
And if we can't answer it and tie it directly to like their
performance, then it either doesn't belong there.
(12:44):
We haven't refined it enough. And so that's really how we've
been working on that. But I think that's what's been
so important because I think up until this point, our game just
tried to rely on whatever dashboard that tech created and
that tech company created. And a lot of times that just it
wasn't thoughtful. And again, this isn't really,
we're all for knocking people that don't invest enough in the
(13:06):
game, but this isn't really a knock because I just don't.
Why would the product developer know what matters for training
like that? You know, there is a level
that's just not a real ask and so you can't use the data like
that. And so we all kind of ran into
where it was like, well, you know, we saw a lot of things
people were just using on the spot too much or they just
(13:28):
weren't using data in the way that is really going to help
performance. And so that to me is the most
exciting thing in addition to which we already referenced.
We keep saying giving it back toathletes, but yes, a lot of them
have access like if they have used that tech, they can log in
in some way. But what we've seen about the
way that the tech companies kindof developed the data and
different platforms do that is that it's not actually the
(13:49):
athletes. And so a lot of times I go to
camps or something with data, I go to like different places and
I get all this data. And there's no sort of like
singular place that I, it's not that even like own the data, but
that I can access my data in a way that makes sense for me.
And so that's been really important to us.
(14:09):
Again, it's kind of through our sources and it's not like people
get access to it forever. But when they're in our systems
that they just, they get it, it's theirs, they get to see it.
We're not withholding anything, we're not hiding anything.
We're not pulling back what theycan see.
We're not sort of doing any of that.
But it's really like anything inputted for that athlete goes
right back out to the athlete. And that's been important to us
to kind of, you know, visualize the data in those ways because
(14:33):
we hadn't always seen that. And it felt a little bit like we
were kind of in that ball game when we weren't doing that of
like, you're getting better, trust me.
Like, you know, here, here's what I see, but you don't.
And that they're able to kind ofsee that and ask questions for
it about it now has really been an important journey for us.
Did I miss anything, Laura? Did we miss anything from sort
of like your standpoint of kind of data input, data
(14:56):
visualization from the standpoint of like, you know,
the scientists on staff here? I think.
The. The accuracy of being three
years into a 5 month app launch is so accurate, so accurate.
You know, this our systems, I think are it's so important, I
think to keep in mind that like.This.
(15:18):
Athlete management system, whichit primarily is for us
internally and part of that athlete management is data
visualization. It's being able to put that
information back in front of thecoaches, internal or external is
really irrelevant, but back to the coaches and then the
athletes of course, where it canbecome actionable.
And I think it's so important. We didn't start, we didn't start
with the data visualization. We started with the
(15:40):
fragmentation of all of this technology and built the systems
kind of piece by piece around it.
So then when it came time and you know, actually referenced
that we built this all in Sheetsand, and there was a lot of
limitations to that. You know, I am sometimes I feel
cursed and blessed to have gone through that, but I learned a
ton just about what it means to scale systems like this.
(16:03):
And obviously, you know, Google Sheets have its limitations as,
as it, as it does as a visualization platform, but it
served us so well for so long because it didn't really cost us
much, right? We just really used and leverage
the systems that we had internally based on what we
already had access to, built thethe training platform, built the
principles, the theories, the applications, and then said, all
(16:26):
right, here is our, our second round of this.
Let's go into this athlete management concept system
visualization, we're going to call it with a different and
updated lens because we had experience being in Sheets.
And I I still remember some of the transitions just in talking
with Ryan of things that we could never do in Sheets just
simply because of its limitations that now are so much
(16:48):
more scalable and, and availableto us.
Something as simple as, you know, there are are places where
our coaches and our admin can basically go in and, you know,
take a custom pitch for a particular pitcher that Rap Soto
calls custom 1 and relabel that a custom pitch for her and those
types of things that make the data so personal to them.
It's not just like, you know, oh, here's this picture over
(17:09):
here that is no, a label. It's not relevant to me.
It can be so unique and individual to them and that lens
that we kind of came through of this, like we need to have it
facing the athletes. It's why it's taken as long as
it has. You know, we've had bits and
pieces of it, but the scalability of deploying sheets
based dashboards to 100 plus athletes is a lot and
(17:29):
unsustainable and you know, rifewith errors and issues.
But the the giving it back to the athlete has taken us a long
time because we have had to really internally work.
This has pushed our business practices, it's pushed our
training protocols. It's forced us into in a very
positive way. It's forced us into me being
very clear about some of the logic of our decisions because
(17:51):
it's got to get programmed right.
It's got to go into A10 concept,so to speak.
And so it's helped us, I think to kind of get out of this, you
know, yes, we still absolutely make changes and evolve our
processes and do tons of qualityimprovement.
But we're at this point now withthe training systems and seeing
with our eyes the impact that we're making that now we can
(18:12):
start to pair. Okay, here's how we show that
and show that story. And it's helped us, like I said,
refine our business practices, refine our athlete management
systems and how we, you know, want to approach this data.
It's been amazing to support ourcoaches in in their data
literacy and starting to understand, like when you see
this stuff in the wild, because it's going to start coming for a
(18:33):
sport here, you know, more frequently than it has, you want
to be educated. You want to make sure that
you're, you know, you're going into that with a good lens.
So then working with it every day.
It just it separates them so much as coaches being able to
make those decisions. And obviously the power for our,
you know, our college partners, particularly right now that, you
know, that information is at their fingertips and it's it's
all in one place. And I think that's the most
(18:55):
important thing is that it's it's a centralized place that we
are trying to, you know, make sure is literally a headquarters
for our athletes and coaches. Yeah.
I was thinking like we were at aplace where we were like, we
have so much data, you know, we have so much data.
We have all this, this, you know, inputs coming in about
each of our athletes. And again, that ranges from, you
(19:16):
know, objective ball flight monitors to, you know, like I
said, Wellness questionnaires, first periods.
Like there's a, there's a whole host of, of things that we can
draw from. And so in preparation for this,
like what, what Google Sheets and our sort of manual systems
didn't require in the way that we have had to sort of revamp
(19:37):
things and really prep for this was to put the data in a way, in
a relational way where we're able now to ask those things.
And so when we get stopped or stuck, you know, and we've
started as we've been in the system, I think that's been some
of the most exciting parts of it, which, you know, we're just
kind of hitting the surface of. And it's going to continue to,
to grow for us is when we, you know, sometimes we get to places
(19:59):
where we're like, we don't. We don't have the answer like we
would assume based on the level of information we've had up
until this point that this pitcher would be having success
with this pitch or this hitter, you know, would be having
success in the games based on what we're seeing.
And we're able now to dig a little deeper because of the way
that the data is sort of speaking to each other to dig to
(20:20):
see what is the nuances here that maybe we're missing.
Is there something correlating that we haven't considered
before because we weren't able to pull that data in in this way
and that that isn't yet the things that were necessarily
visualizing. But it gives us that point
where, you know, when we're hitting a roadblock with our
athletes, we're able to dig a little deeper and not just in
(20:42):
sort of the the small amounts ofdata that we had, you know,
deemed relevant, I guess up until that point.
But really like the whole systemof inputs that are coming in to
start to ask some some bigger questions on that.
And so I'm really excited for that because I think, you know,
I think now at this point, like two pitch talks in a row of like
the in game data, the in game data.
(21:02):
And I think it it's like in gamedata mixed with this data being
in a place that is relational, that you can ask a bunch of
questions that, you know, does this break, you know, get this
result, does this wait, get these belows, does this, you
know, like whatever those questions are you're asking.
And I think that's really exciting because it just hasn't
(21:24):
been done before in that way. And so I think, you know, we're
going to be able to continue to grow and iterate on our
protocols like so much faster and so much better by having
access to that. I feel like you're getting ready
to jump in there, Ash. I was just thinking about like,
you know, I think outside of thethe consulting piece, which is
(21:45):
huge, which is not just limited to the college side.
Like I mean facilities who have this, travel organizations who
have this, which we have had a few who entered into our like
beta phase to do this. Their ability to now do like
consulting and make decisions oftheir athletes based on someone
helping them understand that data.
There's no doubt about it. This is huge for that, but I
(22:06):
would argue that the number one thing that we just haven't
gotten to yet, we're just now like starting to give our own
internal OGX athletes access to this.
The number one thing that I like, can't wait to see how it
like influence. I'm saying influences because
that's professional. But what I want to say is like
blows the hell up, you know, is this like recruiting world?
(22:28):
So that has just, I think been really challenging for me
individually this year is spending the majority of the
time in college, in game data, seeing what works, what doesn't
at all different levels, workingwith the coaches to help their
athletes get the, you know, achieve things XYZ.
(22:52):
And then switching over into thehigh school world where I'm
like, is this the same sport? It is a huge disconnect often
times outside of when I feel like OG athletes are being
advised by us in OGX because we're operating under this
(23:14):
umbrella of being educated ourselves by the in game data.
The in game college data is whatfilters down for us to know what
then an athlete should be striving for when she's 12
versus 14 versus 16 versus 18, right?
So it influences the training journey and how you're advising
(23:34):
goals. It influences if an athlete is
like someone that matches the standards of your program
recruiting wise. Like it's just it's everything.
And So what it often feels like is that the college game, at
least in the bubble of OGX and our college.
Partners. Is shifting so much into being
(23:55):
educated around data. The in game data is now
dictating to us where we need togo, what works, what doesn't,
what's good, what's not. And then over on the high school
level, it is still feels so archaic.
It feels like not, it feels likeit is.
It's just based on whether or not you're getting results in
(24:18):
your travel ball tournaments, which is an absolute circus.
It's just athletes who are exhausted, just flailing, doing
whatever. And so that you know, that might
be a little bit harsh, but it, it is not a great show of like,
if you have success in travel ball, you're definitely going to
scale up the college level. It's not that simple.
And so we just base results off of, of that.
(24:41):
And then at the instructional level, we just are like, whether
or not our instructor like tellsus that's good.
And then we're hiring agents to help us with recruiting who
don't know anything maybe about the data of that level.
That's what Staffs look like, you know, like they don't maybe
know that that staff is really just looking for a drop ball
(25:03):
pitcher. Is your athlete actually a drop
ball pitcher? Not does she have a pitch she
calls a drop? Does she legitimately have a
drop ball that would scale at that level?
So it's like this combination ofjust taking instructors,
subjective thoughts on if you'regetting better, you're not
paired with the results of travel ball, paired with like
(25:24):
recruiting services. It's just a mess.
And I can't wait for us. And I think it'll take a little
bit of time, but for us to startinfluencing this world and
starting to connect, use data toconnect the youth level to
reality. And that that's really the path
to getting into college and playing at that level.
(25:47):
Yeah, I think at the end of the day, it's there's not enough
people right now at the youth level actually in systems that
are designed to get them better.Like it's just, and it's all
about the, it's just about grind.
It's about I work hard. I put in the time, I put in the
hours. Like look how hard I'm working,
Look at all the extra work I do.Look at the all the lessons I go
(26:08):
to, look at all the practices I go to.
Look at this 5:00 AM workout I did like it's.
Just the money my parents and the money I spend.
It's just. Input, input, input, input.
And I think that it's so it's just so random.
And each of those inputs and listen, we're a business.
So like, you know, there's no shame in this.
(26:29):
This is not like, and, and we'rea nonprofit where no one pays to
work with us. So I mean, and we're all
businesses here, but each of those inputs is a separate
business and they have their separate incentives for keeping
you in that loop. And it's not like everyone's out
there maliciously. I mean, there certainly are
people doing that, but it's thisis not like, and we're all out
(26:49):
here malicious, actually trying to take advantage of kids, but
sort of unknowingly we think like, yeah, this kid works so
hard, they're going to keep getting better.
And there's never been a measurement of what that even
means except that I continue to play the game, you know, and I
continue to walk on the travel ball stage and have some level
(27:11):
of success or do OK. And then, you know, I think what
often happens and what we see onthe flip side of that is in the
sort of worst case scenario, they then don't get, it's early
and they don't get recruited theway they thought they were going
to do. They get hurt.
You know, there's there's something that happens to them
in that high school journey thatreally surprises them because
they felt like up until that point I was doing all the things
(27:34):
I was supposed to be to, to go on that path.
Or. It doesn't even, you know, for a
variety of reasons. They do get recruited.
They have enough to get over that hump.
It's a college program that doesn't know about data or what
works or whatever. And they get to the other side
and they are surprised by the sort of lack of success they
have or, or whatever gets, you know, on the other side.
(27:56):
And I think there's just such a a better way to approach that to
make sure that you're actually developing in a way that makes
sense that you're going after things that are going to
actually input, you know, what you are able to do.
And we can tie that gap so much closer now because we have in
game data at these levels. Like it's just this is not, you
(28:16):
know, we're still making some inferences because most of the
in game data right now is reallytargeted at the highest level of
our collegiate game. And so of course, as you're
thinking about what does that look like at each of the tiers
and things like what's going to have success, there's some
nuance to that. We're where we're going to have
to make inferences, but we have such a tighter gap of that.
It's just not like it's not a question mark.
So this idea of like I just am cranking, I'm I'm going for it.
(28:40):
I one time on the pocket radar popped 70.
So shouldn't I be getting recruited and having success at
this level? Like all of those things that
you think you're doing, you can just relieve some of that as you
really start to understand your story and you track it along the
way that's tied to something that relates to the scale you're
trying to go AT and that you're able to keep doing that.
(29:01):
And I think that's really what we are built on and what we've
been trying to do and really putthat information into the hands
of the athletes and their and their families.
And I think that's what's so important.
But we see it all the time. It's just, you know, it's hard
not to just get into that cycle when you're in at the youth game
because that's what you think success is.
(29:23):
And a lot of times the kids thatare grinding in that way are,
are the ones who are having success on the field because
they're the ones tough or resilient enough, I guess.
And so survivorship bias way. Yeah, right.
Survivorship bias, right? Yeah, I'm thinking about.
Some of the like, like the categories of athletes that we
(29:45):
see at the youth level. And a big category is the like,
I work so hard, but I'm not getting recruited.
And then they come for an assessment, they get access to
see what they're and we can say to them like this is where this
stacks up. And it's often times it's just
average. And so the reason why you're not
really getting recruited, it's often.
Times it's like everyone keeps pitching it's like and this is
(30:06):
same for hitting. To be honest, I don't think
these are different. It's slightly above average
below. And so when you were 14, yeah,
everyone was like you throw 63 or you hit the ball 70 mph.
Like we've talked about this before, the like 70s kind of as
people are starting to get comfortable with exit below,
(30:28):
people reference as 7 like you like.
And so in their minds, one, theywere, they, they are because of
that getting recruited by at that stage, they're getting
followed by high because that isabove average at that stage.
And then it's like, but then that's it.
And one, you can't just assume that's going to keep going up.
And two, how you've got access to that because of this like
(30:51):
grind mentality and all of thosethings like for sustaining often
you don't have any brake. You know, there's a bunch of
things that come with that. You don't brake, you might not
have change of speeds and hitting it's similar.
You don't have adjustability with that.
And so it's like it doesn't one,it's not going to be that easy
for us to keep getting your exitbelow to go up unless now we are
thoughtful about it. And then two, sure, if you run
(31:13):
into a ball, you're going to hitit really hard, but like you, I
could throw you in 42 spots and you wouldn't touch it.
So that's not going to work. And so there's these, you know,
I think that happens all the time when you say they come and
they're like not it's not like people that come to us and they
have extremely low velocity or something like that are like, I
really thought it's, you know, it's the large majority.
(31:36):
And I think you have pitchers that come to us throw high 50s
into, you know, 64. They throw in that range.
And so they if they threw that at 14, they're like, I'm right,
yeah, throw at 64. I'm smoking percent.
And a lot of times in those conversations, I'll say to them,
like if you were, if I was in the admissions director at
Harvard and you were like, I have a 24ACT.
(32:01):
I don't even know if they do ACTanymore, but like there's an
example, right? And like my GPA is the 3.1 that
is maybe like, you know, nothingwrong with that, but you can't
get into Harvard. And everyone's like, yeah,
obviously everyone knows there'slike these qualifications.
Now, of course, once you get in,once you hit that threshold, how
(32:21):
hard you work, how much of A competitor you are, like all
these things are going to weigh into your success when you get
there. But there are thresholds because
now that we have the data, this isn't like anyone who works hard
has success at these levels. It's very clear.
Objectively, the combinations ofit's not just if you have high V
low, we see low V low successfulwhen the break profile looks
(32:43):
like X&Y, when you're right-handed versus left-handed,
your change of speed does this versus that your break profile,
it looks like this and that there's a combination of all of
these factors, but they're there.
We're not seeing it's very like mediocre, you know, like, you
know, we're not seeing examples of that where it's just like
very average data and we're likethis should not be scaling.
(33:03):
And it is it might have that picture, might have a good game,
they might have a good outing, you know, but but to like this
ongoing success. And so everyone seems to get
those conversations when we're talking about like the academic
and we're like, it's the same. The challenge is that you don't
have access to like those thresholds and that criteria at
the youth world. So what you think it looks like
(33:24):
to actually get recruited acrossthat threshold is to show up at
camp and hustle hard. You know, like hustle, have a
good attitude. I'm not.
Saying, well, you don't have access to those.
You don't have access to those thresholds, right?
Many, many programs didn't have access to that thresholds, and
still don't. And they.
Don't or they don't. We hadn't around stuff like
that, right? Right.
(33:45):
And we and we hadn't trains based on the influence of data.
And so I also think what the what the game has had to rely on
leading up until these past few years is, as you said, like, OK,
I have the 14 year old who throws 64 is the assumption that
that kid is going to be on the linear path into my program.
(34:06):
And so I'm going to by the time I get her her freshman year of
college, she's going to throw 6870.
And that's what scales. And as we've been able to have
this really, you know, gathered so much more information about
the bio mechanics behind hittingand pitching, the how different
programs influence, you know, wecould see what works.
(34:27):
And the data is, you know, what is leading the show for us of is
it working or not? You know, performance data
specifically is leading the showof if it's working or not.
Then we can also now the collegecoaches even have a more in the
in the highest level of college coach that has access to this
information has it even more sort of like there's a, you
(34:48):
know, difference between what I have as a high school and what I
have as college. Again, this is not every college
program of understanding of like, yeah, you're not going to
scale like this. And so you can't come to me
versus all of a sudden a kid where you're like, wait, how'd
that kid that kid was, you know,under me and performance until
this point. But now that kid gets recruited
(35:09):
because we have this better ideaof what are the things that we
can influence? Is the person in the right
system for us to be able to influence it?
Are they going to keep developing?
And that is is why it's important for it to be in the
athletes hands too. Is like, let's not just know the
thresholds, but how can I influence where I'm sitting
realistically, not just like some random go put a bunch of
(35:30):
work in and and hope it sticks. Christy, you mentioned like, you
know, the, the bio mechanics data that were, you know,
obviously that's one of our major databases and different
forms and different types of collection.
And, and I, let me preface this,I'm just as guilty as it is very
much a, it's a research thing towant to group people together
(35:50):
and makes it easy to compare. It makes it, you know, easy to
draw conclusions ideally. And so this, this concept, when
I see, you know, like, you know,normative values or here's
everyone you know, here's ball flight ranges and it's broken up
by playing level. I just think like, no, no, no,
no, no, we've missed the boat because there is a we can no
longer assume and we should not assume that the, the, you know,
(36:15):
projection of high school hitting or pitching patterns
improves organically to the college level that somehow the
college level is this comparisonideally pro, of course, but that
we should be comparing between playing levels.
I mean, with, with we've been, you know, revamping our reports
and, and with our improved technology.
(36:36):
Like it's not about playing level.
Let's be really clear here, right?
You when you're bucketing, particularly bio mechanics
patterns, you're looking at things like what velocity bucket
does she fit in? There's something she's doing
that is unique to her that allows her to throw 65 plus.
What does she look like comparedto someone else who throws 65
plus? That then helps us to understand
(36:56):
the next Velo tier below that. What does this person not have
because she's not throwing 65 plus?
Or is that just what her Max is for how she's built?
And so going by playing level. And I see these like comparisons
of like, you know, 14 years, 16,just like they're garbage.
And then comparing high school to college is even worse.
And look, and I'm just as guiltyof it.
(37:17):
There are published, you know, pieces of information with my
name on it comparing playing levels.
But in this in this scenario where we have this data now, you
can't, you cannot just assume that high school to college
players get better. You can't assume that anymore.
And so when I think of it from the coaching lens too, I think
of what you just said, Krista, it's like all of a sudden, you
(37:39):
know, she taps into some, she's a hidden gem.
And it's like if you just make the assumption everyone gets
better, you're going to be really disappointed.
Because the reality is they don't.
They don't. Yeah.
Well, and it's I was going to say, and that's really specific
to bio mechanics and we see thisa lot.
I actually think we just saw this last week, I saw this with
some of the, the hitting assessments we did down in Tampa
(38:01):
is that the, the, the patterns look exactly the same sometimes
as a 22 year old, like there's no difference between their
patterns and it's, it's actuallynot their patterns.
So this what's holding them back.
It's that when we measure their strength, it's look, it's very
low because they are young. And so there is like, OK, there
(38:21):
it, there might be some linear path we can take for you to hit
the ball harder or throw the ball faster.
That's just related to whatever mass you might need to put on
some, you know, power numbers need to go up.
We need to fuel the fire of those patterns, you know, and
that happens very often. And there is often, you know,
the an older player who has really poor patterns and that's
(38:45):
blocking them. And it's just this the the
pattern specific data is, is really unique to that.
And you have to have which is again, all back to the point of
why you want your data to speak to each other is like you can't.
You know, we've said this beforeand we were guilty of this
before our data was in this and we're still putting more data
sources and still getting betterat this.
But like, you know, you look at some just like I collect a bunch
(39:09):
of bio mechanics data. It's not in visualization, it's
not speaking to each other. It's just line, you know,
numbers on the line. And all of a sudden I see like
this kid throw 70 and there's one number on that line that
looks different than everyone else's.
And I'm like, that's it, you know, that's the thing, you
know, or is that someone's doingbad and you see something that
seems out of range and you go after that.
(39:30):
And it's like, that can be really, that's really where data
did live for a long time, even for us.
And that can be so dangerous because that might have, that
might be different than someone else's, but that might have no
correlation to the thing you're chasing.
And so the putting the data intoa database where it's constantly
speaking to each other and you can and really ask like, does
that matter? Does that matter to whatever
(39:52):
you're trying to influence is soimportant?
Because then data, if you don't do that, data becomes exactly
what we're talking about. It's just like, you know, you're
just throwing things at grab this data number, grab this data
number and I'm going to go chaseyour arm didn't move as fast as
that kid. And so obviously that's the
thing. It's like, is it that the thing
does that correlate to what we're trying to go after?
(40:13):
And you really, you know, you have to have your data in the
system like this to be able to ask those questions.
And it's why it's so important, because.
Then, and I think we, we see this often and, and probably
we're pretty guilty of it at thebeginning of our journey, then
data, you know, training with data becomes no different than
let's try this random drill thisweek and, and see what happens.
It can. It can really become random if
(40:34):
that's the kind of way you're inputting and outputting the
data, which can get really dangerous.
I think it's important to differentiate the like ball
flight piece from the the bio mechanics piece because I think
from a recruiting standpoint, I think the bio mechanics like the
bio mechanics piece really informs training a lot where it
can inform the recruiting piece.And I think we're just a ways
(40:56):
away from this is understanding like efficiency.
So if you're like she has the ball flight to be my like 3
times through a lineup kid, but her patterns show to be so poor
that her efficiency is so low that I don't know if she can
sustain the workload of an ace even though she has the ball
flight of an ace or I at least have to know that I'm going to
have to manage her. So.
(41:18):
Hard, like really put a lot of energy and capacity.
I think we're a couple years away from that.
We're we're doing that like main, you know, like really
helping coaches understand that we have these direct
relationships with them. But for coaches to just see that
and to know how to do that. I think we're a little bit away
from that. Bio mechanics and and athletes
being able to see that data really informs the like their,
(41:38):
their training and their trends,their their progress.
The recruiting world is kind of X's and O's.
And I think the challenge right now is like, you'll go to a
single camp and just like hand that data and that's just like a
snapshot of a single day. I'm picturing this world where
obviously in HQ you can just youcan click multiple dates, you
see trends over time. You can show a coach, I've been
(42:00):
working on this. Look at my, I'm on the up like
you can just show them that I'm picturing this world where our
athletes, and this is not a likefuturistic world.
This is the world now, now that we've built this platform where
coaches now see in athletes their their story with their
buff light data. They determine if they meet that
threshold and then they can go see them play and see how they
(42:23):
compete, how they handle adversity, what type of athlete,
what kind of a kid they might be, you know, of course, all
those things that also matter. But this idea of just like them
being able to see a snapshot of your like ongoing, not just
data, but progress and evolutionof your data because you're a
kid, you're a child. You are not just going to be
stuck in something. You're not recruiting an athlete
(42:46):
at 16 with like, she has the skills right this minute to
GAIL, you have to see that it's going to keep growing into what
works at your level. And now this is allowing us to
see that. I also just had this snapshot,
took everything not to just likecrack up laughing while one of
you was talking because I was inmy own head thinking about this.
Our VHS tapes when we were recruiting, what we did was we
(43:06):
had like someone with like a Rep.
It was like we're. Grand and yeah.
We we spliced only the good ones, you know what I'm saying?
Like just like to show people swinging and missing.
But the reality is like we have athletes and This is why I'm
like HQ is amazing. I'm using it on a daily basis in
college consulting, but can we please hurry the hell up with
(43:28):
getting it to our athletes? Because if one more athlete
sends us a A very similar, it's like going back to 1996 or 1990,
seven, 1998 where we were like showing videos of us striking
people out, handing that to colleges and being like C.
And we still have athletes that are like, I was going to send
this to coaches. I'm going to post this on on X.
(43:48):
Is that good? I'm like, no, it is not good.
The reason it is not good is because we have no idea the the
kids that are swinging at those pitches are probably just
average high schoolers. If that pitch is 6263 with like
a little bit of horizontal run or just like happens to have
just like it's bullet and so that bullets hard at the high
(44:10):
school level, you know, but it'ssomething that's really not like
that tells us nothing. It tells us nothing.
So stop sending videos of yourself striking random people
out with pitches that show nothing.
It's just like, I think things like that are so frustrating to
me. I'm like, Oh my God, this is
what I did 800 years ago when I was trying to get recruited.
That's how like the needle has moved very far in our game.
(44:33):
But at the youth level, it freaking hasn't.
It hasn't. And I think it's just like, I
don't know, it's been a tough world.
I think to see the youth world operating in some dinosaur age
travel ball dictating who you play for, what's good and what's
not. And I'm not saying that
especially some of the like big time organizations, they have
experience working with athletesthat then scale.
(44:55):
They know what it looks like. There's this top tier of
athlete. We're just using our eyes,
telling coaches it's probably always going to work for.
Right, their exceptions, their exceptions, not the rule.
Top 3% of athletes we're like coaches are like she's got
something you want her but there's a giant pool out there
of athletes that have some like unique tools things that don't
(45:18):
look typical and that we now know scale we.
I have to tell, I have to tell the Lawrence Gurman story and
then we're going to wrap it up because it's just so good.
And so like by me using her name.
But I think it, this is this, this can and should happen more.
I mean, I, I do think, you know,Lauren is a pitcher at Iowa
(45:40):
State right now and played on myteam and trained with us when I
was still coaching and wasn't like the recruiting process just
it wasn't going her way. And I, you know, she came to us
with not, you know, her ball fight changed significantly
while she was with us and and started to get really outlier
with what she was able to throw.We don't have to get into all of
(46:00):
that or what she threw, but it just didn't always show in
trouble ball. Now when once you this is not
taking credit, but I knew what her ball fight was.
So once she played for me, I will say it did show she struck
out a lot of people and we were able to throw the pitches that
were the outlier pitches in thathad success.
But up until that point, she didn't play for me until her
senior year. They were throwing the wrong
pitches. They weren't sort of doing that.
(46:22):
She's not. Throwing whatever he throws
curves right. She's not a.
She's not a very high V low kid.Like she's not over the top V
low. And so like that's not, no one's
going to hold up their pocket radar and be like, Yep, sign
this kid up. And we were frustrated because
we were like, I just, I mean, I remember being just like, I
would send people like 2 performance summaries like
nasty, like which of these kids would you want?
(46:43):
And one of them was like, currently in the, you know, a
Power 4 school and what was her?And they were like, well,
obviously her. I'm like, yeah, no kidding.
What is happening? You know, so frustrated about it
because it was like she didn't fit the mole for what people
wanted at the time. And then this was a few years
ago before we really had access to that.
And so eventually, kudos to IowaState is that they they lost a
kid with a similar pitch profileto her because they did use
(47:05):
data. And so they recruited her not
having seen her like she did not.
They never came to watch her play before they signed her.
They just went purely on data. Now, I will say, and I've said
this to everyone that Lauren is also like a very big gamer.
So in addition to the data, you know, if she backs it up, she
(47:25):
shows up for games and competes her ass off and is a gamer too.
So that's great that she was able to do that.
She's a major contributor there now.
And it's purely based off the pitch profile works at that
level. And and that is that can happen.
And I think, you know, to me, asyou're describing this pool, she
was in the sort of like giant pool about their kids.
And often because they just called random pitches that for
(47:48):
her weren't her exceptional pitches.
It was like 62 and a ball outside like, Nah, you know,
like this is not what I'm going for.
And and until we were able to really lead with the data and
say, like, this is actually the pitch that she throws that is
off the chart. This is the.
Show to compare with that and show it time and time again.
It's not just, you know, actually rough.
(48:09):
It's not just a snapshot. These are stable tools.
It's not just one piece in time.And what's so powerful about
this is that at that time, Krista, when you were like
asking coaches, it's because coaches were also not informed
in in game data. The exciting thing now and the
reason why I'm like, let's just blow up the recruiting space in
a positive way, meaning blow up what's not.
(48:29):
What's archaic about it is because college coaches are now
they're able to see the in game data, more schools have access
to it. Obviously we're showing it more.
Schools are starting to understand it more.
So now they get it way more thanthey did 345 years ago,
obviously. So now they get it.
They know what they should be looking for, but the athletes
(48:50):
are in the dark. They don't get it.
The coaches don't get it. You know, their parents don't
get it. That's the gap that I think I'm
trying to identify. And so it's like we're at this
place now where the college coaches are are in a much
different world now. We need to connect that to the
athletes so that that they can see whether or not they're on
path for a. Conclusion The thing with Warren
(49:11):
should happen all the time. Absolutely.
The fact that I have to go back a few years for that story and
it was a fluke. It was a fluke.
You know, I mean, in all honesty, like there was we, you
know, like all the stars alignedfor that sort of story to
happen. But that should be happening all
the time. The College.
Know that it can now. Yeah, it can.
The high schoolers can. They can see their data, they
(49:31):
could train to the data, they can understand it and we can all
speak the same language. And so we're excited for HQ to
keep like pushing that forward that, you know, the top of our
game, that our athletes, we all have access to this.
Our coaches are being held accountable from a training
standpoint of what we're doing. And that's really the world.
We can close this loop and gap and everyone can be informed and
making those decisions. So we chat a lot.
(49:52):
I'm going to wrap this up OK, But this is and this.
Is a good one your data. This is like game changing.
Big. Powerful piece to the next
evolution of our game. Yeah, so excited to see where it
continues to grow. I'm sure we'll talk about where
it continues to grow as it grows.
Yep, on here, I'm sure. And until next time, like and
subscribe the OGX podcast anywhere you listen to your
(50:14):
podcast. It helps us and we appreciate
it.
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