June 30, 2025 • 37 mins
In Episode 60, the OGX team reflects on the podcast journey and digs deep into the role of biomechanics and sports science in athlete development. They break down why actionable data matters, the disconnect between coaches and labs, and how substance always outweighs style when it comes to performance metrics.You'll hear insights on the importance of partnerships in data collection, understanding norms, and why academic research often misses the mark in practical coaching. This episode is a must-listen for coaches, trainers, and anyone navigating the performance data landscape.Key Topics:• Actionable insights vs. flashy data• Why norms matter in evaluating athletes• Bridging the gap between research and results• Authenticity in coaching and development#ogxpodcast #sportsperformance #biomechanics #athletedevelopment #datadriven #coachinginsights
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:04):
All right. We are back and it's our 60th
episode since the rebrands. I was like to say that, so like
Asterisk sort of our 60th episode and we just finished
quarterly planning. So kind of like 60th episode
going into Q3. Ashley got home from our flight
at 4:00 AM. So she's alive, alert and ready
to go as always. So I in that sort of thinking
(00:29):
about our 60th episode, my question for you in 60 seconds.
So Ashley, 60 seconds, this is, Yeah. 60 seconds with a slow
brain. Like no shot, but go ahead, OK.
In 60 seconds, when we were liketalking about starting a podcast
in the sort of this format, whatwere your like thoughts on that?
What were you excited about? What did you think about it?
(00:51):
Sure. I mean, honestly, from a from a
like a personal standpoint, because I feel like I deliver
information and I deliver what'sin my brain best verbally.
So I mean, I think what I was most excited about was just like
for myself, just like be who I am.
Just express things the way I want to express them, say things
the way I want to say them. Talk about the complexities of
what we do. When you say to me like, oh,
(01:13):
write a thread on XI had to sit there like with my hands frozen
on the keyboard for 8 hours to produce 4 sentences, you know,
but then you jump on a podcast and they can just roll.
So I think for me, the idea of apodcast has always been
exciting, even going back to theRedefine the Circle days because
it's just personally where I feel like I can just get really
(01:33):
connect my brain to my mouth vest and be able to really share
what we do, what we're thinking about the like holistic nature
of OGX, the totality of the things that we're working on.
And so I love this platform for that reason.
And I just think it's great because I think it's a great
opportunity for people to like hear so much information, so
much meat all in one space and just like do it from their car
(01:57):
or do it just like as we're going out their day.
So I just think the nature of podcasts are great, but for me
personally it just really like fits how I best express myself,
I'd say. All right, Laura, same question.
Good one. I think, I think for me, I was
like, cool, we get the opportunity, like, you know, the
three of us with guests or whatever to like, I don't know,
let the world see some of our like internal conversation and
(02:19):
the way that we talk about the business and the game and, you
know, player development in general.
And I was like, I think, I thinkthat'll be fun, you know,
because obviously I enjoy, you know, our conversations.
And I think the, the like, one of my favorite thing is, is when
someone's like, Oh yeah, I've got it.
Like I've got a road trip and I've got the OGX podcast just
like queued up. I'm like people I think really
(02:40):
enjoy one. I enjoy podcast.
I like the listening component, multitasker.
And so I just like the like diversity of the way we deliver
this information. And I think like it's just, it's
just been like just a fun experience for us every week
that when we are are sort of like elbow deep in the business
to be able to talk about the game and to talk about it from
(03:00):
our lens and it I just, I like this every week.
It's a blast. Yeah, Yeah, it's fun.
OK. Our topic today is we.
I think that this is like a fitting time because we
obviously have been really hard at work with Driveline and
building out the reports that are coming from the launchpads.
This higher level of sort of markerless motion capture,
(03:24):
relying on a lot of the information that we already had
about pitching and hitting from the selfless standpoint and
building these reports that are coming out of the to really give
like very clear information out of the bio mechanic.
So I think it's fitting in that and we've had conversations
lately, I actually was thinking of like a line I use and I'm
explaining our bio mechanics assessment to someone who's
(03:45):
considering coming. I'll layout like, OK, we measure
this, we measure this, you'll get this and here's all the
things we measure. And then I was like, and all
that's kind of irrelevant because the most important part
is that then we deliver that information to you in a report
and in a way that's super actionable.
That gives you clear guidance onkind of where to go in this
snapshot of time and sort of what to target and make sure
(04:07):
we're targeting the right things.
And I think what we have seen both in our own journey and also
still kind of out there in the game, is that sometimes what we
are pushing as a community or asa, you know, different companies
or things is the is just the data collection piece.
And what we are often finding isthat, you know, our
(04:29):
conversations are around like that.
It's great. We need data.
So it's, it's not to say people shouldn't be collecting data
like having in game data at the start before you maybe even knew
what it was or having both like data before you knew it was or
by mechanics data. Like you have to have data
collection, which I'm sure we'regoing to get into plenty with
this conversation. But but this idea that the
product for the athlete is the data collection is, you know, I
(04:55):
think getting misunderstood or even Laura, maybe, you know,
this is to some of the conversations you've had when
when a school talks to us about their sports science department,
like we're going to take our athletes to our sports science
department. And it's like you can both be
excited for that because it's it's some data for that sports
science. It's supple, it's really
(05:15):
exciting. And also I think for us, we're
always like to the coach, what'syour expectation of what you're
going to get out of that? Because and then sports science
department, right, That's sportsscience.
It's it's one thing for the sports science department,
doesn't mean it's bad that they're doing that, but the
likelihood that you as the coachare going to get.
Something be really clear, what will come out, Laura, What will
(05:36):
come out if the school just goesto their sports science
department and runs through their like lab setup, whatever
they determine, that lab setup is literally what will come out
of that. That's a great question.
The, the number of scenarios that I've had or, you know, I've
had a consulting call or, you know, whether it's a player
development person who's, you know, related to the softball
team or it's with someone in thesports science lab.
(05:59):
The, the disconnect is always the the sports science lab will
often serve the entire athleticscommunity or even the entire
university. It could be, you know, a lab
that's like baked in the academic side.
It could be something more sports science.
That's the athletic side. It can be a mix, but the
challenge is always, you know, the coach will say, we know data
is important and we have these resources on campus and they'll
(06:21):
make the connection obviously with the sports science side or
that, you know, particular lab. And the lab will say, OK, well,
what do you want to see? And the coach is like, well,
don't you tell us, don't you tell us what it is that you want
to see? And so the disconnect is like
the sports science lab is general, right?
It serves multiple teams, multiple athletes.
The likelihood and I could be, Icould be misspeaking, but the
(06:42):
likelihood of like really softball or any sport specific
stuff is probably limited without the team, the coach,
someone coming in and saying we need this, this and this.
Can you deliver that? And being very clear about that.
Otherwise that that lab mechanism is really for general,
(07:03):
you know, things that serve the general athlete population or
are from a research standpoint. And so it's not that like the
you know, the equipment or the intent is bad or wrong or
whatever. It's just that there's there is
no, there's no, no communicationor standard or knowledge of
like, what are we getting out ofthis process?
The coaches like I know it's important.
This is valuable. Tell me what it is and the smart
(07:26):
science life is going, but we don't know your sport, right?
And that is where you know, whenthen I usually am that bridge of
like, OK, then what we think is XYZ.
Let's start with an assessment we can bring, you know, you can
bring us out and have a sense oflike, what is it that you guys
collect and have a place to start?
It's not that every data point we collect is perfect, but it's
a place to start and to at leastbe educated about how our data
(07:50):
points work together. Because that's really the
conversation you have to take back to the sports science lab
or the academic lab. It's like the, the, the, the
coach or the team or the player development person, whoever it
is, they have to guide what you're asking for from those
people. And that's the, that's the
barrier in my opinion. So often times my conversations
are usually with a player development staff member is
(08:12):
like, Hey, let's just start somewhere.
What's important to you? What are the questions that
you're interested in with the questions your coach is
interested in? And I, I'll be honest, I don't
see a lot of successful partnerships in that way.
And I, I think it's, it's just tricky.
It's not a lab built specific tosoftball, you know, right?
Well, there's also like even I was thinking as you were saying
that like what questions do you want to go?
(08:32):
It's like, well, how did those people know what question and
they're like, I'm interested in whether or not her hands are
dropping. It's like, well, what we have
found is that's never how we we are never we have not been able
to or have been very careful notto go into sort of the initial
data collection, the building ofthe report and the building of
(08:54):
the norms with those love that level of subjectivity or bias to
what we were looking for. It's more just starting to
understand what generally does it look like for everyone and
then learning over time with tons and tons of data of what
matters and what, what you can influence and different things.
And so it's also like I have a college program or I have this
(09:18):
one piece of data or I have whatever and I and going to then
base decisions off these five people I collected.
It's probably not, not as probably it's not a good way to
go about it. And, and so it's, you know, for
us, the report, I mean, the softball report that we've
gotten to at this point has taken us over five years of data
(09:41):
to, to get to the place where itis.
You know, we, we could tell the story because we saw it so much
early on. The first few years we saw
hundreds of pitchers and hundreds of hitters and we could
start to see what what made sense and what was important.
And, and we could tie that to training.
That was a really powerful piecefor us of like, we think this is
important. Then we tried to influence it
(10:03):
and whether it worked or not, and we did that a lot, but it
took us five years of data to get to a place where we're like,
this is important and these are the like 12345 things that are
the most important and they're going to have the most influence
on XYZ. And here's how you look at it.
And there's just you can't. There's not many other like.
(10:24):
I was going to say, Laura, it's very obvious that you started
your world in academia because that was very diplomatic and
professional and thank. You very much.
Because what my response would have been and is going to be is
my. Answer.
You can't get shit from that process.
I was going to say my. Answer is the answer.
My true answer, Ashley, is like you get frustration, that's it.
(10:46):
You get time loss and frustration.
I mean, you can't get anything of value as a coach or anything
that's applicable. And I think that's the actual,
so very professional and diplomatic Laura.
That's one of the things I love about you is your light switch
on and off. But with the the real answer is
that you can't get anything fromthat.
And there's just a reality to that.
I often think when people are like, you know, I think
(11:06):
sometimes there's like a social media world because social media
is all built off of like what's sexy.
And if something is sexy on social media, everyone's like,
that must be cool. I always think to myself, like,
I wish I was in a room. Like, how about we get in a room
with the people that are collecting that data?
Just be curious to like ask themsome questions and maybe they're
doing some great things. But I, I'm always really curious
(11:27):
of like, does it go beyond the like social media world?
And the answer is probably no, right?
Well. And I think a big asterisk that
this is this conversation is specific to softball in the
sense that there are labs out there, there are pandemic labs,
There are labs out there in a sport where enough data has been
collected and there is the ability to license a report that
is helpful to deliver information that can deliver it.
(11:51):
What I will say is it doesn't, Ithink for a while we were like,
oh, well, in supple pitching that's, you know, we know that
doesn't exist for supple pitching, but but maybe it does
for supple hitting. Well, I will say we've talked
about this a little bit on some other episodes and conversation.
Is that in our process of building out and sort of
elaborate, you know, elaboratingour hitting report with
(12:13):
driveline and sort of some of the work they've done with
baseball, is that not wholesale sweeping?
Like nothing applies and softball is totally different.
But there, there are differencesin the norms and the
understanding in the software report.
So even in hitting where where maybe and I think there's not
actually that many labs doing hitting data because it's so
(12:35):
hard for the setup in an academic lab.
So, but even if there's an ability to license a hitting
report, but if you are using baseball norms, what we have
seen is like that you, you are probably delivering some
inaccurate information because we are seeing that the softball
swing and some nuances are really important when you're
saying, when we're talking aboutthe things that matter in bio
(12:57):
mechanics, you know, timing matters or posture matters.
And it's like, OK, you could sayin baseball, like we, we hinge,
we side bend, we hold posture, like generally the concepts are
all the same. We counter rotate in the swing.
We know all of these things. But what we are seeing is like
the amount you do that is different because of the timing
(13:18):
you have and the ball path you have.
And so that really matters to the story because it's it's not
just about that you do the things, it's the range that you
do those things, the timing thatyou do those things.
And all of that is a little different supple.
So I think also as an asterisk to, to be clear, there are
places you can go into a pandemic setting and you can get
a report that that is based off of a data set that is very large
(13:40):
and that delivers a meaningful product to you.
And I think probably there are many baseball programs that in
some ways have had the benefit of some of those, although I
don't think there's that many and I don't think it's that easy
to get into the lab. And there's there's limitations
there, but but that exists, but that doesn't exist in softball.
So that's been our thing is thatthen people are like, oh, great,
now softball has this lab and ithas this and has that.
(14:00):
It's like sort of like big asterisk.
What is the output that you're getting?
And like, yes, we can start collecting information.
Yes, it's exciting that programswant to collect the information
now It's like great for our sport.
And also I am a coach. I needed something actionable
out of this. I'm an athlete.
I need something actionable out of this.
Also, if it was just as simple as collecting the data, why
would we have a partnership withDriveline?
(14:22):
So basically what they could do in the hitting space, they are
like, we already know, we've done this work, we've collected
enough. We've seen the norms.
The norms mean it shows you the range that people are in,
whether it's posture, speed, rotation, whatever position is
what is typical. And then when someone's out of
that typical range, right, they know that they could just say,
(14:43):
hey, we're going to collect, youknow, like what?
Whatever it is, 5, 100 hitters and softball and then get those
ranges and we have the same report.
They could do that, right? And so really the main piece of
this is that they're like one, they want us to advise on that,
obviously, and they want us to be like, because we're, you
know, the brand in the space, but on the pitching side, they
could never do that because theywere, if they could, they
(15:07):
wouldn't come to us, right? If they could just like we have
the equipment, we have everything we need to collect
the data, but they're like, we don't even know what we would be
asking to look at. Of course we could get there,
but it will take years, right? And we've got the data
collection system, we've got theability to visualize a report,
but it will take years to get enough data to ask the question.
And the reality is we've alreadydone that work.
So for us, we started with doingsensors which are definitely a
(15:31):
lower level of a wearable sensors of a lower level of bio
mechanics collection. But we have access to it's field
friendly and we just started getting norms right of like
okay, when using sensors, right,not comparing to like inside of
the lab force plates etcetera. When using sensors, we started
to get norms and when we startedto get norms and it was manual
as hell and it took forever and it probably took it two years,
(15:52):
three years maybe. And now we started to be able to
tell people when you were out ofrange based on those sensors.
Then the next step for us is that our partnership with Uno,
the University of Nebraska Omaha, and their marker lab with
force plates. So the first thing that we did
was, OK, let's get some athleteson the sensors and in your Uno
lab and start to understand whatare the norms, what are the
(16:15):
differences? Well, the same thing applies for
Uno is if it was, why would theycome to us?
And. So we went in with two pieces
with Uno one, what does, what's the language between sensors and
the marker, right? Like how do they speak to each
other? And what's the differences to
let the we, we opened up what welooked at, right?
(16:36):
Because we're like all we're able to look at on the sensors
is XY and Z. But now that we have the marker
data and the force point data, we can look at everything.
So then we opened up all of these variables to now tell us
what else should we be considering and looking at that
matters. So then we got to that phase and
then now that puts us in position that when driveline
came to us, we knew the variables.
(16:58):
It was not. We knew like you want to look at
this, this, this in this time frame, doing this.
And it's not because it's a guess.
And So what you're saying, Laura, is like, when they're
like, well, what do you want to look at?
It's a guess of what you think matters.
But we have spent, we started with the guesses like maybe
seven years ago. And so now it were so much
further along with knowing like it's, it's not a guess what
(17:19):
we're looking at. And that's why we were able to
collect data and driveline and within two months create an
incredible report with them. It's because we did that work
already and they're like, we're not doing all that work.
Why would what's, you know, likeit's years worth of not data
collection, not data collection.They could do the data
collection in a weekend if they really wanted, right?
So it's not the data collection.It's not even having it's really
(17:41):
understanding all the variables you should be looking at in the
timing. You should be looking at them.
What matters, how this variable ends up linking to what the arm
is doing, how that matters. And so that's the work we have
done. That's the power of what we do.
And because of that and that that journey in sensors,
manually figuring out our own norms into the Uno lab now into
(18:05):
the marker data, that's the power.
So I think, you know, I don't mean to sit here and be like, I
don't know, like shit on everybody who's out there trying
to collect bio mechanics data, but I'm kind of like, good luck.
And honestly, if you feel like you've got something good, I'd
love to get in the room with youbecause like we, you know, this
is the kind of stuff that we're like we're elbow deep in it
every single day. And it's been years and years
(18:26):
and years. So, you know, I get AI do get a
little bit charged because I in all things life, as most people
do, you know, I, I like substance and authenticity and
the like flashy, sexy social media.
Look at our lab. I'm like, Oh, give me a break,
you know, because I hate to be cynical, but I in the on, you
know, I kind of know like that'san Instagram post.
(18:49):
The substance of that. And I think the challenge of it
is that the the customer, the person getting that product,
they don't freaking know, right?They don't know.
But even when we're talking ballflight, like in this
conversation, we're kind of talking about bio mechanics
data, of course, but it happens with ball flight.
We have so many athletes that wework with at OGX.
They're remote athletes, so theydon't maybe even have access to
(19:09):
data all the time though, like Iwent to this camp at a school or
I went to a camp that's with a private entity or whatever that
looks like. Here's the report I got.
Can you help me understand, understanding?
And I'm like sure. And honestly, this came up last
week and I was like, this is terrible.
This is a terrible report. They collected all the data.
I think it was, I think they gotyak data.
So it was just like we look at yak data all the time.
(19:31):
But what they chose to show themwas just I'm like, you can't get
anything out of this. Let me explain why.
Let me tell you why this is likethere's probably 10 things you
could have gotten out of this and they showed you like .3 of
the things that matters, you know?
So I was like, this is not a good report.
This is just a bad visual of theactual data, you know, they
collected and so, but they didn't know that.
(19:52):
And they're OGX people, right? But they were like, what was
this good or was this bad? I'm like, no clue based on what
they're showing you here, you know?
Your point, Ash, I think, I think it's we to clarify,
there's sort of two approaches we take in the in the way that
we communicate the story and it's something we've worked
really, really hard on with the pitching side is like you've got
the individual story, right. You have the individual story of
(20:15):
her timing, you know how fast she's moving, all the parameters
about how she organizes the motion.
That's one part of the story. And what you just says, like, is
this good or bad? That's against the norms.
And there are times we've, and This is why volume of data
collection is so important, is that you need to test when
someone violates your norm is basically, is it important?
(20:37):
Does that matter? And does that mean that that
norm is right because you're notjust asking the question, is
your data against the norm good or bad?
Are the norms good or bad? Right?
So what we have found so far, atleast in the kinematics is like
the load phase is highly variable.
It's really hard to find, you know, this like consistency
among amongst the load, amongst all the pictures that we've had
(20:58):
so far and that that's OK. We just have to be careful then
about how we interpret if someone is out of range in the
load phase. You got to keep looking down the
chain because you have to know if that thing that was out of a
norm way back here in the beginning of the motion, does
that matter at the end of the motion?
It might, it might not. Maybe she compensates and it's
irrelevant, but it's not just about.
(21:21):
And I, I think this is somethingto transition that I see with
our, our evolution with the volume you've got right now with
the, you know, the driveline partnership and the marker list
data. This evolution I see is like we
have been able to stop sort of talking about data just at
certain events, just at certain points in each of the motions.
And now we are talking about these continuous graphs and the
(21:42):
relationship maybe of like shoulder and elbow or trunk and
arm positioning. And we're talking about how
these things interact and intersect with each other.
It's not just OK at Max this or your peak, whatever was this.
It's not at a discrete point. It's talking about the entirety
of the motion. And that's taken us, as we've
said, years to get to because inthe beginning it was, it was
(22:05):
just the sensor data that has some limited application because
of the numbers of sensors we had.
And so there's, there's two parts of it.
There's the individual story that has to be told connected to
ball flight. I mean, I don't know that you
can have any conversation about bio mechanics that's meaningful
unless it is connected to an endpoint, it's connected to some
(22:27):
performance data. So either it's hit tracks or,
you know, whatever it is from a skill standpoint.
And then you have to then give the the perception of like,
well, is this good or bad? Is this in range or out of
range? And that's a different
conversation. So you have two parts of our of
our reporting. It's like, what's your
individual story? And then how do you match up
(22:47):
against other people who maybe we've we've talked about like a
Velo tier above. I think we're not going that
direction, but we've talked about how we bucket these
athletes and these norms to givethis, this concept of like this
is where you stack up against other people, but it's who they
stack up against that is difficult.
It's difficult to decide that. So it's, it's an individual
(23:09):
story and it's. There's also, you know what I
was going to say, there's also this world of like in academia,
often times when there are conferences, this happens like
an NFCA and there's like a pitching bio mechanics and
everyone's like, I want to know about that, right?
But it's very academic based andacademia, as you know, Lars,
about publications, it's about the correlation between certain
(23:31):
variables. Does this correlate to this?
Like it's this very and that's that's for a research paper,
right? That is answering a specific
research question. The influence or the
correlation, the connectivity ofcertain variables to another.
That is a entirely different world.
Like we're not here to shit on that world.
That's just publication world. That is an entirely different
(23:53):
world then measuring someone's bio mechanics for the sake of
practical application in the bullpen and training.
I mean literally on two different planets.
So I think what often people getwrong is when they're like, oh,
we're doing this like panel of biomechanists or we're getting
(24:14):
whatever, and it's academic base, which is great, except
coaches. You might as well stand up there
and talk, speak a language that coaches literally do not speak
because that's not going to helpthem.
It doesn't mean the research is bad.
It doesn't mean the process of collection was bad, right?
Well, it's great. It's what we said it's it's
exciting and great to have research.
Sure. Our partnership with Uno is 2
(24:35):
sided right that all that data that goes feeds.
Their their publication. Assistance and their things to
do research and getting researchpublications out about supple
pitching for instance. It's amazing and we want that to
happen and it should be happening.
It's really exciting. And also where I was going,
Laura, based on yours, and this is related to this concept is
(24:56):
let's even say that I'm going togo and I'm just going to get a
bio mechanics report or I'm going to go and I'm just going
to get ball like I'm going to goand I'm going to get one piece
of this puzzle, right. And I I can go to X place and I
can get this information and I have that.
Maybe that in and of itself is not the the negative.
It's you know, we tie everythingtogether and that's how we've
that's how we've kind of got to what's important.
(25:18):
I think that's what it is in biomechanics is by tying together
how that influences fall path. I mean in both flight, in both
hitting and pitching. So maybe we could at this point
deliver a bio mechanics report without the ball fight and say
like, OK, here's some things. I think what is important though
is we are talking about data andsort of the understanding of
data in this like snapshot assessment concept.
(25:39):
I'm going to go get the snapshotof me and what's what then the
carryover. That's really important and why,
Ashley, you're saying it needs to be applicable and the bullpen
it needs to be all these things is what am I gonna how am I
gonna know if the I took this information and it says I'm out
of the norm in this time period and that does influence XYZ.
It's making my arm come in slower.
(26:01):
It's making my back come in slower.
It's infecting my backpack. How am I going to know if I am
fixing that? Am I going to look at my
backpacks going to be fixed? Am I going to whatever those
things are? And we just had this
conversation yesterday because at the end of the day, like we
have college kids right now training and they're sending me
video and they're coming in. I'm not always in.
They're like sending things. And I gave a story that this kid
(26:22):
one time, like I could look at her video still and I could say
like, Nope, we didn't do enough.Your path still doesn't look
perfect. You know, like we got it.
We, we got to fix more of this, you know, the, the mechanics,
except she hit one one day and turned around was like, was that
better? And I said, well, it was a 79
mile per hour home run off the machine.
(26:43):
So that's the highest exit velocity you've hit yet this
summer and the furthest ball you've hit.
So I think it was better. Like seems good and there's
right. And so there's a level of then
the understanding of, OK, we're going to come back and we're
going to keep, we might do another bio mechanics assessment
in a few, you know, 12 weeks or whatever it is to kind of see
what the snapshot is then and get hyper focused on the right
(27:05):
training again. But this is understanding, I
think where you get sometimes when you're when your whole
thing is just the assessment or just the bio mechanics or you
just this piece is where our process has started intertwining
is understanding what are we even trying to influence?
And then is the training that they go do doing that and what
(27:27):
are we watching? We don't put them in the maybe
eventually at a college that will be able to tweak mechanics
this way and put them in the labmore frequently.
But we don't put teenagers in a lab every week and just keep
trying to perfect their mechanics.
It's like, here's your snapshot right now.
This is the training program we know has influenced that in the
past. And then what we are determining
(27:47):
whether it's getting better or not is performance.
This is the way we think it's going to influence performance
and is it doing that or not and you can't.
I think that's the pieces, you know, all of that back and forth
and understanding. Ultimately, as you're saying,
Ashley, the reason we are measuring these things, the
reason we want this information is rooted in influencing
(28:07):
sometimes injury prevention and and performance.
And that is it. It's not just because it's fun
to research things. We are researching.
We are gathering that information and trying to
deliver a report that is hyper focused on that that and that
takes a long time to get to because it took this back and
forth. It took a lot of data and it's
still evolving. We said like we we still are
(28:29):
going to continue to get new pieces of information.
Sure, right. You know, three years ago we
were like no one can throw backspin.
And now every single picture we talked to throw backspin because
we learned that. So now that's not a true
statement anymore. So we learned how you throwback
spin and what that looks like has has changed and evolved and
our understanding of that has evolved and things like that are
(28:51):
always going to happen. Five years ago we were like 70
mph exit velocity. Whoo, that's amazing.
And now it's like, well, everyone's stronger, the bats
are hotter and now 80 is the threshold and we have kids
almost say 90 now. So things are constantly
evolving and changing because the game is changing and
training is changing and, and all of these things are
changing. So the reports are going to
(29:11):
continue changing. But that's really, I think where
our hesitation has come from. It's, it's twofold.
It's do you have the informationand and volume of data to even
deliver something helpful? Because just getting the data
and saying like here you go is not helpful in a variety of
ways. One sort of at best, it's like
is what does this mean? What am I going to do with it?
(29:32):
And at worst, as a more hot asterisk is that sometimes you
then miss bad data because you don't understand the data.
And now you hand over something that is bad data to someone.
And that's like the the worst case scenario, so.
And also your question Krista oflike do you have the answer is
no. I think the reason why I get
charged one, I'm low on sleep today so that I'm extra feisty 2
(29:56):
because I hate when shit is not rooted in substance and it gets
sold to somebody, right? I just I can't stand that
concept. And so the reality is if you are
looking for bio mechanics information that is going to
directly impact performance training, your plan, your player
development. If it is not a lab data report
(30:22):
that is connected to us, I don'tbuy it because and if that, if I
am wrong about that, maybe I am that.
I'd love to sit in a room with the people who think that that's
not true and see what they've got.
And I'd love to be proved wrong.But I just know what the process
has looked like and what it's taken to get to this point.
To know again, not to just collect data, not to just create
(30:43):
a research question, create a publication.
I'm not doing, I'm not. But I am talking about for what
we said, the ability to collect the data, have something really,
really, really clear to deliver to the athlete.
That's like, OK, this is what you need to go after right here.
Not this and not this, but this.And if you do that, this is what
we should see on the ball. If you think there is a product
out there from someone else, we have to get in a room with those
(31:04):
people because I would love the conversation is how I feel when
I think about that because thereisn't there isn't.
That pack runs through OGX, which is what I'm so proud of us
for. And so I think that's why I get
really charged. It's just like, don't buy that,
don't do that. That is just, it's either
something that's not going to help you and it's just for their
research purpose or it's a gimmick.
(31:26):
It's probably one of those sides.
So anyway, I think that's the reason why these types of things
sort of charge me a little bit is because everybody can make
something look real good on social media.
But the reality is people shouldknow what substance looks like,
what it means for something to be a value.
And this is not just me being like, oh, I think we're the
best. No, we are well.
(31:46):
Here's a, here's a perfect example, I think and maybe we
can end up this which I think isis tied to a little bit more of
where we have, we see this limitation in ourselves and we
are cautious about it. It's force flight data.
So I think like we've seen, we saw years ago force like data
came out from hitting and they were like good, bad, this you're
putting so much force into the ground.
And even then I was like, how dowe know this is good and bad?
(32:07):
Like, you know, you're, you're taking a good hitter and then
you're saying it's good that they do this.
They put this force into the ground.
It's like that's not really how it works.
And so we've with you and O we've been collecting force like
data for pitching and we don't report on it yet, right?
You don't know what it means. And I think that's the reality.
It doesn't mean it's bad to do that.
And and I was going to say, and if someone says, Hey, we got
(32:30):
this lab and we're like, we needto start collecting data for the
good of the game, go get in the lab.
Like you're not going to get a product out of it.
And but that's fine. I mean, do some things.
We've needed people to do those things all along the way.
So I think go give to the game and the research and things, but
from a product standpoint like we are, we are closer now
because of the volume of data that we've clicked into seeing
(32:52):
what matters and what we want toreport on.
But you can't just even though the very first pitcher that ever
walked in, you know, was on force plates, we couldn't just
say like see how much force you put into the ground here and
because you throw hard, that's good.
That's not real. That's not how it works.
So it's how long have we been collecting data that you know
now? Three years.
Almost 2 1/2 of the I say the force plates.
(33:14):
We probably had about a year anda half or so of full data
collection with full motion capture and force plates.
Right. And so it's like we're we're not
even in a place after that long.So it's still, you know, we
still want to post like this is amazing.
We're getting force like data. This is so exciting.
We're getting the full motion. We're getting all these things
and also from a product standpoint and the information
(33:36):
and the story and how much, you know, what ties or what you're
going for, how important that isto performance or whatever.
We don't know yet because we don't have enough data and we
don't have the norms and we haven't looked at it or been
able to ask the questions yet atthe volume.
And we're getting closer to that.
But that I think is an example of, yeah, that will evolve our
data when we get to that and we understand how that matters and
(33:58):
it will change things. But you can't just just because
you collect it, you can't just say this is is important and you
can't just say this kid throws 70 and she puts a ton of force
into the ground. So that's what we should be
doing. Right.
You can't do those things and you it takes time background and
so well, you can, Yeah, you can.Yeah, sure you can.
And so that's kind of the story,I think of just being like the
(34:20):
questions of asking of these places when you're going.
And it's the same for ball fight.
It's it's the exact same for ball fight, which is like Once
Upon a time four years ago, everyone was like the very first
time we ever kept got exit velocity of pocket radars and we
thought it was like amazing. We were doing everyone's getting
off the teeth. And so then they were like, this
is a good hitter because she had70 and it's a kid swinging out
the teeth. And so even that it's like we
(34:43):
delivered. You went to camps and and data
collection things and they measured your exit velocity of
the tee. And so it's like, that's not
good. That's not that doesn't tell us
anything. That's not a real measurement of
whether a hitter is good or not the power expression, but that's
not measuring anything. And so even in ball play, it's
like, if you don't understand sort of like what is good and
(35:04):
you don't have the volume of hitters to say whether that's
good or bad in the context of the skill, it just doesn't mean
a lot. And so that's kind of I think a
a good example of something we've withheld and not shared as
part of the story or not made any sort of conclusions on
because we're not a place where we can do that yet.
And so I think we just have to get to that place.
(35:25):
And and that's kind of what you're saying, Ash is that, you
know, if if you don't have that volume and that understanding
and you haven't seen how it whatmatters and how it interacts
with things, then just delivering the data to someone
is like, OK, what am I going to do with this?
Yeah. And any borderline I respond
there well. Yeah, that's, as I say, that's
(35:46):
the best case scenario. If someone just hands you the
information and you don't have to do with it.
The worst case scenario, we've seen this in plenty of cases,
even with some of the like fieldfriendly data, is that when they
then interpret it without that data and say like good, bad,
based on the three informations I've had, that's where it gets
like, OK, now we're we're starting to get into sort of
(36:07):
dangerous is probably pushing it.
But into category where you're taking this information, you're
telling them it's objective and it's definitely subjective and
you're sending them in a in a path that's not going to be
good. In summary, again, if you are
interested in getting your bio mechanics legitimately measured
and given back to you in a way that is related to your
(36:30):
performance goals and how training will connect to that
and what even matters in your motion, and just because
something looks odd doesn't necessarily mean that's
something we need to chase. If you want clarity on that
story in a way that's legitimately going to help with
training, I know exactly where you should go.
So you can book your assessment five places now, right?
So we got Romeoville, Omaha, Seattle, Phoenix, Tampa, any.
(36:56):
Of those places, not all of those, not all of those for
pitching. So, right.
So Arizona and Seattle not pitching yet, but hopefully
soon, but at least Tampa, Omaha and then Chicago are three
places where if it's not our ownOGX facility, it is our process
and our reporting and our debriefing, which is what really
matters. Yeah.
(37:17):
All right. See you there.
OGX Nation, we love you, we appreciate you, but we'd love
you even more if you would like subscribe to this podcast, give
us reviews, send us comments on what you want to hear, then we'd
really, really love you. Got to do it.
Do it now.
All right. We are back and it's our 60th
episode since the rebrands. I was like to say that, so like
Asterisk sort of our 60th episode and we just finished
quarterly planning. So kind of like 60th episode
going into Q3. Ashley got home from our flight
at 4:00 AM. So she's alive, alert and ready
to go as always. So I in that sort of thinking
(00:29):
about our 60th episode, my question for you in 60 seconds.
So Ashley, 60 seconds, this is, Yeah. 60 seconds with a slow
brain. Like no shot, but go ahead, OK.
In 60 seconds, when we were liketalking about starting a podcast
in the sort of this format, whatwere your like thoughts on that?
What were you excited about? What did you think about it?
(00:51):
Sure. I mean, honestly, from a from a
like a personal standpoint, because I feel like I deliver
information and I deliver what'sin my brain best verbally.
So I mean, I think what I was most excited about was just like
for myself, just like be who I am.
Just express things the way I want to express them, say things
the way I want to say them. Talk about the complexities of
what we do. When you say to me like, oh,
(01:13):
write a thread on XI had to sit there like with my hands frozen
on the keyboard for 8 hours to produce 4 sentences, you know,
but then you jump on a podcast and they can just roll.
So I think for me, the idea of apodcast has always been
exciting, even going back to theRedefine the Circle days because
it's just personally where I feel like I can just get really
(01:33):
connect my brain to my mouth vest and be able to really share
what we do, what we're thinking about the like holistic nature
of OGX, the totality of the things that we're working on.
And so I love this platform for that reason.
And I just think it's great because I think it's a great
opportunity for people to like hear so much information, so
much meat all in one space and just like do it from their car
(01:57):
or do it just like as we're going out their day.
So I just think the nature of podcasts are great, but for me
personally it just really like fits how I best express myself,
I'd say. All right, Laura, same question.
Good one. I think, I think for me, I was
like, cool, we get the opportunity, like, you know, the
three of us with guests or whatever to like, I don't know,
let the world see some of our like internal conversation and
(02:19):
the way that we talk about the business and the game and, you
know, player development in general.
And I was like, I think, I thinkthat'll be fun, you know,
because obviously I enjoy, you know, our conversations.
And I think the, the like, one of my favorite thing is, is when
someone's like, Oh yeah, I've got it.
Like I've got a road trip and I've got the OGX podcast just
like queued up. I'm like people I think really
(02:40):
enjoy one. I enjoy podcast.
I like the listening component, multitasker.
And so I just like the like diversity of the way we deliver
this information. And I think like it's just, it's
just been like just a fun experience for us every week
that when we are are sort of like elbow deep in the business
to be able to talk about the game and to talk about it from
(03:00):
our lens and it I just, I like this every week.
It's a blast. Yeah, Yeah, it's fun.
OK. Our topic today is we.
I think that this is like a fitting time because we
obviously have been really hard at work with Driveline and
building out the reports that are coming from the launchpads.
This higher level of sort of markerless motion capture,
(03:24):
relying on a lot of the information that we already had
about pitching and hitting from the selfless standpoint and
building these reports that are coming out of the to really give
like very clear information out of the bio mechanic.
So I think it's fitting in that and we've had conversations
lately, I actually was thinking of like a line I use and I'm
explaining our bio mechanics assessment to someone who's
(03:45):
considering coming. I'll layout like, OK, we measure
this, we measure this, you'll get this and here's all the
things we measure. And then I was like, and all
that's kind of irrelevant because the most important part
is that then we deliver that information to you in a report
and in a way that's super actionable.
That gives you clear guidance onkind of where to go in this
snapshot of time and sort of what to target and make sure
(04:07):
we're targeting the right things.
And I think what we have seen both in our own journey and also
still kind of out there in the game, is that sometimes what we
are pushing as a community or asa, you know, different companies
or things is the is just the data collection piece.
And what we are often finding isthat, you know, our
(04:29):
conversations are around like that.
It's great. We need data.
So it's, it's not to say people shouldn't be collecting data
like having in game data at the start before you maybe even knew
what it was or having both like data before you knew it was or
by mechanics data. Like you have to have data
collection, which I'm sure we'regoing to get into plenty with
this conversation. But but this idea that the
product for the athlete is the data collection is, you know, I
(04:55):
think getting misunderstood or even Laura, maybe, you know,
this is to some of the conversations you've had when
when a school talks to us about their sports science department,
like we're going to take our athletes to our sports science
department. And it's like you can both be
excited for that because it's it's some data for that sports
science. It's supple, it's really
(05:15):
exciting. And also I think for us, we're
always like to the coach, what'syour expectation of what you're
going to get out of that? Because and then sports science
department, right, That's sportsscience.
It's it's one thing for the sports science department,
doesn't mean it's bad that they're doing that, but the
likelihood that you as the coachare going to get.
Something be really clear, what will come out, Laura, What will
(05:36):
come out if the school just goesto their sports science
department and runs through their like lab setup, whatever
they determine, that lab setup is literally what will come out
of that. That's a great question.
The, the number of scenarios that I've had or, you know, I've
had a consulting call or, you know, whether it's a player
development person who's, you know, related to the softball
team or it's with someone in thesports science lab.
(05:59):
The, the disconnect is always the the sports science lab will
often serve the entire athleticscommunity or even the entire
university. It could be, you know, a lab
that's like baked in the academic side.
It could be something more sports science.
That's the athletic side. It can be a mix, but the
challenge is always, you know, the coach will say, we know data
is important and we have these resources on campus and they'll
(06:21):
make the connection obviously with the sports science side or
that, you know, particular lab. And the lab will say, OK, well,
what do you want to see? And the coach is like, well,
don't you tell us, don't you tell us what it is that you want
to see? And so the disconnect is like
the sports science lab is general, right?
It serves multiple teams, multiple athletes.
The likelihood and I could be, Icould be misspeaking, but the
(06:42):
likelihood of like really softball or any sport specific
stuff is probably limited without the team, the coach,
someone coming in and saying we need this, this and this.
Can you deliver that? And being very clear about that.
Otherwise that that lab mechanism is really for general,
(07:03):
you know, things that serve the general athlete population or
are from a research standpoint. And so it's not that like the
you know, the equipment or the intent is bad or wrong or
whatever. It's just that there's there is
no, there's no, no communicationor standard or knowledge of
like, what are we getting out ofthis process?
The coaches like I know it's important.
This is valuable. Tell me what it is and the smart
(07:26):
science life is going, but we don't know your sport, right?
And that is where you know, whenthen I usually am that bridge of
like, OK, then what we think is XYZ.
Let's start with an assessment we can bring, you know, you can
bring us out and have a sense oflike, what is it that you guys
collect and have a place to start?
It's not that every data point we collect is perfect, but it's
a place to start and to at leastbe educated about how our data
(07:50):
points work together. Because that's really the
conversation you have to take back to the sports science lab
or the academic lab. It's like the, the, the, the
coach or the team or the player development person, whoever it
is, they have to guide what you're asking for from those
people. And that's the, that's the
barrier in my opinion. So often times my conversations
are usually with a player development staff member is
(08:12):
like, Hey, let's just start somewhere.
What's important to you? What are the questions that
you're interested in with the questions your coach is
interested in? And I, I'll be honest, I don't
see a lot of successful partnerships in that way.
And I, I think it's, it's just tricky.
It's not a lab built specific tosoftball, you know, right?
Well, there's also like even I was thinking as you were saying
that like what questions do you want to go?
(08:32):
It's like, well, how did those people know what question and
they're like, I'm interested in whether or not her hands are
dropping. It's like, well, what we have
found is that's never how we we are never we have not been able
to or have been very careful notto go into sort of the initial
data collection, the building ofthe report and the building of
(08:54):
the norms with those love that level of subjectivity or bias to
what we were looking for. It's more just starting to
understand what generally does it look like for everyone and
then learning over time with tons and tons of data of what
matters and what, what you can influence and different things.
And so it's also like I have a college program or I have this
(09:18):
one piece of data or I have whatever and I and going to then
base decisions off these five people I collected.
It's probably not, not as probably it's not a good way to
go about it. And, and so it's, you know, for
us, the report, I mean, the softball report that we've
gotten to at this point has taken us over five years of data
(09:41):
to, to get to the place where itis.
You know, we, we could tell the story because we saw it so much
early on. The first few years we saw
hundreds of pitchers and hundreds of hitters and we could
start to see what what made sense and what was important.
And, and we could tie that to training.
That was a really powerful piecefor us of like, we think this is
important. Then we tried to influence it
(10:03):
and whether it worked or not, and we did that a lot, but it
took us five years of data to get to a place where we're like,
this is important and these are the like 12345 things that are
the most important and they're going to have the most influence
on XYZ. And here's how you look at it.
And there's just you can't. There's not many other like.
(10:24):
I was going to say, Laura, it's very obvious that you started
your world in academia because that was very diplomatic and
professional and thank. You very much.
Because what my response would have been and is going to be is
my. Answer.
You can't get shit from that process.
I was going to say my. Answer is the answer.
My true answer, Ashley, is like you get frustration, that's it.
(10:46):
You get time loss and frustration.
I mean, you can't get anything of value as a coach or anything
that's applicable. And I think that's the actual,
so very professional and diplomatic Laura.
That's one of the things I love about you is your light switch
on and off. But with the the real answer is
that you can't get anything fromthat.
And there's just a reality to that.
I often think when people are like, you know, I think
(11:06):
sometimes there's like a social media world because social media
is all built off of like what's sexy.
And if something is sexy on social media, everyone's like,
that must be cool. I always think to myself, like,
I wish I was in a room. Like, how about we get in a room
with the people that are collecting that data?
Just be curious to like ask themsome questions and maybe they're
doing some great things. But I, I'm always really curious
(11:27):
of like, does it go beyond the like social media world?
And the answer is probably no, right?
Well. And I think a big asterisk that
this is this conversation is specific to softball in the
sense that there are labs out there, there are pandemic labs,
There are labs out there in a sport where enough data has been
collected and there is the ability to license a report that
is helpful to deliver information that can deliver it.
(11:51):
What I will say is it doesn't, Ithink for a while we were like,
oh, well, in supple pitching that's, you know, we know that
doesn't exist for supple pitching, but but maybe it does
for supple hitting. Well, I will say we've talked
about this a little bit on some other episodes and conversation.
Is that in our process of building out and sort of
elaborate, you know, elaboratingour hitting report with
(12:13):
driveline and sort of some of the work they've done with
baseball, is that not wholesale sweeping?
Like nothing applies and softball is totally different.
But there, there are differencesin the norms and the
understanding in the software report.
So even in hitting where where maybe and I think there's not
actually that many labs doing hitting data because it's so
(12:35):
hard for the setup in an academic lab.
So, but even if there's an ability to license a hitting
report, but if you are using baseball norms, what we have
seen is like that you, you are probably delivering some
inaccurate information because we are seeing that the softball
swing and some nuances are really important when you're
saying, when we're talking aboutthe things that matter in bio
(12:57):
mechanics, you know, timing matters or posture matters.
And it's like, OK, you could sayin baseball, like we, we hinge,
we side bend, we hold posture, like generally the concepts are
all the same. We counter rotate in the swing.
We know all of these things. But what we are seeing is like
the amount you do that is different because of the timing
(13:18):
you have and the ball path you have.
And so that really matters to the story because it's it's not
just about that you do the things, it's the range that you
do those things, the timing thatyou do those things.
And all of that is a little different supple.
So I think also as an asterisk to, to be clear, there are
places you can go into a pandemic setting and you can get
a report that that is based off of a data set that is very large
(13:40):
and that delivers a meaningful product to you.
And I think probably there are many baseball programs that in
some ways have had the benefit of some of those, although I
don't think there's that many and I don't think it's that easy
to get into the lab. And there's there's limitations
there, but but that exists, but that doesn't exist in softball.
So that's been our thing is thatthen people are like, oh, great,
now softball has this lab and ithas this and has that.
(14:00):
It's like sort of like big asterisk.
What is the output that you're getting?
And like, yes, we can start collecting information.
Yes, it's exciting that programswant to collect the information
now It's like great for our sport.
And also I am a coach. I needed something actionable
out of this. I'm an athlete.
I need something actionable out of this.
Also, if it was just as simple as collecting the data, why
would we have a partnership withDriveline?
(14:22):
So basically what they could do in the hitting space, they are
like, we already know, we've done this work, we've collected
enough. We've seen the norms.
The norms mean it shows you the range that people are in,
whether it's posture, speed, rotation, whatever position is
what is typical. And then when someone's out of
that typical range, right, they know that they could just say,
(14:43):
hey, we're going to collect, youknow, like what?
Whatever it is, 5, 100 hitters and softball and then get those
ranges and we have the same report.
They could do that, right? And so really the main piece of
this is that they're like one, they want us to advise on that,
obviously, and they want us to be like, because we're, you
know, the brand in the space, but on the pitching side, they
could never do that because theywere, if they could, they
(15:07):
wouldn't come to us, right? If they could just like we have
the equipment, we have everything we need to collect
the data, but they're like, we don't even know what we would be
asking to look at. Of course we could get there,
but it will take years, right? And we've got the data
collection system, we've got theability to visualize a report,
but it will take years to get enough data to ask the question.
And the reality is we've alreadydone that work.
So for us, we started with doingsensors which are definitely a
(15:31):
lower level of a wearable sensors of a lower level of bio
mechanics collection. But we have access to it's field
friendly and we just started getting norms right of like
okay, when using sensors, right,not comparing to like inside of
the lab force plates etcetera. When using sensors, we started
to get norms and when we startedto get norms and it was manual
as hell and it took forever and it probably took it two years,
(15:52):
three years maybe. And now we started to be able to
tell people when you were out ofrange based on those sensors.
Then the next step for us is that our partnership with Uno,
the University of Nebraska Omaha, and their marker lab with
force plates. So the first thing that we did
was, OK, let's get some athleteson the sensors and in your Uno
lab and start to understand whatare the norms, what are the
(16:15):
differences? Well, the same thing applies for
Uno is if it was, why would theycome to us?
And. So we went in with two pieces
with Uno one, what does, what's the language between sensors and
the marker, right? Like how do they speak to each
other? And what's the differences to
let the we, we opened up what welooked at, right?
(16:36):
Because we're like all we're able to look at on the sensors
is XY and Z. But now that we have the marker
data and the force point data, we can look at everything.
So then we opened up all of these variables to now tell us
what else should we be considering and looking at that
matters. So then we got to that phase and
then now that puts us in position that when driveline
came to us, we knew the variables.
(16:58):
It was not. We knew like you want to look at
this, this, this in this time frame, doing this.
And it's not because it's a guess.
And So what you're saying, Laura, is like, when they're
like, well, what do you want to look at?
It's a guess of what you think matters.
But we have spent, we started with the guesses like maybe
seven years ago. And so now it were so much
further along with knowing like it's, it's not a guess what
(17:19):
we're looking at. And that's why we were able to
collect data and driveline and within two months create an
incredible report with them. It's because we did that work
already and they're like, we're not doing all that work.
Why would what's, you know, likeit's years worth of not data
collection, not data collection.They could do the data
collection in a weekend if they really wanted, right?
So it's not the data collection.It's not even having it's really
(17:41):
understanding all the variables you should be looking at in the
timing. You should be looking at them.
What matters, how this variable ends up linking to what the arm
is doing, how that matters. And so that's the work we have
done. That's the power of what we do.
And because of that and that that journey in sensors,
manually figuring out our own norms into the Uno lab now into
(18:05):
the marker data, that's the power.
So I think, you know, I don't mean to sit here and be like, I
don't know, like shit on everybody who's out there trying
to collect bio mechanics data, but I'm kind of like, good luck.
And honestly, if you feel like you've got something good, I'd
love to get in the room with youbecause like we, you know, this
is the kind of stuff that we're like we're elbow deep in it
every single day. And it's been years and years
(18:26):
and years. So, you know, I get AI do get a
little bit charged because I in all things life, as most people
do, you know, I, I like substance and authenticity and
the like flashy, sexy social media.
Look at our lab. I'm like, Oh, give me a break,
you know, because I hate to be cynical, but I in the on, you
know, I kind of know like that'san Instagram post.
(18:49):
The substance of that. And I think the challenge of it
is that the the customer, the person getting that product,
they don't freaking know, right?They don't know.
But even when we're talking ballflight, like in this
conversation, we're kind of talking about bio mechanics
data, of course, but it happens with ball flight.
We have so many athletes that wework with at OGX.
They're remote athletes, so theydon't maybe even have access to
(19:09):
data all the time though, like Iwent to this camp at a school or
I went to a camp that's with a private entity or whatever that
looks like. Here's the report I got.
Can you help me understand, understanding?
And I'm like sure. And honestly, this came up last
week and I was like, this is terrible.
This is a terrible report. They collected all the data.
I think it was, I think they gotyak data.
So it was just like we look at yak data all the time.
(19:31):
But what they chose to show themwas just I'm like, you can't get
anything out of this. Let me explain why.
Let me tell you why this is likethere's probably 10 things you
could have gotten out of this and they showed you like .3 of
the things that matters, you know?
So I was like, this is not a good report.
This is just a bad visual of theactual data, you know, they
collected and so, but they didn't know that.
(19:52):
And they're OGX people, right? But they were like, what was
this good or was this bad? I'm like, no clue based on what
they're showing you here, you know?
Your point, Ash, I think, I think it's we to clarify,
there's sort of two approaches we take in the in the way that
we communicate the story and it's something we've worked
really, really hard on with the pitching side is like you've got
the individual story, right. You have the individual story of
(20:15):
her timing, you know how fast she's moving, all the parameters
about how she organizes the motion.
That's one part of the story. And what you just says, like, is
this good or bad? That's against the norms.
And there are times we've, and This is why volume of data
collection is so important, is that you need to test when
someone violates your norm is basically, is it important?
(20:37):
Does that matter? And does that mean that that
norm is right because you're notjust asking the question, is
your data against the norm good or bad?
Are the norms good or bad? Right?
So what we have found so far, atleast in the kinematics is like
the load phase is highly variable.
It's really hard to find, you know, this like consistency
among amongst the load, amongst all the pictures that we've had
(20:58):
so far and that that's OK. We just have to be careful then
about how we interpret if someone is out of range in the
load phase. You got to keep looking down the
chain because you have to know if that thing that was out of a
norm way back here in the beginning of the motion, does
that matter at the end of the motion?
It might, it might not. Maybe she compensates and it's
irrelevant, but it's not just about.
(21:21):
And I, I think this is somethingto transition that I see with
our, our evolution with the volume you've got right now with
the, you know, the driveline partnership and the marker list
data. This evolution I see is like we
have been able to stop sort of talking about data just at
certain events, just at certain points in each of the motions.
And now we are talking about these continuous graphs and the
(21:42):
relationship maybe of like shoulder and elbow or trunk and
arm positioning. And we're talking about how
these things interact and intersect with each other.
It's not just OK at Max this or your peak, whatever was this.
It's not at a discrete point. It's talking about the entirety
of the motion. And that's taken us, as we've
said, years to get to because inthe beginning it was, it was
(22:05):
just the sensor data that has some limited application because
of the numbers of sensors we had.
And so there's, there's two parts of it.
There's the individual story that has to be told connected to
ball flight. I mean, I don't know that you
can have any conversation about bio mechanics that's meaningful
unless it is connected to an endpoint, it's connected to some
(22:27):
performance data. So either it's hit tracks or,
you know, whatever it is from a skill standpoint.
And then you have to then give the the perception of like,
well, is this good or bad? Is this in range or out of
range? And that's a different
conversation. So you have two parts of our of
our reporting. It's like, what's your
individual story? And then how do you match up
(22:47):
against other people who maybe we've we've talked about like a
Velo tier above. I think we're not going that
direction, but we've talked about how we bucket these
athletes and these norms to givethis, this concept of like this
is where you stack up against other people, but it's who they
stack up against that is difficult.
It's difficult to decide that. So it's, it's an individual
(23:09):
story and it's. There's also, you know what I
was going to say, there's also this world of like in academia,
often times when there are conferences, this happens like
an NFCA and there's like a pitching bio mechanics and
everyone's like, I want to know about that, right?
But it's very academic based andacademia, as you know, Lars,
about publications, it's about the correlation between certain
(23:31):
variables. Does this correlate to this?
Like it's this very and that's that's for a research paper,
right? That is answering a specific
research question. The influence or the
correlation, the connectivity ofcertain variables to another.
That is a entirely different world.
Like we're not here to shit on that world.
That's just publication world. That is an entirely different
(23:53):
world then measuring someone's bio mechanics for the sake of
practical application in the bullpen and training.
I mean literally on two different planets.
So I think what often people getwrong is when they're like, oh,
we're doing this like panel of biomechanists or we're getting
(24:14):
whatever, and it's academic base, which is great, except
coaches. You might as well stand up there
and talk, speak a language that coaches literally do not speak
because that's not going to helpthem.
It doesn't mean the research is bad.
It doesn't mean the process of collection was bad, right?
Well, it's great. It's what we said it's it's
exciting and great to have research.
Sure. Our partnership with Uno is 2
(24:35):
sided right that all that data that goes feeds.
Their their publication. Assistance and their things to
do research and getting researchpublications out about supple
pitching for instance. It's amazing and we want that to
happen and it should be happening.
It's really exciting. And also where I was going,
Laura, based on yours, and this is related to this concept is
(24:56):
let's even say that I'm going togo and I'm just going to get a
bio mechanics report or I'm going to go and I'm just going
to get ball like I'm going to goand I'm going to get one piece
of this puzzle, right. And I I can go to X place and I
can get this information and I have that.
Maybe that in and of itself is not the the negative.
It's you know, we tie everythingtogether and that's how we've
that's how we've kind of got to what's important.
(25:18):
I think that's what it is in biomechanics is by tying together
how that influences fall path. I mean in both flight, in both
hitting and pitching. So maybe we could at this point
deliver a bio mechanics report without the ball fight and say
like, OK, here's some things. I think what is important though
is we are talking about data andsort of the understanding of
data in this like snapshot assessment concept.
(25:39):
I'm going to go get the snapshotof me and what's what then the
carryover. That's really important and why,
Ashley, you're saying it needs to be applicable and the bullpen
it needs to be all these things is what am I gonna how am I
gonna know if the I took this information and it says I'm out
of the norm in this time period and that does influence XYZ.
It's making my arm come in slower.
(26:01):
It's making my back come in slower.
It's infecting my backpack. How am I going to know if I am
fixing that? Am I going to look at my
backpacks going to be fixed? Am I going to whatever those
things are? And we just had this
conversation yesterday because at the end of the day, like we
have college kids right now training and they're sending me
video and they're coming in. I'm not always in.
They're like sending things. And I gave a story that this kid
(26:22):
one time, like I could look at her video still and I could say
like, Nope, we didn't do enough.Your path still doesn't look
perfect. You know, like we got it.
We, we got to fix more of this, you know, the, the mechanics,
except she hit one one day and turned around was like, was that
better? And I said, well, it was a 79
mile per hour home run off the machine.
(26:43):
So that's the highest exit velocity you've hit yet this
summer and the furthest ball you've hit.
So I think it was better. Like seems good and there's
right. And so there's a level of then
the understanding of, OK, we're going to come back and we're
going to keep, we might do another bio mechanics assessment
in a few, you know, 12 weeks or whatever it is to kind of see
what the snapshot is then and get hyper focused on the right
(27:05):
training again. But this is understanding, I
think where you get sometimes when you're when your whole
thing is just the assessment or just the bio mechanics or you
just this piece is where our process has started intertwining
is understanding what are we even trying to influence?
And then is the training that they go do doing that and what
(27:27):
are we watching? We don't put them in the maybe
eventually at a college that will be able to tweak mechanics
this way and put them in the labmore frequently.
But we don't put teenagers in a lab every week and just keep
trying to perfect their mechanics.
It's like, here's your snapshot right now.
This is the training program we know has influenced that in the
past. And then what we are determining
(27:47):
whether it's getting better or not is performance.
This is the way we think it's going to influence performance
and is it doing that or not and you can't.
I think that's the pieces, you know, all of that back and forth
and understanding. Ultimately, as you're saying,
Ashley, the reason we are measuring these things, the
reason we want this information is rooted in influencing
(28:07):
sometimes injury prevention and and performance.
And that is it. It's not just because it's fun
to research things. We are researching.
We are gathering that information and trying to
deliver a report that is hyper focused on that that and that
takes a long time to get to because it took this back and
forth. It took a lot of data and it's
still evolving. We said like we we still are
(28:29):
going to continue to get new pieces of information.
Sure, right. You know, three years ago we
were like no one can throw backspin.
And now every single picture we talked to throw backspin because
we learned that. So now that's not a true
statement anymore. So we learned how you throwback
spin and what that looks like has has changed and evolved and
our understanding of that has evolved and things like that are
(28:51):
always going to happen. Five years ago we were like 70
mph exit velocity. Whoo, that's amazing.
And now it's like, well, everyone's stronger, the bats
are hotter and now 80 is the threshold and we have kids
almost say 90 now. So things are constantly
evolving and changing because the game is changing and
training is changing and, and all of these things are
changing. So the reports are going to
(29:11):
continue changing. But that's really, I think where
our hesitation has come from. It's, it's twofold.
It's do you have the informationand and volume of data to even
deliver something helpful? Because just getting the data
and saying like here you go is not helpful in a variety of
ways. One sort of at best, it's like
is what does this mean? What am I going to do with it?
(29:32):
And at worst, as a more hot asterisk is that sometimes you
then miss bad data because you don't understand the data.
And now you hand over something that is bad data to someone.
And that's like the the worst case scenario, so.
And also your question Krista oflike do you have the answer is
no. I think the reason why I get
charged one, I'm low on sleep today so that I'm extra feisty 2
(29:56):
because I hate when shit is not rooted in substance and it gets
sold to somebody, right? I just I can't stand that
concept. And so the reality is if you are
looking for bio mechanics information that is going to
directly impact performance training, your plan, your player
development. If it is not a lab data report
(30:22):
that is connected to us, I don'tbuy it because and if that, if I
am wrong about that, maybe I am that.
I'd love to sit in a room with the people who think that that's
not true and see what they've got.
And I'd love to be proved wrong.But I just know what the process
has looked like and what it's taken to get to this point.
To know again, not to just collect data, not to just create
(30:43):
a research question, create a publication.
I'm not doing, I'm not. But I am talking about for what
we said, the ability to collect the data, have something really,
really, really clear to deliver to the athlete.
That's like, OK, this is what you need to go after right here.
Not this and not this, but this.And if you do that, this is what
we should see on the ball. If you think there is a product
out there from someone else, we have to get in a room with those
(31:04):
people because I would love the conversation is how I feel when
I think about that because thereisn't there isn't.
That pack runs through OGX, which is what I'm so proud of us
for. And so I think that's why I get
really charged. It's just like, don't buy that,
don't do that. That is just, it's either
something that's not going to help you and it's just for their
research purpose or it's a gimmick.
(31:26):
It's probably one of those sides.
So anyway, I think that's the reason why these types of things
sort of charge me a little bit is because everybody can make
something look real good on social media.
But the reality is people shouldknow what substance looks like,
what it means for something to be a value.
And this is not just me being like, oh, I think we're the
best. No, we are well.
(31:46):
Here's a, here's a perfect example, I think and maybe we
can end up this which I think isis tied to a little bit more of
where we have, we see this limitation in ourselves and we
are cautious about it. It's force flight data.
So I think like we've seen, we saw years ago force like data
came out from hitting and they were like good, bad, this you're
putting so much force into the ground.
And even then I was like, how dowe know this is good and bad?
(32:07):
Like, you know, you're, you're taking a good hitter and then
you're saying it's good that they do this.
They put this force into the ground.
It's like that's not really how it works.
And so we've with you and O we've been collecting force like
data for pitching and we don't report on it yet, right?
You don't know what it means. And I think that's the reality.
It doesn't mean it's bad to do that.
And and I was going to say, and if someone says, Hey, we got
(32:30):
this lab and we're like, we needto start collecting data for the
good of the game, go get in the lab.
Like you're not going to get a product out of it.
And but that's fine. I mean, do some things.
We've needed people to do those things all along the way.
So I think go give to the game and the research and things, but
from a product standpoint like we are, we are closer now
because of the volume of data that we've clicked into seeing
(32:52):
what matters and what we want toreport on.
But you can't just even though the very first pitcher that ever
walked in, you know, was on force plates, we couldn't just
say like see how much force you put into the ground here and
because you throw hard, that's good.
That's not real. That's not how it works.
So it's how long have we been collecting data that you know
now? Three years.
Almost 2 1/2 of the I say the force plates.
(33:14):
We probably had about a year anda half or so of full data
collection with full motion capture and force plates.
Right. And so it's like we're we're not
even in a place after that long.So it's still, you know, we
still want to post like this is amazing.
We're getting force like data. This is so exciting.
We're getting the full motion. We're getting all these things
and also from a product standpoint and the information
(33:36):
and the story and how much, you know, what ties or what you're
going for, how important that isto performance or whatever.
We don't know yet because we don't have enough data and we
don't have the norms and we haven't looked at it or been
able to ask the questions yet atthe volume.
And we're getting closer to that.
But that I think is an example of, yeah, that will evolve our
data when we get to that and we understand how that matters and
(33:58):
it will change things. But you can't just just because
you collect it, you can't just say this is is important and you
can't just say this kid throws 70 and she puts a ton of force
into the ground. So that's what we should be
doing. Right.
You can't do those things and you it takes time background and
so well, you can, Yeah, you can.Yeah, sure you can.
And so that's kind of the story,I think of just being like the
(34:20):
questions of asking of these places when you're going.
And it's the same for ball fight.
It's it's the exact same for ball fight, which is like Once
Upon a time four years ago, everyone was like the very first
time we ever kept got exit velocity of pocket radars and we
thought it was like amazing. We were doing everyone's getting
off the teeth. And so then they were like, this
is a good hitter because she had70 and it's a kid swinging out
the teeth. And so even that it's like we
(34:43):
delivered. You went to camps and and data
collection things and they measured your exit velocity of
the tee. And so it's like, that's not
good. That's not that doesn't tell us
anything. That's not a real measurement of
whether a hitter is good or not the power expression, but that's
not measuring anything. And so even in ball play, it's
like, if you don't understand sort of like what is good and
(35:04):
you don't have the volume of hitters to say whether that's
good or bad in the context of the skill, it just doesn't mean
a lot. And so that's kind of I think a
a good example of something we've withheld and not shared as
part of the story or not made any sort of conclusions on
because we're not a place where we can do that yet.
And so I think we just have to get to that place.
(35:25):
And and that's kind of what you're saying, Ash is that, you
know, if if you don't have that volume and that understanding
and you haven't seen how it whatmatters and how it interacts
with things, then just delivering the data to someone
is like, OK, what am I going to do with this?
Yeah. And any borderline I respond
there well. Yeah, that's, as I say, that's
(35:46):
the best case scenario. If someone just hands you the
information and you don't have to do with it.
The worst case scenario, we've seen this in plenty of cases,
even with some of the like fieldfriendly data, is that when they
then interpret it without that data and say like good, bad,
based on the three informations I've had, that's where it gets
like, OK, now we're we're starting to get into sort of
(36:07):
dangerous is probably pushing it.
But into category where you're taking this information, you're
telling them it's objective and it's definitely subjective and
you're sending them in a in a path that's not going to be
good. In summary, again, if you are
interested in getting your bio mechanics legitimately measured
and given back to you in a way that is related to your
(36:30):
performance goals and how training will connect to that
and what even matters in your motion, and just because
something looks odd doesn't necessarily mean that's
something we need to chase. If you want clarity on that
story in a way that's legitimately going to help with
training, I know exactly where you should go.
So you can book your assessment five places now, right?
So we got Romeoville, Omaha, Seattle, Phoenix, Tampa, any.
(36:56):
Of those places, not all of those, not all of those for
pitching. So, right.
So Arizona and Seattle not pitching yet, but hopefully
soon, but at least Tampa, Omaha and then Chicago are three
places where if it's not our ownOGX facility, it is our process
and our reporting and our debriefing, which is what really
matters. Yeah.
(37:17):
All right. See you there.
OGX Nation, we love you, we appreciate you, but we'd love
you even more if you would like subscribe to this podcast, give
us reviews, send us comments on what you want to hear, then we'd
really, really love you. Got to do it.
Do it now.
Popular Podcasts
On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Special Summer Offer: Exclusively on Apple Podcasts, try our Dateline Premium subscription completely free for one month! With Dateline Premium, you get every episode ad-free plus exclusive bonus content.
24/7 News: The Latest
The latest news in 4 minutes updated every hour, every day.