0:00

as soon as you have a strength conditioning

0:02

coach, who's a really good strength in conditioning coach,

0:04

having to also manage a

0:06

ton of data means you take away their time from

0:09

doing probably what you really hired them

0:11

to do and makes them less effective in that

0:13

space. So I would just say if we're not speaking

0:15

to someone in a physical preparation or the sports

0:17

medicine department, I'm speaking to an owner.

0:20

Sporting director or something, I would say, you want the

0:22

people that you hire as practitioners, being practitioners.

0:26

and you probably want people to do that to do

0:28

that.

0:40

Welcome

0:40

to the Paci Performance Podcast.

0:43

The podcast that dives into the philosophies

0:46

ideas and practices of

0:48

some of the best practitioners in high

0:50

performance spa.

0:52

Data science and data scientists

0:55

with no thought of their own, have got

0:57

mystique around them.

1:00

So members of staff sat in a darkened

1:02

room, not speaking to anyone, is

1:04

after a misconception of what data

1:06

science is. So what what is

1:08

data science? So we get today's episode,

1:10

we've got Johan Win. who

1:13

is head of Performance Data Science

1:15

at the Vancouver Whitecaps. So

1:17

where does data science fit into an organization? what

1:20

do they actually do? So we talk about

1:22

subject to evaluations, technology

1:25

evaluations, maximizing data

1:27

quality, And most importantly,

1:29

how data science influences various

1:32

aspects of the organization and

1:34

how it helps other

1:36

departments with their job and

1:38

makes their jobs easier by providing

1:41

good quality data. So

1:43

really interesting chat coming up. And one

1:45

thing that I think is the biggest takeaway

1:47

from this is how data science is

1:49

used to evaluate the effectiveness of

1:52

certain interventions from a spot science perspective

1:55

and also from a strength and condition perspective.

1:57

So a really interesting episode coming

1:59

up with you on.

2:02

This episode of the PACE of Performance podcast

2:05

is sponsored by Hydro. Have

2:07

you ever tried a blood flow restrict for

2:09

recovery. Hatrol has developed

2:11

the world's first BFR wearable

2:13

unlocking the recovery benefits of BFR

2:16

to spot athletes. BFR

2:18

is no longer just for one on one

2:20

physio rehab. Hydraul allows

2:22

teams to use this safe and scalable

2:25

sports BFR device, both exercise.

2:27

to dramatically enhance recovery.

2:30

So whether in the changing room post game

2:32

during a way game travel in the hotel

2:34

or at home, HATRO has

2:36

created a simple and effective tool to allow

2:38

BFR to be delivered to athletes

2:40

and squads simultaneously safely

2:43

and more convenient than ever before.

2:46

Check them out hitroome dot com or

2:48

email warren on warrenhydro

2:51

dot com to find out how

2:53

hydropie FR can give your athletes

2:55

a competitive edge. And

2:57

this episode is also sponsored by

2:59

Hocken dynamics. Hocking Dynamics

3:02

is the world's first wireless force

3:04

plate testing system. The Hocking

3:06

Dynamics system is built for coaches to

3:08

test in the real world, not just

3:10

in the lab. Capture reliable

3:12

data on all your athletes in matter

3:14

of minutes. and monitor their progress

3:16

in the cloud from anywhere in the world.

3:19

Dark

3:19

and Dynamics Forceplates are wireless,

3:21

portable, and trusted by teams

3:23

at every level of sport.

3:25

Integrating force plays into our athlete

3:27

monitoring program has never been

3:29

easier or more affordable. If

3:31

you want to see the Harking Dynamics BOSS

3:33

Play System in action, head over

3:35

to their website harking Dynamics dot

3:38

com. to schedule a demo or

3:40

for them on Twitter at harking

3:42

Dynamics. Also, sponsoring this episode

3:45

is Fusion Spark. Smartabase from

3:47

Fusion SPORT is the premier human

3:49

performance optimization platform

3:51

for elite sports teams and military

3:54

organizations. Built an

3:56

infinitely configurable framework,

3:58

Smartabase is the most flexible

3:59

software on the market. Create

4:02

an adaptable solution to support

4:04

your unique strategy, process

4:06

and culture for a fraction of the oftentimes

4:09

it takes to build your own. Centralize

4:11

your performance and health day by

4:13

easily integrating with other tech and

4:15

data systems using

4:17

smarter bases, robust

4:19

API and custom built connectors.

4:21

In proof performance and reduce injury

4:23

by enabling better communication and

4:25

decision making with role based access,

4:28

custom workflows, mobile

4:30

apps, and

4:30

personalized visual dashboards.

4:33

And with the Smartabase success

4:35

guarantee, you can be confident in

4:37

your human performance solution and

4:39

the people who stand behind it. Visit

4:42

fusionspot dot com forward

4:44

slash Smartabase to learn

4:46

how Smartabase can help you improve athlete

4:48

performance and service member

4:51

combat readiness.

4:52

So now further ado, over to the episode

4:55

with Johan. Johan

4:56

went Welcome to the pacing

4:58

performance podcast. It's a long time coming

5:00

that I'm delighted to have you. Hey.

5:02

Thanks for having me, Rob. It's a real pleasure.

5:05

No. Thank you for coming on. Thank you for coming on.

5:08

lots of back and forth on text.

5:10

Talk about

5:11

our journey through Father Hood.

5:13

You are far more experienced than me.

5:15

but

5:15

it's good to where it's good to get you on the podcast

5:17

and have a little chat about what you've

5:19

been doing at the at the football club,

5:21

what you've done in the past, and what kind of good

5:23

stuff. So and one that doesn't know who you are.

5:25

Would

5:25

you mind just giving us a brief bio?

5:28

Yeah. Happy to, Rob. I think first and

5:31

first and foremost, you've already flagged that probably

5:33

the most prominent parts of my life is

5:35

is right now husband and father. So I

5:37

I married my childhood sweetheart, and we're

5:39

now currently still trying to figure out this

5:41

parenthood thing with with a

5:43

couple of young ones. So if you let me know if

5:45

if you do figure out, please let me know. because

5:47

I mean, also, it's a trouble. I

5:49

can clearly describe it as a a very fast moving

5:51

target. So I don't if I if I

5:53

figure it out, I'll text you. And by the next day, it'll

5:55

be different. So I I don't know how much help

5:57

I can be.

5:58

But, yeah, professionally,

5:59

I think my

6:02

background would probably be described

6:04

as as

6:05

varied and then pretty consistent

6:07

with one foot in an academic space

6:09

in one foot and applied. So if we go,

6:11

like,

6:11

all the way back, moved around

6:14

a lot, grown up, like born in South

6:16

Africa, rooted

6:16

away when I was three, lived in

6:19

Canada for a year, moved to New Zealand,

6:21

lived in New Zealand for five years,

6:22

became a rugby and cricket fan. then

6:25

move to the middle of nowhere in Northern

6:27

Alberta

6:28

back in Canada and then spent

6:30

high school in the Vancouver kind of lower mainland

6:33

area.

6:34

And then underground went

6:37

to a school here in Langley,

6:39

did exercise science, physiology, with

6:41

my undergrad, like, a a ton of the the people

6:43

you've had on the pod. And then during that

6:45

whole time, tried to get as much

6:47

applied experience as I could. So I spent three

6:49

years as kind of a strike conditioning intern

6:51

during that time, trying to get on

6:53

the floor. Got my CSCS. Did

6:56

the the strength and conditioning route

6:58

and then graduated through

7:00

there,

7:01

tried to figure out what I was going to do next,

7:03

was either looking at academia, which

7:05

would have been the master's approach or in

7:07

an applied space of Physio.

7:09

An MD tried to go to

7:12

med school, potentially my dad was a doc, so

7:14

that was always one

7:15

of the discussions. Spent,

7:16

like, six months out of school,

7:19

trying to figure that out, and

7:21

then Professor Kieran Paun,

7:23

from Procter and Conn's Clinical Sports

7:25

Medicine, British Journal Sports

7:27

Medicine, all the things that he was doing at the time.

7:29

He gave

7:30

lectured through a bunch of physios and

7:32

gyros in in Avbotsford where I

7:34

was at the time. And I was like, I'm gonna

7:36

do MD, PHD. This

7:38

guy is literally both of those. He'll probably have

7:40

some wisdom to share about what's a good idea

7:42

in

7:43

that conversation.

7:44

sparked a kind of follow-up. He just said, look,

7:47

a

7:47

master's degree is not a bad choice if you're gonna

7:49

go either of those groups. So why don't you send

7:51

me some of your stuff? And and that led to a master's

7:53

degree with him. He said, look, if you got

7:55

a master's that sets you up for med school or

7:57

if you wanna do a PhD, do the master's first.

7:59

So that was

8:01

a really, really great conversation or

8:03

really, like, pivoting

8:04

point kind of in in my

8:07

career path. So I did the masters with them.

8:09

It was in

8:09

physical activity promotion. We work with

8:11

family practice physicians

8:13

trying to convince them that,

8:15

hey, one of the most

8:17

important things you could do in a fifteen

8:19

minute consultation is encourage your patients

8:21

to be physically active. It has, in

8:23

many instances, as much or more

8:25

benefit than many of the pharmaceutical interventions,

8:28

especially when there's nothing major acute

8:30

going on.

8:30

It's one of the major investments you

8:33

can do for physical activity promotion.

8:34

thought

8:35

it was super important as a topic.

8:37

I didn't necessarily love it. I think my

8:39

heart was still more in that sports space,

8:41

but that was

8:42

kind of the thesis and then

8:44

again,

8:44

tried to maintain that applied space. I was

8:46

a strength and conditioning coach at a local

8:48

college. I proposed

8:51

and then developed and taught a health

8:53

and fitness class at the college where I was teaching

8:55

as well, so I tried to do that.

8:57

And then

8:58

any random consulting

9:00

I could do in this kind of sports science, SNC

9:02

space. I just pick up and say, yes, to far too

9:04

many things.

9:05

And then during

9:07

that master's grad, this this interesting

9:09

hiatus. My my wife and I traveled to

9:11

South Africa. I got a research abroad scholarship

9:13

and we had, like,

9:14

five months living the life that

9:17

probably

9:17

would have had had we never left

9:19

South Africa in the first place. So I was

9:21

at the University of Cape Town where my dad

9:23

studied at the Sports Science Institute, the

9:25

Tim Nokes started My supervisor

9:28

there at the time was Martin Schwalnoz who

9:30

was just on his way out to to move

9:32

to Pretoria and Wayne German, the guys

9:34

that my dad studied with, but I've

9:36

spent time with all the other students. They have

9:38

noakes hour. So on, like, Friday afternoon,

9:40

Tim will sit down with all of the students

9:42

and staff that wanna go with the Sports Science

9:44

Institute and just kinda outline

9:45

his career journey and path for all the

9:48

students that are there for the year. So we'll talk about the

9:50

hyponatremia and the the

9:51

dehydration thing. We'll talk about central

9:54

governor theory and all of things that he's had there,

9:56

obviously, now with the low carbohydrate

9:58

diet approach and how he's

9:59

handling all of those things. So it's just really

10:02

interesting

10:02

stint

10:03

about, like, what life could have been, which

10:06

was really, really cool. And then, again,

10:07

we're

10:08

kinda at that discussion kind of at the end of

10:10

the undergrad. Like, what do I do next?

10:12

And we're trying to figure that out. And then while I was

10:14

in South Africa, I found out I had BHE

10:16

funding. And Karen

10:18

agreed to supervise PHD. So,

10:20

again, we moved back. We went to Vancouver.

10:22

I started the PHD, moved out of

10:24

the physical activity promotion space, and kind

10:26

of moved into the injury

10:28

prevention space. So I was co supervised, Tim

10:30

Gabbett, and then Karen

10:31

Pond was the other one

10:34

there.

10:34

And then pretty soon after signing up, I got

10:36

embedded as a an intern with the

10:38

Vancouver Whitecap. So I spent the first two years in my

10:40

PhD with the club and I'm kind

10:42

of a sports science role on the ground each

10:44

day collecting wellness measures on

10:46

the pitch with with catapult and trying

10:48

to get

10:49

a handle on how can you collect this

10:52

information and kinda support the team. So it's getting the

10:54

applied experience

10:55

then then on

10:56

the academic side,

10:59

there's some huge coffees. Put

11:01

another one. I'm

11:03

I'm afraid I'm gonna run on. I think I drank too

11:06

much before the podcast. I

11:08

have

11:08

a backup water. So we'll see how this goes.

11:10

Six AM is six AM for you. How's

11:13

it's just a bit of context.

11:15

Sorry if I carry on. No. Of

11:17

course. It was it was interesting. Start of the PHD

11:19

was another one of those conversations again

11:21

with with Karen though is really helpful and kind of

11:23

pivotal for where my career ended up going. So

11:25

I definitely didn't see it going in in this

11:27

direction. But I think I was like a lot of

11:29

graduate students like you and that

11:31

conversation with them. A lot of students get

11:33

involved in academia and they learn

11:35

kind of the bare minimum enough

11:37

statistics.

11:39

to publish

11:40

a paper that they need. So in my

11:42

master, I did that. I learned how to do, like, a Macomart

11:44

test prepared comparison so that I published my paper,

11:46

and then I forgot about it. I learned r, I forgot

11:48

all of it,

11:49

and lost the statistics

11:51

stuff. And

11:52

then early in the PHE, it's like, okay. Well, I'm gonna

11:54

have to get back into this coding fast

11:56

thing

11:57

in talking with families. Like, you could

11:59

do that and a lot of

11:59

students do it. But look, academically, this can

12:02

become essentially one of your

12:03

superpowers that people often don't

12:06

consider

12:06

it. Like, you can dive

12:07

in, actually, learn, and develop

12:09

this as a skill, not as a, like,

12:12

a chore you have to do.

12:14

And that actually led into

12:16

me taking a sub specialization here at

12:18

UBC in the measurement evaluation research

12:20

methodology. substream.

12:22

So it's run by education and psychology. They

12:24

have a really strong psychometrics

12:26

department. It lets a conversation with

12:28

Professor Bruno Zumbow who's one of the world leaders

12:30

now in unified validity theory.

12:32

He's a psychometrician mathematician

12:35

really, really brilliant

12:36

You guys ended up being on my PhD

12:38

committee. So I learned about kind of the statistics

12:40

and research methods in a field

12:42

completely unrelated to support, which

12:44

actually I think it's come to pay dividends

12:46

for for how I think about different

12:48

sporting constructs because they're that's where

12:50

they live. In second metrics, you're trying

12:52

to quantify and

12:54

capture things like

12:56

emotional intelligence or even just general

12:59

intelligence or empathy.

13:01

It's like these things are constructs. We all think

13:03

and identify that they exist, but

13:06

you can't just strap

13:08

on a GPS and get someone's be on the other

13:10

end. You can't do that. So you have to develop things. And

13:12

then you have to say, how confident are

13:14

we that this survey we developed, captures

13:16

this thing that you can't have in that. I

13:18

think we end up doing that in support. We'll probably

13:20

get to that. But, like, learning about

13:22

that space was was really helpful during

13:24

that PhD. and then

13:26

trying to upscale myself thinking, hey, if the

13:28

statistics thing is helpful, this coding

13:30

thing might also be helpful to stop me from

13:32

doing all this manual I've done what

13:34

everyone does early in their careers in the

13:36

sports science space of, like, go

13:38

into software, download CSV,

13:40

all parameters. copy all of the

13:42

posts, paste it into the other spreadsheet,

13:45

manually clean the data, which is a

13:47

disaster, hope for the best, and

13:49

the publish

13:50

up the thing. Like, I I did that and I was

13:52

like, maybe there's a better more efficient way to

13:54

do this because this is a lot of time every

13:56

day doing the same thing.

13:58

So spent

13:59

a lot of

13:59

time just upscaling during the PHD,

14:02

trying to become more efficient and and helpful

14:04

in that space, and then wasn't

14:06

done two years into the PHD two years into the

14:08

Whitecap. So I was approached. I was at conference in Belfast.

14:10

Dr.

14:10

Dustin Knapp had at the time he was with the United

14:13

States Olympic and Paralympic committee. He said,

14:15

look, We

14:16

have a job opening in the sports medicine

14:19

department for a data analyst. We're trying to run

14:21

and generate kind of this athlete monitoring program

14:23

across and support national governing

14:25

bodies across the country. We think it'd be a

14:27

really good candidate. Would you be interested and

14:29

had that conversation? One thing led to

14:31

another, we packed up our bags, like,

14:33

a month later and moved to Colorado Springs.

14:35

So then I was

14:36

working in at

14:38

the Olympic training center there, trying to

14:40

develop and and build up these things, spent a

14:42

lot of time building Qualtrics surveys

14:44

for athlete wellness and athlete

14:46

load across a ton of different

14:48

sports have really good conversations

14:50

there also had the privilege of working with Dave

14:53

Taylor when I was, like, we've talked about him in

14:55

the past as well. So he's went on to Golden

14:57

State Warriors now. And then

14:59

Bryce Murphy, who went on to the

15:02

Orlando Magic now, IMG Academy.

15:05

it was, like, this this very condensed

15:07

period. We were only there. I was there

15:09

fourteen months in total. I was there crossing

15:11

paths with Dave for about a year and Bryce for

15:13

five months, but I is one of those we

15:15

work together for, like, a year, maybe less,

15:17

but we felt like we got three years of work

15:19

accomplished. It was just this really

15:21

fun office with

15:23

a lot

15:24

of Tableau dashboards and probably a little bit

15:26

too much espresso. But

15:28

it

15:28

was it was a really good time there.

15:31

And then tried to do the

15:33

the PHD right up the dissertation on

15:35

evenings and weekends, and we had a coffee shop that we'd

15:37

go through each weekend to try and get through

15:39

that.

15:39

And

15:40

were working there

15:42

less than a year end, and that kind of led to

15:44

this current role. I got a call

15:47

back from doctor Ben Spohr who had

15:49

taken over as VP of

15:51

performance here at the Vancouver Whitecaps. And

15:53

at

15:53

that time, they were having a bit of a

15:55

revamping in terms of the performance strategy. Some

15:57

staff had left to move to the

15:59

warriors and they were

15:59

reshaping and kind of planning what that

16:02

future for the performance side of the organization looked

16:04

like and, Vincent, but I've

16:05

had discussions with senior leadership. I've

16:07

pitched what I want this look like in one of the

16:09

pillars that I want within the performance

16:11

strategy and at the higher levels, I wanna make sure

16:13

that we have a data science to and there's

16:15

that we want to accomplish as an organization

16:18

strategically, whether

16:19

that be evaluate the effectiveness

16:21

of all the things that we're doing to have data

16:23

informed decision making to

16:25

look

16:26

at

16:27

these things in a in a long term

16:29

process perspective, looking at

16:31

what is the actual output just

16:33

the the outcome on a on a game to game basis.

16:35

And he said, look, to to do this, one of the things we're

16:37

gonna need is is to have

16:38

a data science

16:40

department or group dedicated to this. Would

16:42

you come back and kind of take it on

16:44

and and build it? And that's been

16:46

now since the start of twenty nineteen, three

16:48

and a half years of change my current

16:50

role and Yeah. Again, I've

16:52

managed to try and keep one foot in in that

16:54

academic space. I think now it's it's

16:56

largely applied. I teach a couple courses

16:58

here at UBC, I enjoy the teaching

17:00

side. We have an

17:02

embedded PhD students in partnerships with the

17:04

university to try and maintain this

17:06

kind of balance a daily training environment

17:08

here that we have to make sure that we deliver

17:10

on each and every day at a high level,

17:12

but also this research innovation space, which can

17:14

be really enjoyable as long

17:16

as it kinda can be used and supported

17:18

in in that the

17:20

actual daily operational environment if you can

17:22

bring it back. So long

17:24

winded answer, but that's the the story to

17:26

this point. No. It's

17:27

interesting. III like, especially

17:29

someone who has a

17:32

nonconventional route

17:33

into that position. So, yeah,

17:35

really interesting. One thing that I

17:38

that when I was listening, it

17:40

was interesting what you were saying in terms

17:42

of that.

17:42

foot in both camps. And that's something

17:45

that Robyn Thoth, I don't know if you've come across

17:47

Robyn, worked to Manchester United over in Red

17:49

Bull now. was very adamant

17:51

how important that is for a spot scientist

17:53

to have that

17:54

research one for in the

17:56

research. one foot in the applied so

17:58

that both can can aid each other.

17:59

So I think definitely echoed echoed

18:02

Robin starts there. But move moving on to

18:04

the data science piece.

18:06

data

18:06

science department

18:09

within the organization. Where

18:11

does that fit? Where do you

18:13

where does your influence

18:15

start and finish? And how is it kind

18:18

of sprawling to other

18:20

areas that may not be what people

18:22

imagine? Yeah.

18:23

No. A hundred percent. I think it's a it's

18:25

a great question. I think it's especially good

18:27

just because, like, that doesn't look the

18:29

same in every organization. And

18:31

I think one of the things before you can answer

18:34

where data science does or should I

18:36

think it's helpful that you just step

18:38

one level up to just even say, what do we mean

18:40

when we say data because it's weird. I

18:42

think a lot of people know what you mean when you

18:44

say like a deadlift or

18:46

a squat or a physiotherapist, you have a

18:48

pretty good sense. But it's

18:50

helpful to say, like, what do we mean when we say data

18:52

scientist? because, again, I think in support, it

18:54

probably looks slightly different than it does at

18:56

Google or Facebook just from the

18:58

nature of how many staff

19:00

can work in this space, what the bandwidth

19:02

is, what the scope of the organization

19:04

is. So when when I would refer

19:06

to data I I would refer

19:09

to

19:09

the application

19:10

of the data pipeline being

19:13

collection, analysis, and modeling, and

19:15

then communication of that data to inform decision.

19:17

And that's what I would do. So you you might

19:19

hear in a in a really technical space, you'd

19:21

probably differentiate, like data science is the modeling,

19:24

the machine learning model

19:27

generation process. And then, often, you'll have a

19:29

a data analyst or even a data visualization

19:31

expert for the communication side, and you

19:33

would have a data engineer and a database architect to

19:35

handle some of the collection and and

19:37

data basing of that information. So I think

19:39

you see more specialization in

19:41

other fields I think

19:43

in sport, at least

19:45

in a lot of the circles that I've had

19:47

discussions. To this point, it's the data

19:49

science department's kinda responsible for all

19:51

of those. Like, you have to figure out how to collect it. You have to figure out how to

19:53

manage it. You have to figure out how to analyze and model

19:55

when appropriate. And then communicate it to all of

19:57

the stakeholders. You have to do all

19:59

of it. And

19:59

in

20:00

that case, then I

20:02

think

20:02

i think yeah. I

20:04

think my answer and a lot of my thinking, we

20:06

we were talking about Patrick Ward before the podcast.

20:08

And some of my my own

20:10

thinking came out of a conversation. I think it was

20:12

twenty seventeen. I was in my PhD intern

20:15

here at the time. It

20:16

was the Saunders Sports Science Conference, and we were

20:18

just having this with this chat over drinks after

20:20

one of the sessions.

20:22

the And at the

20:24

time, I was musing over this conversation.

20:26

I I heard again, it was in

20:28

the S and C space. So Dan John, and it was

20:30

describing this conversation he had and said, what's

20:32

the of a strength coach, it's really easy to

20:34

quantify, help develop physical capacities like strength

20:37

and power, aerobic capacity, etcetera.

20:39

And then he said, but what's the impact

20:41

of a strength coach? and that might be harder.

20:43

Right? Like, you can influence the culture, you

20:45

can you can have these relationships

20:47

that change the dynamic of an organization, you

20:49

can help develop people and players,

20:51

all of those things. the impact's a different

20:54

thing than the role. And at the time, I was trying

20:56

to move more into this data science,

20:58

sports science space. Patrick was a

21:00

bit experienced and had been in it longer. I said, what do

21:02

you think the role and impact

21:04

is of someone in a sports science space?

21:06

Because, again, it's harder to quantify these things

21:08

at times. And

21:09

that conversation

21:10

actually then led to two papers. So

21:12

we Patrick and

21:14

I with Tom Canton published this one about

21:16

business intelligence saying, look, sports

21:18

science. We

21:18

in that one, we were talking about sports

21:21

science, is literally the application of, like,

21:23

data collection, data analysis, data

21:25

communication and then even using that as a decision

21:27

audit after the fact to help

21:29

inform decision making and support it. Same

21:31

thing right now, business intelligence does

21:33

in

21:33

that space.

21:35

That's kind of what I define

21:37

data science as well. But the other

21:39

part that came out is is when we're having that

21:42

discussion in terms of an an

21:44

IS team. I think all of us are familiar like, an athlete centered support

21:46

team. There's athlete, and then there's a sports

21:48

medicine team, and you're a physiotherapist,

21:50

and maybe you have physical preparation

21:52

coach or multiples. You have support

21:54

nutrition staff. You have technical coaching

21:56

staff. And you have video analysis team.

21:59

You have for

21:59

psychology, mental performance support. You have all

22:02

of these different

22:03

departments and supports and you put that

22:05

around an athlete. So I think the logical next

22:07

step in most people's mind is oh, it seems like

22:09

a lot of people are getting these sports science or

22:11

data science departments. Let's put them

22:13

in and then we have another cog within this

22:15

wheel of this athlete center support team.

22:17

And I Like, I I don't I think that happens

22:20

a lot. I think it can't function to start like that

22:22

will never work. I just don't think that's

22:24

optimally where data

22:26

science. If we think about that informing decision making pipeline, I

22:28

don't think that's the optimal fit. I I put

22:30

them as almost this glue

22:32

that

22:32

sits underneath

22:35

and

22:35

outside of all of those other practitioners

22:38

that that end up being the decision

22:40

makers when you have a physical preparation

22:43

they design the periodization, the plan loading,

22:45

the implementation of a lot of the

22:47

on pitch and in gym fitness work,

22:49

the sports medicine, team have to make

22:51

decisions about return to play, about what

22:54

type of prevention strategies are we going to

22:56

implement the coaching staff, the scouting and

22:58

recruitment, which players are we going Like, they're

23:00

the decision makers. And then our

23:02

job, and I think data science

23:04

optimally fits then, going to

23:06

all of those practitioners and saying, look, how can

23:08

we, as a data science department, make your

23:10

job easier or more efficient? Like,

23:12

what are the decisions you have to make?

23:14

What information can I come and give to

23:16

you? Either make it easier to make that decision.

23:18

Maybe it helps make it

23:19

faster, maybe it it challenges, or

23:22

maybe it encourages what you're already doing just.

23:24

And sometimes it's

23:24

literally like can I

23:26

set up

23:26

a folder structure on our shared

23:29

space just so your document

23:31

storage is a bit more

23:33

a

23:34

bit more organized and in line with the rest of what we're doing. And

23:36

somebody I need that. I'm

23:39

desperate

23:39

for that. But

23:42

I think when you're in a generalist space, right, like, it it isn't

23:44

always like, hey, can we build the most sophisticated

23:47

convolutional neural network and support?

23:49

Like, that's not where we

23:51

spend the majority of our time. And I think

23:53

that's that's a really healthy space. Just to be

23:55

one step below, we wrote a editorial in

23:57

BGSM, a few of us that have kind of

23:59

been in the space,

23:59

and that's I think organizationally where

24:02

I think it's optimal for data science and

24:04

support to sit. I

24:05

know people will have different perspectives. It's

24:07

how we sit

24:08

here and that's been

24:10

Yeah. I think it's been really nice

24:13

to transition into a role where that

24:15

is realized. And yeah.

24:16

Again, I I could go on. I'm gonna,

24:19

like, I

24:20

think if I can take a smaller

24:22

side, this this only works. So I think

24:24

it's optimal,

24:25

but the caveat is you you can't

24:28

do it if the

24:30

organization isn't set up for that to

24:32

work. Right? So as

24:34

a data science department, within a structure

24:37

like I'm not the primary decision

24:39

maker almost

24:40

in any realm. Right? So

24:42

what

24:42

that means is I can have

24:44

this really,

24:45

like, Let's say,

24:48

intellectual is

24:49

the wrong word. I can have this really broad

24:52

scoping role, which I really enjoy of being

24:54

almost like this glue between multiple departments to

24:56

have these discussions across the board and

24:58

understand what's going on, and try and

25:00

help as many people as I can. And

25:02

that extends again within each department,

25:04

but it if you have

25:05

an organization that has

25:07

a bunch of people that just think

25:09

they have all of the answers already,

25:11

and if they're instead of like,

25:14

encourage to look at other sources of

25:16

information or want to take information and

25:18

data onboard,

25:19

the like,

25:20

the data science department becomes

25:22

completely helpless.

25:23

It's useless to spend resources and

25:26

time. And then you have a frustrated group of

25:28

people in the data science space trying

25:30

to build dashboards or reports or something

25:32

and a bunch of decision makers who are like, why is

25:34

this guy in the room? I

25:35

already know what I'm doing. So

25:37

why is he here? And then that that

25:39

breaks down the perfect organizational structure if

25:41

you don't have a culture in

25:43

place and a structure in place and if you don't

25:45

hire the people that are willing to take that information

25:48

on board, then that it doesn't work. It'll

25:50

just implode, and I think that that can happen as

25:52

well. It's it's not just organizational

25:54

structure. I think it's it's culture. And I'm

25:56

I'm really fortunate here at the club. That's

25:58

when I talk back

25:59

to when they

25:59

revamped what the performance department

26:02

looked like, kind of when Ben took over

26:03

as

26:04

SVP And just within

26:07

the culture, I think I can list people

26:09

within every one of those departments

26:11

that have that have been brilliant

26:13

to work. like, in the physical

26:15

preparation space, John Foley and I have worked

26:17

together now since my first

26:19

joined. He's been here even before that. So he's been

26:21

brilliant as a physical preparation coach.

26:23

He's

26:24

he's brilliant to work with,

26:25

and that's been a really great experience. You'll know both

26:27

Steven Fletcher and Tom Ryan, the

26:30

some of UK invasion has begun. So they they've come

26:32

over and they've again, we we bring

26:34

on really quality people that are willing to take that

26:36

type of information on board. We can have these really

26:39

helpful discussions about how can I make their

26:42

jobs a little bit easier, and they've been

26:44

brilliant to have. So

26:46

that's

26:46

been great. Chris Branks James

26:48

Gartner has joined as

26:50

as an a t. Jose Yimin is on the sports

26:52

medicine side. Again, each of these departments have

26:54

these

26:54

practitioners that are great practitioners,

26:56

but also really good people that

26:58

are like you can have

26:59

these conversations with. Our

27:02

our director of outing, spent time working at what's

27:05

considered the one of the most data driven

27:07

football clubs in Brentford, F. C. Michelin,

27:09

owned by Matthew Benhama. a

27:11

a sports gambler turned owner. Obviously,

27:14

he takes a lot of data on board, and then he moved

27:16

from there into working directly with stats

27:18

on one of the a lead

27:20

data providers in sport. Right? Now he's come

27:22

on. So you can guess he's he's very

27:24

open and comfortable taking data onboard

27:26

to make decisions when we look at

27:28

look at players. We have a technical staff.

27:30

Not every coach is the type of coach that's

27:32

going to say, hey, what

27:34

you do the

27:35

data have to say about this performance,

27:38

about this player about these things and we have a group of technical staff

27:40

that are willing to have those discussions that we can be

27:42

in a room and just have

27:44

these

27:44

these chats about,

27:46

hey, where are we? What are we doing? How

27:48

can we improve? Where do we

27:50

see that

27:50

we've grown? And what can we say both

27:53

positively and negatively? And translate

27:55

that where necessary to other members of staff

27:57

or to the players. And those those

27:59

conversations don't

27:59

happen in every organization. We work

28:02

incredibly closely with, like, our video analysis

28:04

team with with Luke and Drew, and I

28:06

think from a date of his perspective, we'll

28:08

probably get there in this conversation. But,

28:10

like, if people

28:11

always say, like,

28:13

picture is

28:14

worth a thousand words kind of thing. And

28:16

and I was just, like, the other day thinking, like,

28:18

a a video in some cases is worth, like,

28:20

a thousand dashboards. Like, you

28:22

could take the most sophisticated model, you could build a

28:24

thousand dashboards to show up. But then when you

28:26

wanna translate that sometimes to a a player or

28:28

to a coach, you can

28:30

show one video example of saying, we want to do

28:33

this more. And that's

28:34

gonna be way

28:35

better than, like, my most sophisticated Tableau

28:38

visual.

28:39

in many of those cases.

28:40

But that again only happens if you work

28:43

with the other practitioners and

28:44

the staff that are one going to help

28:47

you create

28:48

that content and translate that across

28:50

to the other practitioners or to the

28:53

players. And

28:53

I think that's the other thing

28:55

from the structure. I know it's a bit of an aside here

28:57

on that space. But I think

29:00

in that

29:01

structure, if you go and you make

29:03

data science this supporting structure with

29:05

all of the other practitioners,

29:08

the it it actually

29:10

does remove us one step

29:12

from an athlete. Right? So

29:14

if

29:14

now I help make Jampoli's life

29:17

easier or loops

29:19

like easier or our coaching staff

29:21

their life easier.

29:23

And

29:23

by helping all of them,

29:25

what

29:25

it means is often, I think the information

29:28

that we provide is most effectively

29:31

translated by the person that usually works

29:33

in that avenue with an athlete. So John

29:35

Polley can communicate, hey, here is

29:37

where your loading is at. Here's why

29:39

we think you should come on for sixty

29:41

minutes this next game because we're in this

29:43

congested schedule. we think you'll play

29:45

sixty, then you'll be ready on the weekend. Hey, we're going

29:47

to implement this new Arova

29:51

capacity training top of what you're doing because, hey,

29:53

we've identified this on your screening or this

29:55

on your testing or we've looked at your loading over

29:57

time and here's where here's why we're making

29:59

this decision. So

30:00

I help build out the dashboard

30:02

or report that identify those things.

30:04

But I also need

30:05

to be comfortable that I'm it

30:07

probably means

30:07

more to the athlete coming from

30:10

John Pope. And when the coaches say, hey,

30:12

we want you to do this behavior more. I might

30:14

have some information that helps

30:16

guide the decision to say, hey, we want to

30:18

encourage this action or this type

30:20

of play, it means more when

30:22

a coach, like Ricardo Clark, who's

30:24

played it in the world. Cup goes to the player and

30:26

says, hey, you're doing this. Maybe you

30:28

wanna do this more than

30:30

having a data science background and be like, you know

30:32

what I found? Let me pull up my dashboard and talk

30:34

about this. It's it's it's a different

30:36

role. And sometimes, like, having come out of the

30:38

strength conditioning realm where you're on the floor, you have these

30:41

discussions every single day with the athlete. I'll

30:42

have less interaction with athletes

30:44

in structure than I have in the past. And

30:47

that's also just

30:47

personally some of the things that you need

30:49

to to work

30:50

through and kinda kind

30:52

of work with. But is is that sometimes a criticism

30:55

of this

30:56

type of structure? I think

30:58

I think it

30:58

can be because I think Ryan Curtis

31:01

had that discussion with you as

31:03

well because the more embedded you are, the more

31:05

you're there with the athletes, like, the more you're on the

31:07

floor, the more likely when

31:09

you do have something you want to communicate. Look, they're more

31:11

likely they are to list. Right? That

31:13

that that's a

31:14

that the that's going to

31:15

happen. You like Ryan mentioned, you you're

31:18

more likely see some of the

31:20

challenges, the hiccups, the pain points,

31:22

especially on, let's say, the physical preparation

31:24

sports medicine side if you're in that room and on

31:26

the floor every single day. I

31:29

think the way we try and balance

31:32

that is like we have a a sport

31:34

scientist we've hired, Luke petty, He's

31:36

in his PhD here at UBC. He has extensive

31:39

experience, both as, like, a national

31:41

level swimmer, but also now helping

31:43

in kind of the sports science supports

31:45

SaaS based and and swimming. Now he's moved over

31:47

into the the football realm. So he he's

31:49

within our data science department,

31:52

but he's he's our sports scientist and he's on the ground with the

31:54

athletes every single day. So what we do

31:56

then if within that structure is

31:59

loop is primarily our conduit with our sports

32:01

medicine staff and our physical preparation

32:03

staff. So we as a department

32:04

spread all the way through, but to

32:06

make that as efficient

32:07

as we can,

32:09

Like, Luke then is on the floor communicating every

32:11

single day. So one of us as a

32:13

data science department needs to help

32:16

with force plates or warm ups. Like, he he's kinda

32:18

first line there. So he's gonna spend the most time

32:20

with the athletes. So he'll rep the data

32:22

science department. It's not always loop. So we just this

32:24

past week, we've rolled into academy testing. So

32:26

there's whatever eighty

32:27

academy athletes that have arrived. We need to run them

32:29

through a kind of testing. So all three of the

32:31

data science department alongside Tom

32:33

Ryan or they're doing whatever we need to to make

32:35

the testing function. We're willing to do that, but

32:37

I think we try and

32:40

have representation in each department. So Luke's there in the

32:42

sports medicine space and in the physical preparation

32:45

space. He's on pitch. He's in the way of room. He's

32:47

doing all of those things. But

32:49

we also, again, in that structure, don't just live in that space. We also

32:51

have to help with the coaching staff and with

32:54

opposition analysis, with scouting and

32:56

recruitment. So In that case, we also have

32:58

Alexander Henson who's a

33:00

more brilliant and better data scientist than

33:02

I will ever be. We hired him after he finished

33:04

the masters in economics and he finished the

33:07

master and data science. During that time, he got to do a Capstone

33:09

project with us. It was a brilliant

33:11

eight week time. He was a member of a group

33:13

that worked with us. And he was one of those

33:15

as you spend eight weeks with him. Like, if we can get him

33:17

as part of the organization, there'll be a big win for

33:20

us. So we we we talked and we

33:22

told him we got him here, and he's been he's been a

33:24

great addition. But there, as our senior

33:26

data scientist. Now he spends more of his time

33:27

really closely with our coaching staff, with our

33:30

video analysis team on

33:32

opposition

33:32

scouting. spends more

33:34

time building some of our models off

33:36

events or tracking data or doing some of

33:38

the database architecture. He spends more time doing those

33:40

things. and being our primary conduit down

33:42

other avenues. And then I'll have the

33:44

same, like, communicate in certain avenues

33:47

more so than others. to

33:49

try and identify. But I think back to your

33:51

point is a criticism. I think if you were

33:53

completely removed, so we do sit in a different

33:55

office a lot of the time when we plug

33:57

away if we only sat in this room and we never got out, we

33:59

never spoke to the other practitioners,

34:01

it's really

34:01

hard to have positive relationships and

34:04

ask the questions about how we

34:06

can make their life easier if they don't know who we are and

34:08

we don't experience any

34:09

of those things. So I think it's it's a

34:12

balance point that

34:12

you have to hit. We've tried our best to

34:15

do that with our structure, but I think some moving

34:17

target kinda like like parenthood

34:19

as well. No. No.

34:22

I understand. So we're just gonna

34:24

take a very quick break in the chat with

34:26

Johan Hope in Joint Power one. So we

34:28

have a little chat around

34:30

data visualization in Power two. some

34:32

principles that Johan and the team at

34:34

the Whitecaps live by, which is not

34:36

ugly and efficient. What that

34:38

actually means how they live by it,

34:40

an example how you can

34:42

improve your dates visualization as

34:44

well. So really interesting part too

34:46

coming on.

34:48

This

34:49

episode of

34:51

the Pasty Performance podcast

34:53

is sponsored by Omega Wave.

34:55

Omega Wave is the only non

34:57

invasive at rest technology on the

34:59

market that analyzes readiness to train via both brain and

35:02

cardiac analysis. Using DC

35:04

potential and HRV to understand

35:08

your brain's energy level and autonomic nervous system

35:10

balance allows you to use objective

35:12

data on recovery and

35:14

readiness that

35:16

in turn helps you to

35:18

truly individualize your training and thus optimize performance.

35:20

A megawave also measures

35:22

ECG from the v six

35:24

position. this

35:26

data can be used by the medical profession to cardiac health

35:28

on a frequent basis. The

35:30

measurement takes only four

35:32

minutes to perform

35:34

and results are visualized in an intuitive way,

35:36

thanks to our windows of trainability

35:39

concept. A megawave

35:40

used by hundreds of

35:42

elite sports, military, and law

35:45

enforcement organizations. A

35:46

Mega Wave, also the official partner

35:48

of the UFC performance in YouTube.

35:51

Learn more about A Mega Wave

35:51

by visiting their website at megawave dot

35:54

com and their social media

35:56

channels. And this episode is also

35:58

sponsored by

36:00

Tanta College. Tanta College, led by doctor Liam Hennessy,

36:03

provides international recognized qualifications in

36:05

strength in conditioning and

36:08

performance science, to

36:10

master's level. Castle designed

36:13

by industry leaders such as Des

36:15

Ryan and Professor Iain Jefferies,

36:17

ensuring students and

36:19

graduate at the cutting edge of technology and learning

36:21

the most current methodologies from

36:24

world renowned

36:26

practitioners. Instacology's

36:26

unique blended learning approach allows you to take

36:29

the next step in your career, in your own

36:31

time and in your own pace.

36:34

Lectures are delivered in an online classroom,

36:36

while residential workshops provide

36:39

the perfect opportunity for

36:41

practical application of your studies with

36:44

guidance from experts in the field

36:46

of sports science and performance

36:48

coaching. With

36:50

campus location across Ireland, the UK,

36:52

USA, India and

36:53

South Africa. Applications are now

36:55

open for courses in

36:58

including the BSC in strength and conditioning,

37:00

MSC in

37:01

performance

37:01

coaching, and MSC in applied

37:04

spa and

37:06

exercise physiology. visit

37:08

santa college dot com for more

37:10

information on how to

37:12

apply. And now back to the episode

37:14

with Yoam.

37:16

So there are many people out there.

37:18

There are probably more people out there who are one man bands

37:20

who are one man band

37:21

plus an

37:24

intern. versus the data science department. So just to

37:26

finish off this little area that we're

37:28

we're we're chatting in regarding data science,

37:31

what would be

37:34

the

37:34

what would be your

37:36

y'all

37:38

recommendations

37:38

for those people

37:40

to upscale

37:42

not only for now, but to future proof

37:44

themselves with

37:45

new technology, the amount of

37:47

data that's been processed, generated,

37:51

and has to be analyzed, visualized, cleaned, all

37:53

that kind of stuff. What would be

37:55

your recommendations for those

37:58

people to take on as a I suppose

37:59

the

38:00

in situ default data

38:03

scientist because there's there's only

38:06

one there. will be our recommendations. I think it's a I think

38:08

it's

38:08

it's a really important thing to consider because

38:10

I think that is the case in many

38:13

many organizations that The real

38:15

quick caveat I think is organizationally. I don't think it's

38:17

optimal because as soon as

38:19

you have

38:19

a strength conditioning coach who's a

38:21

really good strength initiative

38:24

coach. Having to also

38:25

manage a ton of data means you take

38:27

away their time from doing probably what you

38:29

really hired them do and it makes

38:31

them less effective in that space. So I would just say

38:34

if we're not speaking to someone in a

38:36

physical preparation or the sports medicine department,

38:38

if I'm speaking to

38:40

an owner, sporting director

38:40

or something, I would say you want the people that

38:42

you hire as practitioners, being practitioners, and

38:45

you

38:45

probably want people

38:46

to do that to do that.

38:49

that's an organizational thing. It's not the reality. Sometimes it's budget.

38:52

Sometimes it's just organizational

38:54

constraints. So you you do run

38:55

into this this avenue. And

38:57

then I think to speak

38:59

to the practitioners, they have to do what

39:01

they're great at on the floor with

39:03

the athletes on the pitch, whatever that might be, and

39:05

then also have to have some data

39:08

skills. Or let's say if almost like

39:10

myself, if you're in a practitioner role

39:12

and you want to gain those skills and say, I

39:14

actually want to do more of this the

39:16

future. I think

39:16

what I would

39:17

what I would say is I've come back to

39:19

that description of data

39:20

science being that full pipeline.

39:23

So I think if you Google, like, what is

39:25

data science or, like, upskilling in data science, you're

39:27

really quickly gonna get into, like, a coding course

39:29

and pipeline about

39:32

machine learning. which is fine. Like, you can learn that and understand what a

39:34

random forest does or it's, like, decision

39:36

tree. Oh, obviously, you can

39:38

do that. But I I think in

39:40

support, your day to day isn't just sitting back

39:42

and and fitting models. It's actually managing

39:44

data from its onsets to

39:46

its conclusion where you're trying to inform

39:48

some decision making often the

39:50

modeling doesn't necessarily have to be as

39:52

sophisticated as what it might be.

39:54

There's gonna be a time and space for that. So I

39:56

would say, you wanna in

39:58

that space, I think the most

40:00

helpful thing that I have found and the thing

40:02

that I've often recommended when people ask that

40:04

question, is

40:05

find projects. It can either be at your work or

40:07

it could even be personal projects. Like, if you

40:10

have a a

40:12

Fitbit or some wearable

40:14

technology, even though you have on your own, think

40:16

about what do I want to

40:17

know about whatever my own training or what do

40:19

I wanna know about the athletes that I currently

40:21

work with and,

40:23

like, build out something. Like, imagine what it

40:25

would look like at

40:28

the end and then try and figure out a way to collect the data. Maybe

40:30

for the first time that is going to be a

40:32

download CSV. But when you do it, you might find that

40:34

the spreadsheet's

40:36

a mess. that

40:37

you have six header rows that things

40:39

are out of place, and you have to

40:41

change a bunch of stuff in order to even

40:43

get it into another platform. And then

40:45

when you do that, maybe you're like, oh, I need

40:47

to create a new variable ranker to talk about

40:49

that. Like,

40:50

i 0II

40:52

my

40:54

speed or velocity. Like, maybe I wanna do

40:56

force velocity profile. I need to create some other

40:58

variable. Maybe I need to estimate B02

41:01

max from a thirty fifteen result have

41:03

these thirty fifteens or whatever, and I wanna get someone view

41:06

it to max that you

41:06

have to create the equation to get there. Maybe you have

41:08

to do some sort of analysis, create new

41:11

variables, describe what that is and some basic summary

41:13

statistics, and then you have to build

41:15

the visualization of dashboard or

41:18

report or whatever that is to

41:20

communicate that at the end. And I think you

41:22

if you break apart your learning into,

41:24

like, trying to solve real world problems,

41:27

whether they

41:28

be for yourself or for the

41:30

organization, then I think you encounter and learn problems

41:32

of what you actually do in sport. because

41:34

it's it can be one less motivating

41:36

to just

41:36

open up this online coding course

41:40

from track and just learn how

41:41

to type print hello world into

41:42

the coding platform and then go

41:44

through all of the syntax.

41:47

that's

41:48

not really how we learn on the ground either. It's a lot of time. Like,

41:50

I need to solve this problem. So how do

41:52

I do? Right?

41:53

So this one won't

41:54

be solved by Excel.

41:56

maybe

41:56

a survey that has a is a powerful enough platform will

41:58

be a great way to collect

41:59

this data. What we encountered here is some of

42:02

the stuff we wanted to collect is a bit too

42:04

sophisticated. So

42:06

a SurveyMonkey and a Qualtrics doesn't have what we need. So

42:08

then

42:08

we have to learn how to build apps

42:10

because then we can build it more to

42:12

what we need it to be. So then you

42:15

learn how

42:15

to do it. You learn it because you're solving a problem, not

42:17

because you're like, oh, I need to learn how to build

42:19

a a web application, and I'm

42:21

gonna

42:21

spend ten hours doing it. So I I think when I

42:24

think about learning. If

42:26

you think about those actual project oriented, what it'll

42:28

look this look like in the real world, I think you

42:30

start to learn and develop skill sets along

42:32

the way that will serve you well in the

42:34

future. That's kind of the big

42:36

things. And, again, it's across that pipeline. Ryan Curtis

42:38

is a lot of people, the

42:39

Jose Fernandez, to meet the

42:42

minimum required Well, sure. If you want the list Scott referenced that again. I just

42:44

he comes with every I wanna get in every week.

42:46

I want the first of the list. There's

42:48

a list. You want the list.

42:50

I I often go to, like, look,

42:52

find a problem, try

42:53

and solve it

42:55

with data.

42:56

the And

42:58

then, look look, last thing. There's just a last point

43:00

on this. If you're a one

43:02

man show and you're in charge of, like,

43:05

developing all the programming, end

43:08

loading, and

43:09

gym work, all

43:10

of those things, and then data's laid on

43:12

top. Like, I think it's just a really

43:14

important thing to come back to. It's a similar question

43:16

I asked with technology. Like, let's pretend you did

43:18

have all of the data and you you

43:20

do all of the work to put

43:24

it into and then you build the whatever report. what

43:26

in your practice as a physical

43:28

prep or a strength conditioning coach is going to

43:30

change if you have all of that data? Like, it's nice

43:32

to have

43:34

sometimes if you at the end of the day are,

43:36

like, I could dump

43:36

ten hours a week into trying to collect all of this and you'd

43:39

come to the conclusion

43:40

the the conclusion that like that, like,

43:42

I'll

43:42

still program this way. Like, this is still gonna be a

43:44

maintenance block. This will still be a build

43:47

block. I'm still gonna emphasize power and speed

43:49

here, and then I'm gonna emphasize

43:52

size the robot capacity over here. If you say, you're unlikely

43:54

to change most of your operational

43:56

things regardless of that ten

43:58

hours of

43:59

data spent. I

44:01

think it's worth would great to have all of the

44:03

data, but maybe it's not that important that

44:05

I need to spend ten hours away from

44:07

other things I could be either

44:10

at work or personally to say,

44:12

like, it's

44:12

not worth it at this point. And

44:14

then to have that conversation,

44:17

with the

44:17

leaders of the organization. Like, I

44:19

think

44:20

it would be helpful to have someone to manage

44:22

this, but I can do it because I've been hired

44:24

to do this. and I'm willing to

44:26

do this. But I

44:27

can't do all of the other job as

44:29

well. And I think that's the space

44:31

people have to consider and ask. before

44:34

they just jump headlonging it. Of

44:36

course.

44:36

One thing that I wrote

44:38

down pretty early when we when we started

44:40

talking was about

44:41

Ben when when you had the initial

44:43

chat with Ben was for

44:46

for this department and for you to

44:48

evaluate the effectiveness of

44:50

what we do, And I

44:52

think that is a really interesting

44:54

point across many conversations that I've

44:56

had, not justifying

44:58

what we do, but trying to understand

45:01

how we do

45:03

exactly that. Like, look at

45:05

the effectiveness of Australian conditioning coach or a

45:07

a spot scientist and that feeds

45:09

many other conversations around salary, around working

45:11

conditions, around staffing, all that

45:13

kind of stuff. So

45:16

just bringing that back into the frame from that that initial comment.

45:19

How do you

45:22

monitor, manage

45:22

the effectiveness

45:24

of the interventions that are going on

45:26

all over the ecosystem

45:28

within the performance department. I

45:31

think

45:32

that's yeah.

45:33

If if there's a quote about

45:35

our performance strategy, I think that that integrates with probably two or

45:37

three to come up, but, like, our ability

45:40

or our desire to do

45:42

that drive so much of

45:44

what we try and do as a department to

45:46

say, like, Yeah.

45:48

Why why do we build these apps

45:50

to collect information about, like, what we're

45:52

doing in sessions about the time spent

45:54

with athletes doing different modalities? or

45:57

why do we have

45:58

technology that that

45:59

captures this stuff? Or why do we use in match

46:02

data the way that we

46:04

do it? and often it

46:06

comes back to this idea. Right?

46:07

Like, there's so

46:08

many things that we do in sport.

46:10

If you think about that IST, there's things that

46:13

we're going to do to try encourage the the right

46:15

mentality of our athletes. There's things

46:17

that we do

46:18

from a physical preparation and and a

46:21

prevention standpoint. So for

46:23

every one of

46:25

the

46:25

interventions that we think about. Right? Like,

46:27

there's there's some outcome we're trying to change.

46:29

So within within the way that we frame this

46:31

at our our performer strategy. We consider the

46:33

the ninety minutes on

46:34

the pitch. How we play is

46:36

is ultimately what we mean by performer.

46:39

Right? It it doesn't mean what someone gets on any

46:41

test score, some

46:44

isolated

46:44

individual players. And

46:45

it's like, how

46:47

does our team perform for ninety minutes on the pitch over the course of

46:49

the year. That's what we mean, and that's what we're trying

46:51

to optimize. We

46:54

understand

46:54

that that is essentially

46:55

this coming together of

46:58

a cohesive eleven players on the pitch that all

47:00

interact. I want to it can be really challenging to

47:02

do. We can talk about how that team

47:04

performs.

47:05

and then And then we

47:06

can think about each athlete that has this kind of profile

47:08

that we think about into

47:10

like a physical, technical, tax

47:14

and mental domains of how this

47:16

app

47:16

is coming into that team environment. And

47:19

we think within each of

47:21

those domains, there's characteristics constructs

47:23

that we we value. Right?

47:24

So if you think about the physical bucket,

47:27

like an athlete's gonna come into that

47:29

with a given aerobic capacity

47:31

and anaerobic power, a top

47:33

speed that they can run an acceleration,

47:35

change of direction, deceleration ability.

47:37

There's all these things, their constructs within

47:39

a within a physical domain and

47:41

we think that we

47:42

have interventions, that we can

47:45

change those things. Again,

47:46

it doesn't help to change just this

47:49

isolated physical construct for the sake

47:50

of the physical construct. We have to identify that that physical construct

47:53

is important for this player because

47:55

what they're expected to do within

47:57

our overall team strategy is

47:59

to do

47:59

x. During the ninety minutes, our team

48:02

strategy, our game model demands

48:04

this player do this

48:05

in this

48:07

position. So

48:08

therefore, this player is

48:10

unable to do it because maybe they are

48:12

simply not fast enough to recover in

48:14

defensive transition or this player simply isn't fitting

48:16

up like, ninety minutes in this position that that demands within our game model

48:18

are are really demand. So

48:20

then you identify

48:21

what the construct is that you

48:22

want to change, and then you develop the

48:26

intervention. So in some cases, that might be a a top speed. In some

48:28

cases, it might be an aerobic capacity. In some cases, it might

48:30

not be a physical thing at all. It might be

48:34

that this player needs additional technical training

48:36

because this action is

48:37

missing from their game. So we might they might

48:39

be in the right position. They might do the right

48:41

thing. They might be fit enough to

48:43

do that for a hundred and eighty minutes consecutive.

48:45

But, hey, when we get to this moment, they

48:47

are unable to

48:49

consistently perform this action. So what they

48:51

need is a dedicated technical

48:53

work again, the reason they need

48:55

that is because in our game model, at

48:57

the team level, that's

48:58

what it

48:59

requires. So that's really what the

49:01

performance strategy is is about is trying to understand

49:03

all the way from saying, are we adhering to the principles

49:06

of play? What does

49:09

that principle with blade demand from

49:11

each of the positions, then what are the different constructs

49:13

that we think in each bucket that

49:15

relate to the athlete's ability to

49:17

do that on pitch? And then

49:19

we have to think one step

49:21

lowercase,

49:21

can we intervene and change these

49:23

things? Like,

49:24

an aerobic capacity is a more

49:26

malleable thing. than a max velocity, especially in a

49:29

short term thing. It's just easier to

49:31

change. And

49:31

they're same thing on the technical or

49:33

on the practical. components

49:35

like these things might be really easy to change

49:37

and they might take a little bit of time. They might be

49:39

harder to change and take

49:41

longer to get to. But

49:43

if you're a data science department, you

49:45

have to be able to

49:47

say, did

49:48

we intervene? How

49:49

much did we intervene? And what

49:52

measure, if any, do we have, to latch

49:54

onto this construct

49:56

to say, did this change? Like, did this

49:58

get better? Because first, you have to

49:59

say, like, Did

50:00

they get better? Did they get they get at crossing? Did they get better

50:03

at this thing? And then you can ask, okay,

50:05

now are they

50:05

doing that in

50:08

a game? at speed, with defensive pressure, and is that

50:10

translating to our team being

50:12

better? And and that's a fascinating process. It's

50:13

really fun to talk to you radically, but

50:16

obviously that

50:17

it a long time

50:18

to build the ability to do that both

50:20

at a really discreet level

50:22

and at a macro scale. macro

50:24

scale so that So that's it's

50:27

a challenge and it's everything from

50:29

the small component parts all the way to

50:31

the the integrated piece. I don't think we're there yet.

50:33

I think that we're we have nice incidents and

50:36

nice examples that we can we

50:37

can highlight

50:38

along the way and we're getting in that direction.

50:40

But a lot of, as you can imagine, the

50:42

first couple years in setting up

50:44

a data science department is like, Do we

50:47

have data from the matches that we can use to say whether

50:49

a player is or isn't doing

50:52

this? What's

50:54

good? for this and what's necessary. And, oh, we don't have information

50:56

about this. So what

50:58

do we need to do to get? And then once

51:00

you start

51:01

building up components that

51:02

kind of get to both the intervention

51:04

and the performance, you can start to answer those

51:06

questions down the line to say, hey, we've

51:09

really consistently been successful

51:11

here and hey, this seems to be a bit more

51:13

challenging to to

51:14

get after and change.

51:16

Really interesting point.

51:17

I'm gonna use deceleration

51:19

as an example. So we we identify

51:21

that deceleration is an issue because

51:24

it's linked to this

51:28

imaginary athletes change

51:30

direction ability that we've identified in

51:32

game. That a code does come from a

51:34

coach. No. That probably would,

51:36

but it has this time. So

51:38

we go, okay, we need to measure it.

51:39

So we measure

51:41

it with a

51:44

suitable declaration

51:44

oregon test. We isolate it. We

51:46

go, okay. This person is here on the

51:49

scale compared to the rest of

51:51

the team. That's

51:52

fine. But can number on that.

51:54

But the difficulty then comes to

51:56

go, have we improved it

51:59

in game? and

52:01

we can count them. And we can say, are they doing

52:03

more than they have done? The deceleration intensity is

52:05

higher based on this

52:08

number and and whatever. We can

52:10

link that back to

52:11

video. But then how

52:13

do we still bring these

52:16

physical constructs into the

52:18

game and go that's

52:19

got better. We can always look at the training

52:21

and and the testing and say it's got better.

52:23

But how are you going about answering

52:25

that question? Okay. that's

52:27

got better. But did it does it actually

52:29

now happen in game? And

52:31

I don't wanna

52:32

take you down the path that you don't wanna go in terms of

52:34

saying things that you do in club that you don't wanna

52:36

talk about, but why is that

52:38

process to get there? And

52:43

Yeah. How did you go how did

52:45

you go about doing that? Did you come

52:47

back to subjective? because it come back

52:49

all the way to a subjective Yeah.

52:51

I think

52:52

that's a it's a really nice segue into something

52:54

that I know we're probably gonna get into on that

52:56

subjective space.

52:57

So I think for deceleration. Let's stay

53:00

on your example. Right? So when

53:02

you say, like, we've

53:04

changed

53:05

objectively this this

53:08

like, set environment deceleration

53:10

capacity of this athlete. We've

53:12

we've identified that, hey, actually,

53:14

they're middle of the road, they're deceleration really. Maybe we've

53:16

even worked on it and brought it to average. So they're

53:18

they're an average player within our environment

53:21

for for deceleration

53:22

the salaries the capacity

53:24

capacity. The question

53:25

becomes in

53:28

a match. So

53:28

there's two ways that you consider and we'd probably

53:30

want to look at both. One is

53:33

that deceleration

53:34

test might measure their max deceleration ability

53:36

when they're fresh, fit, whatever in

53:39

a close environment. It could be

53:41

that in a match,

53:42

the their fatigue

53:43

levels actually the limiting factor. So you'd actually want

53:46

to combine that with some other measures of

53:48

of different fitness or different capacity

53:50

to say, actually,

53:51

when we use the tracking or the GPS data, seems like their deceleration

53:53

intensity does decrease and drop throughout a

53:55

match. Like, that is one theoretical

53:57

scenario that

53:59

maybe it they don't decelerate properly, but the reason they don't decelerate

54:02

properly is actually some other physical

54:04

limitation. That being fitness is

54:05

is one

54:06

key marker. But

54:09

The other question could be, the coach

54:11

is saying

54:12

this athlete isn't decelerating properly,

54:14

and it's not actually their deceleration ability.

54:16

It's something around their tactical awareness

54:19

of knowing when to decelerate.

54:21

Or in this moment, they're just they're getting

54:24

distracted by the fact that someone else is making a

54:26

run-in behind them and there's a player with

54:28

the ball they're closing down and not

54:30

a physical deficiency.

54:31

It's a tactical deficiency. In

54:33

which case,

54:35

that gets

54:35

harder. So some of these constructs are really nice

54:38

and easy, the max velocity one minus

54:40

the noisy tracking or

54:41

GPS data. Is this a

54:43

nice everyone understands what you

54:44

mean when you say, hey, this player is

54:46

really fast at the top end speed and this

54:48

player slow. Like, we get that.

54:52

Is this player effective pressing?

54:54

Like, closing down opponents when they're

54:56

pressing to guide the shape of play? Or

54:58

something like that as a construct is

55:02

much more challenging answer. And that type of construct

55:04

is where I think the subjective

55:06

measures become brilliant because

55:08

you you you think

55:10

and I think we all intuitively know that a

55:12

really experienced wise coach and a practitioner

55:14

when they see something. They can say, hey,

55:17

that was a really good well executed performance, and

55:19

that was that was worse. And then from a

55:21

data science department, the

55:22

fascinating question that I have is,

55:24

like, That's awesome.

55:26

I believe that there's absolute gold

55:28

in that brain when you evaluate

55:30

and you assess a performance.

55:32

Now I want that. Like, that needs

55:35

to be in our database so that

55:37

I can use

55:38

that for something. Like, I don't just want it

55:40

to be comment that's made in a locker room, I want that to be something

55:42

that we can use as information.

55:44

And that's

55:45

the big discussion, the

55:47

road, about subjective measures,

55:50

which Yeah. If

55:50

you want to that, but that's fascinating topic that

55:53

I think is is still

55:54

wide open. Yeah.

55:56

And

55:57

I think it isn't it links into

55:59

so many things. I mean, never chat last

55:59

night with with three

56:02

guys on a round table with the guys

56:04

to agility. Exactly

56:06

that. Kinda educated in the in the

56:08

science with the science head. Okay. We

56:10

wanna change something. So

56:12

first, we have to test it. But

56:14

then when it comes back to the training, it

56:16

becomes so chaotic and and try and encourage coaches

56:19

to be happy in

56:21

this chaotic space. So

56:23

then we go, well, how

56:25

how do we

56:26

link that testing them testing numbers into

56:28

this chaos that we're seeing in

56:30

the training environment

56:31

because we've made it that

56:33

way. because it links

56:34

to the game because the game is also

56:36

chaotic. So this, like,

56:39

uncomfortableness.

56:39

And I I was actually last night

56:41

when we were talking fighting in my

56:43

brain to go, loosen up. It doesn't have to be

56:46

this stiff environment

56:48

that we test and we

56:51

intervention

56:51

and then retail and and and the

56:54

kind of comb to comb

56:56

drill versus the chaotic

56:57

activity. So I

57:00

find this this

57:00

messy space really interesting.

57:03

And I

57:03

think as as pro code just get and like

57:05

people like yourself get more

57:08

experienced, they become more comfortable in

57:10

this messy zone.

57:12

Yeah. I

57:13

I think that

57:15

It's in

57:16

between. It's it's kind of back to that or

57:18

you kind of alluded to it earlier. So what's the

57:20

breaking point for that athlete when you get to this

57:22

acceleration change of direction? one like, hey. They're not doing this

57:25

in a game. The first question,

57:26

this kinda gets

57:28

back to, like, how we evaluate these

57:31

interventions. You you have to figure out what

57:33

the intervention point is. So if they if

57:35

they can't decelerate or change

57:38

direction

57:38

around a cone, that they know

57:40

the root. It's just to

57:42

be that much harder to do it

57:44

dynamically with an uncontrolled

57:47

environment with pressure with some contact is gonna be that much

57:49

harder. So first, you have

57:50

to say, hey, they're are they capable of doing

57:52

it in this? And then are they capable of

57:55

doing it in training or with some constraint where you introduce an

57:57

opponent or you add some uncontrolled thing. And then

57:59

you get into, hey,

58:01

are they doing this effectively game,

58:03

but then you run into the, hey, that's a lot harder

58:05

to quantify than

58:07

when they were

58:08

just with the cones and you had the time.

58:11

So you

58:11

have the spectrum from easy to quantify

58:13

to, like,

58:14

actually the relevant point when you

58:16

get to the match. So that's yeah. there's

58:18

some gray in there, but I think it's important to go through those steps because it I

58:21

think if you only evaluate let's say

58:23

we have the perfect subjective measure,

58:26

you know back, we like, oh, this

58:28

player is our worst player at decelerating when they close out opponents

58:30

in a match. It's helpful.

58:32

But if

58:33

you wanna intervene

58:35

to know that

58:36

is. Like, why are they not

58:38

able to close down opponents?

58:40

And

58:41

your intervention point

58:43

could probably be more

58:45

efficient if you identify that limiting

58:47

step and

58:47

then attack that rather than just

58:49

having them practice closing out a bunch of opponents every time

58:51

of the session. Like, there's a way to

58:53

do it. But if it's if it's overall capacity

58:54

fitness thing, just having them do

58:56

close ups might not be the most efficient

58:58

way to change their fitness level.

59:01

If it's a if it's an awareness issue, maybe you

59:03

have to introduce those game moments. If it's

59:05

a, again, a deceleration capacity, maybe you want to

59:07

do some other dedicated. Maybe it's literally that they just

59:09

need to get stronger. and

59:10

then the time opponents could actually

59:13

be better spent

59:13

developing a bit more of a

59:16

robust

59:18

physical base in a wav room set

59:20

that then they can transfer. But if you only assess

59:22

the in mass performance, it's hard to get

59:24

to what the right limiting step is that then

59:26

would

59:26

inform what your intervention could be. So it's

59:28

trust Like,

59:29

I I challenge being assessing the game performance. Yeah. I mean, I

59:31

wanna I wanna continue down

59:32

this track because I think it's an interesting

59:36

one in I'm just thinking of examples using the deceleration. So I'm

59:38

a defender. I'm a tech is coming

59:40

towards me. I wanna go and

59:42

meet them.

59:44

but a coach

59:45

identifies that I'm not

59:47

able to slow down quick and get that quick

59:49

enough and slow down quick

59:52

enough to being a relevant or optimal position to be

59:54

able to defend that

59:55

attacker. But

59:56

for me to do that, that may be nothing

59:58

to do with my ability to

59:59

get there quick and slow

1:00:02

down. That may

1:00:03

be something

1:00:05

mentally

1:00:05

from my point of view to think

1:00:07

I don't wanna

1:00:09

go as as possible

1:00:10

because I think that person's quicker than

1:00:12

me.

1:00:12

Therefore, I'm

1:00:14

going to hold back

1:00:17

to give myself more time to prepare

1:00:19

body position, etcetera, so that person doesn't

1:00:21

whip past me. But how

1:00:22

do we get into

1:00:23

a position where we

1:00:25

are subjectively

1:00:28

evaluating

1:00:28

something like that, so we can go, yes,

1:00:30

it's improving on the pitch. No, it's not

1:00:32

improving on the pitch. Yes. And

1:00:33

and to even follow-up on that, it might

1:00:36

be that literally within the game model,

1:00:38

your coach has communicated that in

1:00:40

this scenario, you do not

1:00:42

go and press. the

1:00:43

attack. In those moments.

1:00:45

Right? So you just like

1:00:47

that is a different that's

1:00:50

a different structure trying to get to.

1:00:52

And I think now we're getting into the space where I think having some subjective

1:00:54

ability to evaluate and

1:00:57

capture becomes absolutely vital

1:01:00

because it it's it's a lot easier to get to some contracts. But if I say,

1:01:02

hey, is this defender adhering

1:01:04

to our game model more

1:01:06

than they

1:01:08

were before? Right now, I would venture against as much as we

1:01:10

try with event or tracking or etcetera data to

1:01:12

try and get to these things. So

1:01:14

probably the most

1:01:15

reliable is to ask

1:01:17

the manager, ask some of the coaching staff

1:01:19

in this

1:01:20

game. How well did

1:01:21

this player adhere to principles or

1:01:23

use video to say? In this example, did they did

1:01:25

they not do what they were post and

1:01:28

use

1:01:28

that. So so

1:01:30

from a data science provider to entrepreneur

1:01:32

and surgeon drug, from a

1:01:34

data science perspective, what are you doing to

1:01:37

help get that information

1:01:38

out of the coach's head and like you

1:01:40

say the analogy that you you

1:01:42

described, put it into our

1:01:45

database. Yeah. Hundred

1:01:46

percent. So that

1:01:48

that's really

1:01:48

the

1:01:49

impetus behind. We we just published we

1:01:52

have a a PhD soon right

1:01:54

now at Keith Hamilton. He's a PhD student with doctor David

1:01:56

Cox, who's an

1:01:58

unbelievable

1:01:58

sports

1:01:59

psychologist. before psychologists He's

1:02:02

had

1:02:02

a very prolific career. We we're very fortunate to have him as

1:02:04

as part of our performance staff

1:02:07

here. Chuck, he's

1:02:08

the applied brilliant

1:02:10

academic grade. He he

1:02:12

has a student, Keith Hamilton, and his PHE is

1:02:14

really trying to get into this space. And we started

1:02:17

with this paper

1:02:18

we just released to say, look, subjective evaluations. I

1:02:21

think one

1:02:22

are everywhere

1:02:22

in sport. We do

1:02:25

this. when select the starting lineup. We do this when

1:02:28

we announce an MVP

1:02:30

award. We do it at the end of every academy

1:02:32

season when we say, okay, this player

1:02:34

is gonna come back next year and this player

1:02:36

isn't those

1:02:37

decisions are based

1:02:40

on the

1:02:42

Like,

1:02:42

the collection

1:02:43

of information in the brains of

1:02:45

our practitioners as they make decisions. And we believe

1:02:47

that there's the coach's eye. We believe that someone that's

1:02:50

really experienced has the

1:02:52

capability of doing

1:02:53

it. And they're just

1:02:54

done all the time. It's on every radio or

1:02:57

every talk

1:02:57

show. It's like this player's awesome and

1:02:59

this player's terrible. what that is is

1:03:02

it's a subjective evaluation based on what you've seen as you assign

1:03:04

this categorical rating

1:03:05

of terrible or excellent

1:03:07

or average

1:03:08

to a player. Now

1:03:10

I would liken

1:03:12

this back to, like, my

1:03:14

time during my PHD was sitting in

1:03:17

like, psychometrics

1:03:18

classes as they talk about how do we develop

1:03:20

stuff to capture empathy,

1:03:22

emotional intelligence, general intelligence, etcetera.

1:03:26

And

1:03:26

how do

1:03:27

we know

1:03:28

what we're collecting? Is getting to this contract

1:03:30

that you can't really measure? And like, tactical

1:03:33

adherence is really hard or

1:03:35

you get into this these spaces of,

1:03:37

like, hey, is this player a

1:03:39

good teammate? Or is this like, you

1:03:41

get to

1:03:41

these things that are harder to measure, and that's what I

1:03:43

would liken it to. And I would say, like, I think

1:03:45

the jury is up. So when

1:03:46

I say there's there's moments where a

1:03:48

survey doesn't do it or an Excel workbook doesn't do

1:03:50

it. And we we've tried to develop things

1:03:54

in house that can capture. I

1:03:55

don't I don't think we

1:03:57

know yet what's best

1:03:58

practice. What I would say

1:03:59

is say is

1:04:01

I've seen

1:04:02

I've seen

1:04:04

surveys ruin subjective evaluation.

1:04:07

I we

1:04:08

don't yet in sport have

1:04:10

what a gold standard. This is what it should

1:04:13

look like. So, like, for

1:04:13

example, I've seen before,

1:04:16

hey, can you rate this

1:04:18

player's performance in this given match

1:04:20

or in this

1:04:22

given competition? Did they

1:04:22

perform better than expectation, at expectation,

1:04:25

worse than expectation. And this was

1:04:27

collected for a long

1:04:27

period

1:04:30

of time. in

1:04:30

a given environment.

1:04:31

And essentially, then what you would have

1:04:33

is you'd have some

1:04:34

of the best players

1:04:37

would

1:04:37

end up over the course of the year

1:04:39

with a degrading performance.

1:04:40

And they

1:04:41

would be, like, kinda, like,

1:04:43

your bottom third of performers. And then

1:04:45

you go in and be like, I thought this was your best player. They would

1:04:47

say, well, he he was. But, like, as because they're so

1:04:49

good, the expectations rise,

1:04:52

and they continue rising over the course of So a given period of

1:04:54

time or given match, their performance is

1:04:57

further below the moving

1:04:58

bar that is the expectations placed

1:05:02

on. and you

1:05:03

might have an average player that performs above expectation. But if you were

1:05:05

using that at the end of the year to say,

1:05:07

okay, which players stay, which players go,

1:05:09

which player should start,

1:05:12

you're

1:05:12

not capturing the construct you're

1:05:14

interested in, which is which of

1:05:16

the players perform the That's

1:05:20

it.

1:05:20

That's what you're trying to get to, but the

1:05:22

survey or the question is

1:05:24

not

1:05:24

capturing. It's capturing something

1:05:26

else which might

1:05:27

be relevant, but it's this

1:05:29

construed ratio of

1:05:32

coaches' evaluation

1:05:32

with player performance that

1:05:35

gets you something else. You

1:05:36

see this this is in

1:05:37

the in the athlete's self

1:05:39

report space as

1:05:40

well. Right? So Aaron Foods has been on. You've

1:05:42

had guys pop well, like, use the

1:05:45

the scales

1:05:45

that have some validity related evidence versus

1:05:48

scales you create on your own. And I have you've seen,

1:05:50

like, sometimes one is good and

1:05:52

five is good, bad, and then sometimes

1:05:54

five is and one is good or

1:05:56

whatever. And vice versa, sometimes it's a one to ten anchor, sometimes it's a zero to

1:05:58

seven, sometimes it's this, sometimes

1:06:00

it's

1:06:02

that And

1:06:02

I've seen data sets before where all of a sudden an athlete goes from, like,

1:06:05

being fantastic to being terrible.

1:06:07

And then terrible

1:06:09

you're, like, what's going on on that day

1:06:11

and you're on top of them box and they're like, oh, no. I've

1:06:13

been misinterpreting this scale completely since I

1:06:16

started recording

1:06:18

data.

1:06:19

They're like, well, that's

1:06:20

it's gone now. So I I've

1:06:23

seen many examples of, like, hey, here's subjective

1:06:25

evaluations. And because of the way

1:06:27

that the survey

1:06:27

or the scale or the question was

1:06:30

designed, you've lost the

1:06:31

ability to capture what you're

1:06:33

trying

1:06:33

to get to.

1:06:35

The question

1:06:35

that we're after is how do you develop

1:06:38

the survey to scale in a

1:06:40

way that

1:06:42

does capture That's what we're working on at the club. I think the jury's out. think can

1:06:44

learn a lot from the psychometric space. We're

1:06:46

partnering with Bruno. It

1:06:48

was one of

1:06:50

the authors on that paper that we now

1:06:52

to try and at least get a framework to

1:06:54

think about it because there there's actually

1:06:56

a lot of similarities with

1:06:59

objective data when you think about how data

1:07:01

is generated and how it can actually

1:07:04

help

1:07:04

inform an

1:07:05

inference. There's a lot of

1:07:07

crossover. But what we have and I think we

1:07:09

said this in the paper was in

1:07:12

the

1:07:12

objective space. You have this data collection process.

1:07:14

Right? The data is generated.

1:07:16

We

1:07:16

run a sprint. We do a fitness test. Right? That athlete

1:07:18

runs on a treadmill. Right?

1:07:20

To

1:07:21

then capture

1:07:22

that

1:07:24

information. It's the calibration side. Which case is, like, is the is

1:07:26

the treadmill calibrated? Does the mask

1:07:28

fit on the mouth? What is are

1:07:31

we doing the

1:07:31

Bruce protocol or something

1:07:33

else. And we've spent so much time in exercise

1:07:36

science talking about the

1:07:38

calibration of

1:07:39

objective tests. Then there's

1:07:40

the inference, which I think can't

1:07:43

forget, and that's a a lot of the unified validity theory, psychometrics talking,

1:07:45

which is another giant conversation.

1:07:47

We'll leave that. then

1:07:49

you have to make an inference.

1:07:50

This VO2 max or this player

1:07:52

is from

1:07:53

this very calibrated

1:07:56

bike, whatever fifty two, then

1:07:57

you have to make an difference. This player

1:07:58

is, isn't fit enough, needs

1:07:59

work, we're gonna use that to do something.

1:08:02

It's not just about the score, it's about what you're

1:08:04

trying to do

1:08:06

with it. and then you

1:08:07

make an inference. The subjective thing is the same thing. Right?

1:08:08

So you you watch a game back to

1:08:10

your example. The defender does

1:08:12

or does not close

1:08:14

out. on the attacking plan.

1:08:16

Then maybe you have this,

1:08:17

whatever, the

1:08:18

survey that asked the coach. In

1:08:20

this environment, how

1:08:23

effective was this defender? at

1:08:25

doing what you've asked.

1:08:27

Data is then captured in that

1:08:28

survey, in that

1:08:30

question, in that whatever, That's

1:08:34

the calibration. And then there's an inference. We're gonna compare that with all the other times. The defender did or didn't or in

1:08:36

the

1:08:36

situation that say they are getting

1:08:38

better or are not getting better.

1:08:43

In the sports

1:08:43

science space, we've spent like

1:08:46

so much time calibrating bikes

1:08:47

and

1:08:48

building protocols, developing field

1:08:50

tests, and lab tests, and

1:08:52

saying, what are all the other things

1:08:55

that go

1:08:55

calibrating so can trust test? And in the subjective

1:08:57

and then in the subject of space space,

1:09:00

we skip

1:09:00

that for the most part. It's like stuff is stuff

1:09:02

happens. People play games. We look at their performance, and then

1:09:04

there's a lot of hot takes. Like,

1:09:07

this player is terrible. They're getting

1:09:10

worse every game or this player is great.

1:09:12

That was a brilliant performance.

1:09:14

This was above

1:09:15

average. And often,

1:09:16

we don't capture when we capture it,

1:09:19

even in the academic literature, there's

1:09:20

almost no scales

1:09:23

that have had dedicated effort

1:09:25

to saying, does this scale itself have

1:09:27

any evidence related to its ability to capture

1:09:29

someone's technical or tactical performance. And that's

1:09:32

the space we're living in

1:09:34

now. It's like, we're trying to borrow best practices from

1:09:36

psychometrics, having these discussions, and

1:09:38

saying, what do we know

1:09:40

from survey design and scale

1:09:41

design and many things that we

1:09:43

can apply here But I

1:09:45

think

1:09:45

best practices is up for discussion, up for debate, which makes it a really

1:09:47

exciting space, but

1:09:48

for discussion upper debate which makes it really

1:09:50

exciting space for that are we that's how

1:09:52

we at

1:09:52

least how we try and answer

1:09:54

the question as we build tools. I think it's

1:09:55

fascinating. I think it's

1:09:57

a fascinating area

1:09:59

and

1:09:59

something that just the more

1:10:02

you talk about, the more you kinda get level down, level down, level down, then I go

1:10:05

my mind just

1:10:07

goes, oh, no. Yeah.

1:10:09

So this is so deep, but no, it's tackling it. So good work. Good work. I've

1:10:11

kept you for an hour already, but we

1:10:14

still have one more I

1:10:18

think important point to cover. And

1:10:20

I think it's I'll I'll reference

1:10:22

your article that you kindly wrote

1:10:24

for Sportsmith. which has done very well. So

1:10:27

clearly, this is a point that people want more information on and it's data visualization. And I think

1:10:29

if we can keep it to a

1:10:31

couple of minutes, that great

1:10:34

and just get the I hate

1:10:37

to say it, the

1:10:39

hot takes. But your

1:10:41

principles, when it

1:10:42

comes to debt visualization. I know you some a lot

1:10:45

in the in the

1:10:46

article itself, but what what

1:10:48

do you

1:10:49

live by when it comes to visualizations?

1:10:51

And how do you evaluate whether that's been

1:10:53

successful or not?

1:10:54

Few minutes. Mhmm. Few minutes.

1:10:57

I'll try to keep it as

1:10:59

brief as I can. I

1:11:01

think taking

1:11:01

care in what you're doing by taking

1:11:03

private is is one

1:11:06

of the the overarching things.

1:11:09

you're using this again as one

1:11:11

end of the data pipeline. So your ability

1:11:13

to communicate all the back end

1:11:15

work in terms of, like, collecting

1:11:19

aggregating,

1:11:19

analyzing, building the thing,

1:11:21

all ends up coming down to your

1:11:23

ability to communicate that.

1:11:26

So

1:11:26

taking pride in that and just realizing you're

1:11:29

hoping that

1:11:30

you can

1:11:32

convey a

1:11:32

message effectively to the end user.

1:11:35

They're more likely to look if

1:11:37

it looks nice. That's just the reality. Like, you it

1:11:39

it's just they're more likely to look longer, and

1:11:40

the longer they left, but

1:11:42

hopefully, more likely they are to

1:11:45

take

1:11:46

on the information that you're trying to

1:11:48

convey. So there's this

1:11:50

base level where you have

1:11:52

to

1:11:52

reach before someone goes, I

1:11:54

think I said

1:11:55

in the article, if not, it's been in

1:11:57

hard conversations. Like, it's

1:11:58

it's efficiently

1:11:59

beautiful. So you

1:12:02

you have to We would describe our

1:12:03

structure. So the number of dashboards or reports are

1:12:06

things we have to deliver is is pretty high.

1:12:08

So

1:12:09

if we spend

1:12:11

every last minute kinda fine

1:12:13

tuning to the nth degree, like, we will run out of

1:12:15

time in the day to do that. So

1:12:18

the that gets into

1:12:19

just having templates in place. So having color schemes, having

1:12:22

general font recommendations, having these general

1:12:24

templates for

1:12:26

how

1:12:26

we build. gets you eighty percent of the way there. It takes no extra time to

1:12:29

use the default g g plot, whatever, or

1:12:31

Tableau out of the box. It takes

1:12:33

a little

1:12:33

bit of upfront work to

1:12:36

build templates you can build on that gets you eighty percent

1:12:38

of the way. And

1:12:38

then it gets to that, like, at the right time, hey, this

1:12:40

visual should

1:12:41

be fine too, and then you can take

1:12:43

that any expert twenty percent. And

1:12:45

then I think the other

1:12:46

principle that I I hope we all continue going

1:12:49

with, and this

1:12:52

is like when I write an

1:12:54

article like that, the impostor syndrome is real. The note at the end of the article, like, I think the biggest principle I try and live by constantly

1:12:56

learning. I think

1:12:59

John Polley would love

1:13:00

hollywood love to

1:13:02

say, and the joke around here is I'm

1:13:04

not even the best date of this person

1:13:06

in the room I'm

1:13:07

in. So, like, Luc PD arrived and

1:13:09

produced a better looking foreplay dashboard on, like,

1:13:11

week number two of MVP. No. So I hope that I can

1:13:13

at least continue to learn and grow and

1:13:15

get better. So it's

1:13:19

when it's done brilliantly, like, I think we all know what

1:13:21

that looks like. You know? Like, when you look at

1:13:23

Cedric here, create

1:13:26

a

1:13:26

plot in g two plot two? Or

1:13:28

you even see, like, I

1:13:30

think, right now of the inspirational

1:13:32

view

1:13:33

of, like, how data can

1:13:35

inform and, like, in this quote, inspire. But,

1:13:36

like, capture people. If you

1:13:39

think about John Murdoch's, like,

1:13:41

like plots

1:13:42

on COVID incidence rates across

1:13:44

the world during the pandemic. Like,

1:13:46

you've never seen so many people interested

1:13:49

in exponential growth rates.

1:13:51

Just

1:13:51

able to turn What's this

1:13:53

global pandemic and a ton of

1:13:55

information into a really digestible form to talk about whatever it'd be,

1:13:57

like cases.

1:13:59

But that's or increasing that, like, all those things, and

1:14:00

it's done in a way that

1:14:02

just, like, captures people and

1:14:04

helps them to

1:14:07

understand what's going on. And

1:14:08

then from, like, the

1:14:09

beautiful side, we we published

1:14:10

a a preprint on this this abstract thing. I

1:14:12

called it. A lot of conversations that Dave

1:14:14

and I had at USOC, like, Well

1:14:18

done data

1:14:19

visualization can be such a great communication medium, and

1:14:22

and

1:14:23

science often offers from, like, a knowledge

1:14:26

translation problem in some instances. So can we capture that? So we tried to take a dataset

1:14:28

and communicate it in a

1:14:30

in

1:14:30

a visualization. So I had

1:14:34

phone

1:14:34

call with Lisa Crescribe. She won

1:14:36

IronBiz, which is like

1:14:38

Tableau's

1:14:39

data visualization contest, which is

1:14:41

of

1:14:41

sight to behold. I was at a

1:14:43

New Orleans, so there's, like, ten thousand data science, data analyst, data biz

1:14:45

people in a room and

1:14:46

people get twenty minutes to build

1:14:50

their

1:14:50

best looking visual. And the stuff that people

1:14:52

can build in front of me is absolutely

1:14:54

fascinating. So Lisa, actually, won, I am Biz.

1:14:57

I reached out and said, I'm trying to

1:14:59

developing. Can we

1:14:59

turn a research, abstract research paper into a really

1:15:01

nice news? If we published this using

1:15:04

player

1:15:04

maker data that

1:15:06

was publicly available, Steve Barrett,

1:15:08

Chris

1:15:09

Allison, they had this publicly available data. So we took it and we created a Tableau vision. You can see you

1:15:11

take one look at it and you'll probably say there's no way

1:15:13

Johann actually produced

1:15:14

this. So I had to as

1:15:19

I did a very small part. Lisa did a lot of the heavy lifting. You'd see

1:15:21

how beautiful it is. So it's like, as a

1:15:23

principal, I hope

1:15:24

I, our team and people that

1:15:25

are in this space can be liberated

1:15:27

to say, look, It's another

1:15:29

one

1:15:29

of those moving partners. You're never gonna be arrived and say, like, I am now, like, the

1:15:32

perfected date of biz artists. Like, there's

1:15:34

so many examples of people doing it

1:15:36

well. so

1:15:39

many different ways to do it well that you can continue to learn and

1:15:41

evolve and then think what looks what's

1:15:43

perfect in one instance

1:15:45

for one user is gonna be different than another.

1:15:47

I kinda end on that.

1:15:47

Like, it goes back to idea of, like, a video

1:15:50

can be more effective than

1:15:52

the

1:15:54

best pristine Tableau dashboard that's as

1:15:56

interactive and as sophisticated or as simple

1:15:59

as you want.

1:15:59

Because it comes down to can

1:16:01

you take the information on board and

1:16:03

change the decision you're making.

1:16:05

For a player, for a coach, for different practitioners, sometimes it's it's

1:16:07

literally a a video

1:16:09

and say,

1:16:11

do this more.

1:16:13

do this less, do this differently or a picture of

1:16:15

a pitch and say, we

1:16:15

want you to be here rather

1:16:18

than, hey,

1:16:19

here's this trend

1:16:21

line over time that's contextualized to the other assets. So I'll

1:16:23

end there. I'm missing principles,

1:16:24

contextualize

1:16:28

the data. be

1:16:30

honest with your reporting. There's a lot there, but

1:16:32

you told me to keep it brief and I'm already over,

1:16:34

so I'm gonna stop with with

1:16:35

that. That's fine. That's that's loads

1:16:38

of good loads of good stuff there. And I think

1:16:40

people can people can read the article, and I'll link it

1:16:41

in the show notes and on iTunes, it's WiFi, and YouTube, all that

1:16:43

kind of stuff. where

1:16:46

people are listening so people can have a little look and and dive into it. But Johan, we've

1:16:48

been on the

1:16:49

phone for an

1:16:50

hour and twenty five minutes. an

1:16:55

hour

1:16:55

and ten has been recorded. So I really appreciate

1:16:57

your time. I really appreciate you getting

1:16:59

up early. And as

1:17:01

the Is that big coffee gum? That big coffee

1:17:03

gum has a big time to go

1:17:06

to the water. Nice. Nice. Well, thank

1:17:08

you very much. I really do appreciate it.

1:17:10

But last but not least, where can people get know

1:17:12

more about you, where can people keep up to

1:17:14

date with projects you got going on, research

1:17:18

gay, all that kind of stuff? You'll probably

1:17:20

be sadly

1:17:20

disappointed if you follow me on social

1:17:22

media because something will come on

1:17:24

once every six months or

1:17:27

something along those lines.

1:17:29

Yeah.

1:17:29

I guess there's probably

1:17:30

the question on the on the academic space, like the research gate

1:17:34

or the whatever the

1:17:37

the

1:17:37

Google scholars or whatever the people wanna see. Like, if I if I'm

1:17:39

fortunate enough to push out a publication, then those are

1:17:41

gonna be the avenues

1:17:43

to find them.

1:17:45

in

1:17:45

the applied space. If I say

1:17:48

anything publicly, it's probably on Twitter. But again, it's

1:17:50

probably

1:17:50

with disappointing frequency. When I say anything, but also,

1:17:52

like,

1:17:55

if you if people really wanna connect,

1:17:56

like, I'm I'm happy to have a chat, always happy

1:17:58

to to hop on a call or

1:17:59

send

1:17:59

some some messages.

1:18:02

So that'd be direct message on Twitter, do the LinkedIn

1:18:04

thing or whatever. Like, it's it's always fun

1:18:06

to connect

1:18:06

with people in this space. So those

1:18:09

are all options

1:18:11

in avenues.

1:18:12

Sweet. Right? I'm gonna

1:18:13

find and let you go and get on with some work.

1:18:15

But I really appreciate your time. It's great to have finally the chat and great to get

1:18:17

you on the podcast and, yeah, really appreciate

1:18:19

your help and speak soon.

1:18:22

Awesome. Thanks

1:18:22

for having me, Rob. Yeah. Take your time. Thank

1:18:25

you. Yeah. Thanks

1:18:27

to your ninja episode

1:18:29

four hundred and fifteen. of the Pacer performance

1:18:31

podcast. Really appreciate you tuning in. Hope you got

1:18:33

as much out of this episode as I

1:18:36

did. Big thanks to

1:18:38

Johan for giving up his time. very busy guy getting to the office early,

1:18:40

so it fits in with with

1:18:42

everyone's kids, bedtimes, and all that

1:18:45

kind of things. I really appreciate his time. Big

1:18:47

thanks to Hocking Dynamics, Fusion Sport, Omega

1:18:50

Wave, to Tensor College,

1:18:52

and Hydro

1:18:54

Fast Response and happy so today. The podcast could not run its

1:18:56

code following out these guys, so I really

1:18:58

do appreciate all their support. Big thanks to

1:19:01

you've tuning in and look forward to

1:19:03

chatting to you next time.