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If you’ve ever dreamed of working in sports analytics, this episode is for you. Nick walks through how he got hired by doing the work first, plus advice for breaking into the field and hopes for a strong Reds season.

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⌚ TIMESTAMPS

02:00 — Nick's role with the Cincinnati Reds and how his work fits into the organization

05:00 — Pivot into sports analytics and early career decisions

17:00 — How personal projects and initiative directly led to getting hired

21:00 — What the day-to-day really looks like beyond "just baseball data"

35:00 — Advice for aspiring sports analytics professionals on standing out

🔗 CONNECT WITH [GUEST]

🎥 YouTube Channel: https://www.youtube.com/c/NickWan

🔗 CONNECT WITH AVERY

🎥 YouTube Channel

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📸 Instagram

🎵 TikTok

💻 Website

I applied to like 20 or so jobs and

I got calls back from all of them.

2

Wow.

3

And Nick Wan.

4

Nick Wan.

5

Nick Wan.

6

You are the senior director of baseball

analytics at the Cincinnati Reds.

7

You kind of have a lot of people's

dream job combining sports and data.

8

I'd love to just hear your story

from beginning to end, how you

9

went from like a psychology PhD.

10

To ultimately being in charge

of data for a baseball team.

11

It was a different era, you know?

12

How hard is it today to

land a sports analytics job?

13

I think compared to what people are

putting in for time, it seems so

14

much more difficult now than it was.

15

I'd love for you to start at

the beginning of like how you

16

ultimately landed that first job.

17

I was writing a blog at the time, and

someone from the New York Times said,

18

you're on the front page right now.

19

I'm like, what?

20

Let's say the Cincinnati Reds.

21

Had a job opening right now.

22

Uh, and some of these listeners applied

like what would they need to do in

23

order to stand on, like, teams are

always looking for person in and people

24

putting out really cool stuff on.

25

Avery: All right, next.

26

Thank you so much for coming on the pod.

27

You are the Senior Director of Baseball

analytics at the Cincinnati Reds, which

28

is a pretty sweet title if you ask me.

29

You also, uh, create a decent

amount of content around data and

30

sports analytics, and that's how we

became friends via like this data

31

creators meetup that we both went to.

32

We'll have all of your links in the show

notes down below, but you gotta have.

33

A lot of people's dream job,

like combining sports and data.

34

I think a lot of people

would love to do that.

35

So first off, I'd love to just hear like

your story from beginning to end of how

36

you went from like, no offense, but maybe

kind of a boring role as like a psychology

37

PhD student to ultimately like being

in charge of data for a baseball team.

38

And I know it's, it's kind of a long

story, but I'd, I'd love for you

39

to start at the beginning of like

how you got interested in sports

40

analytics and you know, how you

ultimately landed that first job.

41

nick wan: Yeah.

42

Um, thanks.

43

Starting me on Avery.

44

This is awesome.

45

And yeah, that was, uh, uh, I always,

I, I watch these all the time, so I, I'm

46

like, oh, this is cool that I'm on it now.

47

So, um.

48

But yeah, I was at, uh, I was

doing, I was at grad school.

49

I went to, uh, Utah State, uh,

for my PhD and I was working

50

on, neuroscience and doing, uh.

51

Neuroscience of strategy formation.

52

So, how do, how does a person

come up with a strategy?

53

When does it form?

54

Where in the brain does it form?

55

Uh, and about like, I don't know, call

it like two or three years into it, I.

56

Was like, am I really

the best in my class?

57

Not to say I, you know, was horrible

at school or anything, but academia,

58

especially in cognitive neuroscience,

it's like extremely competitive.

59

And I, I kind of had like a, uh, an

honest realization of like, I don't

60

know if I'm the best at grant writing.

61

I dunno if I'm the best

at publishing papers.

62

I really like the research,

but I don't know if I could

63

do all of this other academic.

64

important stuff.

65

so I started looking at, uh,

other jobs or other, uh, potential

66

paths for, for employment.

67

And, uh, I was looking

at some journalism stuff.

68

I was looking at some other,

you know, tech related stuff.

69

And finally, like, I, I've always been

interested in sports and sports analytics.

70

Uh, and, uh, it was, uh.

71

was writing a blog at the time and I

was putting up blog posts and someone.

72

Uh, from the New York Times said, Hey,

this is a really cool blog post you have.

73

Can we use it as a part of, uh, a blog

post over at the Upshot, which is like

74

the New York Times, they were kinda

like competing with 5 38 at the time.

75

It's like their data viz political

blog thing they were doing.

76

I think it's still going by the way, but.

77

at least when I last checked, my

article was still up, so that was, nice.

78

Um, so I was like, absolutely,

you could definitely use it.

79

I'm just a student and I, I, I

would love more reach for my blog.

80

Uh, so that was really cool.

81

And then like a couple days after

they had put it up, uh, Justin Wolfer

82

is the guy who, who wrote it all up.

83

He was like, Hey, if you're near

that sells the New York Times,

84

you're on the front page right now.

85

Like.

86

What, so I ran to like a hotel

that You didn't have one.

87

I went to a coffee shop.

88

Uh, they, like, I had the last issue of

like a New York Times and it was true.

89

I was like bottom full, talking

about free throw shooting, at Arizona

90

State and their student section

and the curtain of distraction.

91

So.

92

that kind of made me realize

that sports analytics wasn't this

93

inaccessible, you gotta know who's who.

94

Networks kind of career path.

95

I, like, oh, people are really

actually interested in different

96

perspectives and uh, uh, methodologies.

97

Avery: be.

98

nick wan: uh, I started going to

different sports conferences on, uh.

99

budget I had remaining in my student

budget and then uh, uh, kind of fork

100

in the road was I could have gone to a

postdoctoral fellowship, uh, which is very

101

common for, for cognitive neuroscience

people, uh, trying to get into becoming

102

a professor, um, or at least becoming

like some sort of like, you know,

103

tenure track to researching person.

104

Um.

105

So I had this postdoc lined up or, uh,

or sorry, not, or, uh, I had this postdoc

106

lined up and it was going to start.

107

18 months from when I

got accepted into it.

108

They were, they still had to like,

make sure they had the grant money

109

and they had other students and

other people in their lab who were

110

leaving, but it wasn't for a year.

111

So I was like, alright, 18

months is a long time and I

112

couldn't just work at the lab.

113

So what job would I do for, you know.

114

An hour for a year before I had to go to

those postdoc, started looking at jobs.

115

And then when I realized I was getting

a lot of, you know, responses back,

116

uh, from different teams, uh, and

being like a sports analytics person,

117

for being a data scientist for a

sports team, I decided like, what if

118

I just didn't do it for only a year?

119

What if I just did it for like,

maybe the rest of my life?

120

So, um.

121

So that's kind of how I ended up

as my first role in, uh, as a data

122

scientist for the Cincinnati Reds.

123

And, uh, it's, I, I've

kind of been doing that.

124

There was a small moment in 2020

125

but for the most part it's been,

it's been working with red, so.

126

Avery: Very cool.

127

Yeah.

128

During that stint you were

analyzing fried chicken at KFC.

129

We, we can talk about that in a bit,

but, uh, I want to go back to kind

130

of like where you're, you're doing

this blog, so you're writing this

131

blog and was it just about sports

analytics or was it about whatever.

132

nick wan: Yeah, I, uh, a little,

so the whole point, uh, someone, a

133

very smart person once told me like,

write down everything, you know,

134

like, whatever you learn, like, to

write it down, whether it's like.

135

Taking notes and then just like

transposing the notes into something

136

written, like, make sure whatever

you hear or whatever you're

137

learning, try to write it down.

138

And so, this blog I was writing, uh,

it was, uh, anything I learned in stats

139

or in psychology or neuroscience, I

would try to write kind of just like.

140

As notes, but I always would try to

convert what I knew into, uh, translate

141

it into like what it would mean if we

were talking about a score in sports

142

or stats in sports or, uh, what would

the translation be if you did this to

143

like a music, uh, a music file or like

some sort of song like how do these

144

manipulations, how do these processing

techniques change things that like.

145

We listen to or we hear,

we see all the time.

146

And, uh, it helped me be able to

explain like different processing

147

techniques, different stats

methods, uh, a lot more clearly.

148

Rather than talking about like

what this does to a neuron or what

149

this does to an area of the brain.

150

I could say if you did this to a bunch of

players on a football field, like this is

151

what their movement would look like now.

152

Or this is like.

153

things that you're making more

clear versus the things that you

154

would making, uh, more obscure from,

from, from a processing technique,

155

Avery: I guess, I guess we should

also clarify that when you're doing

156

the psychology research, um, I'm, I'm

inferring here, so correct me if I'm

157

wrong, but you're, you're like, you're

looking at brainwaves and when we look at

158

brainwaves like that basically is just a.

159

It's just data, right?

160

You have to interpret the data

in a certain way or another.

161

And so there's actually a decent amount of

data in psychology and and in neuroscience

162

because the only way you can really

study the brain is by checking, I don't

163

know what, what it's called, but these

different signals the brain gives off.

164

And basically these signals can be

interpreted to mean different things, but

165

in order to extract that signal, you have

to be using some sort of data science.

166

Is that correct?

167

nick wan: Absolutely.

168

So I use, uh, I use the big, the big

thing I was using was electro Ence.

169

Holography or EEG.

170

And like, it's like you said, you

put this cap on and there's all these

171

electrodes and they're recording the, uh.

172

The, uh, electrical impulses that are

coming off the top of your brain, and then

173

it's trying to read it through your scalp.

174

It's trying to read it

through your hair and skin.

175

Um, and so the signal itself is very

weak, but it is still electrical impulse.

176

So like, how do you know what's.

177

Um, real, like actually coming

from your brain versus what's like

178

muscle movement or what's just like

the machine shaking or something.

179

So you have to do all these like

cleaning, pre-processing techniques.

180

A lot of this is signal processing

from the world of like physics.

181

So you'll, you'll, if, uh, if people

are like, interested in time series

182

analysis or, or anything like that, just

cleaning, uh, signal over time, uh, then,

183

then, uh, EEG or fni or any of these,

uh, neurocognitive imaging techniques,

184

they, they collect all this data.

185

It gets loaded into a CSV.

186

You download the CSV, you clean it, you

send it through pre-processing stuff.

187

You ban, pass, filter it.

188

You do all this like, you know,

things you would typically do for,

189

for a time series, uh, filter.

190

And uh, it's exactly the same.

191

It's all data at the end of the day,

192

Avery: Side note, this is funny

because this is very similar

193

to what my first actual.

194

Analyzing data job was, I, I worked

at a biotech startup and they had

195

basically an electric nose, like

where basically if different chemicals

196

would hit it, the electrons would

either take or donate electrons and

197

that would cause a change in signal.

198

And so my job, well actually my job was to

just be the lab tech and just do the tests

199

and hand it off to the data scientists.

200

But when did the data scientists

quit And we couldn't find a new one.

201

And I was like, how hard can this be?

202

And I was like, I can figure it out.

203

And uh, it turns out it was

a lot harder than I thought.

204

But that's basically how I started.

205

So That's interesting.

206

Very.

207

Very similar analysis to,

to what I, I used to do.

208

Um, okay, so you're, you're

documenting your learning.

209

You're basically like taking notes

via a blog and, and, and maybe

210

relating it not only to neuroscience,

but to sports analytics as as well.

211

Um, I guess how good of

a programmer were you?

212

Because

213

nick wan: I

214

Avery: I read you studied psychology

as an undergrad, is that right?

215

nick wan: Yeah, I

216

Avery: Okay.

217

nick wan: my undergrad in psychology

and then my grad school of psychology.

218

Avery: Okay.

219

There was a break in between where you are

a music journalist or am I making that up?

220

nick wan: In while I was in

college, I was, uh, I had a blo

221

uh, yeah, I was a journalist.

222

I was, I, I had, there was this blog

that we were, it was a different

223

blog, uh, not the same blog that I

was writing the science stuff in.

224

Uh, we all had this, we all

were writing for this blog.

225

There was like six of us writing and then,

uh, a handful of us doing photography.

226

Yeah.

227

Avery: Okay, so like, were

you a good programmer?

228

I guess, did they, did they teach

programming and math to undergrads

229

or is that just more of a PhD thing?

230

nick wan: Um, really, at least

when, maybe now I would hope

231

that it's a little changed.

232

I know some programs of study, they

do a little more like, you know,

233

they're working in Python, they're

working with r, they're working with

234

some sort of like actual programming

language that they're doing stats in.

235

But back when I was doing it, they didn't.

236

Really teach much of it.

237

They, we had like a little exposure to

SPSS or SaaS or something, but it was

238

really just like a means to an end.

239

I felt, uh, the, if you really

wanted like different statistical

240

techniques or if you wanted, uh, more

in depth statistical techniques, you

241

would have to learn how to program.

242

So I ended up learning.

243

Matlab, uh, in grad school.

244

Not because I was told to, but

because I felt like I had to.

245

And, uh, and so, so that

was really good for me.

246

Uh, but I, before that I

had, I was like an Excel guy.

247

I was just living in Excel,

living in the spreadsheet world,

248

doing the spreadsheet thing

249

Avery: It.

250

I was a MATLAB guy too, so

this, this is super funny.

251

I think, I think we must be

extinct now, especially with ai.

252

Like, I can't imagine there's a lot of,

a lot of new MATLAB people out there.

253

Um, okay, so, so you taught

yourself some programming.

254

You're doing this blog and, um.

255

You, you wrote an article on your blog

about the curtain of distraction at

256

at, is it University of Arizona that

has like a really good crowd that

257

basically makes you miss free throws.

258

nick wan: Arizona State, uh, Arizona

State University, current distraction.

259

The whole thing is like, uh, when the.

260

Visiting team is shooting

into the, the student section.

261

They have this curtain.

262

Have you ever seen the movie basketball?

263

It's kind of like that, where they're

trying to like distract you from making

264

the free throw shot, but they open

the curtain and it could be anything.

265

It's like people kayaking and

people like, there's this like

266

unicorn thing that's happening.

267

I think Michael Phelps was like a part of

the curtain of distraction at some point.

268

So it is very distracting and loud.

269

Uh, but uh.

270

The whole question was, does it have

like an effect on the actual game?

271

Uh, and, uh, while I didn't find

a significant effect, the, the

272

New York Times article will, will

suggest that there's actually more

273

larger effects that I, I didn't find.

274

So do.

275

Avery: Interesting.

276

Okay.

277

And you're just, did you like share

the blog article, like on social media?

278

How did this this guy find it?

279

nick wan: Yeah, I don't know.

280

Uh, like I don't, in the neuroscience

community at the time, uh, social

281

media wise, it's very tiny and small.

282

And so, uh, if one person shared

something like all the neuroscientists

283

on social media would see it.

284

But I'm not like a popular guy in

social media, so I really have no idea.

285

Like I.

286

Uh, I'll always kind of share the email

that I got from him, and it was quite

287

literally, Hey, uh, really like your blog.

288

Um, can we use this?

289

And I was like, who is, how does he know?

290

I still don't know how

he found it, so, uh, but.

291

Avery: But he did find.

292

That's, that's the point is like you,

this is, so when I try to help people

293

end their first data job, a lot of people

are just like, oh yeah, I learned Excel

294

and I learned SQL, and boom, I can get a

data job and if I don't, if I don't get a

295

data job, it's 'cause I'm not good, good

enough at Excel or not good enough at sql.

296

And it's like.

297

nick wan: It's like

298

Avery: Well, you're missing out on

two thirds of the equation, which

299

in my opinion is creating projects

into kind of your network effects.

300

And so you created a project, essentially,

you put it online, someone noticed

301

you, it got read by, you know, millions

of of people around the world and.

302

That experience didn't necessarily like

lead directly to a job, at least the

303

way you told it, but it sounded like

when you started applying for jobs, you

304

could be like, oh, hey, you might've

seen me on the front page of the New

305

York Times doing sports analytics.

306

I think I'd be a good fit for your role.

307

Here's some ev like, here's

the article if you missed it.

308

Is that kind of how it went?

309

nick wan: Yeah, I, yeah, mostly,

uh, I, I like how you said it.

310

It, uh, uh, at least in my mind, uh,

on top of making sure I was writing

311

stuff down, I also wanted to make

sure that people saw me learning.

312

Um.

313

no other reason than like, this is

something I'm very interested in and maybe

314

other people are uh, in the same path as

me and I wanted to meet other people, so.

315

Uh, so sharing out projects, sharing

out research I was looking at,

316

uh, I think was always important

and it was extremely helpful.

317

So, uh, it was, this was

easily the biggest thing that

318

someone had discovered me for.

319

Uh, and to this day, I've, I can't say

I've done anything as crazy as being on

320

the front beach of the New York Times.

321

Maybe some people might attest.

322

Or contest that, uh, I think I've

done maybe a few things that would

323

be on the same scale, but to be

being a New York Times front pager.

324

So, uh, but yeah, I definitely

used that in my portfolio

325

when I was applying to things.

326

It was a conversation starter.

327

It was an easy thing to

talk about in interviews.

328

Like, here's a project that I did.

329

ask like, all right,

where's the data from?

330

How did you clean the data?

331

Did you consider all

these other aspects of it?

332

So, uh, just having a full

project that I did from beginning

333

to end was really important.

334

Uh, just, you know, breaking

the ice, talking to people

335

who were interviewing me,

336

Avery: With the Red's job originally,

did you just apply online or did you

337

like talk to someone at a conference?

338

nick wan: apply it online.

339

Like there's this website that most teams,

put their thing, put their job ads on.

340

It's, uh.

341

Uh, can I like say the link and stuff?

342

Is that fine?

343

Avery: a hundred percent.

344

Yeah.

345

It's like, yeah, it's like teamwork

online or something like that.

346

nick wan: it.

347

It's teamwork

348

Avery: Yeah.

349

nick wan: So, uh, so it

was on teamwork online.

350

Uh, I was literally just going through

all the jobs related to, to working

351

in some sort of player operations.

352

So whether it was baseball operations

or basketball operations or whatever.

353

And, uh.

354

Yeah, I applied to, I must

have applied to like 15 or so.

355

Now, this is gonna sound like rookie

numbers to a lot of people, but, uh, I, at

356

the time, the market was the way it was,

um, I applied to like 20 or so jobs and,

357

and I got calls back from all of them.

358

So, uh.

359

Avery: Wow.

360

nick wan: Yeah, it was a different

era, you know, like we're talking

361

like almost a, like we were

talking like over a decade ago.

362

So, uh, data.

363

Avery: And, and I will say like you

were just having that, that like bone of

364

defeats of like, look at here's my sports

analytics project, you know, published

365

in like the biggest newspaper in the us.

366

Like even if they couldn't see that, like

that's, that's really powerful I think.

367

Um.

368

So it makes sense.

369

It's like, hey, you, you had someone

else thought you were good already.

370

And when someone else thinks you're

good, you're way more attractive

371

to everyone else for some reason.

372

nick wan: It's very true.

373

Avery: Okay.

374

Wow.

375

Okay.

376

So then you got the job.

377

Um, let's talk about the job a little bit.

378

Um, like what exactly do you do?

379

Like what are you kind

of like a manager now?

380

Do you still find yourself like

actually doing analysis yourself?

381

Do you, are you just like a communicator,

like between parties nowadays?

382

nick wan: Yeah.

383

Um, a little of all, definitely LA

lot less hands on keyboard these days.

384

Uh, as the director, I try to

keep one project just, you know,

385

something that keeps my brain going.

386

So I do have like a project that I'm the

lead on still, uh, which, which is great.

387

Uh, it has to do with psychology,

so, uh, right up my alley.

388

Um, but.

389

In terms of all the other work that

we do, whether it's, you know, helping

390

the team from the day-to-day advanced

scouting planning, uh, whether it's,

391

you know, backend engineering, working

with the new sources of data that

392

comes in and cleaning it, creating

new metrics out of whether it's.

393

You know, the new bat path swing kind

of data or anything with a pitch or

394

positioning of players, um, anything

related to our biomechanics data, uh, all

395

of that can also be turned into metrics.

396

not really doing a lot of that anymore.

397

Um, there's projection systems, so

being able to say what's happening

398

in the future, uh, making sure that

you're very accurate, um, and, uh.

399

able to explain, not just the,

the estimate, the point estimate,

400

but the range of estimates.

401

there's also that, which I also

don't do at, at all anymore.

402

and then overall the communication of it,

which I'd say I do a lot more of, but.

403

Um, really the, the analysts, the

data scientists, they, they do a

404

lot of that, mostly do that as well.

405

So, out to stakeholders, decision

makers, coaches, players, uh, people in

406

player development, people in scouting.

407

Um, I do a lot of that, but I, I'm also

supported by my department who does

408

probably way more than I do on that front.

409

So, I, uh, I tend to think of my job

more as like the, the person who does all

410

the dirty work that no one wants to do.

411

Uh, someone once said, uh.

412

I get to do all the not baseball

stuff so everyone else can

413

get to do the baseball stuff.

414

Um, which it's like, uh, I wear

it on, uh, I wear it on my sleeve.

415

It's, there's all the business of

baseball that happens and it's not

416

as fun or, or sexy, the talk about,

but, uh, it, it's, uh, it what makes,

417

it's what makes the team operate.

418

So whether it's, you know,

budgeting or contract stuff, or.

419

You know, just talking to, uh, different

groups who might not be related to

420

the players or the coaches being able

to, to translate things for them.

421

Uh, it frees up my group from having

to do all this administrative stuff

422

and, and I, I, I get to go do all of

that so they get to do the fun stuff.

423

So, I do all of that.

424

And then I'd say the biggest thing

that's the definitely the most fun is.

425

Uh, talking about player personnel being a

part of the group that helps, uh, bring in

426

players being a, a part of the group who

helps promote players up to the big league

427

club or promotes them through our system.

428

So doing more roster construction

and player personnel decisions.

429

That's, that's probably like.

430

My biggest contributions to, to

our organization, me and everyone

431

else who works on that kinda stuff.

432

There's a small group on every

team who works on that kinda stuff.

433

And I'm, uh, pretty fortunate

to be a part of our small group.

434

Avery: You're basically like real

life Moneyball guy for the Reds?

435

nick wan: It's funny

436

Avery: Yeah.

437

nick wan: uh, our president of baseball

operations, Nick Crawl, was on the.

438

A So he was a

439

Avery: Oh, wow.

440

nick wan: yeah, he was a part of the

Moneyball, uh, the Moneyball team.

441

So,

442

Avery: Very cool.

443

That's awesome.

444

Um, what tools would you say you

and your team use the, the most

445

if I if in, in sports analytics?

446

Like what are the top two tools that

you're using on like a day-to-day basis?

447

nick wan: yeah.

448

Um, I love that you mentioned sql.

449

'cause like we're absolutely in

the database ripping out data, so.

450

It doesn't matter where you like what

level you are, you gotta know how to

451

like, rip out data from the database.

452

So, a lot of us, I'd say like 90,

call it 95% of us are doing SQL

453

and if we're not in SQL or using

Spark, so a lot of us are using PIs.

454

Um, but

455

Avery: Which for those, if you've

never heard of that before, just

456

basically think of it's SQL using

Python, but really big data.

457

nick wan: Exactly like where

it's like frame by frame data

458

in baseball, every game's like.

459

Upwards a terabyte of data now,

and, you know, you have points from

460

biomechanics and, you know, every

player has like 29 points on them.

461

So, it's a lot of data to store.

462

SQL isn't necessarily the best for

cutting through all of that data, so

463

there's a handful of us who use Spark,

but, um, most of the people, this

464

is a, this is probably a, a secret

that the, the Spark people don't.

465

to say, but we use sql.

466

We just write in sql and then that like

magically gets converted into Spark.

467

So I think most of us

are still writing sql.

468

So SQL's one, definitely one.

469

Uh, and then, uh, after that it's

kind of your flavor depending

470

on the team you're working on.

471

Our projections team works in R

so uh, they're doing a lot of.

472

Bayesian inference with r and Stan.

473

and then our, uh, research and

development team, they're all in Python,

474

so that's just straight up Python.

475

Avery: Cool.

476

Okay.

477

So sql, Python, RI mean, kind of the main

stack for data scientists right there.

478

So that, that makes a lot of sense.

479

Um, okay.

480

I wanna go and talk a little

bit about like how hard, 'cause

481

like you said, you were getting

interviews when you were applying.

482

Now you were a very qualified

candidate, but like how hard is it

483

today to land a sports analytics job?

484

nick wan: Yeah.

485

I, I go through an exercise every

year with my team of like, uh,

486

here is what my, uh, application

looked like and I got a job.

487

Right.

488

mainly because it's important

to, to kind of understand that.

489

least for me, I don't think my

application in retrospect was that,

490

you know, mind blowing, especially what

we're expecting of interns, what we're

491

expecting of entry level people today.

492

and so it's important to, like's know

that people do have the ability to grow.

493

They're not just like who

they are on their application.

494

Um, and uh, I do think it's way harder.

495

The expectations are way higher,

and the process of getting a job

496

now seems all like longer, harder.

497

it's more difficult, I would think

today than it was, know, a decade plus

498

ago where, uh, you know, my friend, I

have a friend, uh, Brad, and he, uh.

499

He was one of the first data scientists,

uh, for Uber, and this was back before

500

they had the term data scientist.

501

He was a data evangelist and, uh,

uh, he, his job interview was just.

502

Literally drinking coffee with a person.

503

And then they said, okay, well

we wanna bring you in as a, you

504

know, someone who helps us with,

uh, a quantitative analysis.

505

And he was like, well, I

think you need like 20 of me.

506

Uh, ended up getting the job.

507

But yeah.

508

Uh, back then it was that as a

nerd review, so mine was like.

509

all this positioning data in,

uh, for baseball, where would

510

you put these players if you were

hitting against these batters?

511

give, and then you had to estimate,

you know, how fast they were, uh,

512

you know, their accuracy in terms

of catching their, all their, how,

513

their, their throwing abilities.

514

where would you put them when you

were facing, I don't know, like.

515

Schwarber or Bryce Harper.

516

I dunno why I'm naming

Phillies, but GT Realdo.

517

Um, but where would you put them?

518

And, uh, mine was super easy.

519

I just did like some clustering,

and I said, all right, well the

520

best of these players play in

the center and then kind of.

521

Put these players on these other sites.

522

So, it was very not comprehensive.

523

It was one page, and the analysis

itself was extremely short.

524

Uh, I, I didn't think I did the.

525

I, I thought I did a fine job, a good

job, like people would understand,

526

like I knew what I was doing and how

to explain things, but I didn't think I

527

knocked it out of the park or anything.

528

I, uh, and uh, truth be told, still being

a PhD student, I was also trying to be a.

529

A PhD student trying to finish

all my dissertation work.

530

So I think my, compared to what people

are putting in for time now for,

531

uh, technical assessments, uh, doing

all sorts of lead code stuff, are

532

all things that I never had to do.

533

Uh, and, uh, it seems so much more

difficult now than, than, uh, than it was.

534

Avery: Yeah, I think, I think one of

the hard things also, it was so funny

535

that you talked about this earlier, but

kind of you were like, yeah, psychology

536

is like really competitive and like

academic psychology is really competitive.

537

So I was like, yeah, sports analytics.

538

And I, I'm like, well, I think

sports analytics, it's like you

539

look around, there's like, what,

five major, major leagues in the us.

540

Each league has about 30 teams, so

there's like 150 different teams.

541

Let's just ignore college for

right now, although that's becoming

542

more businessy every single day.

543

Let's just, let's just go pro.

544

So you have 150 teams.

545

How many people do analytics at

the Reds right now, would you say?

546

nick wan: I'll at uh, 12.

547

Avery: Okay, I'm just

gonna do 10 for easy math.

548

So that means there's like 1500

open sports analytics jobs in the

549

US and a lot of people like sports

and a lot of people like data.

550

And so if you combine

the two, that's great.

551

So it's like those are very

competitive, you know, 1,500 jobs.

552

So to me it feels near

impossible because it's like.

553

nick wan: You

554

Avery: You.

555

You have to be really

good to even get the job.

556

And then the other issue is when there's

a high demand for a role and a low

557

supply for the role, one thing that

does oftentimes is drive down salaries.

558

Now, some teams and some organizations

do a really good job where they're

559

like, yes, we value analytics,

so we're gonna pay really well.

560

But like there's other jobs where it's.

561

nick wan: it's like,

562

Avery: They're just not

gonna pay you very well.

563

Like, that's, that's the,

that's the big give and take.

564

It's like we can go find someone else

who will do it for, for this amount.

565

So, to me, from the outside,

you know, I, I interned with the

566

jazz like, what, five years ago?

567

But just from the outside,

kind of looking over the, the

568

view right now, it seems hard.

569

It seems competitive.

570

It seems stressful.

571

nick wan: Yeah, I, I, I agree and I

think like, uh, you know, something that,

572

not to say that this is any better, but

usually people who are applying to sports

573

analytics jobs on teams, they're all.

574

The, this isn't the only career

path they're looking for because

575

of all those reasons, right?

576

Like the, the extremely

saturated limited role.

577

when you talk about the 1500 roles,

578

roles, but really it's like.

579

I don't know, call it like roles

at most, uh, available at any year

580

across like all of the leagues.

581

Uh, because some teams aren't hiring.

582

Some teams are all full.

583

Some, some jobs are or not the

job that you're interested in.

584

Uh, so.

585

So there's really very small pool of,

of open jobs in, in the market versus

586

like tech where some companies are

just infinitely hiring for like data

587

analyst, data scientists, it seems.

588

So, um, so I think you're right

that I, I went from a competitive

589

industry to a a com, a competi,

more, more competitive industry.

590

Uh, I didn't see it that way at the

time, but I, I think you're right.

591

Uh, and then the, the ability to just

stand out above everyone, it's, you

592

know, having all the feathers in the cap.

593

It's, uh, I think it's a, a lot more

difficult these days, but it goes back

594

to the things we were just talking about,

like, how do you put yourself out there?

595

Putting your projects out there,

making sure that that allows you

596

to start conversations and meet

different people, new people, uh,

597

and then hopefully something like

that helps you along the way.

598

Uh, maybe directly, maybe

more likely indirectly.

599

So.

600

Avery: That's actually what I was

gonna ask you is let's say the

601

Cincinnati Reds had a job opening

right now, uh, and some of these

602

listeners applied, like what would

they need to do in order to stand out?

603

Like what are some things that they

could actually, you know, tangibly do to

604

try to land some sort of an interview?

605

nick wan: yeah.

606

Uh, I think the, we do our, the

way we do it at the Reds, we're

607

all the technical stuff first.

608

So you go through like a very

basic, like, do you know how to

609

program that filters out like 90%

of the people applying to a team.

610

Uh, so if you know how to do a little

database stuff, if you know how to.

611

Make a function in Python,

you're more than likely gonna

612

get through to the next round.

613

then, uh, the next part is

that take home assessment.

614

So where you send out a problem

set and then, uh, you work

615

through it, it's one problem.

616

Uh, and then we ask, uh, a

couple different questions

617

within the same dataset.

618

Uh, and then that's pretty open-ended.

619

That's your typical, you know, think of it

kind of like a kago competition in a way.

620

Like, here's a data set.

621

We want you to predict this

thing, predict this thing.

622

Or like, here's a problem.

623

There is no target.

624

Invent the target and try

to train a model to it.

625

So,

626

Uh, that's been the standard for us

since early, like 21, 20, 22 ish.

627

And then, um, uh, the way people

stand out the most in that process

628

is doing all the technical stuff,

uh, and being really diligent

629

about their answers and responses.

630

We're typically trying to

highlight certain aspects of

631

the technical assessment, like.

632

You know, some years we want to bring in

people who are more data visualization

633

heavy because we have a lot of data

visualization or reporting projects on,

634

uh, for the year, or sometimes we want

people to have more Bayesian expertise.

635

So, uh, having some sort of Bayesian

inference or understanding like Bayes

636

related stats, uh, is important and

we want people to showcase that.

637

Or if it's just like straight up.

638

engineering stuff like,

all right, take a model.

639

How would you create a set of, uh,

functions or packages that would call

640

a model back and then like put that,

push that into a database or something.

641

So.

642

really the technical skills are

the things that we weigh up most,

643

but once you get past all of that,

we do, uh, we, we have interviews.

644

Uh, the best things that stand out for

us, uh, at least for me one, is, uh,

645

just the, the ability to communicate

clearly is always like a, a huge.

646

Aspect.

647

So because we do all the technical

assessments first, we're always

648

talking about your technical

assessments as your project.

649

So if, even if you don't have this

gigantic portfolio, it doesn't

650

matter because if you're just.

651

You know, a student who never had the

time to, to jump into extracurricular

652

stuff, you have this technical assessment,

which is a gigantic project and we

653

could talk to you very clearly about it.

654

Everyone we're talking to, we're talking

about, their technical assessment.

655

So that kind of levels the

playing field for the kind of

656

work that we're talking about.

657

And who could speak most clearly

about their methods, like they

658

kind of start standing out more.

659

Just because we're talking about

the same project every time.

660

So those who could communicate their

results, those who are able to, uh,

661

to understand where the con founds

are, those who ask questions about

662

the dataset, like, is this dataset,

you know, how complete is it?

663

How accurate is it?

664

How, how relevant is it?

665

Is this similar to the

day-to-day that they'd be doing?

666

Uh.

667

the answer to that last

question is, yes, it is.

668

Um, that's all really important to us too.

669

So just being thoughtful and, and

engaging, uh, when we start engaging you

670

about how you went about your project.

671

Uh, it, it goes a really

long way, I would say.

672

Avery: Very cool.

673

I I like that whole process.

674

Thanks for giving us that.

675

A little bit behind the scenes, uh,

sneak into it, sneak peek into it.

676

Um, and I, I'm curious also, like what are

your best recommendations and resources

677

for people who are interested in getting

into sports analytics, like books or blogs

678

or podcasts or those types of things?

679

nick wan: yeah.

680

Um, there's a lot of people

doing a lot of cool work in the

681

sports analytics space still.

682

Uh, if it's not.

683

Over at my YouTube, you could

definitely check out different

684

websites like fan graphs, they're

fan graphs, community articles.

685

You have a bunch of people

contributing to research there.

686

Uh, those are always really great reads.

687

Baseball prospectus, uh, also

always puts out great research.

688

Uh, it's, uh, very interesting to read.

689

From there, analysts and writers,

uh, all the things that they're

690

seeing from the public point of view.

691

I, in terms of podcasts, uh, the, I think

the number one podcast a lot of people

692

in the baseball world will listen to

is rates and barrels, which is put on

693

by Eno C uh, it's, uh, it's baseball.

694

It is very baseball forward, but.

695

There's nuggets of analytics,

uh, threaded through the podcast.

696

Uh, of course fan

graphs, effectively wild.

697

a, that's a very much more analytic

forward podcast for baseball.

698

and then, uh, I, I, I don't have a ton

of places in terms of, uh, uh, online

699

resources or analytics, sports analytics.

700

I know you and I do a ton, so, uh.

701

Outside of us.

702

I, I, I, I'm unsure.

703

I don't, I don't really, I don't

really have a ton to recommend

704

Avery: I think, I think those

recommendations you gave are great.

705

And yeah, I would just encourage

you guys to check out some of

706

Nick's links in the show notes.

707

Done like his YouTube, he just

released like this like very fun walk

708

and talk podcast episode and like

some of the stuff he talks about in

709

that episode are really interesting.

710

I think like there's some really

good nuggets kind of buried

711

inside of that episode that I

think people should listen to.

712

And then, and then the

other thing I suggest.

713

I think is really useful.

714

That helped me, not that I've made

it in the sports analytics world.

715

I did one internship with Utah Jazz.

716

Um, but I think anything in life

is just finding people that do the

717

things you're interested in and

following them on social media.

718

So like, for instance, follow Nick on

Blue Sky or YouTube or whatever, right.

719

And, and see what, see

what they're talking about.

720

See like what they mention.

721

Um, I'm really into basketball and

so I really like, um, oh crap, and

722

I'm gonna forget this guy's name.

723

Uh, well, there's Kirk Goldsberry, uh,

who used to work for the San Antonio Spurs

724

and he teaches at the University of Texas.

725

He does like analytics light, like,

I wouldn't say like anything crazy

726

in analytics in the MBA, but I like

a lot of the stuff that he publishes.

727

And then there's another guy on Instagram

who publishes like a ton of MBA content.

728

He does it anymore 'cause he just

got hired, I think, by the Nuggets.

729

And then I think they like

asked him to stop posting all of

730

his useful, insightful things.

731

They're like, we wanna keep all this

stuff to ourselves, but like these

732

people are like literally posting like.

733

As good of analysis that they

would be doing for teams.

734

And they like, we'll even like

show you the GitHub of how they do

735

everything and it's like, oh, I can

just study what this person does and

736

learn a whole heck of a lot from them.

737

So, uh, I think, I think the, like

fan graphs and other things that you

738

mentioned are, are really good resources.

739

Um, so yeah, hopefully, hopefully

people if they, if they want to and they

740

really want to commit, yes, I'm into

sports analytics, I think there's enough

741

resources out there that they can, they

can learn quite a bit on their own.

742

nick wan: I agree.

743

And that's a great point about people

on social media who post a lot,

744

who end up getting hired by teams.

745

Teams are always looking

for the next person in.

746

And while I just talked about our

interview process, that doesn't

747

dissuade us from seeking out or

scouting really, uh, people putting out

748

really cool stuff on, on social media.

749

So if you do have the time to do

that, uh uh, it could take you.

750

Uh, in definitely different places.

751

Avery: I, I love that.

752

I love that.

753

That's what you did.

754

That's how you got hired, basically.

755

So.

756

nick wan: That's how I did it.

757

Avery: It co it comes full circle.

758

Well, awesome.

759

Dick, thank you so much

for all your insights.

760

We'll have all your links and the

description down below, and, uh, hopefully

761

some people will, uh, will become sports

analytics analysts after watching this,

762

and hopefully the Reds have a good season.

763

Good luck to you guys

in your upcoming season.

764

nick wan: I appreciate it, Ru.

765

Thanks for having me on.