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Takeaways:

• Player tracking data revolutionized sports analytics.
• Decision-making in sports involves managing uncertainty and budget constraints.
• Luke emphasizes the importance of portfolio optimization in team management.
• Clubs with high budgets can afford inefficiencies in player acquisition.
• Statistical methods provide a probabilistic approach to player value.
• Removing human bias is crucial in sports decision-making.
• Understanding player performance distributions aids in contract decisions.
• The goal is to maximize performance value per dollar spent.
• Model validation in sports requires focusing on edge cases.
• Generative models help account for uncertainty in player performance.
• Computational efficiency is key in handling large datasets.
• A diverse skill set enhances problem-solving in sports analytics.
• Broader knowledge in data science leads to innovative solutions. 
• Integrating software engineering with statistics is crucial in sports analytics.
• Model validation often requires more work than model fitting itself.
• Understanding the context of data is essential for accurate predictions.
• Continuous learning and adaptation are essential in analytics.

Chapters:

11:58 Transition from Academia to Sports Analytics

20:44 Evolution of Sports Analytics and Data Sources

23:53 Modeling Uncertainty in Decision Making

32:05 The Role of Statistical Models in Player Evaluation

39:20 Generative Models and Bayesian Framework in Sports

46:54 Hacking Bayesian Models for Better Performance

49:55 Understanding Computational Challenges in Bayesian Inference

52:44 Exploring Different Approaches to Model Fitting

56:30 Building a Comprehensive Statistical Toolbox

01:00:37 The Importance of Data Management in Modeling

01:03:21 Iterative Model Validation and Diagnostics

01:06:53 Uncovering Insights from Sports Data

01:16:47 Emerging Trends in Sports Analytics

01:21:30 Future Directions and Personal Aspirations

Links from the show:

• Luke’s website: http://www.lukebornn.com/ 
• Luke on Linkedin: https://www.linkedin.com/in/lukebornn/
• Luke on Wharton Moneyball: https://knowledge.wharton.upenn.edu/podcast/moneyball-highlights/luke-bornn-part-owner-of-ac-milan/
• LBS #108 Modeling Sports & Extracting Player Values, with Paul Sabin: https://learnbayesstats.com/episode/108-modeling-sports-extracting-player-values-paul-sabin

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

uh Today, I am excited to host Luke Bourne, a true pioneer in sports analytics and based

in statistics.

2

Luke started his career as a statistics professor at Harvard and Simon Fraser before

pivoting almost entirely to sports analytics over a decade ago.

3

Luke has worked across multiple sports roles including quantitative gambling, analytics

leadership at A.S.

4

Roma and the Sacramento Kings and co-founding

5

obviously Zealous Analytics, which grew to a 75 plus person company before being acquired

by Teamworks.

6

He was also part of the ownership group at Toulouse FC, where he's applied data-driven

decision-making to build a competitive club on a budget, winning both Ligue 2 and the

7

Coupe de France.

8

In this episode, Luke takes us through the evolution of player tracking data and the

application of basic methods in decision-making under uncertainty.

9

We dive into portfolio optimization for player acquisition, the challenges of model

validation, and the role of generative models in forecasting performance.

10

Whether you're into sports analytics or just fascinated by decision-making in high-stakes

environments, this episode is packed with insight from one of the best in the field.

11

This is Learning Basics and episode 131, recorded December 10, 2024.

12

Welcome Bayesian Statistics, a podcast about Bayesian inference, the methods, the

projects, and the people who make it possible.

13

I'm your host, Alex Andorra.

14

You can follow me on Twitter at alex-underscore-andorra.

15

like the country.

16

For any info about the show, learnbasedats.com is the last to be.

17

Show notes, becoming a corporate sponsor, unlocking Beijing Merch, supporting the show on

Patreon, everything is in there.

18

That's learnbasedats.com.

19

If you're interested in one-on-one mentorship, online courses, or statistical consulting,

feel free to reach out and book a call at topmate.io slash Alex underscore and Dora.

20

See you around, folks.

21

and best patient wishes to you all.

22

And if today's discussion sparked ideas for your business, well, our team at Pimc Labs can

help bring them to life.

23

Check us out at pimc-labs.com.

24

Luke Bourne, welcome to Learning Bayesian Statistics.

25

Yeah, thanks for me on.

26

Yeah, thank you so much for taking the time.

27

This is a blast to have you here.

28

uh I never thought I would be able to uh interview you on the show, to be honest.

29

uh You're kind of a uh superstar to me in my world, you know, so I'm very happy to have

you here.

30

And thank you so much to Patrick Ward for...

31

us in in contact.

32

Patrick, if you're listening, thank you so much.

33

if you when you come to Miami, I definitely owe you a good dinner.

34

know, anything Patrick asks, I do.

35

So and as prep for this, I went back and I looked at all your previous, listened to a

bunch of the previous episodes and really kind of

36

really cool to me to see your podcast in some sense resembles my career like the last you

recent episodes you have a lot of people that I call colleagues and friends now Robbie and

37

Patrick and ah Paul Saban and then I look farther back and it's like

38

Julian Cornerbees, and I worked together in North Carolina, what he was doing was

post-doctoral at UBC.

39

We actually wrote a short paper together.

40

Andy Gellman and I shared an office when I started at Harvard.

41

Nicholas Chapan and I have had many dinners together over the years.

42

Kevin Murphy, know well from my time at UBC.

43

So was really cool to kind of, it really blends my sort of two phases of my career, of

first as an academic and now sort of in sports.

44

Oh yeah, okay, that's good to know.

45

Yeah, for sure.

46

So I didn't know you knew um Nicolas and Julien so well.

47

um I remember Andrew Gelman, um which much has been really cool.

48

honestly, working in the same hallway as Andrew because each time I get to see him, it's

really cool and I get a lot of, uh you know, it's just awesome because he always has so

49

many stories and I can ask him any questions I have.

50

So working with him.

51

must be incredible.

52

Yeah, and we shared an office my first my first semester at Harvard.

53

I was actually on sabbatical kind of a parental leave because I just had a kid.

54

And I was going back and forth between Vancouver and Boston sort of we're in the process

of moving.

55

And so yeah, I shared an office with him.

56

It was a really cool experience.

57

Yeah, I mean, I guess.

58

And actually something Patrick told me a while preparing for the episode he told me so you

can ask any Luke anything about spots.

59

He knows a lot about that in stance.

60

But don't ask him anything about guitar playing, because I'm way better than him,

something like that.

61

Do you know what he's referring?

62

Yeah, so I used to play guitar quite a lot.

63

For those watching this on video, we'll see the guitars in the background.

64

um

65

But Patrick is actually a properly trained musician.

66

Actually, I have an interesting story about Patrick and guitar which is so I was at the

Sloan Sports Onlives Conference This is probably five six years ago and we were upstairs

67

at Trillium Brewing, is the sort of a you know, nice nice restaurant brew pub near the

conference and I was sitting at the corner of the bar and on one side of me was Patrick

68

who works for the Seattle Seahawks I think was on the your podcast what a couple months

ago or something and Sonny Maida was on the other side

69

of me and he's an assistant general manager for the Florida Panthers, incredibly

interesting guy, just two incredible guys on both sides of me.

70

And at some point, late into the evening, it occurred to me that these guys had just met

and they had this incredible unknown connection, is Patrick actually studied jazz guitar

71

at Berklee in Boston and so formally studied jazz guitar.

72

Sonny Maeda was actually a professional jazz guitarist in New Orleans for years.

73

In addition to being a

74

professional poker playing, all sorts of other cool stuff.

75

ah And so I said to these guys, said, whoa, like you guys are both like leaders in sports

analytics, but you have this really bizarre connection that neither of you realizes, and I

76

do.

77

And see how long it takes you to figure this out.

78

So they started on like, know, academic stuff.

79

It took them probably a good 20, 30 minutes before they realized.

80

then I couldn't keep them apart for the next like four hours as they just, you know,

compared teachers and the musicians.

81

they played with and so on.

82

you know a long history of course of math and music being tightly tied together but that

was a cool example of two of my favorite people and Sonny and Patrick sort of like come

83

together on a musical interest.

84

Yeah this is really cool.

85

For those watching the video it's the first time that happens.

86

I'm in a new flight now and I have a picture behind me.

87

uh

88

Like there is a face on that picture and basically the iPhone is freaking out because he's

seeing two faces and so he doesn't know if he should focus on myself or the painting.

89

It's literally going back and forth between the weird painting and you.

90

So I think I should take that personally, right?

91

Because I look nothing like the painting and the painting is pretty awful so it's like I'm

taking it quite personally.

92

I'm selling my iPhone after the episode.

93

I'm going to get rid of that painting actually while you're talking, which is way too

distracting for people.

94

So anyways, yeah, great, great story.

95

I love that.

96

And yeah, as you were saying, yeah, of course, deep history of music being actually very

mathematical and mathematics also being very creative and artistic in a way.

97

We've talked about that already actually on the show.

98

Yeah, at Zellas, which we'll talk about in a bit.

99

there's a very active music culture, a lot of people who play instruments that are hooked

on music, one of them being uh Daniel Lee, who you've also had in the podcast.

100

Really cool guy, but just really super passionate about music as well, and I think it's

pretty common connection.

101

Yeah, yeah, yeah.

102

So as you saw, could not get shoot of the paintings like really into the wall.

103

So we'll have to do that with that.

104

That's going to be a collector episode, I encourage you to check the YouTube video of that

one.

105

uh Yeah, mean, Daniel is actually incredible.

106

Each time I come to New York and I get to meet him, he always gives me...

107

tons of recommendations, know, of stuff to do in New York, very artistic.

108

He's also very well versed into in architecture, which is something he and I have in

common.

109

yeah, he's like, knows, he knows really well architecture.

110

And so that's really cool because he always gives me amazing things to go see in New York.

111

So yeah, thank you.

112

Thank you so much, Daniel, for enlightening me all the time.

113

Did you get a private concert when you were at the Sloan Conference from Patrick in that

other name?

114

don't remember.

115

From Sonny, no.

116

Patrick, though, in the past has sent me videos of himself playing guitar, and he's a much

better guitarist than I am, so I have never reciprocated.

117

What's talent of yours actually that you know like something that's I mean you're

obviously talented in your job and everything you do related but like a non-professional

118

talent that you could send a video to Patrick.

119

man it's good question.

120

Yeah and I know I'm putting you on the spot here because it's not at all it's the first

time I ask these questions and that was not in the questions I sent you.

121

I'm trying to get sort of obscure talent you know I'm

122

These days, it's my whole life is job and family.

123

So I'm extremely good at losing to my kids at Mario Kart and horrendously bad at putting

together IKEA furniture.

124

So maybe it's like good at being bad at child-related things.

125

So yeah.

126

I mean, I don't know if you're already at the part where you're losing on...

127

you're not losing anymore on purpose at my account against your kids.

128

I am at that point, my kids a little older now, they're eight, 10 and 12.

129

And so certainly the older two are, you know, super smash bros.

130

have no chance.

131

I just mash buttons and hope I get lucky.

132

So yeah, I'm well past that point.

133

They're definitely better gamers than me.

134

Yeah.

135

I mean, they probably don't listen to the show.

136

So if you tell them you do it on purpose, I think you can still do it for a few years, you

137

I didn't purpose that one.

138

Exactly.

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I made you win.

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uh Actually, yeah, so can you tell us what you're doing nowadays?

141

Because you do so many things, uh very original.

142

yeah, how, what do you tell people when they're asking you what you're doing nowadays?

143

And also how you ended up doing what you do and working on this?

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Yeah, I tend to give people, it depends on how long of a conversation I want to have with

someone.

145

If I want to sort of end it quickly, that's what I do.

146

I say I'm a statistician and it ends the conversation right there.

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For longer conversations, you know, I'm really fortunate that I have sort of a one word

description of what I do, which is moneyball.

148

So certainly people outside of the field, what do do?

149

Yeah, you know, I'm the moneyball guy and that's sort of a really easy way to sort of

describe what I do.

150

But yeah, if I give sort of a longer background,

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I did a PhD in stats and machine learning under Arnaud Doucet and Jim Zidak at UBC.

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Then I went on to spend some time on the tenure track at Harvard and then Simon Fraser.

153

through that time sort of made a transition into sports and we can talk about that

transition later if you want but sort of pivoted over to sports analytics and since then

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have worked for a bunch of NBA teams, spent a few years with the Sacramento Kings, some

consulting games with others, spent a little over a year with AS Roma in Italy, a geek in

155

quantitative gambling.

156

And then the last four or five years with some partners, we banded together alongside some

investors and we bought Toulouse FC in the South of France and another club more recently.

157

And alongside that, I co-founded a company called Zealous Analytics, which uh we built up

to about 75.

158

employees and we actually sold the Teamworks to, sorry, we sold Zellis to Teamworks back

in the summer.

159

sort of just came full circle on that.

160

So now my time is spent.

161

I continue to work a little bit with Teamworks with the sort of old Zellis crew and

continuing to be part of the operations of Toulouse.

162

Hmm, yeah.

163

Okay, so Toulouse and not Milan.

164

Not terribly involved in Milan at the moment, that's right.

165

Yeah.

166

Yeah, because you still cannot be involved in both at the same time, right?

167

Because of the...

168

Yeah, it's complicated.

169

It's complicated with UEFA.

170

Yeah.

171

Yeah.

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Okay.

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Yeah, that's just a great...

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Just such a great path.

175

yeah, we'll definitely dive into these lightest activities that you were talking about

because I'm really interested in how...

176

are the kind of models we're going to talk about and we talk about on this show all the

time and I do personally in my in my everyday life uh how they used how they consumed by

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um the people I make the models for you know and that's that's something we have

178

I get asked to do interviews and podcasts quite regularly, but it's almost always in

sports and I turn it down.

179

This was like, get a chance to sort of like be nerdy again and talk about things that,

know, like technical things that I'm super interested in.

180

So, yeah, cool.

181

So then thank you.

182

Thank you so much.

183

That's an honor.

184

Yeah.

185

And I think also if people are interested in a bit more about your background and stuff

like that, I would refer to the

186

I think it's the Wharton Monable podcast where you were a few months ago I listened to

that one to prepare for the show so I'm not gonna ask you the same questions uh this

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interview is really great because you go into detail how you ended up doing what you did

your time at Sacramento, your time at S-Roma and that's a great interview so

188

Yeah, for people, I put that in the show notes for people who want to get a bit more

detailed with your background.

189

Today, uh let's talk about patient stats a bit, you know, like how were you introduced to

these weird worlds of peace?

190

When I got married and my brothers gave a speech, part of the speech was...

191

they said he's a proud Bayesian and I can't remember, I just remember them like

pronouncing the word Bayesian like really strangely.

192

So it's part of my identity when it's part of a wedding speech.

193

But you know, I did my PhD at UBC and at the time I was there,

194

It was like this incredible hotbed of people in this domain.

195

So, you know, did my PhD with Arnold de Se.

196

And actually at the time there was like this really, was him, Jim Zidak, who also did

another co-supervisor of mine, Rafael Guitardo, Paul Gustafson, Nando De Freitas, Kevin

197

Murphy, you know, and many others.

198

So these people at the time, it's kind of all I knew.

199

So I didn't kind of realize how special that situation was, but it was sort of,

200

you know, got to spend essentially six years of my life surrounded by those people and

kind of deeply ingrained in me a sort of Bayesian way about thinking about the world.

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oh yes, so that was, yeah, that was quite fast basically and that's the way, that's kind

of the way you learn statistics if I understand correctly.

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Yeah, you know, I did an undergrad in mathematics and then my master's and PhD were both

in stats and

203

I sort of, my PC thesis was sort of divided between sort of spatial stats and Monte Carlo

methods and.

204

So you sort of those two things together when you think about sort of Bayesian spatial

statistics, combined with Monte Carlo, it sort of covered the whole gamut of sort of

205

Bayesian modeling techniques.

206

And of course, with Kevin Murphy there and Nanodifera, just like also a lot of exposure

to, you know, what Kevin might call like probabilistic machine learning or statistical

207

machine learning kind of thing.

208

yeah yeah okay yeah so that's funny because it's a bit like myself also where I didn't

have to unlearn you know all the stuff I had learned very confusingly in in undergrad like

209

basically learn patient sense from the from the start um

210

kind of make it simpler on the teacher, would say, as a teacher now, like it's way easier

to teach people like us than to teach people who learned a lot of frequent distance and

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now are trying to switch because I mean, naturally they always try to come back to

something that's familiar and sometimes it's very different.

212

So you're like, okay, try to, you know, try to forget that, that paradigm.

213

That's hot.

214

UBC was interesting because you had sort of this Bayesian cohort, but then you also had

people working on like robust estimators like M estimators and Taoist estimators, all

215

these kind of very frequentist ideas.

216

So it was kind of being exposed to both worlds.

217

I think it was actually quite useful because it helps you really understand the sort of

philosophical differences as well as the, you know, the pros and cons of different ways of

218

thinking about things.

219

Yeah.

220

yeah.

221

I think that's definitely super interesting and super worth it.

222

And, as always, as always,

223

say for people interested in kind of the epistemological kind of side of things.

224

The two best episodes to start with that that we have are episode 50 and 51.

225

50 is with David Spiegelhalter, the only sir we had on the podcast for now.

226

Maybe we'll get Alex Ferguson, know, one day.

227

And 51 with Aubrey Clayton, the author of

228

the book Bernoulli's Fallacy.

229

If you want to start with some epistemological topics, would definitely recommend these

ones, So yeah, just go on the website and look for those.

230

But yeah, I think in my experience that I only give that to the students when they ask for

it.

231

There's usually the way that works for people is there are many interesting in the, you

know, in the practical side of things.

232

Um, and unless they are very nerdy like me, they're like, okay, but why is that actually

interesting?

233

And why is that working better in these cases?

234

You know, and interesting in really the why then I'll, I'll give that to them very happily

because I love that.

235

But, uh, in my experience and especially also for my bosses or else, you know, I'm like,

just.

236

I just show the model and show why that's interesting and why that would help their

decision making.

237

uh And actually you personally, as we've seen, you've been involved in sports analytics

for years.

238

So I'm curious about so many things.

239

First, what's your vision on how the field has evolved, ah especially with the rise of

different sources of data?

240

in different spots.

241

another question I have you as a decision maker is how do you approach modeling

uncertainty in your decision making, uh whether that's with Toulouse or, uh well, Milan

242

less now, the different things and projects you're working on, and whether we're talking

about evaluating players or planning strategies.

243

Yeah, so the first thing is about sports analytics.

244

So when I sort of getting into sports analytics at the time, it was the early days of

player tracking data.

245

So prior to that, the vast majority of the data up to that point had been event data, hand

tracked, maybe a couple hundred or a couple thousand events per game sort of true across

246

sports.

247

Now baseball may be the exception there, but for most sports it's sort of very simple

data, count data, that kind of thing.

248

And about 2012, I was really fortunate.

249

One of the first people I met when I started at Harvard was...

250

guy named Kurt Goldsbear and he had just been handed the NBA's player tracking data.

251

And this data captures multiple times per second the location on the court of every player

in the ball in three dimensions.

252

And so to me, when I looked at this, I like, this is like, I'm actually not that big of a

sports fan, but to me it was the richest space time data I'd ever seen.

253

And the amount of structure that exists in this data because of the sport itself, both the

rules as well as the sort of tactics and strategy.

254

It was just like the most fascinating and challenging problem I think I had ever seen ever

come across.

255

So for me, sports was not, uh my path into sports was not, I want to work in sports or

sports is really cool.

256

It's like this is really interesting and hard and unsolved.

257

Keep in mind my PhD was essentially on modeling things in space-time along with SMC

methods and so on to handle these things computationally efficiently.

258

And so I had the right toolkit to handle this type of data.

259

So yeah, that's kind of how I made the transition from academia to sports is fundamentally

with these new data sets that are really complex and spatially and temporally rich, uh

260

having the right skill set and just realizing actually there's lots of interesting

problems.

261

After a while I actually consulted in sports and continued academic gigs and just found

that I enjoyed the sports stuff more.

262

The problems were more interesting.

263

I never enjoyed going to committee meetings and reviewing papers and revising papers.

264

oh The peer review process in academia is fundamentally broken.

265

uh

266

So it was an easy decision when I decided I'm gonna go full time into sports.

267

So anyway, that's the sort of sports piece, how I sort of pivoted to sports.

268

The second part of question, which is like handling uncertainty in decision making.

269

I really think about that as the end user.

270

We wanna try and construct team.

271

that's gonna outperform its budget.

272

So let me use Toulouse as an example.

273

So Toulouse has a payroll of about, you know, 15, 18 million a year.

274

That's euros.

275

our goal with that- Which is, so to give perspective to people who don't know, you know,

sports.

276

That's not a lot.

277

Right.

278

If you compare to the We are the lowest or second lowest in the league.

279

And our goal is to have...

280

the are like way bigger.

281

Like they have way bigger payrolls than you two, the big clubs.

282

You might know a big club in France that has an insanely inflated payroll.

283

There's a few of them.

284

It starts with a P and ends with a G.

285

And so, ah know, just to make the math easy, we're spending 15 a year.

286

We want to perform like a mid-tier club, like a sort of an average club in the league,

which is typically spending 40 or 50 million.

287

So the way I think about us is like, how do we spend 15 million a year?

288

So that's like the lowest or second most budget performed, like the 10th or 11th best team

and that's sort of 40, 50 million.

289

And to do that, we sort of have to have a distributional sort of.

290

assumptions around the value that each individual player creates.

291

And essentially it becomes like a portfolio optimization problem where you have a set of

players which sort of act like individual uh assets in a way.

292

And you're trying to sort of find the combination of players that you can have some with

some reasonable confidence that you're going to be able to perform to the level that you

293

need.

294

Certainly the very least avoiding relegation, sort of assembling these things in a way

which may, you you're trying to sort of de-correlate these assets that these player

295

perform.

296

is, and so on, sort of such that you minimize your chance of relegation and maximize your

chance of performing at the level you want to perform at.

297

Yeah, yeah, and that's funny because that's also a lot how I personally, you know, try and

explain the issue to, I mean...

298

what what I do to to people and why actually it's interesting for clubs to model with you

know the kind of models we do um because often people don't know at all what's going on

299

you know in the front office is like especially in europe in the us I think you know the

influence of money bowl as you were saying um not only the book but also the movie is is

300

much more pregnant but in europe

301

especially soccer, especially continental Europe is like, really?

302

People do that.

303

And so, yeah, like trying to explain them in a way, as you were saying, as a portfolio

management, or I'm saying, well, if you're, know, when you have less, remember when you

304

were a student and you were broke, you had to be very careful about any dime that was

leaving your account.

305

So that's basically why also for the clubs which have way less budget, it's actually much

more interesting to

306

do these modeling because they have to be much more careful about what they spend, whereas

PSG or Real Madrid, it's not that big of a deal for them, at least at the beginning.

307

uh

308

such a high budget relative to rest of league, you can actually be fairly inefficient with

it and still be quite successful, right?

309

um I think they are.

310

think that's good description of the club.

311

Yeah, and to boil it down to a really simple example, let's say you want to acquire a

player, want to fill, say you're going for striker, have player A, player B.

312

The traditional approach would say, okay, let's say player A costs one million a year,

player B costs two million dollars a year.

313

And traditionally the Scots would

314

Well, we like player B better so let's get them but they say hold on one guy cost two

million a year that one cost one million a year one of them's you older young girl these

315

different factors how do we choose one and they have to sort of Either kind of comparing

apples to oranges.

316

They have these sort of financial traits and then they have the scout saying I like this

guy better He's got a nice he's got a good left foot and he has good spatial awareness And

317

he said well, how do I turn that into dollars?

318

Like how do I decide whether I'm gonna spend two million or one million on player B or

player a?

319

whereas Statistical methods come along and you can sort of very precisely say hey

320

there's a 10 % chance that this player performs about his contract, there's a 30 % chance

that this other guy performs about his contract.

321

So you can actually be much more explicit and probabilistic about the value a player

brings, most importantly, how they bring relative to their contract.

322

Yeah, yeah.

323

And so I'll show you how...

324

how does that work for you?

325

I'm curious as the decision maker, as one of the decision makers, how do you actually use

the models to make decisions?

326

And I think for me, you would be one of the best bosses I could have, right?

327

Because you come from the modeling side.

328

So that would make my work, of course, way easier because I could talk to you in technical

terms, of course.

329

But I'm very interested in how you personally consume the models.

330

What is actually useful

331

For you, know, are those the posterior distribution?

332

Is this something that you are more interested in the comparisons?

333

You're more interested in the tail probabilities?

334

What's actually something you care about when you make the decisions?

335

Yeah, I think the most useful way to think about

336

like for the end user is really sort of a distribution on future performance value.

337

And what I mean by that is you can imagine saying, we think next year this player's

performance on our team and ideally in dollars would be to say, you know, some, some

338

distribution over the value they will add in dollars.

339

So if you look at basketball, it's like, you know, maybe, maybe LeBron James is worth 40

million a year, but, and, we might say, Hey, we think that there's a 20 % chance that

340

he's, you know, he's thinking some distribution.

341

There's a 20 % chance that

342

he's worth 60 million or more.

343

And there's a 10 % chance, because of injury or whatnot, that he's actually worth 5

million or less.

344

So understanding that distribution and then seeing how it progresses through time.

345

That's the key information you need to make a decision about whether to, you know, it's

not going to sell the player or sign that player or to extend their contract or

346

renegotiate the contract.

347

So that's sort of the key bits of information.

348

And ultimately we want to use that information as much as possible.

349

I sort of think about...

350

A lot of what I do now is actually just removing humans from decision making.

351

You know, sort of, there's so much work now in behavioral economics and elsewhere that

humans are just quite biased in the way that they go about decision making fundamentally.

352

so, you know, lot of what I do is sort of focusing on how do we...

353

How do we remove that bias from the decision making process?

354

How do we take the good bits of subjective information and remove those biases in a way

which is ultimately going to allow you to create some arbitrage opportunity in player

355

value and ultimately to create shareholder value.

356

Okay, I see.

357

If I try to summarize.

358

Basically something like a war metric is how you summarize the contribution of the player

then but that's still in terms of you know goals or wins or whatever and then transforming

359

that contribution to dollars in and then making the decision based on that and probably

making a decision that's helped a lot by uh

360

computer assistance to try and limit the decision, the biases that are very well

documented in how humans make decisions.

361

Exactly.

362

One way to think about it on a player, if every single player on your team is underpaid,

you are going to have a very good team.

363

Right?

364

Like assuming you're paying, you know, have a certain payroll, right?

365

Conversely, it's easier to think about the other way, right?

366

If you have a fixed payroll, if you have every single player on your team, if you have a

low payroll and every player in your team is overpaid, you're going to be very, very bad,

367

right?

368

So the goal is essentially to getting as much performance value as you can for the dollars

you spend, right?

369

history has shown that that statistical models and sort of probabilistic reasoning are a

much better way to go about that process than sort of the traditional gut-feel scouting

370

approach.

371

Yeah, yeah, but that's really fascinating to really see that research now pan out in, you

know, in

372

in real uh clubs like you're doing so that's why I'm really fascinating by that kind of

work and also you know yeah I think these are great organizations as modellers at least to

373

work in because

374

That's also where your work has more impact, I would say.

375

Yeah, in sports, you just get a tremendous amount of super interesting modeling problems.

376

Like, of thinking that you talk about how to use Bayesian methods and so on.

377

That's...

378

Fitting the model is not the sort of interesting part, right?

379

It's like even defining the model is not necessarily the interesting part.

380

Where you get in, just, it's all sort of the issues that come around it that become super

interesting.

381

It's like, if you've defined your estimate, like slightly incorrectly, you can end up with

wildly wrong conclusions.

382

Conclusions that can cost you millions of dollars.

383

Or there can be like,

384

there's probably lots of examples we can get into, where this sort of standard model, like

think about what you're doing in sort of traditional validation of models.

385

You say, okay, we fit this model and then we sort of say, hey, this thing predicts well by

some metric, like, you know, RMSC or blog loss or whatever it might be, right?

386

We say, hey, this thing works well.

387

And that's sort of a summary across all of the data.

388

But think about what you're doing in practice when you're running the sports team.

389

saying, I want the players who have really distinctly act like

390

surplus value to add to my team.

391

Ultimately, you're really you're grabbing players from sort of the edges of the prediction

space.

392

If you think about the joint space of like performance and dollars, the players you want

are sort of at that threshold where they're cheap and they're good.

393

Right.

394

And so if you're so you don't care about the bulk of predictions from the model, what you

care about is the predictions in the space where you're actually actioning.

395

Right.

396

So it's the it's the predictions in the space of players that you're actually going to be

able to action with.

397

players on your team or players you're acquiring or of course that extends ideas of

strategy and so on as well, right?

398

Sort of unique strategies and so on.

399

So you care about pathological behavior and you really care about edge cases.

400

So, and a lot of times in sort of an academic settings, it's like, hey, if this model, it

works, it performs well, great.

401

Here you're sort of saying like, I'm actually fine if the model in aggregate is slightly

worse if in the sort of space...

402

of the in the covariate space that we care about or the prediction space we care about, it

performs better.

403

Right.

404

So there's lots of little issues like that that come up in sports that are like super kind

of interesting and make the modeling part of it, you know, much more, uh much more

405

interesting.

406

I'll give you another example.

407

This is like so basing but unbasing and see if you can see if we can sort of wrap our

heads around this, which is there's this idea in sports called the messy test.

408

You've probably heard this phrase.

409

Maybe it's like the trout test in baseball or something, right?

410

But the idea is that if you build a model for overall player skill and you know, this is

like, you know, five years ago or whatever, but if you build a model for player skill, if

411

Messi's not like at the top or close to the top, your model probably sucks.

412

Right.

413

It's like a very simple, like it's the eye test, right?

414

Like you build a, if you build a model for, you know, best player, you want messy to be at

the top.

415

And you can sort of simply, you could imagine doing the same for individual skills, right?

416

If like, if I went basketball and I said, if I built a model for the best three point

shooter, if I didn't have like Steph Curry right near the top, like I should probably be

417

concerned about my model, right?

418

And that's actually like really insightful information.

419

But if you think about what that is,

420

It's typically not because, know, unless you're sort of like have some latent space of

player skills.

421

Typically in this case, what you're actually doing is having some model that's like

weighting different parameters and so on.

422

And then you sort of like calculate it for each individual player to see where they land

on this ranking.

423

What you're actually doing is you're putting a, you have prior information about which

players are good.

424

Okay.

425

But when you're showing these rankings, essentially that's like the posterior predictive

of the model, right?

426

It's like the predictions from the posterior.

427

So it's the posterior predictive.

428

And you're saying, have information about what that posterior predictor should look like.

429

So you're putting a prior on the posterior predictive, right?

430

Which is like any like traditional Bayesian would say like, that's just, you know, heresy.

431

But it's like there's something there and it's actually valuable.

432

So there's all these super, super interesting things that come out in sports where you

have to think really deeply um about the problem you're trying to solve and how your model

433

sort of dovetails into that problem and really sort of working on...

434

um

435

sort of those issues.

436

I'll give you one more example here and I could come up with more if we could keep going

where the modeling just becomes super fascinating, which is one thing I've seen quite a

437

bit is that there's a lot of these models that sort of capture overall player value, and

they do it on the same units.

438

So they say like, in basketball, for example, it might be like points per hundred

possessions.

439

So like you could have, you could put a whole bunch of grad students and say, hey, you're

all gonna build a model for player performance with points per hundred possessions.

440

And then they will do like they might have all other analyst team go out and build these

models and then they say, okay, now we're going to put, we're going to wait these things

441

to create an overall model, sort of like an ensemble laying or an averaging idea, right?

442

And they might say, but because these things are all in the same scale, let's make sure

these weights sum to one, right?

443

Or, know, a triangle, triangle, prior on the simplex, right?

444

But that's actually not necessarily the right thing to do because each of these models in

and of themselves has some shrinkage typically built into them.

445

And so it's very possible that on the whole, once average, there's actually more

information in the sort of aggregate model than there's in the individual.

446

So there's no reason actually that you should do that.

447

So the sort of the,

448

if you don't kind of understand what's going into these models, why they're shrunken, why

sort of multiple models combined might not wanna be shrunken.

449

In fact, in this case, it's not even obvious to me that you should enforce positivity on

the weights, right?

450

So there's like all these things that I think if you go and just say like, we're gonna

throw the obvious solution into some of these problems, you oftentimes end up with the

451

wrong solution.

452

So you need to sort of like...

453

deeply understand the models you're building as well as the data you have as well as sort

of the underlying sport.

454

And that's, that's, think where it becomes super, super interesting.

455

Hmm.

456

Yeah.

457

Indeed.

458

That's fascinating.

459

Um, and what's really interesting to me is then, and that hints a bit about how useful,

um,

460

these models and the Beijing framework is in your work and at Teamworks in particular.

461

So I'm curious, like, if you can tell us a bit more about how you leverage these methods

in your work at Teamworks uh or for Toulouse and how you...

462

like, and maybe to come back, you know, to what you started doing in your career.

463

uh How did do they help account for uncertainty and thinking about the generative graph of

the model with in particular, spatial temporal data?

464

Yeah, I think the word generative that you said is really useful.

465

That's kind of how I think about modeling problems, right?

466

I very much think about like, what is the generative model here?

467

And how do we think in sort of

468

This may be a bit over simplistic, but I sort of think about generative model as the way

of specifying the model and Bayes is kind of the way of giving the data how you sort of

469

work backwards, right?

470

Yeah.

471

Sort of the Bayesian, the sort of way of thinking, right?

472

Where you're sort of pooling information across maybe across players or across matches or

across seasons or across space or this is all like incredibly important in sports where

473

you have what might look like a lot of data.

474

but there's so much sort of contextual variation combined with a tremendous amount of

players.

475

So our database in soccer is about 80,000 players.

476

And if you sort of, you know, just think about like, oh, I want to fit even like an adjust

plus minus model, which is essentially like a giant regression model.

477

It's a really, you know.

478

large P, small N type of problem.

479

But of course there's a tremendous amount of structure there, right?

480

You have a lot of insights based on the league they play on, the positions they play on,

how good the team is, as well as sort of individual event information that happens in the

481

game.

482

you get like all this sort of...

483

super universal information uh that comes from the structure of the game and you want to

be able to use that to get sort of better, better information, right?

484

So uh when you talk about Bayesian models, that's sort of where it comes into play.

485

Now, I should say that oftentimes what we end up doing in the end is sort of finding this

balance between, uh want to, in my head, maybe I've specified this perfect model for this

486

problem.

487

But then either it's like, it's too slow to fit because of the size of this data is

especially tracking data can be absolutely massive.

488

So it's like, how do we get sort of poor man's bays or like some, so oftentimes it's like,

can we get 90 % of the value of this model for 5 % of the computational cost or 10 % of

489

the computational cost?

490

Right.

491

So, you know, there's a lot of those types of conversations go on.

492

And then the other things that will happen is again, is you'll sort of fit this model.

493

And then you might back to sort of the earlier conversation, you look at the

494

these edge cases and sort of the the perimeters of the covariate space and you're like

there's some bizarre behavior um in the model just doesn't look right in for various

495

reasons in areas that we ultimately care about and So then you sort of like have to do

think of okay.

496

How do we solve this?

497

Right?

498

Do you like up weight that area that covariate space in terms of the likelihood?

499

Do you do sort of ad hoc corrections?

500

Do you?

501

uh redefine the model right like there's uh

502

There's like all these sort of interesting things that arise.

503

I'll give you an example, one fairly recently, which is that we're building, you can

imagine you have some model that says, okay, I'm going to build component models for

504

different skills.

505

And then I'm going to sort of have those skills try and predict like player performance or

team performance or something.

506

So you can actually think of this as like multiple likelihoods in the same model, right?

507

A likelihood for, there's some like latent, there's some latent skills.

508

Think of them as like speed and ball control and whatever, right?

509

And then you might be observing like uh actual speed data and maybe some of the, you know,

team performance and much of things, right?

510

So there's all these different likelihoods sort of stack on top of those variables.

511

And it turns out that because of this, whether it's like model mis-specification or

collinearity or something, you can end up in these situations where, um okay, the speed

512

data in theory should define everything you need to know about speed, but because of the

sort of incompleteness of the data or who knows what, you end up sort of, likelihood for

513

team performance, what can actually happen is to say, actually the speed data says this

player's slow, but because he's so good, this latent variable for speed

514

actually going to think he's really fast.

515

And so you end up in like situations like no, no, I don't want the players performance or

team performance to sort of flow into the speed like in variable because I, you know,

516

subjectively, I think all the information should be captured from this other thing.

517

And so you sort of end up doing things which are kind of unbazing actually, which are like

cut models where you're saying, yeah, I won't like even within say like a Gibbs sampler,

518

right?

519

Where you're saying I'm not going to allow this information to affect this variable.

520

or this parameter to sort of flow into this other parameter.

521

So based on the underlying models, you can sort of hack these models to do very, very

interesting things.

522

Actually, this is an old idea.

523

If you remember, WinBugs used to have this like cut option, which sort of was largely for

computational reasons, but allowed you to sort of like, in some sense, uh sort of portions

524

of the graphical model to sort of produce what you want.

525

There's actually some really interesting papers.

526

I'll see if I can find it later by Pierre Jacob that looks at these models, these cut

models.

527

They're sort of like unbazing in a way, but sort of shows how they behave and how they're

super useful in certain cases.

528

Yeah, yeah, that is very interesting.

529

It's like, yeah, I'm not sure it's unbazing.

530

To me, it's just specifying prior information and making sure it goes through the sampler.

531

The issue you have usually, and that's actually an active area of research, is

532

If the model gets complex enough, it gets hard to set the priors on something you really

care about.

533

know, like setting the priors on the standard deviation terms of a multivariate normal is

like, you have to do it, but...

534

sometimes not interpretable.

535

What you have an idea about is the standard deviation of the whole data, right?

536

So your whole generative graph, you know, more or less the prior you want to have on the

standard deviation.

537

but you don't know how to specify that for each of the parameters.

538

so ideally you would want to specify one big standard deviation and that one is just

allocated then up upstream to the rest of the model.

539

This is even more weird though, because this is like saying we have some Gibbs sampler.

540

like maybe you're alternating through parameters.

541

Maybe it's these latent variables, right?

542

And you're saying, okay, if we write the full joint posterior and then we break it down

into the conditional so we can do Gibbs sampling.

543

And we say, this one,

544

All the Gibbs samplers we're gonna sample with, whether, whatever, maybe it's conjugate or

maybe you can do rejection sampling, it doesn't really matter.

545

But this one conditional that we wanna sample from, we're actually not gonna sample from

the true uh conditional as defined by the full posterior.

546

We're actually gonna sample from some version which looks like that, but we're actually

gonna cut out the dependency on this particular data source.

547

So you're actually like, you're sort of creating this posterior, sort of artificially

creating this posterior which is actually not the full posterior you originally defined.

548

you're of saying, you're defining some sort of new posterior, is uh by removing certain

dependencies within these conditional statements.

549

Now it's actually not necessarily true that you end up with like a valid joint posterior,

but you end up with better performance.

550

So it's just a super interesting problem for me where, I have this, like, know how things

work on a broad scale for sort of the full Bayesian model, but I also know I can get, I

551

can solve problems by sort of hacking these things and sort of pinning certain variables

here and tweaking things here.

552

and sort of breaking things, but breaking them in a way which actually produces better

results and ultimately better decisions.

553

Yeah, that's fascinating.

554

I love that.

555

So basically it's like...

556

If I understood correctly, when you say cutting the posterior, it's like making sure we're

only taking a subset of the full posterior that the model was actually defining.

557

Kind of, yeah, but only on one of the conditionals, only on one of the variables.

558

you're saying, when we learn about the speed...

559

So normally it's saying, if I'm updating, let's say the variable for players

decision-making, you have to condition on everything.

560

It's the data, know, to give sample, you're also conditioning on the previous draws of all

the other variables.

561

But you're saying that when saying, for speed, I actually only...

562

when I update the speed latent variable I only want to condition on the other variables

and the speed data I don't want to condition on these other data so you're sort of

563

dropping the conditioning on these other variables and sort of just removing them from

that conditional uh sampler.

564

So kind of a really interesting case where you're sort of like just taking this thing that

looks like a well-constructed

565

posterior distribution and sort of just like hacking it in ways, which is like kind of

unprincipled, but like makes sense and leads to the performance that you want.

566

Yeah, yeah.

567

Damn, that's very cool.

568

So I'm guessing that means a lot of custom code, whether that's in Stan or Pimcee or

Python or stuff like that, right?

569

Yeah, certainly, right?

570

A lot of these things aren't necessarily handled well with Stan, right?

571

Because Stan is great for, or any these small PMC, whatever, they're great for, hey, let's

build like a Bayesian model that does this, this, and this, but it's not great for when

572

you're like, hey, we need to fix this edge case, we need do this, and we need to of hack

these things.

573

So first up, to do those types of hacks, you have to like deeply understand the problem

and deeply understand the models, that's sort of step one.

574

But as you say, it oftentimes falls outside of these automatic inference engines.

575

So you oftentimes have to do things custom, right?

576

So, you know, it actually reminds me

577

bit of like, you know, think a lot of times because of Stan and PIMC and others, like you

oftentimes don't need to understand what's going on with the hood with HMC or any of these

578

other algorithms, right?

579

But I always thought it was important for people to understand, like at the very least,

like what's going on when you're fitting sort of basic models.

580

Like I'll give you one example here, which is when I taught spatial stats, it was a grad

course.

581

I would give people like a very simple multivariate normal where they're trying to like

estimate the mean or the covariance or something, right?

582

It's like, okay, here's a whole bunch of data and you know, maybe P is 50 and N is a

thousand.

583

Hey, learn the mean and covariance.

584

And that's like, that's a trivial problem.

585

It's conjugate, blah, blah, blah, right?

586

And then I say, okay, now I'm going to give them another data set, which is kind of the

same thing, but P is now a thousand and N is maybe 10,000.

587

And all of sudden it's easy to pull in the data.

588

The data is actually relatively small, but they go to fit the data and it just doesn't

work.

589

And they say like, what's going on here?

590

Why doesn't this work?

591

It's a conjugate problem.

592

why doesn't it work?

593

And they say, well, you're trying to invert a thousand by thousand covariate matrix.

594

Maybe that's not a lot.

595

Maybe it breaks down at 5,000.

596

Whatever it is, it's a pretty relatively low number on sort of a typical laptop where

these things start to fail.

597

And then I okay, well, that doesn't work.

598

So go fit it with stochastic gradient descent.

599

And then they go do that and they say, actually that works way better for these large data

samples sizes.

600

And I said, but what about for the small data?

601

And they say, well, actually then just use the sort of the conjugate one, right?

602

So it's like realizing that, there's different ways to solve the same problem.

603

And you might use...

604

this sort of direct sort of analytical solution in some cases, and you might prefer to use

SGD in another.

605

And then I go back and say, actually, there's a piece of information I didn't actually

tell you.

606

there's actually a certain correlation structure in this data.

607

And it turns out to be just an AR1, it's a time series.

608

Oh, now you can just fit this thing with like, you know, common filter-esque kind of,

message passing kind of ideas, typically like common filter.

609

So you can do things in sort of linear time in P, because P is sort of T now, like time,

right?

610

So, and then there, and all of a sudden they go from an exact solution to something which

actually is exact and very, very fast.

611

solving the same problem fundamentally, but understanding sort of there's different ways

of actually doing the computation, right?

612

And I think having those skills and sort of uh thinking holistically, not just about the

modeling, but as well as the sort of fit uh is really important.

613

Like in sports where we're dealing with space all the time, you're sort of like, you're

always having to discretize space in some way or another, right?

614

Whether you like it or not, whether you sort of think about it as like...

615

we are projecting onto some basis or something, ultimately you're discretizing.

616

you put it into the computer, it's discretizing it.

617

So uh people oftentimes will say, I really don't like having a continuous model, but like,

hold on, you're to have to sort of come up with some low basis approximation to it at some

618

point.

619

So might as well be sort of transparent that you either sort of either having a sort of

simple model that you can do like really accurate exact computation on or a complicated

620

model that you got to do approximate computation on and sort of understanding those

trade-offs.

621

think is like is critical in anyone who's working on this problem.

622

Yeah, no, for sure.

623

And that makes me think, you know, in your experience, what what have been the different

ways, you know, to get these uh

624

90 % of the results with 5 % of the computational cost of the model.

625

What have you found particularly helpful?

626

And I'm guessing these are cases where you cannot run classy nuts sampler, for instance,

on a model.

627

Yeah, a lot of this looks like...

628

uh

629

It's like making sure you have a big enough toolbox so you can say, here's the perfect way

of doing this.

630

Maybe it's like some big model built in Stan.

631

ah But hey, that's not going to work as the data scales.

632

So can we fit this with like a penalized regression?

633

Or what happens if I sort of

634

In a lot of these samplers, where things start to get really slow is when you're sort of

sampling hierarchical parameters as well as sort of like, like in a lot of player, if you

635

think about like a situation where you have like one variable per uh player, right?

636

Those types of problems are oftentimes easy because you can marginalize them out.

637

But as soon as you start sampling like hierarchical parameters as well, where you sort of,

you want like some, you know, uh

638

some like group level variances or something like that, then those conditional updates can

be super expensive to calculate because then you're sort of conditioning on the full data,

639

And so it's like, okay, what happens if...

640

What happens if we just like do some hack and just find an estimate for that hierarchical

parameter, just plug it in, right?

641

Do we lose a lot by just sort of specifying that directly rather than sampling it and

having it posterior over it, right?

642

So that's like lot of the types of things we're thinking about is like, where are the

computational bottlenecks, especially as the data scales and how can we make it work?

643

Oftentimes work sort of in sequential updating, because essentially we want to update

these things.

644

weekly as new matches come in.

645

How can we do this sort of in really efficient way without losing sort of prediction

fidelity, right?

646

And so yeah, it looks like things like, hey, actually, if it's a big regression problem,

can we do sort of ping-wise regression?

647

And sure, we won't get full characterization of uncertainty, but can we get like a point

estimate and 1 % of the time?

648

And then is there sort of ways, can we bootstrap or something else to get some notion of

uncertainty around it?

649

Or as I said earlier, can we pin some hierarchical parameter to speed up the sampling,

even though we know that, you know, that's

650

But we're gonna end up with some bias that results.

651

So those are the types of conversations we're having constantly.

652

Hmm.

653

Okay.

654

Yeah.

655

Yeah.

656

Yeah.

657

That tickles a lot of the conversations I'm having also.

658

In my work for sure.

659

so if you and curious, know, if you would, um if you would be mentoring or teaching right

now, these students, you know, um what would you

660

recommend them to invest their learning time on providing conditional and then knowing

patient stats and being fluent in a probabilistic programming language already, which is

661

the case of a lot of the listeners here.

662

But so if you had to tell them, it's something that can actually help you also complete

your toolbox for these cases, for instance, what advice would you give them?

663

Yeah, one thing I've seen with like a lot of

664

of students that are coming out of master's programs or data science programs is that

they're sort of quite good at give them a data set, hey, either use this method or choose

665

a method that's going to give you a good prediction performance.

666

so people are good at maybe tuning neural networks or Gaussian processes or...

667

you know, penalized regression or what have you, they're sort of quite confident with

these, with sort of fitting methods and looking at predictions and saying, hey, this has a

668

better RMSE.

669

But where they can really sort of struggle is that a lot of times at sports, actually,

there are certainly prediction cases, but oftentimes what we're doing is fundamentally as

670

inference.

671

You're saying, hey, we observe some team level performance where you want to infer what's

causing this.

672

So you have some latent parameters, which are like player skills or player performances,

and you're essentially trying to learn those.

673

It's, it's fundamentally an inference task.

674

And so, you know, thinking about more sort of statistical problems and sort of building

that statistical toolbox, you know, is the first thing I would say is if you're sort of

675

the bulk of your time has been spent building prediction algorithms is sort of broadening

your toolbox beyond that, right?

676

And then learning as much as you can, you know, if you've some of the best ideas I've ever

had in sports have come from like text modeling.

677

Right?

678

Where it's like using like Duryshy processes to model plays in basketball was like one of

the coolest things, I think, projects that I've ever been involved in.

679

Again, that wasn't my idea.

680

was a genius PhD student named Andy Miller.

681

there's like those types of things, right?

682

If you want to be able to sort of creatively solve these problems to find like, to find...

683

computationally efficient solutions, you have to have way more than just one tool in your

toolbox.

684

Read broadly, study broadly, and you can get into the weeds on things.

685

Learn about language models, learn about text models, learn about image modeling, learn

about spatial statistics, learn about robust estimators, as we talked about earlier, learn

686

about all these different areas.

687

And sure, you won't necessarily be an expert in any of them, but if you sort of

understand, I know what these models do, and I sort of understand their pros and their

688

cons, and what they work on, what they don't work on.

689

then it becomes much easier to sort of solve these sort of unique problems that arise.

690

Yeah, yeah, yeah.

691

That's really, really interesting to hear that.

692

And how do you...

693

You know, in your experience, what have been very interesting uh open source software that

maybe you've used outside of the patient framework?

694

curious.

695

Yeah, early days for me was not open source.

696

I started when I started my master's, it was like MATLAB programming, right?

697

That's what I was like the tool de jour.

698

And since then I've sort of been...

699

combination of R and Python has been the large bulk of my work.

700

But a lot of stuff is built on packages designed for those two languages as well as Stan

and others.

701

And these days, a lot of what I do is also sort of broader technology, which is just

revolutionized the way we do things, whether it's cloud computing or Docker.

702

Like all these sort of standard tools in broader tech, think, also, you if you want to

work in industry in particular, are incredibly valuable, incredibly valuable to learn,

703

right?

704

Like...

705

At Zell's, example, now Teamworks, even have sort of our models themselves are like

Dockerized, they're containerized.

706

So you think about like, I'm building a Bayesian model.

707

Well, we think of it as like a container object that holds predict functions and test

functions and train functions and allows you to like very simply uh probe these models as

708

well as to sort of aversion and control them.

709

And so it's like blending ideas from statistics and machine learning with sort of

710

software engineering ultimately to solve a lot of interesting problems that come up in

sort of uh production-alized machine learning.

711

And so, to this it's a lot, right?

712

But it's a lot also outside of what you might think of as traditional stats and ML

toolbox, right?

713

But broader tech stuff.

714

Heck, I spent a weekend, this is not work-related, but I spent a weekend flashing a

Raspberry Pi and messing around with a Raspberry Pi last weekend.

715

So I'm deep into this.

716

stuff.

717

Yeah, I mean, that makes sense to me in the sense that, you know, a lot actually a

substantial portion of proportion of the modeling work is actually not done on the model.

718

It's much like a lot before, how do I get the data in which format?

719

How do which data do I want?

720

Even before that, what's the generative graph we're thinking about?

721

Which questions are we interested in answering?

722

And then once I have the data, how do I format it in the format that the model can

actually take it?

723

Doing all the EDA, also extremely important.

724

Parameter recovery, simulation-based calibration, all this stuff.

725

So this is something I hear a lot, right?

726

Which is like, data science in the real world is like a lot more data managing and like

all this sort of pre-model fitting stuff.

727

That's certainly all true.

728

I think the analysts that have worked with me in the past, where they're most surprised

when they work with me is that how much work there is after that first model fit.

729

they do all this work getting everything in place and then, hey, I finally built the

model.

730

We're good to go.

731

And I said, well, hold on.

732

I want to see.

733

Calibration, I want to see like all these different, want to explore all the edge cases.

734

I want to explore, you know, what, what happened, how sensitive is it to different

assumptions?

735

How, know, is there like all these sort of like poking and prodding the model?

736

It's like, so you have, yeah, there's all this work that comes before the model and then

they're sort of fitting the model, which is kind of the quote unquote fun part.

737

And then before you sort of productionalize this thing and release it to the world, like

you have to probe it endlessly to make sure that, that it does the things you want it to

738

do and that in sort of predictable and controllable.

739

ways.

740

And that's sometimes more work than is actually needed before the models fit.

741

yeah.

742

Yeah, I was gonna go there.

743

Definitely.

744

And yeah, I think that's almost always even more work.

745

um Because, well, that, that'll that often doesn't work like you want to, know, model,

especially if it's complex enough.

746

So there's definitely some dimension.

747

where it's not working as you would have expected.

748

um And so that's interesting to know, okay, in which conditions do my model uh collapse?

749

Where does it not work?

750

And so that takes a lot of form.

751

think then there is even after that, there is another part that is visualization.

752

If you're doing that as the modeler, it's extremely important because you want custom

visualizations uh depending on the people who are gonna talk to.

753

uh We'll get to that a bit later, but yeah, on that model validation part, I think it's an

extremely important part uh that you bring up.

754

that ah also...

755

goes back to what something you were saying earlier which is actually when we develop the

model we're fine if it's not doing a very good job at the league level for instance but

756

where it does a good job for the kind of players we're interested in so it's like maybe

the model is not that good overall or it's not that good for

757

old players because they have low athleticism stuff like that but we're actually

interested in young players so more bigger like better athleticism and so on in this

758

population the model is doing better so it's actually fine for us so that's the very

important part amateurs what's your workflow there if you have one you know if the other

759

stuff that you

760

always check once someone comes with a model that's fitting well, all the conversions and

diagnostics are all good.

761

Now we're entering that part of model validation.

762

Yeah, we do a lot of stuff pre-modeling, project plan type stuff where there's documents

that we will use, the layout, the types of things we want to do beforehand.

763

Afterwards as well, like calibrations, lot of standard things that we'll do.

764

But lot of it just comes, it's sort of fairly iterative, right?

765

You do the initial sets of plots and then say, actually, we should look at this and this

and then something looks weird and you say, okay, that's weird.

766

Like how else can we slice that in a different way?

767

Can we see it?

768

uh

769

trying to expose what's actually going on under the hood.

770

Hey, can we see this, set of parameters?

771

Is there some, is there some sort of like high leverage data that's maybe driving these,

this weirdness?

772

Like let's explore all that type of stuff.

773

So it's a combination of sort of a standard checklist, if you will, as well as sort of

really custom stuff that really comes from just working with these models and these for,

774

for so long that you sort of start when you, you sort of really spot little issues and you

sort of figure out how to, to sort of pull the thread and to get at what you want.

775

Hmm.

776

Yeah.

777

It's like, yeah, like a detective work.

778

um And what's like, is there a case, you know, that you remember that was particularly

hard, you know, where you were like banging your head against the wall before finally

779

understanding what was going on with the model?

780

Yeah, there's lots.

781

That happens like that's daily.

782

I'll give you one example.

783

It's it ended up being my favorite paper I think that I've ever written is with Patrick.

784

Very simple statistically.

785

There's this idea in sports science of acute chronic ratios and the...

786

It's a pretty simple notion of, how much load on your body has there been?

787

I'm going to simplify a bit, so bear with me, but how much load has your body had in the

last week versus how much has it had in the last month, roughly, or maybe month and a

788

half?

789

Just the idea being sort of like, Hey, are you, you getting, um,

790

is the load that you've experienced recently, is this sort of normal to what you normally

experience or is it high or is it low?

791

You can imagine if you're a runner and you sort of normally run 10 miles a week and then

this last week you ran 50 miles, well you'd have a super high Q-chronic ratio.

792

If you normally run 10 miles a week and this week you ran one mile, well you'd have a

really low Q-chronic ratio.

793

So sort of like a short-term average divided by a long-term average, right?

794

So it's the ratio of the two.

795

And there's a tremendous amount of papers out there that show that if that cute chronic

ratio is outside of some range.

796

So here I'm talking about like load on the players, like amount of physical exertion.

797

It's probably easiest to think of it as like running, right?

798

It's like distance run, it's a little more complicated than that.

799

But basically say, if we have these notions of player load and if you fall way outside of

these bands, that it's highly predictive of injury in the future.

800

And there's like some intuitive logic there, right?

801

Which says that, uh which is like, hey, um if I'm a runner and I go from running 10 miles

a week to all of a sudden running 50 miles, I'm increasing my injury risk.

802

I'm more likely to get injured moving forward, right?

803

Or if I'm training on the soccer pitch for two hours a week and now if I'm going to 10

hours a week, uh

804

to have a higher chance of injury.

805

But, and to be clear, there's like literally hundreds of papers out there that show that

that is predictive of future injury.

806

Okay, great, okay, great.

807

So we have this Kube Chronic Ratio.

808

In fact, just as a little aside, the Apple Watch now, if you have the newest Apple Watch,

has this built in Kube Chronic Ratio.

809

It's like a plot that shows how are you relative to your normal.

810

It's the same idea, and it's using a lot of these same ideas.

811

But it's essentially like, again, the short-term average divided by long-term average.

812

But when we did this internally, a couple of teams I've worked for is that, excuse me, we

found that it wasn't predictive and I couldn't figure out why.

813

And so you look at all these papers and basically what they're at is like, their estimate

is like a zero one.

814

Did the player get injured after some point in time or not?

815

So it's like, if...

816

If my acute chronic ratio is high at time t, do I get injured in time t plus one to time,

let's say, you know, the next month or the next week or whatever.

817

That's how all these papers look.

818

It's like the acute chronic ratio at a snapshot in time, it's past sort of looking

backwards.

819

Does it predict injury in future?

820

And then I realized like, that's actually not what you care about because what these are

often saying is that, okay, if...

821

What we sort of uncovered is that the main reason this is predictive is because if your

load has increased in the last week...

822

It also means that your load will likely be high in the future.

823

so what is actually happening is that your chance of injury per minute of exposure or per

mile run in the running example, it actually stays, oftentimes it stay constant, but your

824

total underlying exposure has increased.

825

these are not actually, this like great hundreds of papers that show this thing super

predictive, it's actually predicting exposure more than it's predicting injury.

826

ah So you end up with this like confounding with sort of it's oftentimes it's like the

time of the season, right?

827

Like you come into training camp, your load spikes and therefore you have higher chance of

injury.

828

It's not that your chance of injury for every minute you're on the field is higher.

829

It's that you spend more minutes on the field after that.

830

Right?

831

so like, you know, I'll give you another example, right?

832

If you, there's like, if you looked at

833

uh So eating ice cream is super predictive of drowning deaths.

834

Like if you look at the data on this, you'd say like, okay, when people eat a lot of ice

cream, there's a lot of drownings as well.

835

Right?

836

And so it's like, what's the mechanism here?

837

Are people like eating too much?

838

People eat all this ice cream, they get bloated, they can't swim.

839

It's like what your grandmother used to tell you, don't swim after eating, you you sort of

like make some choices to make sense of this.

840

And then you step back and like, no, there's actually confounding variable here, which is

like season.

841

It's the summer.

842

People eat more ice cream, they, you know, they swim more, right?

843

And so the ice cream is actually just, it's actually saying that there's, it's it's

predicted not of the drownings, but of the amount of water exposure.

844

And it's the same thing here that the,

845

the Q-chronic ratio is not necessarily predicting a higher, it's not like your chance of

drowning per minute of being in the pool is any different, it's that your minutes in the

846

pool goes away.

847

And here it's saying the same thing.

848

So that's like a simple case of like, uh we did this big project and it wasn't necessarily

this new and novel, like, we got this new thing.

849

It was saying, hey, everyone's using this thing that turns out largely to be garbage.

850

And it's, it's.

851

or certainly at the very least they're putting way more confidence and trust than they

should because fundamentally all these papers have misdefined the estimate.

852

They've defined it in a way which is actually not what you care about.

853

You don't care about whether the player gets injured in the next two weeks.

854

You care about their injury risk per minute of exposure or per minute of gameplay or

whatever.

855

And you need to essentially control for that.

856

And none of these papers have.

857

So that's a really simple example.

858

Again, not terribly Bayesian, but sort of when you

859

really deeply about a problem, sometimes like this, there's like a really simple solution

right in front of you that can, in that case, know, it's essentially telling people to

860

stop doing this thing or at least be much more thoughtful about how you're using this

data.

861

And so that's one example of like something that's still super prevalent in sports, but we

kind of uncovered this really interesting confounding effect.

862

Yeah, it's fascinating.

863

Yeah.

864

And I mean, I love it because it's really, I couldn't find the solution in the data.

865

Right.

866

So I think in a way that's quite patient because you had to something that's fundamental

in base is solutions are not always in the data.

867

And that's why you need priors.

868

And that's why you need the structure of the model.

869

And so here is like literally thinking much more about the generative process of the data

and basically um realizing.

870

like in the ice cream and drowning example where you have basically season is, as Richard

McArth say, uh is a fork, right?

871

So because it causes both viable, you're interested in, and unless you control or

condition on that variable, you're gonna have uh biased estimates.

872

And so here, thinking about that, then you condition on the time plate.

873

the time spent on the field and then you see that this predictive aspect basically

disappears because you've taken care of it.

874

In this paper we also showed that if you use some ideas from, back to the point about

having a broad toolbox, if you use some really basic tools from causal inference like

875

matching or propensity scores that you can actually solve this problem, right?

876

If these studies, instead of saying, let's predict whether a player gets injured or not,

if instead you had said, let's take two players at the same point of the season, one who

877

has a high IQ chronic and one who doesn't, but control for all the other things like

minutes played in the games and all this other stuff.

878

And if you did that, then you can actually control for this effect.

879

so, yeah, it's like, I think it's...

880

just a really simple example of a case like, you know, have a lot of that work a lot of

money to teams and sort of understanding them well and perhaps in this case, like not

881

chasing down rabbit holes.

882

Yeah.

883

Yeah, I love that.

884

That's really fascinating.

885

And that's a solution that's like simple, but it can take so long to get there.

886

So thanks.

887

That's super interesting.

888

That's not a big model.

889

It's a good example because there's no real stats there.

890

It's just like a probabilistic.

891

I guess there is in a way, but it's super simple.

892

It's a very simple case of this really, are you defining the estimate, the thing you care

about properly?

893

Yeah.

894

and end up with wildly wrong conclusions.

895

Yeah, for sure.

896

um So ah I'm going to start winding us down.

897

Now he's like, I've already taken quite a lot of your time.

898

eh Maybe two other questions, if you can, before we go to the last two questions.

899

um

900

I'm curious, what do you see as the most exciting trends or advancement now in your field

and how you think they will impact sports analytics and decision making?

901

Yeah, think there's probably a couple of things there.

902

The first is, as I mentioned earlier, just the growth of this really super interesting

data and all the statistical problems that come along with it.

903

That has sort of been a trend over last five, 10 years, but it certainly continues.

904

And it means that teams are and sort of...

905

Other businesses in the sports space are investing a lot, so you have lot of teams scoring

their analytics departments and media companies and other sort of spending in the space.

906

Another cool thing I've seen, which seems sort of like tangential but is actually really,

I think actually quite nice, is that...

907

When teams are building out their internal resources, every team typically has to do a lot

of the same things.

908

So they have to like set up a database, they have to build all the ETL to ingest the data,

they have to...

909

create like an IDM to sort of standardize across the different data sources.

910

They have to map players.

911

Anyone who's done like player mappings in soccer where you have tens of thousands of

players and you're trying to map, you know, uh data from one data provider to another for

912

some Brazilian player with like six names, like it's just impossible.

913

Right.

914

And so everyone's doing these things sort of on repeat before you can get to like the

interesting statistical problems.

915

Right.

916

And so one thing that actually that I think has been great in that it's a trend that has

just recently started and actually Zellis has been a big part of that, but

917

but there's others as well, uh sort of realizing that's a problem and sort of like finding

centralized solutions for that.

918

So there's been cases, for example, of leagues taking some of that on and sort of saying,

all these teams don't need to each be spending hundreds of thousands of dollars a year,

919

like mapping players are doing this and this.

920

We'll create something.

921

Or Zelis has essentially said, hey, all these teams are doing a lot of the same things

when it comes to data engineering and even sort of early simple states, the disk of

922

modeling.

923

let's sort of democratize that and for a fraction of the cost teams can just sort of

ingest all that.

924

And then they sort of to use the baseball analogy.

925

Like they're not starting from nothing.

926

They're kind of starting on their base.

927

Right?

928

So that's sort of another thing that I think is like a huge progress in terms of creating

the space for sports analysts to actually work on, you know, challenging interesting

929

problems.

930

No, for sure.

931

mean, so first, now that I work in baseball, I can understand that metaphor.

932

So thank you.

933

And second, yeah, I mean,

934

I'm an open source developer so I'm not gonna tell you this is a wrong direction.

935

think, like, know, ideally if you'd ask me I'd be like, eh, I think you know the leagues

should just take over all of that and put all that tracking data and stuff like that.

936

Not the latest frontier because, well,

937

the industry to keep progressing, know, people still has to have to earn money because

they were like the first one to develop that kind of data.

938

know, the old data and stuff like older data and stuff like that, you know, just put that

on the league and then the league could just open source everything so that everything

939

gets access to that.

940

Everything has the same data.

941

Um, and then it's just how you use the data and the combination, not only a light of like

the models, but the combination of the people working on the data, the modelers.

942

the GMs, the coaches, like all these people together, the scientists, that's what really

makes the value out of the team much more than having that exact row in their database

943

that the other team doesn't have.

944

think the rest of the other industries also show that, you know.

945

Yeah, our edge at Toulouse did not come necessarily from having the best data.

946

Like I think we did actually have the best data and the best models, but it came from

execution.

947

There's no question about it.

948

Like, yeah, we probably did a little better than if we'd had simpler data, some of the

models.

949

Yeah, maybe we would have been a little less efficient, but uh there's a lot of clubs out

there, and I certainly won't say names here, but a lot of teams out there that have great

950

data and big analytics groups and so on.

951

And, you know, they treat them like houseplants.

952

They stick them in the corner and ignore them.

953

Right.

954

And you're not going to get any, you're not going to create competitive advantage doing

that.

955

No, no, no, for sure.

956

mean, I see that a bit like, you know, high quality cooking, right?

957

What makes a chef good is not only the ingredients, it's like how they use the ingredients

and with whom they're using that and to whom they are feeding the, the, the plates and the

958

where they are feeding people.

959

know, like, if you go to big restaurants, it's like, it's not only one table where they

just give you the food.

960

It's like a whole experience.

961

So.

962

It's yeah, like I think I think it's a it's a great thing that we're moving in the in this

direction and and yeah, as you were seeing zls is doing a lot of that and that's I think

963

that's really amazing.

964

um Another question I have for you before the last two ones is, well, what's next for you,

you know, because you're a very curious person and uh now you've so you've sold zls so I'm

965

curious are there

966

any upcoming projects you're particularly excited about for the month to come?

967

know, I sort of spent the first 15 years of my career trying to be the best statistician

that I could.

968

I sort of had this personal mantra.

969

I was always personally offended if I didn't understand something or I didn't know a

method.

970

That's part of what drove me to learn so much.

971

It's just like...

972

feeling like I just wanted to sort of cover everything.

973

And so I spent so much of my career doing sort of being that, right?

974

And sort of growing as a statistician.

975

And mostly in academia, but of course with very consulting eggs and all sorts of industry

as well.

976

And then the last 15 years have really been about, and some overlap there, but the last 15

years have been about sort of sports and learning about sort of applying that domain

977

really into sports and sort of how that leads to sort of decision-making, negotiations,

sort of finances and the whole, like the integration of all those things coming together

978

to ultimately go from, hey, how do we value players better?

979

Right through to like running a team and

980

and ultimately outperforming our payroll and all the way through to like creating equity

value for shareholders, right?

981

And I kind of feel like I'm at this point where like I've built a lot of time, know, maybe

it's like hit Malcolm Gladwell's 10,000 hours in both of these things.

982

And I feel like I'm, with Toulouse, we've sort of proven the thesis here.

983

so a lot of right now what's next to me, like I sort of want to keep leaning in on this.

984

I feel like I have this, these two things.

985

things sort of perfectly hybridized together where these technical skills that I've built

over the years combine with the team management skills and it's a super powerful

986

combination and a really valuable combination.

987

I like just love using those tools and these ideas to sort of play Revenge of the Nerds in

real life.

988

Like going out there and just dominating, using essentially, this is gonna sound very not

humble, but using our group's intellectual advantage to win.

989

And to me that's like super, super fun.

990

Yeah, mean, love that.

991

Definitely support it.

992

Like, Revenge of the Nerd, you had me there.

993

But yeah, I mean, of course, yeah.

994

And that's something.

995

Also try to do with the podcast from like trying to...

996

percolate these ideas through more people.

997

um Because I think that's something that's needed a lot.

998

yeah.

999

Awesome.

01:24:11,406 --> 01:24:12,547

Well, Luke.

01:24:13,048 --> 01:24:14,678

I think I think we'll call it a show.

01:24:14,678 --> 01:24:20,620

I would still have so many questions like literally I still have so many questions like I

had for you today I didn't ask you.

01:24:20,620 --> 01:24:22,511

Sorry that's my fault.

01:24:22,511 --> 01:24:24,831

I filibustered a couple of those questions there.

01:24:24,831 --> 01:24:27,152

No, no, That's you know, that's me.

01:24:27,152 --> 01:24:28,202

have, that's also my job.

01:24:28,202 --> 01:24:32,493

have to you know adapt uh to the topics.

01:24:32,493 --> 01:24:33,724

But yeah, I mean that's cool.

01:24:33,724 --> 01:24:42,396

That means you can come back on the show next time you have a cool project to talk about

and I'll get you, I'll get you to you these questions.

01:24:42,736 --> 01:24:47,516

But first, before you leave, of course, I have to ask you the last two questions.

01:24:47,556 --> 01:24:49,996

Ask every guest at the end of the show.

01:24:50,916 --> 01:24:53,356

So I'm going to change a bit the first one for you.

01:24:53,356 --> 01:24:54,556

First time I do that.

01:24:54,556 --> 01:25:00,196

But I think I think that you kind of answered a bit the first one already.

01:25:00,196 --> 01:25:10,596

And I'm curious because you have a particular background and origin story where if I

remember correctly, in this moneyball episode,

01:25:11,568 --> 01:25:25,528

you were saying that you actually started working on spatial temporal data with the

Sacramento Kings, because someone came into your office with like, some question, but the

01:25:25,528 --> 01:25:28,848

question was actually not for you, think, if I remember correctly.

01:25:28,848 --> 01:25:39,464

And that was so that's a very, very random, you know, origin story where that could be in

a in a movie in a way where it's like, the hero never wants to

01:25:39,630 --> 01:25:40,620

be a hero, right?

01:25:40,620 --> 01:25:43,543

It's like the situation imposes onto him.

01:25:43,543 --> 01:25:47,369

So my question is counterfactual for you.

01:25:47,369 --> 01:25:49,411

If that moment had not happened, right?

01:25:49,411 --> 01:26:01,683

If you had not been in your office at that time, at that place, and you hadn't met that

person and ended up working on on sports data, what do you think you would have done?

01:26:02,992 --> 01:26:10,472

Yes, I think everyone's lives are sort of built up with a lot of these just kind of random

events that accumulate into who you are.

01:26:10,632 --> 01:26:11,892

That was certainly one of them.

01:26:11,892 --> 01:26:22,972

I won't retell that story because as you say, it's on the Word and Money Balls podcast,

but I think I'd still be working in sort of very similar spaces, but possibly not sports,

01:26:22,972 --> 01:26:23,072

right?

01:26:23,072 --> 01:26:29,936

Sort of taking all these ideas, bays and so on and...

01:26:29,936 --> 01:26:32,476

trying to apply it to some interesting problem.

01:26:32,476 --> 01:26:41,616

for some reason, I ended up working in a space which is working on a zero-sum game where

billionaire owners are trying to extract value from millionaire players and the

01:26:41,616 --> 01:26:43,996

millionaire players are trying to get dollars from the billionaires.

01:26:43,996 --> 01:26:54,656

So it's like a very strange space to work in, but I think I'd probably be in a very

similar spot but not in sports.

01:26:54,816 --> 01:26:59,816

I was on a path where I was doing a lot of stuff in climate, maybe it'd that, maybe it'd

be...

01:26:59,864 --> 01:27:07,892

sort of some other domain, but I think it'd be sort of the same thing I'm doing now, but

just a different domain other than sports.

01:27:07,953 --> 01:27:09,034

Yeah, yeah.

01:27:09,034 --> 01:27:11,036

Yeah, I was thinking maybe agriculture.

01:27:11,036 --> 01:27:15,261

oh They do a lot of spatial temporal stuff over there.

01:27:15,261 --> 01:27:20,016

I actually published a paper once on crop yield predictions in the Canadian prairies.

01:27:20,016 --> 01:27:21,507

How exciting is that?

01:27:21,870 --> 01:27:23,842

Yeah, yeah, yeah, exactly.

01:27:23,842 --> 01:27:29,968

That's actually, we worked on a similar project when I was at Pimcee Labs.

01:27:29,968 --> 01:27:33,231

it was actually something like that.

01:27:33,452 --> 01:27:34,533

Gaussian processes.

01:27:34,533 --> 01:27:37,916

So that's a cool space because you can use a lot of Gaussian processes.

01:27:38,857 --> 01:27:42,460

That's also very challenging because Gaussian processes are hard to fit.

01:27:42,861 --> 01:27:44,743

But they are such cool beasts.

01:27:46,274 --> 01:27:54,009

And second question, if you could have dinner with any great scientific mind, dead, alive

or fictional, who would it be?

01:27:55,130 --> 01:28:00,533

Yeah, this is interesting because I know you've asked the same question to others, so I

sort of gave it some thought.

01:28:00,533 --> 01:28:08,258

You know, I've been really fortunate that over the last 20 years or so, I've had a lot of

tremendous dinners with people, right?

01:28:08,258 --> 01:28:10,680

Whether that's in academia, like...

01:28:10,882 --> 01:28:22,021

Some of the, I just have great memories of dinners with people that you probably know,

Christian Robert and so I had this amazing dinner in Bristol with Julian Bisseg in the

01:28:22,021 --> 01:28:22,732

last year of his life.

01:28:22,732 --> 01:28:24,103

was a visitor there in Bristol.

01:28:24,103 --> 01:28:25,840

ah

01:28:25,840 --> 01:28:27,660

Peter Green at the same time.

01:28:27,660 --> 01:28:36,120

Just so many incredible dinners and sort of, and then of course now in the sports world

with coaches and owners and so on, I've just been really fortunate.

01:28:36,320 --> 01:28:43,560

So in sort of like the explore, exploit, I think I have a pretty good idea of what the

distribution of good and bad dinners are.

01:28:43,560 --> 01:28:55,500

And so I think there's a really good chance that if I named a name of someone I've never

met, that it would be sort of below my expected return if I were to sort of exploit the,

01:28:55,794 --> 01:28:57,354

explore rather than exploit.

01:28:57,534 --> 01:29:07,174

I think I would have to say to sort of look at some of the people that I've had some of

the most interesting dinners with, and I thought about this a little bit, and think the

01:29:07,174 --> 01:29:09,914

person I would name is Xiaoli Meng.

01:29:10,210 --> 01:29:15,402

So Shelley was the chair when I was hired at Harvard and then later became the Dean.

01:29:15,643 --> 01:29:19,184

And uh he is perhaps one of the most fascinating people I've ever met.

01:29:19,184 --> 01:29:24,620

He's just full of wit and humor and intelligence and just a kind human being.

01:29:24,620 --> 01:29:33,090

It doesn't hurt that he has a uh giant box of scotch uh sitting under his desk, which can

uh come in handy at times.

01:29:33,211 --> 01:29:35,432

And so, yeah, I think I would not explore.

01:29:35,432 --> 01:29:37,052

think I would exploit.

01:29:37,413 --> 01:29:40,354

by that, I mean, I haven't had dinner with Shelley and probably

01:29:40,354 --> 01:29:44,416

a decade and I would love to sit down with him again.

01:29:44,957 --> 01:29:45,898

Nice, yeah.

01:29:45,898 --> 01:29:51,071

Well, I really like the structured answer.

01:29:51,071 --> 01:29:53,001

I think I never had that yet.

01:29:54,163 --> 01:29:55,823

So thank you so much.

01:29:56,604 --> 01:29:57,414

Yeah, awesome.

01:29:57,414 --> 01:29:59,286

Well, I think that's...

01:29:59,286 --> 01:30:01,807

Let's call it a show, Luke.

01:30:01,967 --> 01:30:03,218

That was really amazing.

01:30:03,218 --> 01:30:10,030

I'm really happy because we get to explore a lot of the questions I had for you from a...

01:30:10,030 --> 01:30:15,746

decision-making perspective but also got to be very nerdy so that's great.

01:30:16,347 --> 01:30:17,628

Thank you so much.

01:30:17,628 --> 01:30:22,884

As usual, we'll put links in the show notes for those who want to dig deeper.

01:30:22,884 --> 01:30:26,998

Thanks again, Luke, for taking the time and being on the show.

01:30:27,179 --> 01:30:27,979

Thank you, Alex.

01:30:27,979 --> 01:30:28,940

It was a blast.

01:30:33,796 --> 01:30:37,499

This has been another episode of Learning Bayesian Statistics.

01:30:37,499 --> 01:30:47,988

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01:30:47,988 --> 01:30:52,071

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01:30:54,033 --> 01:30:58,877

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01:31:03,224 --> 01:31:04,204

Alex Andora.

01:31:04,204 --> 01:31:08,424

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01:31:15,690 --> 01:31:18,071

Thank you so much for listening and for your support.

01:31:18,071 --> 01:31:20,382

You're truly a good Bayesian.

01:31:20,382 --> 01:31:23,503

Change your predictions after taking information.

01:31:23,503 --> 01:31:30,530

And if you're thinking I'll be less than amazing, let's adjust those expectations.

01:31:30,530 --> 01:31:43,688

Let me show you how to be a good Bayesian Change calculations after taking fresh data in

Those predictions that your brain is making Let's get them on a solid foundation