Episode #252: Nathan Braun

Summary

On this week’s episode of the show, I talk with Nate Braun, author of several Python books, all having to do with sports. Nate shares his journey from having a background in economics to writing books on sports data analysis and visualization using Python. Despite not initially being skilled in coding, Braun was inspired by his work in environmental issues and modeling, leading him to develop fantasy football models and later educational books on coding and data analysis with a focus on various sports. We cover Nate’s data scraping and writing process, as well as the ins and outs of why he likes to work with Python and the various libraries he uses in his work.

Topics Discussed

• Background and Transition: Nate shares his unconventional journey from working on environmental issues to developing a niche in sports data analytics. His inspiration took root during his work on modeling the impact of the BP oil spill.
• Fantasy Football and Education: The pivot to sports began with fantasy football models. The success of these models led Nate to author books designed to educate enthusiasts on coding and data analysis, specifically tailored for those outside the computer science field.
• Challenges and Opportunities: Nate talks about the difficulties he faced entering the competitive fantasy football advice market. With the rise in betting and fantasy sports advertising, he recognizes the potential for educating people on data analysis.
• Sport-by-Sport Learning Curve: Despite not being an expert in all sports, Braun has written instructional books on a range of sports by dedicating time to write and develop new models, leveraging the success and experience gained from his initial football book.
• Data Gathering and Visualization: Our conversation delves into the varying difficulty levels of acquiring and visualizing data across sports and we highlight Nate’s use of the Python Seaborn library.
• Python Over R: Nate expresses his preference for Python due to its versatility in machine learning, data visualization, web scraping, and content creation, favoring it over R.
• Technical Deep-Dive into Web Scraping: We talk about using Python for web scraping, including dealing with JavaScript-heavy websites, and the other tools Nate uses like Beautiful Soup and Selenium.
• Future Plans: A teaser for a potential Python book on Formula One as Braun’s love for sports continues to drive his writing endeavors.

Resources

Guest Bio

Nathan Braun is a self-taught programmer and data scientist based in Milwaukee, WI.

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Transcript

00:00 – 00:16

Welcome back to the PolicyViz Podcast. I’m your host, Jon Schwabish. Hope you’re well.

00:16 – 00:21

Hope you’re enjoying the beginning of 2024. On this week’s episode of the show, I speak

00:21 – 00:27

with Nate Braun. Nate is a Python programmer and writer who’s written several books on

00:27 – 00:35

how to use Python to analyze and visualize data around sports. So football, baseball,

00:35 – 00:39

soccer, hockey, and basketball. Those are the five, I think. I asked Nate, I reached

00:39 – 00:45

out to Nate because I saw his book on learning to code, learning to code in Python with hockey

00:45 – 00:48

data. And of course, I’m a hockey fan. So I reached out to him to see more about what

00:48 – 00:53

he’s doing in that book. And so after reading that book, I went back to him and said, hey,

00:53 – 00:57

would you like to come on the podcast? Let’s talk about your approach, your background,

00:57 – 01:03

and how you think about visualizing or analyzing data when it comes to sports. Because sports,

01:03 – 01:07

I think, and you’ll hear this in the conversation with Nate, sports is something that many of

01:07 – 01:12

us are interested in. If not fully engaged, maybe, you know, at least we kind of know

01:12 – 01:17

it’s there. But it’s a great way to work with data because for many of these sports, there

01:17 – 01:25

is so much data. And so it offers a fairly unique, I think, opportunity to practice our

01:25 – 01:30

skills and learn new skills when it comes to data, data visualization, programming,

01:30 – 01:36

HTML, data science, because there’s just so much opportunities there. So Nate and I sit

01:36 – 01:39

down and talk about his process and his background and where he came from and how he decided

01:39 – 01:43

to write these books. We talk about the various different sports that he’s written about and

01:43 – 01:49

talk about obviously Python and his process for writing these books and doing the data

01:49 – 01:54

analysis in Python and why he likes Python over some other tools that many of you may

01:54 – 01:58

be using in your day-to-day work. So I think you’ll find this an interesting conversation,

01:58 – 02:03

especially if you’re interested in learning or upping your skills in the Python programming

02:03 – 02:08

language. So here’s my conversation with Nate Braun, author of several books on learning

02:08 – 02:13

to code. Hey, Nate, welcome to the show. Good to meet you.

02:13 – 02:18

Hey, you too, John. Thanks for having me. Yeah, this is exciting. Madison guys come

02:18 – 02:21

together. That’s right.

02:21 – 02:27

Maybe not enough beer for a Madison reunion. Yeah, not this early in the morning.

02:27 – 02:33

Yeah. So I reached out because I saw these books that you’ve written that are data, data

02:33 – 02:38

is coding, but rooted in sports, which is, I think, a really interesting way to get about

02:38 – 02:44

teaching people how to work with data since so many of us like sports. And you were gracious

02:44 – 02:49

enough to send me over the hockey one, which I’ve been really enjoying. So I thought maybe

02:49 – 02:52

we would talk about pieces of the different books, but maybe we can start with introductions.

02:52 – 02:58

Like what’s your background? How did you decide to write not just one book on data and coding,

02:58 – 03:02

but like several books on, on data and coding. And then we can, we can dive into some of

03:02 – 03:07

the specifics. Yeah, for sure. So my background actually is

03:07 – 03:14

not in coding. It’s a little bit into data. I did economics. We, like you said, have the

03:14 – 03:20

same alma mater at UW Wisconsin, but so I did economics. I did some of the modeling

03:20 – 03:24

and stuff, but really not a lot of coding coming out of school. And so my background

03:24 – 03:31

was originally in environmental stuff. I came out of school like working on the BP oil spill,

03:31 – 03:34

but I kind of realized like doing all that and building those models, which do involve,

03:34 – 03:39

you know, a decent amount of coding. We, we did it in state of back then that, you know,

03:39 – 03:44

I enjoyed the kind of modeling and data piece of it more so than like specifically the environmental

03:44 – 03:48

stuff. So I was happy building models, you know, it didn’t necessarily matter what they were.

03:50 – 03:55

And I kind of wanted to do more of that. So, you know, I originally, I started with football,

03:55 – 03:59

fantasy football. So I’ve been in a league for a long time with a bunch of buddies and

03:59 – 04:03

you know, we’re just competitive. We’re all trying to beat each other every year and stuff. So I,

04:03 – 04:08

I started working on some models for that. And then with this kind of entrepreneurial streak

04:08 – 04:14

in me, I decided to sell the models. So, you know, even back then it was just kind of like,

04:14 – 04:17

you know, I didn’t know anything about coding or web development or whatever. So I found some

04:17 – 04:23

WordPress plugins and it would upload the CSV of the rankings every week. And I got some interest

04:23 – 04:27

via that, but like not a ton. I mean, there’s just so much out there with fantasy football.

04:28 – 04:33

It was, but it was a pretty good model and it was kind of, you know, I, I think I was doing

04:33 – 04:37

some cutting edge stuff. So occasionally I would get people emailing me, Hey, this is awesome.

04:37 – 04:42

But what I really want to do is like know how to make a model like this, you know, so can you

04:42 – 04:50

teach me? So, so I eventually decided to get into the books and the first one was learn to code with

04:50 – 04:57

fantasy football. So I had learned Python as part of building all these models and putting them

04:57 – 05:03

online and stuff like that. And meanwhile, doing that, I had kind of after the BP oil spill wound

05:03 – 05:08

down, I’d, I’d use that knowledge in my Python knowledge, learning from doing these football

05:08 – 05:14

models to get, you know, data science jobs here locally in Milwaukee. So I worked at a startup

05:14 – 05:19

for a while and I worked at a bigger company for a while. But it was really kind of doing the same,

05:20 – 05:25

you know, data manipulation and visualization techniques that I had been doing as part of the

05:25 – 05:32

football stuff. So, yeah, I started working on that on the side, like working on these books

05:32 – 05:36

and, you know, I’d get up early and we had, you know, little kids at the time. So it was like,

05:36 – 05:43

yeah, getting up early and working on the weekends and stuff like that. But I kind of just basically

05:44 – 05:51

wanted to, you know, teach all of the data science concepts generally, like using the sports

05:51 – 05:56

application. So it’s not like you would, the football book, it’s not like you’d be able to,

05:56 – 06:00

you know, read that and then work for the Packers necessarily, or something like that. You’re not

06:00 – 06:05

doing like super cutting edge, you know, on like the frontier of these sports where you can work

06:05 – 06:11

for a team, but it’s more teaching these concepts, you know, with like an interesting way, in an

06:11 – 06:17

interesting way. So that’s kind of how it all got started. So this was around what the BP was like

06:17 – 06:22

2012? Yeah. Yeah. 2010 is what had happened. And yeah, I graduated in 2011 and was doing that.

06:22 – 06:28

It took like five-ish years to kind of wrap up. So. So it’s been since then, you know, like a

06:28 – 06:34

decade or so. If you were starting now, do you, this is like totally separate from the books,

06:34 – 06:41

but like, is the fantasy football, like marketplace so saturated at this point with

06:41 – 06:46

models that like trying to build the new models, like kind of impossible? Yeah. I mean, it’s the,

06:46 – 06:51

yeah, the problem is that there’s just so many people, like, it’s like, you want to work on a

06:51 – 06:55

fantasy football model and come up with fantasy advice and like do all this stuff. You know,

06:56 – 06:59

there’s just so many people who are like to do that, you know, and they like to do it for free

06:59 – 07:04

and for fun and all that stuff. So it’s just really hard to, you know, if you want to break

07:04 – 07:10

into the fantasy advice space, it’s sort of like, you know, good luck. Like I hope. Yeah. I mean,

07:10 – 07:15

it’s, it can be a good fun thing, but you’re not, you know, like no one’s everyone is working on

07:15 – 07:20

their own models. And that’s why these books have done so well. Like my, you know, my target is

07:20 – 07:26

people who like, don’t have a computer science background, but do like messing around with

07:26 – 07:32

fantasy stats and sports stats and Excel, you know, kind of like, don’t necessarily know where

07:32 – 07:37

to take it from there. Like how to do, you know, what’s the next level beyond like Excel formulas

07:37 – 07:41

and stuff like that. Right. Right. And so that, I mean, now, yeah, I was gonna say now when you

07:41 – 07:47

watch any sport on TV, all the ads are, you know, betting in fantasy. So it’s like, yeah,

07:47 – 07:52

the industry is kind of like exploded a bit. Yeah. Imagine like how hard it would be now.

07:52 – 07:58

Yeah, for sure. And I don’t, yeah, I’m not a big fan. I don’t personally bet too much or,

07:58 – 08:03

I mean, just the, you know, it’s like the 8% break or whatever it is like the, you know,

08:03 – 08:08

with my economics background, it just, you know, it’s not, it’s not a zero sum game. It’s a negative

08:08 – 08:14

sum game, you know, the fact that you have to beat it by that much. So I prefer, I’m happy just to,

08:14 – 08:18

you know, teach people using this stuff. Right. And that’s really kind of what I try to do is,

08:18 – 08:23

again, like you, you know, you learn the data, like, it’s like, okay, combine two data sets

08:23 – 08:26

together. We have a bunch of hockey players and they have a bunch of their goals. Like,

08:26 – 08:30

here’s how you combine the data together. And here’s how you do a merge. And here’s an

08:30 – 08:34

inner and outer joint and all that stuff. So people, you can learn that. And then you,

08:34 – 08:40

you know, you get a job at wherever. Right. And it’s, you know, it’s a different data source,

08:40 – 08:43

but you still got to learn how to merge them together and stuff like that. Right. So,

08:43 – 08:46

so it seems like your goal, cause there’s a, there’s a book, I’ve got the hockey one,

08:46 – 08:52

there’s a baseball one, there’s a soccer one, there’s a football one. Right. So it was your

08:52 – 08:58

thinking all the way, like people are interested in sports. People want to learn to code. And this

08:58 – 09:02

is a way to tap into that, to that interest. And it could be music could be anything, but like

09:02 – 09:07

sports seems like a pretty universal thing to get started. Yeah, for sure. Yeah. That sort of,

09:07 – 09:11

I mean, that’s definitely what it was for the football one. And then the football one did well.

09:13 – 09:19

And then I would get people reaching out about other sports, you know, like, Hey, like I really

09:19 – 09:23

like football, but football is not my thing. Or I had a lot of people reaching out from Europe,

09:23 – 09:26

you know, saying like, I thought this was about football, you know, like,

09:29 – 09:33

so then I did, I mean, so then I did baseball after that, just because it was, I mean,

09:33 – 09:39

it was all kind of market driven. Like it was selling well enough that, you know, it made sense

09:39 – 09:43

to do. So we had our first kid and I was working on this stuff, you know, early mornings and stuff.

09:43 – 09:47

Then we had our second kid and it was like, okay, I don’t have time to do this anymore. So I went

09:47 – 09:52

down one day, I went down to four days a week at my day job. And I spent one day a week working on

09:52 – 09:57

my own stuff. And this book was one of the first things, right. And then ended up doing well,

09:57 – 10:02

where it was like, Oh, you know, this is 20% of my salary. You know, I maybe I’ll just do this.

10:02 – 10:07

And so then, and then after that, I cranked out the baseball one, you know, that did well,

10:07 – 10:12

all these other sports, they’re not really like, you know, totally my sports. Like I grew up

10:12 – 10:16

following football, I do fantasy football, like, I like that well enough to build the models on my

10:16 – 10:22

own time. But I, you know, don’t necessarily know that much about baseball. Right. So again,

10:22 – 10:26

it’s sort of like, you know, all this stuff, like building the sports model and taking that to

10:26 – 10:30

another job. That’s kind of what I had to do with building, taking this football book and applying

10:30 – 10:34

it to baseball and hockey and everything else. So there was some like deep diving into the sports

10:34 – 10:39

and kind of like figuring it all out. Yeah, so so tell me a little bit about that that process.

10:39 – 10:42

I mean, I’m guessing it’s the case that the football one was sort of the hardest to write

10:42 – 10:48

because just writing it, getting all the code, laying it all out, get all the images. But like,

10:48 – 10:53

did you find it? Was it markedly easier to do each subsequent? Yeah, it definitely was like,

10:53 – 10:58

you didn’t know the sport. So yeah, for sure. Yeah. And I knew the sports well enough. Like I

10:58 – 11:02

don’t know all the NHL players, but I like playing a hockey league, you know, once a week and play

11:02 – 11:06

pick up basketball and all that stuff. So I know it’s not like I didn’t like him. I know him. I

11:06 – 11:10

definitely know him well enough. But it’s like, you know, if you just I had to like look up,

11:11 – 11:15

you know, like, you know, like just like the famous, like no one like make sure I have all

11:15 – 11:21

my bases covered and all that stuff. But so yeah, it was it was they’re all like basically they

11:21 – 11:26

follow the same pattern, you know. So it was the football one was like kind of the path breaking

11:26 – 11:29

thing. And the other one, it was sort of like fitting the sports into that. Right. So I did

11:29 – 11:36

football first and then baseball, like a year or two years later. And then I did hockey, soccer

11:36 – 11:42

and basketball all at the same time. And that and those sports are pretty similar in the fact that

11:42 – 11:47

it’s like very free flowing, you know, like a lot of the data I found was like X, Y coordinates,

11:47 – 11:53

you know, for passes. Yeah. And like you pass in every sport, you shoot in every sport. So that

11:53 – 11:59

that was sort of helpful when doing it. And was finding that data hard to do.

12:00 – 12:06

It’s it’s easier for some sports than others. It wasn’t ever too bad. Yeah. Soccer was probably

12:06 – 12:10

the hardest just because. Oh, OK. I don’t I’m not even sure why. But a lot of these

12:11 – 12:17

like the NHL and baseball and NFL and NBA, they have kind of like undocumented APIs where you

12:17 – 12:23

can just grab a bunch of data and figure it out. Yeah. Yeah. It seems like the hardest part would

12:23 – 12:29

be the data visualization chapter. So like in the hockey one, you’ve got one of the shot graphs

12:29 – 12:35

laid out in a rink, but like you can’t really do that. You know, the exact same thing for baseball.

12:35 – 12:39

Right. You can’t do it for baseball and you can’t do it for football. But I did do it for the ones

12:39 – 12:45

I did do it for soccer and basketball. Yeah. So those were and those were the ones I was doing

12:45 – 12:50

at the same time. And so that’s kind of why I did it that way. Yeah. I mean, it was not too bad to

12:50 – 12:57

do the data visualization in the book. I talk a lot about Seaborn, which is a Python library that

12:57 – 13:05

I like a lot. And so, yes, I’m not a I’m not a Python coder. So tell me, I guess I guess the

13:05 – 13:10

question would be sell me on. Although you’re not going to be able to, but that’s OK. Like

13:11 – 13:18

sell, sell the listener on using Python over, you know, R or, you know, any other language someone

13:18 – 13:25

wants. So I’m not I’m not anti R. I mean, the early football model I did, I did it in R.

13:27 – 13:33

I think my understanding is that, you know, since then, that kind of direction has been sort of

13:33 – 13:40

moving towards Python in terms of market share and stuff. I have a blog post or an email I sent

13:40 – 13:47

out to some people asking, like exploring this question, like our Python. Yeah. And there’s one

13:47 – 13:53

guy I saw, like his name is like open war or something like that. He did a kind of a mini

13:53 – 13:58

study using Stack Overflow, like the kind of questions people ask and stuff like where people

13:58 – 14:04

get into programming, you know, the languages they move to and adopt. Yeah. And all that. He

14:04 – 14:08

found that actually a lot of our movers end up moving to Python. And then so he has this big

14:08 – 14:13

graph of all these programming languages, like where you he calls them terminal nodes, like where

14:13 – 14:19

you you move to and you stop because you can’t find anything better. And for and Python three is

14:19 – 14:25

one of those. Right. And that was my journey. I mean, I did Stata back in the day, which is like

14:25 – 14:31

more so in the economics field. I did SAS in grad school. I did some like all that stuff.

14:31 – 14:35

Right. Right. But yeah, I mean, Python, I think a good thing about Python is

14:36 – 14:41

obviously the machine learning stuff, you know, like the scikit-learn is pretty well developed.

14:41 – 14:48

And then these these data visualization chapters like I or packages like Seaborn is the one that

14:48 – 14:53

I really like. Yeah. And, you know, just the ability to do all that. But I mean,

14:53 – 14:58

they’re really pretty similar. So. Right. So tell me about the actual writing. I can imagine,

14:58 – 15:03

like a lot of the database books that are are they’re using are Markdown or using Cordo to

15:03 – 15:09

actually like basically write the book within the code. Did you approach the these books the

15:09 – 15:17

same way? I wrote all mine. I mean, I wrote all mine in Markdown, like and then I used Pandoc.

15:17 – 15:23

It’s like a thing to convert Markdown to, you know, a PDF. So. Right. So that’s really like

15:23 – 15:30

what I did. I use I like using the terminal and, you know, Vim is my text editor that I

15:31 – 15:38

that I really like to use. I’m actually working on a new book on kind of like tooling, you know,

15:38 – 15:44

that covers Vim and the terminal and all that stuff. Right. So I wrote it all in that. Then I,

15:44 – 15:51

you know, I’d have my Python repl up on the side and I would, you know, copy and paste the input

15:51 – 15:57

and output and put it in the books. Right. Yeah. So, I mean, the thing that I’ve always,

15:59 – 16:02

I guess, heard known about Python is that it’s it’s really good at web scraping.

16:02 – 16:08

So because you’re pulling all these APIs, did you find that like just using Python made that process

16:08 – 16:13

because you’ve got all these different sports like so much easier? Yeah, for sure. Yeah. Yeah.

16:13 – 16:17

Python is is good at web scraping. There’s a package that’s kind of like the, you know,

16:17 – 16:21

some of these packages, they’re third party packages, but it’s like everyone uses them.

16:21 – 16:26

Right. So like might as well almost like be part of Python itself. And so the web scraping one is

16:26 – 16:34

called Beautiful Soup. And it lets you just go to a website and get data and do all that. I mean,

16:34 – 16:39

the problem is a lot of these websites now are with just so much JavaScript on the front end

16:39 – 16:44

and like single page apps, like you can’t really scrape it anymore. Like you like so

16:44 – 16:50

Beautiful Soup works by connecting to the website and getting the HTML. And, you know, you can like

16:50 – 16:54

work with the tags and get the data. But a lot of these websites now are just like kind of

16:54 – 16:59

dynamically populated where like that doesn’t work. So to build a web scraper, you have to like

16:59 – 17:07

actually build some code that like opens up a web browser and like, like automate the clicking

17:07 – 17:13

around and getting it that way. And that’s another package called Selenium that Python

17:13 – 17:20

covers that people do that in. Right. So have you thought about branching out and doing similar

17:20 – 17:27

books for other content areas, other data areas? I’ve thought about it. Like realistically, I think

17:27 – 17:28

the book is well structured.

17:29 – 17:34

And it’s like a good introduction to like all this stuff, like whether you like sports

17:34 – 17:38

or not, you know, and I’ve had people, right. Yeah. And I, like I had early on, I had a

17:38 – 17:43

coworker that I worked with, like she was from China, you know, she like, didn’t really

17:43 – 17:46

speak that good of English. She knew nothing about American football, but she wanted to

17:46 – 17:51

learn pandas. And so I sent her the book and she’s like, Oh, this is great, you know? Yeah.

17:51 – 17:57

And so I’ve thought about, I’ve thought about doing that. And like making kind of a general

17:57 – 18:02

purpose one. I mean, I think there’s maybe I, and maybe I’ll do it someday. I think

18:02 – 18:06

there’s a lot of general purpose stuff out there, you know, and so it’s sort of harder

18:06 – 18:11

to kind of like attract attention or get people in, but maybe I’ll, but maybe I’ll do something

18:11 – 18:13

like that.

18:13 – 18:20

If someone said, I want to learn Python and, but they, you know, they’re kind of, you know,

18:20 – 18:25

they like all sports, whatever. Is there one of these books that you think is more generally

18:25 – 18:29

applicable? And the reason I ask is when I think about the hockey one, you know, you’ve

18:29 – 18:34

got the shot, the shot map, for example, which is like basically just the map. So it seems

18:34 – 18:39

like it’s pretty easy to take that skill and transfer it to, I want to make just like a

18:39 – 18:45

dot map. So are any of these that you think extend well to the person who like kind of

18:45 – 18:50

doesn’t really care about the sport so much, but just wants to get that broad intro?

18:50 – 18:55

I think, no, I think they’re all, I think they’re all pretty good in that. I think,

18:55 – 19:02

I think football and baseball, there’s just a little bit more data, like, you know, again,

19:02 – 19:07

because hockey and soccer and basketball are so free flowing. Like that’s, that’s why I

19:07 – 19:12

actually did a lot with the shot data because it’s like the shot data you have, you know,

19:12 – 19:16

you know, the distance from the goal, you know, that like the type of shot, like, is

19:16 – 19:22

it a slap shot? Is it a wrister in the NBA? Is it like a step back, you know, whatever,

19:22 – 19:26

like, you know, in soccer, is it left foot, right foot or a header? And so like, you just

19:26 – 19:31

have certain like attributes on every shot where you can kind of like break it down like that.

19:31 – 19:36

And so in the books, I actually spent a lot of time with the shot data just because it was like

19:36 – 19:41

a good data set, you know, right. And you can build a model, like, does the shot go in or not?

19:41 – 19:45

And like, how does, does the shot go in like relative to distance away from the goal? Like,

19:45 – 19:52

how does that change? And so with the nice thing about football and baseball is that like,

19:52 – 19:56

everything’s like that, you know, like, it’s not just the shots, like football, you have a play,

19:56 – 20:00

and you can say, like, what’s the down? How far do you have to go? And like, is it a runner pass

20:00 – 20:04

and baseball? You know, there’s even more stats, like, what’s the type of pitch? And like, what’s

20:04 – 20:10

the count and all that stuff. So I would say, like, if people were purely sport agnostic,

20:10 – 20:15

and wanted to just stick with one of them, I would say, you know, football or baseball just

20:15 – 20:22

has a little bit more variety in terms of data. I mean, the football one, I actually, the football

20:22 – 20:30

one, I’ve actually extended where I made, I call it like the developer kit. So, so after you read

20:30 – 20:35

the football book, and like, have this basic knowledge down, I sell another thing that I sell,

20:35 – 20:41

you know, every year, like the 2024 version is out right now, where you get that. And then,

20:41 – 20:47

so you it gives access to an API that I built, where you get access to like these fantasy

20:47 – 20:52

simulations from the old model I did, you know, back in the day, and then we work through using

20:52 – 20:59

that API to build, like a couple of projects that analyze your own team. So, so we work on you know,

20:59 – 21:04

if you’re, if you’re fantasy league is on ESPN. Yeah, one of the one of the first projects we do

21:04 – 21:09

is like, okay, how do we connect to ESPN and get that data, right, you can see who’s on your team,

21:09 – 21:14

and who’s playing this week, and all that. And then we take that and we build, you know,

21:14 – 21:19

who do I start calculators, so like, it’ll run through all your backup options, like run the

21:19 – 21:24

simulation, say, oh, this person increases your probability of winning, right? We do a league

21:24 – 21:30

analyzer. So it’s like, you, you know, you get the probabilities for every matchup, or it’s like,

21:30 – 21:35

who’s going to score the high this week, all that stuff. So that and, and so that, and that’s just,

21:35 – 21:40

I think, due to the, you know, kind of my, my original love and passion for football,

21:41 – 21:46

that that’s why I’ve gone and built all that out there. So people like really didn’t care,

21:46 – 21:51

you know, and you wanted to kind of get the full experience doing the football one, and then doing

21:51 – 21:56

the kit afterwards is pretty cool, I think. And is the kit like, is it, is it videos? Or is it?

21:57 – 22:01

It’s still, that’s the same. Yeah, yeah, it’s the step by step instruction. Yeah.

22:03 – 22:10

So aside from trying to write these books around having job and kids and family and everything

22:10 – 22:17

else, what was what for you was the most fun part about going, you know, going through the process?

22:18 – 22:25

I think the most fun part is, is just, you know, like helping people like, like in getting, you

22:25 – 22:29

know, that early feedback of like, Oh, wow, this is like, really helpful. Like, I think a lot of

22:29 – 22:35

people, like, technically, my books don’t assume any prior knowledge, like we start out like, what

22:35 – 22:40

is data? And like, what is rows? And what is columns? But the sweet spot seems to be the

22:40 – 22:45

people who’ve like, tried to learn to code before, and like, thought it just was like, really boring,

22:45 – 22:49

or like, yeah, you know, like, they call it I got it. I wasn’t even aware of it before. But,

22:49 – 22:54

you know, tutorial hell, like people just going through these, you know, Hello, world tutorials,

22:54 – 23:01

that kind of take forever. And so, you know, helping people, like really kind of get like,

23:01 – 23:06

spun up pretty quickly. And like doing things and talking about why we’re doing things and like

23:06 – 23:11

having people respond to that, I would say has been the most right kind of rewarding and fun part.

23:11 – 23:15

Yeah, that’s pretty great. So last question for you. You’ve got all these sports. So

23:15 – 23:23

so I’m guessing you’re a Bucks fan. Packers fan. Yep. Brewers fan. Yep. Okay, what about what about

23:23 – 23:31

hockey and soccer? Do you have? And hockey, I hockey, I like the Blackhawks. Okay. I’m not,

23:31 – 23:34

you know, not like too plugged in. But I was originally born in Chicago. So that’s

23:34 – 23:39

good enough for me to write but not a Bears fan. No, not a Bears fan. Wisconsin. Yeah, yeah,

23:39 – 23:45

definitely. You get in trouble if you Yeah, yeah, yeah, that’s right. Yeah. And soccer. I don’t

23:45 – 23:52

I’m pretty soccer. I’m pretty agnostic. So in the book, we cover the World Cup data.

23:52 – 23:57

Okay. That’s like the data. That’s the data I found and good. And so I’m, you know, I’m always

23:57 – 24:01

cheering for the US then I have a friend who’s really big into MLS and likes the Minnesota

24:01 – 24:06

loons. So okay, you know, they all have good names. Yeah, exactly. Yeah, yeah, yeah. Well,

24:06 – 24:11

Madison has a team now to with the it’s like a minor league team or whatever the flamingos like,

24:11 – 24:15

Oh, really? When they when they covered Baskin Hill and all the flamingos they

24:16 – 24:21

name? Yeah, yeah. Yeah. Well, if you end up writing a Formula One Python book,

24:21 – 24:26

yeah, my son, my son will be into that one. Yeah, definitely. Which is another one of those

24:27 – 24:32

sports where there’s just once you start getting even knee deep into it, you see how much data

24:32 – 24:38

there is. Yeah, for sure. And can understand a ton about it. I’ve been really into golf recently. So

24:38 – 24:42

I got my putter here in the background. So yeah, I’ve been thinking about that. But we’ll we’ll

24:42 – 24:48

see. Well, they must have I mean, it feels like all these sports now have their own processes to

24:48 – 24:54

have tons of data. Yeah. Not all of which they they release. I did a podcast episode with Micah

24:54 – 25:01

McCurdy earlier in the year who runs hockey viz. And we were talking about, you know, he has a ton

25:01 – 25:08

of hockey data. And the one question that I have is a lot of the I have a theory that a lot of

25:08 – 25:14

hockey play shows up in the front two corners when you’re watching it on TV. Yeah, because the

25:14 – 25:19

opposite side is where the where the benches are. So there’s more space. But but the teams don’t

25:19 – 25:23

release that data, right? That data is like they I’m sure they have it. Yeah, I’m sure they’re

25:23 – 25:26

sitting on it, but they haven’t like they don’t post that they have the shot data, but not where

25:26 – 25:33

they spend the right time. So yeah. Yeah. I mean, the Formula One stuff is is pretty crazy, too,

25:33 – 25:37

because, you know, they’re they have sensors all over the cars. Yeah. And so they’re measuring,

25:38 – 25:43

you know, speed, but heat and wind and, you know, the exhaust coming off the car in front of you.

25:43 – 25:48

And it’s kind of amazing when you when you start to see what they’re measuring. Yeah, for sure.

25:48 – 25:53

There’s a lot of fun, but I think it’s a great idea to to tap into something that people love

25:53 – 25:58

and and be able to teach them to code. So so these are great. I’ll put all the links to all

25:58 – 26:02

the books on the show notes. And Nate, thanks for coming on the show. I appreciate you taking the

26:02 – 26:07

time. Yeah, thanks for having me. And thanks, everyone, for tuning into this week’s episode of

26:07 – 26:11

the show. I hope you enjoyed that. If you’re interested in learning more, check out Nate’s

26:11 – 26:16

website, check out Nate’s books. If you’re interested in any of the sports, I think these

26:16 – 26:22

are great opportunities to learn more about how you can engage with the data sets in baseball,

26:22 – 26:27

hockey, basketball, soccer, etc. So again, I hope you enjoyed that and hope you’re enjoying the show.

26:27 – 26:32

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favorite podcast provider. Just a quick little click there on the five stars.

26:36 – 26:42

Less more people know about the show, which helps me bring in new and better guests each and every

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other week. So until next time, this has been the PolicyViz Podcast. Thanks so much for listening.

26:49 – 26:54

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