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From Economics to Python-Powered Sports Analytics: Nate Braun’s Game-Changing Journey
940 Plays10 months ago
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.
➡️ Check out more links, notes, transcript, and more at the PolicyViz website.
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Transcript
Introduction to Nate Braun and His Work
00:00:12
Speaker
Welcome back to the Policyviz podcast. I'm your host, John Schwabisch. Hope you're well. Hope you're enjoying the beginning of 2024. On this week's episode of the show, I speak with Nate Braun. Nate is a Python programmer and writer who's written several books on how to use Python to analyze and visualize data around sports.
00:00:33
Speaker
football, baseball, soccer, hockey, and
Nate's Journey into Python and Sports Data
00:00:37
Speaker
basketball. Those are the five, I think. I asked Nate, I reached out to Nate because I saw his book on learning to code, learning to code in Python with hockey data. And of course, I'm a hockey fan, so I reached out to him to see more about what he's doing in that book. And so after reading that book, I went back to him and said, hey, would you like to come on the podcast? Let's talk about your approach, your background, and how you think about visualizing or analyzing data when it comes to sports, because
00:01:02
Speaker
Sports, I think, and you'll hear this in the conversation with Nate.
00:01:06
Speaker
Sports is something that many of us are interested in, if not fully engaged, maybe, you know, at least we kind of know it's there, but it's a great way to work with data because for many of these sports, there is so much data. And so it offers a fairly unique, I think, opportunity to practice our skills and learn new skills when it comes to data, data visualization, programming, HTML, data science, because there's just so much opportunities there.
The Educational Power of Sports Data
00:01:34
Speaker
So Nate and I sit down and talk about his process and his background and where he came from and how we decided to write these books. We talk about the various different sports that he's written about and talk about obviously Python and his process for writing these books and doing the data analysis in Python and why he likes Python over some other tools that many of you may be using in your day to day work.
00:01:56
Speaker
So I think you'll find this an interesting conversation, especially if you're interested in learning or upping your skills in the Python programming language. So here's my conversation with Nate Braun, author of several books on learning to code. Hey, Nate, welcome to the show. Good to meet you. Hey, you too, John. Thanks for having me. Yeah, this is exciting. Madison guys come together for a... That's right. Right. Maybe not enough beer for...
00:02:23
Speaker
for a Madison reunion. Yeah, not necessarily in the morning. Yeah. So I reached out because I saw these books that you've written that are data, data is coding, but rooted in sports, which is, I think, a really interesting way to get about teaching people how to work with data since so many of us like sports. And you were gracious enough to send me over the hockey one, which I've been really enjoying. So I thought maybe we would talk about pieces of the different books, but maybe we could start with introductions like
00:02:53
Speaker
what's your background how did you decide to write not just one book on data encoding but like several books on on data encoding and then we can we can dive into some of the specifics yeah for sure so um my background actually is not in coding it's a little bit into data i did economics
00:03:12
Speaker
We, like you said, have the same matter at UW-Wisconsin. So I did economics. I did some of the modeling and stuff, but really not a lot of coding coming out of school. And so my background was originally in environmental stuff.
00:03:27
Speaker
I came out of school like working on the BP oil spill, but I kind of realized like doing all that and building those models, which do involve, you know, a decent amount of coding. We, we did it in state of back then that, you know, I enjoyed the kind of modeling and data piece of it more so than like specifically the environmental stuff. So I was happy building models, you know, it didn't necessarily matter what they were.
00:03:50
Speaker
And I kind of wanted to do more of that. So, you know, I originally I started with football, fantasy football. So I've been in a league for a long time with a bunch of buddies and, you know, we're just competitive. We're all trying to beat each other every year and stuff. So I, I started working on some models for that. And then with this kind of entrepreneurial streak in me, I decided to sell the models.
00:04:11
Speaker
Um, so, you know, even back then it was just kind of like, you know, I didn't know anything about coding or web development or whatever. So I found some WordPress plugins and it would upload the CSV of the rankings every week. Um, and I got some interest via that, but like not a ton. I mean, there's just so much out there with fantasy football. Um, it wasn't, but it was a pretty good model and it was kind of, you know, I,
00:04:32
Speaker
I think I was doing some cutting edge stuff. So occasionally I would get people emailing me, Hey, this is awesome. But what I really want to do is like, know how to make a model like this, you know, so can you teach me? So, so I eventually decided to get into the books. And the first one was learned to code with fantasy football. So yeah, I had learned Python as part of building all these models. And
00:04:56
Speaker
and putting them online and stuff like that. And meanwhile, doing that, I had kind of after the BP oil spill wound down, I'd use that knowledge and my Python knowledge, learning from doing these football models to get, you know, data science jobs here locally in Milwaukee. So I worked at a startup for a while and I worked
00:05:15
Speaker
at a bigger company for a while. Um, but it was really kind of doing these same, you know, data manipulation and visualization techniques that I had been doing as part of the football stuff. Um, so yeah, I started working on that on the side, like working on these books and, you know, I'd get up early and we had, you know, little kids at the time. So it was like, yeah, getting up early and working on the weekends and stuff like that. Um,
00:05:41
Speaker
but I kind of just basically wanted to, you know, teach all of the data science concepts generally, like using the sports application. So it's not like you would, the football book, it's not like you'd be able to, you know, read that and then work for the Packers necessarily or something like that. You're not doing like super cutting edge, you know, on like the frontier of these sports where you would work for a team, but it's more teaching these concepts
00:06:09
Speaker
you know, with like an interesting way in an interesting way. So that's kind of how it all got started. So this was around what the BP was like 2012. Yeah. Yeah. 2010 is what had happened. And yeah, I graduated 2011 and was doing that. It took like five years to kind of wrap up. So, so it's been since then, you know, like a decade or so. If you were starting now, do you, this is like totally separate from the books, but like, is the fantasy football,
00:06:38
Speaker
like marketplace so saturated at this point with models that like trying to build the new models like kind of impossible yeah i mean it's yeah the problem is that there's just so many people like it's like you want to work on a fantasy football model and come up with fantasy advice and like do all this stuff you know there's just so many people who are like to do that you know and they like to do it for free and for fun and all that stuff so it's just really hard to you know if you want to break into the fantasy advice space it's sort of like
00:07:08
Speaker
you know, good luck, like I hope. Yeah, I mean, it's it can be a good fun thing. But you're not you know, like no one's everyone is working on their own models. And that's why these books have done so well. Like my, you know, my target is people who like don't have a computer science background, but do like messing around with fantasy stats and sports stats and Excel, you know, kind of like don't necessarily know where to
00:07:33
Speaker
take it from there, like how to do, you know, what's the next level beyond like Excel formulas and stuff like that. Right, right. And so that now, yeah, I was just saying now when you watch any sport on TV, all the ads are, you know, betting and fantasy. Yeah, for sure. So it's like, yeah, the industry is kind of like exploded a bit. Yeah, I imagine like, how hard it would be now. Yeah, for sure. And I don't. Yeah, I'm not a big fan. I don't personally bet too much or I mean, just the, you know, it's like the 8%
00:08:00
Speaker
break or whatever it is, like, you know, with my economics background, it just, you know, it's not it's not a zero sum game, it's a negative sum game, you know, the fact that you have to beat it by that much. So I prefer, I'm happy just to, you know, teach people using this stuff. And, right. And that's really kind of what I try to do is again, like you, you know, you learn the data, like, it's like, okay, combine two data sets together, we have a bunch of hockey players, and maybe a bunch of their goals, like, here's how you combine the data together. And here's how you do a merge. And here's an
00:08:30
Speaker
inner and outer joint and all that stuff. So people, you can learn that. And then you, you know, you get a job at wherever, um, and it's, you know, it's a different data source, but you still got to learn how to merge them together and stuff like that. Right. So, so it seems like your goal, cause there's a, there's a book, I've got the hockey one, there's a baseball one, there's a soccer one, there's a football one. Right. Um, so it was your thinking all the way, like people are interested in sports.
00:08:56
Speaker
people want to learn to code. And this is a way to tap into that, to that interest. And it could be music could be anything but like sports seems like a pretty universal thing to get started. Yeah, for sure. Yeah, that sort of I mean, that's definitely what it was for the football one. And then the football one did well.
00:09:12
Speaker
Um, and then I would get people reaching out about other sports, you know, like, Hey, like I really like football, but football is not my thing. I had a lot of people reaching out from Europe, you know, saying like, I thought this was about football, you know, like, so, um, so then I did, I mean, so then I did baseball after that, just because it was, I mean, and it was all kind of market.
00:09:35
Speaker
driven, like it was selling well enough that, you know, it made sense to do. So we had our first kid and I was working on the stuff, you know, early mornings and stuff. Then we had our second kid and it was like, okay, I don't have time to do this anymore. So I went down one day, I went down to four days a week at my day job. And I spent one day a week working on my own stuff. And this book was one of the first things.
00:09:55
Speaker
Right. And then ended up doing well where it was like, oh, you know, this is 20 percent of my salary. You know, maybe I'll just do this. And so then and then after that, I cranked out the baseball one. You know, that did well. All these other sports are not really like.
00:10:10
Speaker
you know, totally my sports. Like I grew up following football, I do fantasy football, like, I like that well enough to build the models on my own time. But I, you know, don't necessarily know that much about baseball. So again, it's sort of like, you know, all this stuff, like building the sports model and taking that to another job, that's kind of what I had to do with building, taking this football book and applying it to baseball and hockey and everything else. So there was some like deep diving into the sports and kind of like figuring it all out.
00:10:36
Speaker
Yeah. So, so tell me a little bit about that, that process. I mean, I'm guessing it's the case that the football one was sort of the hardest to write because just writing it, getting all the code, laying it all out, getting all the images, but like, did you find it, was it markedly easier
Challenges and Evolution in Writing Books
00:10:51
Speaker
to do each subsequent book? Yeah, it definitely was. You didn't know the sport. So yeah, for sure. Yeah. And I knew the sports well enough. Like I don't know all the NHL players, but I like playing a hockey league, you know, once a week and play pickup basketball and all that stuff.
00:11:04
Speaker
So I know it's not like I didn't like him. I know him. I definitely know him well enough. But it's like, you know, if you just I had to like look up, you know, like, you know, like just like the the famous like no one like make sure I have all my bases covered and all that stuff. But so, yeah, it was the it was they're all like basically they they follow the same pattern, you know. So it was the football one was like kind of the path breaking thing. And the other one, it was sort of like fitting the sports into that. Right. So I did football first and then baseball.
00:11:32
Speaker
like a year, two years later, and then I did hockey, soccer and basketball all at the same time. Gotcha. And that and those sports are pretty similar in the fact that it's like very free flowing, you know, like a lot of the data I found was like XY coordinates, you know, for passes. Yeah. And like you pass in every sport, you shoot in every sport. So that was that was sort of helpful when doing it.
00:11:57
Speaker
And was finding that data hard to do? It's easier for some sports than others. It wasn't ever too bad. Soccer was probably the hardest, just because I'm not even sure why. But a lot of these, like the NHL and baseball and NFL and NBA, they have kind of like undocumented APIs where you can just grab a bunch of data. Kind of figure it out. Yeah.
00:12:20
Speaker
It seems like the hardest part would be the data visualization chapter. So like in the hockey one, you've got one of the shot graphs laid out in a rink, but like you can't really do that.
00:12:33
Speaker
you know, the exact same thing for, you know, baseball, right? You can't do it for baseball and you can't do it for football, but I did do it for the ones I did do it for soccer and basketball. Yeah. So those were, and those were the ones I was doing at the same time. And so that's kind of why I did that way. Yeah. I mean,
Python for Web Scraping Sports Data
00:12:49
Speaker
it was not too bad to do the data visualization. In the book, I talk a lot about Seaborn, which is a Python, a Python library that I like a lot.
00:12:59
Speaker
And so, yeah, so I'm not a Python coder. So tell me, I guess the question would be sell me on, although you're not gonna be able to, but that's okay. Sell the listener on using Python over R or any other link or something like that. So I'm not anti-R. I mean, the early football model I did, I did it in R.
00:13:27
Speaker
I think my understanding is that, you know, since then, that kind of direction has been sort of moving towards Python in terms of market share and stuff. I have a blog post or an email I send out to some people asking, like, exploring this question, like our Python. Yeah. And there's one guy
00:13:48
Speaker
I saw like his name is like open war or something like that. He did a kind of a mini study using stack overflow, like the kind of questions people ask and stuff like where people get into programming, you know, the languages they move to and adopt. Yeah, and all that he found that actually a lot of our movers end up moving to Python. And then so he has this big graph of all these programming languages, like where you
00:14:12
Speaker
calls them terminal nodes like where you you move to and you stop because you can't find anything better and for in python 3 is one of those right and that was my journey i mean i did stata back in the day which is like more so in the economics field i did sass in grad school i did some r like all that stuff right um right but yeah i mean python i think a good thing about python is
00:14:36
Speaker
obviously the machine learning stuff, you know, like the side kit learn is pretty well developed. And then these, these data visualization chapters, like I heard packages, like Seaborn is the one that I really like. Yeah. And, you know, just the ability to do all that. But I mean, they're really pretty similar. So right. So tell me about the actual writing. I can imagine like a lot of the data of his books that are are they're using our markdown or using Cordo to actually like
00:15:05
Speaker
basically write the book within the code. Did you approach these books the same way? I wrote all mine, I mean, I wrote all mine in Markdown, and then I used Pandoc, it's like a thing to convert Markdown to a PDF.
00:15:22
Speaker
So that's really what I did. I like using the terminal and Vim is my text editor that I really like to use. I'm actually working on a new book on tooling that covers Vim and the terminal and all that stuff. So I wrote it all in that, then I'd have my Python REPL up on the side and I would
00:15:50
Speaker
you know, copy and paste the input and output and put it in the books. Right. Yeah. So, I mean, the thing that I've always, I guess, heard knowing about Python is that it's, it's really good at web scraping. So because you're pulling all these APIs, did you find that like just using Python made that process because you've got all these different sports, like so much easier. Yeah. Yeah, for sure. Yep. Yeah. Python is, is good at web scraping. There's a package that's kind of like the,
00:16:17
Speaker
You know, some of these packages, they're third party packages, but it's like everyone uses them. Right. So it like might as well almost like be part of Python itself. And so the web scraping one is called beautiful soup. Um, and it'll let you, you know, just go to a website and get data and do all that. I mean, the problem is a lot of these websites now are with just so much JavaScript on the front end and like single page apps. Like you can't really scrape it anymore. Like you.
00:16:43
Speaker
like, so beautiful soup works by connecting to the website and getting the HTML. And, you know, you can like work with the tags and get the data, but a lot of these websites now are just like kind of dynamically populated where like that doesn't work. So to build a web scraper, you have to like, actually build
00:17:01
Speaker
some code that opens up a web browser and will automate the clicking around and getting it that way. And that's another package called Selenium that Python covers that people do that in.
00:17:15
Speaker
Right.
Catering to Sports Enthusiasts and Non-programmers
00:17:16
Speaker
So have you thought about branching out and doing similar books for other content areas, other data areas? I've thought about it. Like realistically, I think the book is well structured and is like a good introduction. Yeah. To like all this stuff, like whether you like sports or not, you know, and I've had people. Right. Right. Yeah. And I like I had early on I had a coworker
00:17:39
Speaker
that I worked with, like she was from China. She didn't really speak decorative English. She knew nothing about American football, but she wanted to learn pandas. And so I sent her the book and she's like, oh, this is great. And so I've thought about doing that and making kind of a general purpose one. And maybe I'll do it someday. I think there's a lot of general purpose stuff out there. And so it's sort of harder to kind of like
00:18:08
Speaker
attract attention or get people in, but maybe I'll do something like that.
00:18:20
Speaker
They like all sports, whatever. Is there one of these books that you think is more generally applicable? And the reason I ask is when I think about the hockey one, you've got the shot map, for example, which is basically just a map. So it seems like it's pretty easy to take that skill and transfer it to, I want to make just a dot map. So are any of these that you think extend well to the person who kind of doesn't really care about the sport so much, but just wants to get that broad intro?
00:18:50
Speaker
I think no, I think they're all I think they're all pretty good in that. I think I think football and baseball. There's just a little bit more data, like, yeah, you know, again, because hockey and soccer and basketball are so free flowing like that's
00:19:07
Speaker
That's why I actually did a lot with the shot data because it's like the shot data you have, you know, you know, the distance from the goal, you know, that like the type of shot, like, is it a slap shot? Is it a rister in the NBA? Is it like a step back, you know, whatever, like, you know, in soccer, is it left foot, right foot or a header? And so like, you just have certain like attributes on every shot where you can kind of like break it down like that.
00:19:31
Speaker
And so in the books, I actually spent a lot of time with the shot data just because it was like a good data set, you know, right. And you can build a model like, does the shot go in or not? And like, how does does the shot go in, like relative to distance away from the goal? Like, how does that change? And so
00:19:50
Speaker
with the nice thing about football and baseball is that everything's like that. It's not just the shots. Football, you have a play, and you can say, what's the down? How far do you have to go? And is it a run or a pass? And baseball, there's even more stats. What's the type of pitch? And what's the count? And all that stuff. So I would say if people were purely sport agnostic and wanted to just stick with one of them, I would say football or baseball just has a little bit more variety in terms of data. I mean, the football one,
00:20:21
Speaker
I actually, the football one, I've actually extended where I made, um, I call it like the developer kit. So, so after you read the football book and like have this basic knowledge down, I sell another thing that I sell, you know, every year, like the, the 2024 version is out right now where you get that. And then.
00:20:42
Speaker
So you, it gets access to an API that I built where you get access to like these fantasy simulations from the old model I did, you know, back in the day. And then we work through using that API to build like a couple of projects that analyze your own team. So, so we work on, you know, if you're, if your fantasy league is on ESPN.
00:21:03
Speaker
Yeah. One of the, one of the first projects we do is like, okay, how do we connect to ESPN and get that data? So you can see who's on your team and who's playing this week and all that. And then we take that and we build, you know, a, who do I start calculator? So like it'll run through all your backup options, like run the simulation, say, Oh, this person increases your probability of winning. Right. Um, we do a league analyzer. So it's like you.
00:21:28
Speaker
you know, you get the probabilities for every matchup or it's like who's going to score the high this week, all that stuff. So that and and so that and that's just I think due to the, you know, kind of like my original love and passion for football. Yeah.
00:21:42
Speaker
That that's why I've gone and built all that out there. So people like really didn't care, you know, and you wanted to kind of get the full. Experience doing the football one and then doing the kit afterwards is pretty cool. I think it is the kit. Like, is it, um, is it videos or is it, it's still, that's the same. Yeah. Yeah. That's the step-by-step instructions. Yeah. Yeah. Um, so aside from.
00:22:05
Speaker
trying to write these books around having job and kids and family and everything else. What was, what for you was the most fun part about going, you know, going through the process? I think the most fun part is, is just, you know, like helping people like, like in getting, you know, that early feedback of like, Oh, wow, this is like really helpful. Like, I think a lot of people
00:22:31
Speaker
Like technically my books don't assume any prior knowledge. Like we start out like what is data and like what is rows and what is columns. Um, but I, the sweet spot seems to be the people who've like tried to learn to code before and like thought it just was like really boring or like, you know, like they call it, I got it. I wasn't even aware of it before, but you know, tutorial hell, like people just going through these, you know, hello world tutorials that kind of take forever. And so, you know, helping people.
00:22:59
Speaker
Like really kind of get like spun up pretty quickly. Um, and like doing things and talking about why we're doing things and like having people respond to that, I would say has been the most kind of rewarding and fun part. Yeah, that's pretty great. Um, so last question for you, you've got all these sports. So, so I'm guessing you're a Bucks fan Packers fan, brewers fan. Yep.
00:23:21
Speaker
Okay. What about, uh, what about hockey and, and soccer? Do you have, do you have? And hockey, I hockey, I like the black Hawks. Um, I'm not, you know, not like to plugged in, but I was originally born in Chicago. So that's good enough for me to, uh, but not a bears fan. No, not a bears fan. Yeah. Yeah. Definitely. You'd get in trouble if you, yeah. Yeah. Yeah. That's right. Yeah. Um, and soccer, I don't, I'm pretty, uh,
00:23:47
Speaker
Soccer, I'm pretty agnostic. So in the book, we cover the World Cup data. Okay. That's like the data. That's the data I found and good. And so I'm, you know, I'm always cheering for the US then I have a friend who's really big into MLS and likes the Minnesota loons. So, okay. You know, they all have good names. Yeah, exactly. Yeah. Yeah. Yeah. Well, Madison has a team now too, with the it's like a minor league team or whatever the flamingos like
00:24:18
Speaker
Well, if you end up writing a Formula One Python book, my son will be into that one. Yeah, definitely. Which is another one of those sports where there's just, once you start getting even knee deep into it, you see how much data there is. Yeah, for sure. For sure. And can understand a ton about it. Yeah, I've been really into golf recently, so I got my putter here in the background. Yeah. I've been thinking about that, but we'll see. Well, they must have. I mean, it feels like all these sports now have
00:24:32
Speaker
Oh, really? Yeah, when they covered Baskin Hill and all the flamingos, they kind of adopted the name. Oh, yeah, that's pretty good. Yeah, yeah.
00:24:47
Speaker
their own processes to have tons of data. Not all of which they release. I did a podcast episode with Michael McCurdy early in the year who runs Hockeyviz. And we were talking about he has a ton of hockey data. And the one question that I have
00:25:04
Speaker
is a lot of the, I have a theory that a lot of hockey play shows up in the front two corners when you're watching it on TV, because the opposite side is where the benches are. So there's more face, but the teams don't release that data, right? That data is like, I'm sure they have it. I'm sure they're sitting on it, but they don't post that. They have the shock data, but not where they spend the right time. So, yeah.
00:25:30
Speaker
Yeah, I mean, the Formula One stuff is is pretty crazy, too, because, you know, they're they have sensors all over the cars. Yeah. And so they're measuring, you know, speed, but heat and wind and, you know, the exhaust coming off the car in front of you. And it's kind of amazing when you when you start to see what they're measuring.
00:25:47
Speaker
Yeah, for sure. There's a lot of fun, but I think it's a great idea to tap into something that people love and be able to teach them to code. So these are great.
Conclusion and Resources
00:25:57
Speaker
I'll put all the links to all the books on the show notes. And Nate, thanks for coming on the show. I appreciate you taking the time. Yeah, thanks for having me.
00:26:05
Speaker
And thanks everyone for tuning into this week's episode of the show. I hope you enjoyed that. If you're interested in learning more, check out Nate's website, check out Nate's books. If you're interested in any of the sports, I think these are great opportunities to learn more about how you can engage with the data sets in baseball, hockey, basketball, soccer, et cetera.
00:26:25
Speaker
So again, I hope you enjoyed that. I hope you're enjoying the show. If you would be so kind, take a moment out of your day to rate and review the show on your favorite podcast provider. Just a quick little click there on the five stars. Less more people know about the show, which helps me bring in new and better guests each and every other week. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.
00:26:50
Speaker
A number of people help bring you the PolicyViz podcast. Music is provided by the NRIs. Audio editing is provided by Ken Skaggs. Design and promotion is created with assistance from Sharon Sotsky-Ramirez. And each episode is transcribed by Jenny Transcription Services. If you'd like to help support the podcast, please share it and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcasts.
00:27:11
Speaker
The Policy Vis podcast is ad-free and supported by listeners. If you'd like to help support the show financially, please visit our PayPal page or our Patreon page at patreon.com slash policyvis.