Billy Nayden is a Connecticut-based data professional with over a decade of experience working across various technical proficiencies primarily in the sports and entertainment space.
Across his career he has worked on data analysis and consulting for multiple teams and leagues including the UFC, Professional Bull Riding, numerous international properties, as well as teams from the NBA, NHL, and MLB.
Currently, Billy serves as the Manager of Fan Analytics for WWE where he handles data science and analytics projects across WWE’s direct-to-consumer business lines including ticketing and merchandise.
Billy is a proud graduate of Southern Methodist University in Dallas, TX, with an an Bachelor’s in Marketing and a Master’s in Data Science.
You can also learn more about Billy on LinkedIn.
Here’s a quick summary of key takeaways:
- Creativity is still vital in the industry, even with the rise of analytics, as having the same information and strategy as everyone else doesn’t give a competitive advantage.
- Moneyball and sports analytics have been successful because they approach the game differently, recognizing that creativity has always been a driving force in sports success.
- Analytics are a tool, not a definitive guide, and need to be combined with creativity for the best results.
- Billy pursued a career in sports and started in ticket sales for the Philadelphia 76ers, which led to conversations with the team’s executive, Sam Hinkie, about data and analysis.
- Billy eventually got their master’s in data science, enabling them to analyze sports using the tools he acquired, particularly Python for modeling, correlations, and collinearity in data analysis.
- Understanding statistics is essential for data analysis and modeling, including concepts like confidence intervals and multicollinearity.
- Many prioritize learning to code but overlook the importance of statistical knowledge, which is crucial for translating math into meaningful insights for non-technical stakeholders.
- The process of data analysis in sports involves brainstorming analysis ideas, assessing data requirements, discussing analysis approaches, sharing rough results with the team, refining the results into presentations, and iterating based on stakeholder feedback.
Listen to the podcast here, or find if wherever you get your podcasts:
Ryan Atkinson: [00:00:00] Welcome everyone to the tech guide podcast, where we give actual advice to those wanting to break into tech or looking for their next gig. We have Billy Naden on the podcast today. Welcome Billy. Super, super excited to have you on.
Bill Nayden: Thanks for having me, Ryan. Appreciate it.
Ryan Atkinson: This is going to be a great conversation about data, storytelling, sports, how those are all intertwined in the advice that you have for someone that’s wanting to get into this industry. But I do want to start us off because your LinkedIn bio states that you love storytelling and you’ve loved storytelling.
Ever since like grade school, is there a book or a movie from your younger childhood that stands out to you because of like the story that was told?
Bill Nayden: I’ve always been, I’ve always been a big science fiction fan. I’ve always been a big anime fan. I think it goes hand in hand with just the absolute nerdiness that comes with being in the tech and data world.
But. I, as a kid, remember watching Neon Genesis Evangelion for the first time, which is like a 1997, 26 episode classic Japanese anime, and the story, the depth of the characters , the emotionality of it the way that they were able to string all these like philosophical tropes through this, this journey And what’s ultimately a 26 episode cartoon.
I, that’s the one that really sticks out that I’m like, that kind of sparked me, like really thinking about, how do you tell stories and how can you impart philosophy or impart life advice, through through a story and that’s the one that sticks out for me.
Ryan Atkinson: That’s so awesome. And because now you’re really combining your love for storytelling with analytics. So let’s talk a little bit about your analytics background. Can you take us a little bit more around SMU where you first got really interested in data? Yeah, take us to that time.
Bill Nayden: Yeah. So it really comes from being interested in sports, right?
Like I came of age academically, like my high school and undergrad, I was, square in the middle of the money ball [00:02:00] era, the sports analytics revolution, guys like john Hollinger were. Starting to get really popular and I would sit up at night and debate with my engineer roommate in college You know who the best NBA player was and we’ll look, you know at the time LeBron had just gone to the heat It was like will LeBron ever win one like and it ultimately led led us to figuring out Okay, what metrics should we be using to evaluate players, right?
And so that kind of led me down this path of trying to learn more math, more statistics, just better understanding of, of analytics literally. So I could have smarter sports conversations. Like the, and knowing I wanted to be in sports, I, I pursued that and was told like the best route in is ticket sales.
So that’s what I did straight out of college. Went to go work for the Philadelphia 76ers and ticket sales, straight in the heart of the process. I was there when they drafted Joel Embiid. And then we tell you, man, trying to sell Philly sports fans tickets to a 15 win basketball team. Ain’t easy. Yeah. Sam Hinckley was there.
So I remember like getting conversations with Hinckley where he would come talk to the staff and talk about what numbers he looked at, how he evaluated things. And. I sat there and I said, you know what? I’m a pretty terrible ticket salesman, but I’m really interested in data. So I ended up talking to one of the data guys at the Sixers.
We had lunch together and he said, look, go outside of sports, go learn data. And if you still want to be back in, once you’ve learned it, you’ll be able to come right back. So that’s exactly what I did. I went and worked for a real estate data company in Dallas. Worked for Essilor, which was a, , eyeglass manufacturer and like really honed in on learning SQL, learning Python, learning data structures so that I could then turn [00:04:00] around and get my master’s in data science and like really take off running on analyzing the sports that I cared about all along and just didn’t have the tools to.
to do , and over time, learned the skills I needed to do the analysis that I wanted to do all those years ago when I was having late night conversations with my college roommate.
Ryan Atkinson: Yeah. I’m curious, why would you think that advice was so good to leave the industry to learn about analytics?
Is it because like it’s so hard to break into at a sports gig or is it just Yeah. Like why is it so, why was that advice so good?
Bill Nayden: So I think this is a little bit better now. I’m talking about like 2014, so almost 10 years ago here, but. At the time, sports analytics, the idea of using math to evaluate players to evaluate the business, like in a serious way, was nascent, right? , it was not like a big part of the industry. . For me, it, you had to go outside it because big business and fortune 500 companies were using kind of business analytics and models. And, these deep learning ideas at the time that just didn’t exist in the sports industry.
Yeah. And I think that’s the most important thing when you’re young in your career is it’s not about the entry level job, right? It’s about finding a place that’s going to help you develop the skills that you need to eventually go deeper and be part of the kind of tech decisions that ultimately you want.
And so for me, that just didn’t exist in sports, but it existed outside of it. And it was, skills that I could learn that were directly translatable to the industry I wanted to be in. Interesting.
Ryan Atkinson: Would you recommend someone that, they might be fresh out of school. They’re like, Hey, I want to get into sports analytics.
Would you recommend them to not go into sports right away and try to break in later? Or how would you recommend it
Bill Nayden: [00:06:00] now? It’s very dependent on the, on the company, right? Like I think, I think a lot of people come into sports and, or, and especially sports analytics and their dream is to work on the team side, right?
They want to work for the Yankees. They want to work for the Panthers. They want to work for whoever, like they grew up a fan of. And the reality is, is like on that team side of things, a lot of times, like you’re, you’re brought in to do a very specific role and there’s not a lot of opportunity outside of that.
Like these team organizations run really lean, right. And their front office staffs are, are really lean and that’s. By design where if you go to say, an agency or mm-hmm. , a minor league sports, college, sports organizations like the U F C, the wwe, e professional bull riding, like these kind of touring properties or, or more niche sports.
You’re able to wear a lot more different hat and that really helps you grow and get the skills that you need to Eventually move up but on the flip side. Yeah, if you go into say Finance or you go into some sort of tech company or manufacturing or things like that The dollar amounts are just so much bigger And the, the business need for analytics is so much greater.
You can sometimes find that you get a lot better training, at least on the job. But ultimately, like whatever, whatever path you choose, like to me, I think it comes down to what work are you willing to put in outside of the actual job? That’s, I mean, that’s how pushed ahead.
And in my opinion is. I was spending time every day after work doing coding, practices and thinking of models and thinking of analyses and writing and that sort of thing to, to make sure that I was like really honing my sports analytics skills and answering the questions that I wanted to answer.
Ryan Atkinson: So let’s talk about one of [00:08:00] your roles, like one of your first roles in like a sports organization, like working on analytics, like what were some of the projects that you worked on that you like were really excited about?
Bill Nayden: Yeah. So I think my first like real sports analytics role, right?
I was working for an ad agency called forefront. As we were the agency of record for the UFC. And figuring out working on a touring property is so cool and it’s so underrated, I think, in the industry. Right. Where if you’re the Knicks analyst, right, you deal like Madison Square Garden is great.
It’s my favorite arena in the world, but like you’re working on that one arena. When you work on a touring property, I get to figure out, okay, if the UFC is in Columbus this week. Where are these people coming from? How far out are they coming from? What are their buying patterns? Wait, how do I segment this out and figure out?
Okay, who’s buying our high middle and low price tickets? And then turn that around and then next week we’re in Knoxville and the next week we’re in Tampa and the next week We’re in Atlanta and yeah, you get to do that all around the country. That’s Awesome. You learned so much about how sports fans differ in different parts of the country.
And it’s so fascinating to dive into those buying habits which like really leads to what I do today with WWE on, on such a greater scale.
Ryan Atkinson: Interesting. So when you’re talking about touring, like properties, you’re talking about like a WWE that might have something in Columbus, in Nashville, in Orlando, in Austin, for example, and so you guys are able to take this data from who’s buying like high level tickets, just all these different areas and then.
You’re putting that into a story form and I presume, or like, how are you presenting this data?
Bill Nayden: Yeah. So it’s, I mean, looking at this, I mean, there’s all kinds of different ways you could slice this, right? You can look at market grades, [00:10:00] right? Where you’re saying like, okay, here are all my, a markets and here’s what we can expect for our buying patterns, our sales cycle, or, here’s how many price levels we think we should have. And then there’s B markets and C markets and whatever and figuring out okay, if I’m in, Dallas on Monday and, want to be in Denver on Friday, like what’s in between there, that would be a good market for us to hit.
There’s all that kind of stuff. But then. You can also look at this regionally of, do people in the Northeast buy tickets differently than the people in the Southeast? We, we know these are all fans of the, of the property, right? But it’s figuring out like, what are, what are these regional differences?
What can we expect? Is this a market that buys day of at the box office? Which, I know that’s crazy, but it still exists in a lot of places. And so it’s. It’s really segmenting out as much as possible. What are the similarities? What are the differences? What are the patterns and looking at?
How can we use that to form our promotional strategy, right? If this is a huge walkup market, obviously you want, you always want people to buy tickets earlier because it helps us forecast that it’s money in the hand, that sort of thing. Right. So if it’s a walk up market you’re going to have to push prior to that harder than you would for a market that buys early, as opposed to somewhere where you’re just trying to fill those last sets of seats and day of the show, you want to run a promo for 25 upper deck tickets or something like that.
It all all the data that we get on ticket buying. informs how we promote a show in each individual market. And like when you’re WWE and you’re doing 300 plus shows a year, like that needs to be so templatized and so systematized because. You don’t have time to [00:12:00] dial in on every single event the way you would for a 16 game NFL season.
Ryan Atkinson: Interesting. I’m curious so what the technologies that you’re using to decipher all this data or use this data to visualize it? I mean, what are some of the technologies that you’re using that someone should learn at an early age, so if they do want to do something like this they know how to do it?
Bill Nayden: Yeah always start with SQL. I think I, I do think there will be a day where SQL might go by the wayside. I don’t think we’re there yet, and I just think it’s such a good fundamental coding language to learn first, because ultimately everything you do is going to be around pulling data.
And so it’s, it’s great to just have even just basic SQL to, learn that object oriented programming syntax. From there, once you feel comfortable, at least as an intermediate at worse SQL user. Yeah, Python is the next route to go, I think, because I pull all of my data with SQL, right?
And I’m, I’m making sure I have the right data set with SQL. But anytime I want to model anything, anytime I want to look at. Correlations or look at any kind of co linearity, that sort of thing. Like Python is crucial, but the thing I will caution everyone against is like computer science, coding, all that.
It’s great. It’s it’s very important. It is useless if you don’t understand statistics. Basic AP high school statistics is still crucial for a lot of what I do. I’m talking about confidence intervals, I’m looking at multicollinearity, I’m looking at [00:14:00] what kind of curve is best to model a particular data set.
That’s all high school level and maybe slightly above math. And to me… That is a, a component of the industry that’s really lacking right now, that like a lot of people are in tune to the idea that you need to learn to code. Mm-hmm. , very few people are in tune to the idea that you need to under have at least a base level understanding of statistics.
Because ultimately, like sta like statistics is an English class. Yeah. If you think about statistics as a math class, you’re doing a lot of writing. That’s all business analytics. Sports analytics is is like I’m running analyses. I’m doing math and ultimately I’m turning this into a story so that I can go talk to a sea level executive who hasn’t taken a math class and 15 years.
So I need to take the math and translate it into. Okay, what’s going on here? And what do we do about it?
Ryan Atkinson: And were these all things that you’ve learned, like, how, how did you learn this? Was this through your the master’s like your master’s course with SMU? Or is this something that you learned on the job of oh, I need to learn statistics.
Bill Nayden: So I, I took stats in, in college and high school, right? Yeah. And was always. Interested in it and always saw how it was like immediately applicable. I think the, it’s a combination of experiences, right? It’s, it’s one of these things where I had this idea of what I wanted to do and, and how I wanted to use my skillset.
And what I did was I talked to as many people as possible, like people who are peers, people who are already in the industry. People at like bars, like in, on, at airports, just saying Hey, this is what I want to do. What do you [00:16:00] think? And just get collect thoughts.
And as I, as I talked to people more and more, I realized okay, like what we’re really talking about is. It’s statistical storytelling or data storytelling like that’s like I synthesize that and I said, okay What are the two components that lead you to the insights that you then put in your story?
Okay, it’s computer science and statistics ultimately, right? So I figured out that if I was gonna focus on my hard skills and and really nail down What I wanted those were the two areas so I would take You know, if, if there was like LinkedIn learnings or random stuff work would offer, I’d take them.
I would go to the trainings that, that were offered no matter how applicable I thought it was. And then ultimately that led me to say, okay, I need to like really lock in on this and I’m going to do a master’s because I need the accountability of a degree program to make sure that I. And like continuing to develop these technical skills.
And it’s one of the best decisions I ever made. I think a lot of people put down the idea of additional formal schooling sometimes, especially in the, in the tech world. And I hate to break it to you. Like you’re not Mark Zuckerberg or Bill Gates, like a lot, a lot more of us need that formalized training then.
We like to let on and it gives you a level of accountability that is unmatched if you do it, if you’re doing it self taught and then on top of that, you meet a bunch of people who are in this industry. So it really helps you where I have friends from grad school who I can text and say, hey, I have a, a work problem.
How would you go about fixing this? Yeah. And they can help me through it and, and. [00:18:00] That’s invaluable to have a network of people who are knowledgeable about what you do every day.
Ryan Atkinson: Yeah, we, yeah, we had a Christian Bordeaux on a former episode. He works at analytics at Netflix right now, but he also got his master’s in like analytics from like USC.
And that’s what he also said was that the networking experience like in a master’s program is like one of the most valuable parts that you actually get from it.
Bill Nayden: Yeah, absolutely. And and in in the data science world where everything is so rapidly changing all the time. I mean, there’s so many like the amount of python packages that come out every day is nuts.
So to have that network where it’s I can’t be everywhere all at once and I can’t focus on everything all at once. And there’s going to be solutions that I could use at work that don’t Just don’t come across my radar, but yeah, I have buddies from masters that I can text and call up and they may come across that because a lot of them do very different data science roles to me.
Yeah, and so it’s so good to just have that kind of. Network of okay, I have basically all these people who are watchdog in the industry for me and we have this kind of knowledge share. It’s it is invaluable.
Ryan Atkinson: Interesting. What are some of the projects that like other people are working on right now?
Like your data science friends? What are some of like things that like they’re being exposed to right now?
Bill Nayden: Oh, I have, I mean, I have a buddy in manufacturing who, who works at essentially like a metal working plant and he has to figure out how to like make these assembly lines work.
I have, I have a buddy works in freight logistics. I have a buddy who works in the medical field and, and is literally like testing drug efficacies and things like [00:20:00] that. And looking at how. How drugs react with the body, but there’s, I mean, and then I have buddies who are in like very traditional tech roles that like SAP and companies like that, where it’s you’re literally working on the future of database structures and how to how to organize a data science team.
And so I, I have friends all across this industry working in all kinds of different roles and, and there’s just it’s, it’s one of those things where data science is like , the, the it girl at the ball right now. And every company, every industry is looking to find a way that they can use kind of algorithms and models and math and things like that to improve their business.
And that doesn’t matter if it’s WWE or finance or real estate or manufacturing or medicine. Like it’s, it’s everywhere now. And so there’s, there’s almost like limitless. opportunity. Especially if you find the right firm and find executives who see the value in in the practice.
Ryan Atkinson: That’s what I was just about to say. Actually, you hit it right on the head like data science to me right now is like in data analytics is like the girl at the ball. We’re like The applications for these big analytics to apply to business decisions, I think is like what makes analytics so cool.
And you just gave five perfect examples there, like how it’s being used across so many different industries. And there’s so many different ways that you can get into analytics and usage cases for analytics. You
Bill Nayden: know, I think, and it’s funny to me that there was ever resistance to this because back in the day before we had terms like data science and analytics, we used to just call this evidence it’s really what you’re talking about here.
It’s Hey, this is what’s happening in our business. And here’s the evidence that it’s happening. And then we make a business decision about it. That’s all business analytics is it’s it’s very complex [00:22:00] ways to get the very, very simple solutions and answers, which that’s what you run into.
Sometimes people who don’t think there’s value to it or don’t think it’s like as difficult as it is because you have. If you’re a good data scientist, I can tell you what’s going on in one or two sentences but you don’t see the two weeks of models and work and analysis I had to do to get to those sentences, right?
So that’s, that’s the crucial thing, I think, is making sure that you’re able to effectively communicate, but have someone who understands that this is not just something you can step in and be insightful, right? Like you really do need to. No, no, the technical stuff in order to get to the actual
Ryan Atkinson: insights.
Yeah, well, that’s what I also wanted to ask you because two weeks of working on data. It’s like me is I’m not a huge numbers person to me. That would be very hard to do. So I’m just curious what’s the non glamorous side of working in sports analytics?
Bill Nayden: It’s I mean, this is true of data science in general, but sports analytics is as bad as anywhere.
Like data wrangling is. is awful. Like the process of okay, this isn’t in the exact format that I need. I need to do all this like weird filtering to get to my correct data set. Like all of that work is not fun at all. And it’s usually one of these things where like I put my headphones on, put some put some music on and And grit my teeth and bear it.
But the, the idea that like, I think there’s this idea out there that you’re going to have constantly clean datasets and it could not be further from the truth and especially when you’re in sports analytics, right? Like I said, we do about 300 plus shows a year, but it’s 200 plus arenas, give or take 70% of those [00:24:00] arenas are going to be ticket master.
About 20% are going to be AXS and then the other 10% are going to be God knows what. So you’re potentially looking at 15 to 20 different data structures for ticketing that you need to figure out and put together and make into a nice clean slide because. When I got to send PowerPoint to the CEO of WWE, he doesn’t have time to data wrangle, that’s, that’s my job.
So it’s like that kind of, it’s like very non glamorous of cleaning that up, making sure everything matches, making sure everything makes sense, and then you get into the actual storytelling, which is the fun part. Yeah,
Ryan Atkinson: I wanted to, I can’t imagine setting the CEO of WWE, Hey, take this data for me, but I do want to talk about that.
Talk to me about the projects that like go into, or talk to me about the steps that go into a typical like project, like your data wrangling to like presentation. Can you highlight just some of the steps that go into a typical.
Bill Nayden: Project. Yeah, absolutely. So we work on a pseudo agile model,
where it’s not like truly organized in sprints, but we have a Kanban board we, we have tasks that we meet twice a week as a team to go through, okay, what’s to do. What’s in the backlog? What’s been sent out? That sort of thing, right? So like it’s constant planning. Yep. Yep.
Yep. The basically when you when you Decide like a new project is gonna get done, right? Like we had a night we will spend like usually five minutes just thinking of ideas for analyses in these kind of bi weekly huddles, but we take you know When I when something gets moved from the backlog to to do right?
It’s okay. What data do we need? Like, where does that day to live? Is there anything that we’re missing? Right? I can’t give you a [00:26:00] demographic analysis on customers. I don’t have demographics for, for instance, right? Yep. So we kind of like do that assessment of basically what data do I need?
How do I get to it? Right? And then We usually look at, okay, here’s two or three ways that I would go about this analysis in theory. So it’s I’m going to use XGBoost and I’m going to do a tree based model or I’m going to organize this data into bins.
I think that makes the most sense or. I’m going to use percentiles or whatever, right? And we talked that through and ultimately as a team come to the conclusion of, okay, here’s where the data lives. Here’s going to be our approach, right? Yeah. Then you get into our non glamorous side of, Okay.
I actually have to now go get this data. I got to pull it. I got to make sure it’s clean. All that. Right. And then I’m going to plug it into my model and I’m going to send out kind of like a very rough results usually in, in an Excel, in a Python notebook, something like that to the team to say, okay, here’s my takeaways in bullets.
Here’s the data. Here’s the analysis. Does that make sense? And they say, yep. Looks great. Then it goes into a PowerPoint, where usually it’s a visual, a couple bullet points, like I said, one or two sentences about what’s going on, and then that goes out to whoever the business stakeholder is, right?
Yeah. If they’re like, Hey, I think you should look at it like this. We can sometimes go back to the methods. Sometimes you have to pull additional data sets, that sort of thing. But ultimately it’s like a very collaborative process, but it’s with somebody kind of leading the charge, obviously, like coding and analysis is like a very individual pursuit.
It’s hard to do that in a team. So it’s basically okay. [00:28:00] We, we come up with the plan as a team. Somebody goes and does it on their own. The team checks it and then it gets put in like a pretty nice package and PowerPoint and sent off to whoever it needs to go to.
Ryan Atkinson: Interesting. One project that comes to mind that you may work on, may not work on 300 events a year.
And let’s just say you go to Columbus. I don’t know why I keep using Columbus. We go to Columbus like 2021. And then 2022 comes around. You guys are going to Columbus again. How much do you rely on like the his historical data that you do have? So like huge,
Bill Nayden: huge, it’s massive. What’s amazing to me is even as the technology changes, even as the entertainment landscape changes, like the way fans buy tickets.
Stay so similar in particular markets. It’s like, it’s baked into the fabric of these cities. Right. And so. But we lean heavily on history like that’s how when we’re looking at projections, I’m always looking at, okay, here’s the gross we did there last time. Here’s the sales cycle, roughly like we, I’ve been a lot of data with sales cycles.
It doesn’t make a ton of sense to look at things day to day. Yeah. It’s really about. Looking at on sale, looking at the week of the show and figuring out okay, at this point in on sale roughly how, how many, like what percentage of tickets will be sold using that to project out and figure out, okay, we can expect we’re going to sell 8, 000 tickets in Columbus this time around, something like that, but hugely relying on history, but also Reliant on not just quantitative history, but qualitative.
Interesting. So I I know like [00:30:00] we just, we just got back from Tampa, right? Our, our last event in Tampa was end of 22. Okay. And it was. A humongous event because it was John Cena’s only wrestling match in the WWE E in 2022. Makes sense. So I know, I know in my head this last Monday night Raw in Tampa, which still sold very well, is not gonna do as well as John Cena.
No. And there’s no number that’s telling me that. It’s like I have to like intrinsically know and remember and look through my history of I have literally every show since July of 2021 in a giant massive spreadsheet with notes next to it of if there’s anything that was like special that was advertised.
So I’m not just looking at the history and looking at the numbers, but also able to say like Here’s why the show in 2022 did better than this one, and it’s not because we didn’t promote well or not because we, we didn’t utilize data the way that we could have. It’s there’s sometimes just especially in WWE, you get these special events and it brings people out exactly.
Ryan Atkinson: And it would be like, if Tom Brady’s only plays in one game next year, like it’s, I was going to sell like a crazy amount. I am curious too. So like you had the historical data, but let’s just say you guys are going to test like a new market. Let’s just say you’re going to go to I don’t know if you’ve ever been to Des Moines, Iowa, Des Moines, Iowa.
We have many times. Okay. I don’t market you haven’t been in before. Yeah. Like, how do you guys analyze Oh, like, how do we know this is going to be successful if you don’t have any historical data on it or how does that? So
Bill Nayden: let’s do that. First, first off, I think the. WWE is such a strong brand that like it’s almost never can we go to any city and it’s not successful, [00:32:00] right?
Yeah, it’s obviously varying degrees of success, but very rare that it would be not successful. Well, I, what I look at too is I look at I have similarity scores on markets. Right. So it’s that’s so cool. I know, I know that like a Des Moines, right. Is going to be a very, it’s like a very similar market to like a champagne, Illinois.
Yep. It’s a very similar market to a Madison, Wisconsin. So there are places that we have been that even if we haven’t been to like that exact venue or that exact market Yeah, I can you know, look at similar markets and say okay I think Madison’s maybe a little bit bigger Champagne’s a little bit smaller Let’s find somewhere in the middle there and that’s about where I can project like Des Moines.
We j in Miami. It’s not t Miami before. That would recently have a show at t We usually play with, you without any historicals trying to figure out, okay, should we basically treat this is Coral Gables, are we going to treat that the exact same as we’re going to treat the downtown Miami arena, or is it slightly different and trying to figure that out?
But. Again, it’s it’s a lot looking at similarity and venue similarity and show type like is it, is it a TV? Is it a pay per view? Is it a non televised event? And then you can triangulate projections from there.
Ryan Atkinson: That is so cool. And so if you are going to compare like a Madison and Des Moines, is it?
Based on like demographics and like income ranges, or is that what you guys typically look at? That is so sweet.
Bill Nayden: Yeah. So you look, you’re going to look at census data. There’s a lot of information that we have around roughly, I think it’s roughly 3% of the [00:34:00] United States is a WWE fan, right?
Yeah. So we were able to look at, okay. What’s the population of of a Madison and like we have information based on survey data about how many sports fans there are there, right? Yeah. And so you can start to do some market sizing. If you say, okay, I know there are 200, 000 sports fans in Madison.
3% of those are going to be WWE fans. So we’re looking at 6000 WWE fans, right? Like the and you can do that all over the country. There’s all there’s all different ways to look at that. But essentially, yeah, we’re looking at census demographic data. We’re looking at nationwide research firms that do market sizing on sports fans.
And then we know that that 3% number more or less holds true. Obviously, like there are markets that are more into WWE, there are markets that are less than a WWE, like that much bigger in the Southeast than we are in the West Coast. But ultimately you can figure that out and figure out, okay.
What’s my rough market size of potential wrestling fans in, in a given city.
Ryan Atkinson: I think what’s so neat about this conversation is just like how much like analytics drives like sports, like organizations decisions. Cause some people like they think of sports, it’s like entertainment at the end of the day, like WWD is a company, the NFL at the end of the day.
Is a company you can compare to like a Google or something. So I think it’s so cool how analytics are being used to drive business decisions. And I do want to ask our last question based on this. So not on business decisions, but just on analytics in sports, MLB is like a perfect one where analytics have just taken over the game.
Like people are shifting based on batting statistics or whatnot. So do you think like analytics for sports on just the playing side is good or bad for the sport? Or both.
Bill Nayden: So I think it’s good, but what [00:36:00] I, what I will say is it’s becoming clearer and clearer as people become more analytically driven, more analytically inclined, how much creativity still matters in this industry.
You think of analytics, you think of very black and white cookie cutter things, and it’s really not, because if everyone has the same information, if everyone has the same strategy you ultimately, you actually don’t have an advantage, right? The whole, the whole idea behind Moneyball and the whole idea behind Sports Analytics is they were doing stuff that other teams weren’t doing.
They were thinking of ways to approach the game that other teams weren’t. And so I think it’s great for the game, but I think it’s also potentially very boring if people don’t utilize the creativity that’s always driven success in sports in the first place. So analytics is a tool. It’s not like a guide for here’s the manual how you win the World Series in baseball, right?
It doesn’t work like that. You have to get creative. You have to think outside the box. And then you have to use. The analytical tools, the logical tools that you have to make the best possible decision for your organization.
Ryan Atkinson: Nice. I love that. Philly, this was an awesome episode. Thank you so, so much for joining us.
Where can people connect with you? Where can people learn a little bit more about you?
Bill Nayden: Yeah, I’m on, I’m on LinkedIn. Billy Naden at B Naden on Twitter. I’m always, I’m always tweeting about random things, or I guess it’s called X now, I don’t I don’t know, whatever, but that’s my most active social media by far.
And obviously feel free to, to send me messages there or send me messages on LinkedIn. Happy to help anyone who’s looking to break into sports analytics.
Bill Nayden: Thank you, Ryan. Appreciate it.