Steve (00:00.822)
And as you know, this is not live. we, if anything comes up, you do your thing. I'm just gonna take a minute, just set process.

Steve (00:33.336)
When we think about the disruption throughout every facet of the business of sports today, one of them that's been ongoing for decades has been the area of analytics. And it's interesting when you think about it. An evolution 15 years ago in the realm of what was called data science, this superhuman, if you will, almost,

a metaphor for what was to come in the form of super intelligence. And that was an individual within an institution called a company in which statistics, all forms of computer science, all aspects of analysis, and ultimately artificial intelligence would be integrated in a way

that this individual would literally carry a company to the point that we had new monikers within companies called chief data scientists, all in capturing what was ultimately what my guest today on the transaction report, Dr. David Ford Payton, lives and breathes every single day.

And in the aftermath of Veterans Day, what a wonderful confluence to have him on the transaction report, a former naval man himself. And David, welcome to the transaction report today.

David Peyton (02:00.383)
Well thank you for having me. I'm delighted to be here and share what I can with you and talk a little bit about analytics and how it integrates with sports and not only the sports side but you know the human performance side but also the business side. So there's quite a convergence there as well.

Steve (02:19.99)
And there is, and there's been a, it only hit me recently where the evolution of where we've been as a human race, if you will, in the last hundred years. When you look at technology and sports, and I'm so glad you kind of brought in both the business side and performance side. But one evolution is on the technological side. And I heard one author.

comment. It was really interesting. I'll throw it out to you. He said, think about the first technological evolution in sports. What comes to your mind, David, as to what that might be?

David Peyton (03:00.575)
Well, I mean, you have to think quite often. Well, you know, first of all, in recent years, most of the people in your audience probably are aware of the Billy Bean thing and Billy Ball. I mean, you know, when we had the, you know, the metrics of the Oakland A's, but even going farther back than that, you know, when they started taking statistics, even with like just batting averages and, know,

Steve (03:13.773)
Saber metrics.

David Peyton (03:27.967)
pitch rates and stuff like that and where the ball is and then the TV came into play and everybody could see the close-ups of where the ball was being placed across the plate. I mean, the evolution's been changing ever so slightly. mean, it's crazy now, the Doppler radar that's been associated with a lot of, you can actually pick up spin rates. Now, what people don't realize is that even in my

Navy career, I was a Doppler radar technician. So we could do analytics and I could find you and I can't go into radar with the military with you, the detail that we can use radar systems, but just the application of the Doppler system into the spin rates, the speeds, the speed off the bat, you know, it's just incredible.

Steve (04:19.222)
And I'm so glad again that you mentioned that because in the evolution and I'm going to mention what I learned as an interesting observation of what was the technological revolution of the time. And by the way, that was the stopwatch. And when you look at the stopwatch integrated into sports.

it brought in a whole new ability for us to really, on a performance side with athletes, measure speed, obviously used through track and field, and you look at all aspects of these, just stopwatch, and the evolution, which was so interesting to me, to what you just referenced, of how we went into video analysis, and what this did, I'm totally focused just for our viewers, and a lot of them are very oriented, coming from a background of brand stewardship, and

running budgets, big budgets and all different size budgets for corporations in investing into some form of sports asset to reach their target market effectively or optimally. So it's interesting that evolution for the first few going from a kind of a timing element which brought about metrics.

for analytics led us into video analysis, which brought about enormous evolution of all types of metrics. And then as you just rightly said about the Doppler effect, it's so interesting when you look at IBM's Watson and the evolutions of different AI tools and measurement tools, Hawkeye being another one, and Wimbledon, where you have...

all elements of Doppler effect being deployed in sports and how AI is being used and velocity algorithms, GPS. And obviously today, as I mentioned with the form of AI and its ability to evolve analytics to a level that has become moving at a rate. But I would imagine people like you who direct the Master of Science.

Steve (06:24.758)
in sports business and analytics program at Clemson, it's ever-changing. And yeah, I'd be interested to get to, for you teaching it from a pedagogical standpoint, instilling an analytics mindset in a world that is in such flux in the world of analytics.

David Peyton (06:33.496)
absolutely.

David Peyton (06:50.227)
Yes.

Steve (06:51.448)
How do you approach that when you're building a program trying to help folk in sports better understand all aspects of the deployment of metrics and analytics from a performance-based standpoint, a business-based standpoint? How do you even approach that in a world?

David Peyton (07:09.415)
That's a great question because when I was doing some research to develop this program, I was looking at what the world, basically what our country and institutions were doing with regard to what they were teaching their students. For example, many of the institutions either focus on the human performance side or the venue management side or just throw little bit of business here. Our approach was that

I like to use the analogy of Jerry Jones. Jerry Jones is, whether you like him or hate him, he's a very, very savvy businessman. And a couple of years ago, he paid a quarterback $160 million contract for four years. And the quarterback, Prescott, has never been to a Super Bowl. He's never made a playoff game or won a playoff game.

So in that approach, my question to a lot of my students that I teach is how did Jerry Jones is not going to pay Dak Prescott $160 million just to look good on the field. There has to be something else beyond the field playing that's providing Jerry Jones money. And then in fact, when you look at the money that Dak Prescott brings in for the name, image and likeness, very similar to the marketing of him and

just in the overall brand presence, he brings a lot more value than the 160 million. So when I teach these courses to these students, when we approach these, we want them to have a creative eye. Look at the things that nobody else possibly is looking at and bring them in. let's see, okay, let's look at this statistical and see if it has a footing. And so that's kind of our approach. And let me say something else about data analytics with regard to

decision making and that's where I really lie in my decision sciences or data sciences approach. My job as an analyst, as a data analyst, is to bring you the optimal solution. It may not be the best. I had an NFL coach the other day, two weeks ago, matter of at a seminar with Dave Wharton, was approached and he said, he said, David, it's fourth and three.

David Peyton (09:32.704)
He said, we're on the 38 yard line. The analytics say that we should go for it. And he says, what do you say? And I told him, I said, well, there's four phases of analytics. One, raw data. Number two, information. Second is knowledge and insight. And the last stage, what a lot of people don't realize is wisdom. And this is, this is where we come to the marriage of AI and human decision making. What the, what the analytics don't have is the

data input that the left guard has got a bum knee. The right tackle has a cold. The quarterback's getting his ass kicked all day long and it's just difficult for him to be confident in the movement. Those are the decisions that are not going into the data where we leave that human performance or that human decision making. So our approach as a data scientist is to give you the optimal.

Steve (10:29.326)
Just excellent, so excellent on that aspect of it, because I think when we look at how we're all grappling with the post, primarily OpenAI, chat GPT-3 announcement in 2022, that kind of rocked the world. And it's only now coming up on its third anniversary where chat GPT-3.5 came to the fore.

And all of sudden, people were sending birthday cards that were very poetic, written in Shakespearean prose to friends and thinking, what a cool tool. And then, as I always say, having fluffy teddy bears doing something from a visual standpoint. And how far we've come in less than three years. Which brings me to someone who also enjoys analytics greatly and obviously sports biz as a business. I'm glad you met our CMO.

Dave Wharton. I would say, as I understand it from Dave, outside of himself you are the biggest Clemson fan, although I think he thinks you are superior.

in your fidelity to Clemson. And I guess by the way that you actually are employed by Clemson, the only thing that would lead me to believe that you are the ultimate optimal Clemson fan is you have told me you did a Michael Bloomberg thing as mayor and you waived your salary and decided you'll direct their master's program on a comp basis just to have the privilege of walking throughout Death Valley. But with that said, David,

what I grapple with. And I went through this as someone like yourself, you who got your bachelor's as you shared with me at the age of 41, you have a PhD today. And like you, while I have an MBA from 40 something years ago, I was enrolled in Northwestern's Master's of Data Science program. And a few years back, unfortunately,

Steve (12:36.494)
It was put on hold because I got a severe COVID bout, which prevented me from delving deeper into the calculus I was in, but still very much a part of my daily life. And even back then, a few years back, it was changing every day. And so here's what I grapple with. I grapple with it and my interest is...

David Peyton (12:42.549)
Bye.

Steve (13:04.575)
removed from your program, because I think what you're doing is fantastic. What I'm interested in, and I think this is the challenge for the human, the challenge for the human, and particularly for the educator, is recognizing that yes, the wisdom, the intuition, that sense of you knowing that a player went out.

and I'll be parochial, the New York Giants, Super Bowl, and some guys are out on a boat partying before one of our biggest games, the optics were not great. So, you know, and if you have that knowledge that someone is going through a tough day at home or they've got a cold, yeah, yeah, that's going to influence that. Of course, if you support it with your metrics, which can be jersey-driven or wearables, and you start seeing...

all types of stats that are reflecting that this athlete is not going to have his or her optimal performance, you're absolutely able to, from a human standpoint, advise a coach and say, well, listen, these are variables that are vital for us to undertake. The reality is the AI is increasingly capable of now having that same knowledge, making those same observations.

having those same data point incorporations. And so we are getting into that blurry zone now that says, hear you, but let's just forecast out three, six, nine, 12, 18 months from now. And I would suggest to you, just like we saw with reading lab reports, x-rays for breast cancer, where we've now exceeded a doctor's ability.

David Peyton (14:53.642)
Yes.

Steve (14:53.695)
in many areas of medicine, what happens when the analytics does not have that wisdom factor which the AI lacks?

David Peyton (15:07.008)
That's a great point and let me throw another caveat out there that AI is actually having a difficult point with and that's the human emotion side. And when I teach, for example, when I teach predictive modeling, you have three different kinds of analytics. You have descriptive, which tells you basically what's happened in the past. You have predictive analytics, which is your...

you're forecasting and then you have what you call prescriptive analytics, which basically tells you how to get to a place based upon a selection of decisions and constraints. Well, in actuality, when you're talking about predictability, one of the things that will be, I'm looking forward to and to see now AI takes account, is this little thing called momentum. And for example, if a player fumbles the football, I'm going to go to football here, or

Uh, you know, if a collegiate collegiate player hits a three point shot at a crucial point in time, you know, it changes the momentum. It changes momentum sometimes. And that's that human, uh, integration that changes the dynamics of the forecast. example, you know, uh, you know, you have a team, let's say, for example, you have two teams that have a 50, 50, 50, 50 per probability of winning.

all of a sudden now inject that momentum shift. And there's something about the character of a human that gives a little more when there's a little bit of a, am I making sense here? That momentum actually plays a huge role. The crowd noise all of a sudden changes. The attitudes of the crowd or the fans, or they,

Steve (16:44.493)
You're making total sense, total sense. Yeah, please.

David Peyton (16:58.74)
the team hears the boos, you know, there's all sorts of still unchartered areas of AI. And let me just say something else about as a data scientist, as a scientist who's been involved with computers, your data is only good as the data that's put in. And so when you're talking about all these AI platforms, you have to be very, very the user.

And this is something that I teach in my classes. The user has to be very, very aware of what is being utilized inside that AI platform. And I heard you talk about chat GPT. There's times when I think AI, for example, something like chat GPT is useful, but I'm also wary of chat GPT at times because there's not a boundary.

per se, of what the data is being utilized and put in. So I'm very much a proponent of AI, but I'm also cautious about understanding what data points are being put in as well.

Steve (18:07.865)
So I want to add if I one thing you said I was watching the Indiana game against Penn State actually after actually no it was was it was I was watching game cast from ESPN and to add if I your point David one of the interesting observations is they have the predictability of the win based on the score at the present.

David Peyton (18:19.264)
Okay.

Steve (18:32.749)
And it shifts throughout the entire game. From day one, when they implemented this in their metrics in ESPN, I always thought it was somewhat farcical. I understand it's needed from a gambling standpoint, betting standpoint, that it helps people make decisions. But I'll tell you, when I saw Penn State kick that ball back to Indiana, with a minute and 12 approximately left, I said to myself in my gut,

even though Penn State had, I don't know, a 85 % probability according to this AI metric that they were going to win, my instinct told me, don't think so. I have a gut feel Indiana is going to have some form of momentum and that there's that inner will of the human that's going to drive that ultimate remarkable preventative

That's that victory. Yeah. So to your point, yeah. So there's two aspects of this. And the objective of, as I see it in the conversation, is to understand from my vantage point, who like you, I live this every day along with my colleagues. And I heard such wonderful things about the conference for which Dave Wharton spoke at.

at his alma mater Clemson. By the way, don't know if you know this, and I'm not really supposed to mention this from his vantage point. He doesn't appreciate it so much. But you know, he was the two years the mascot of your football team. So, okay, so he doesn't tell too many people, but he was two years the mascot.

And I understood that at times it was about 118 degrees in that uniform. And that's a whole other legal liability issue that I'd love to discuss. I mean, I never knew that there could be a class action suit for mascots. But setting that aside, what's so interesting is truly to think about...

Steve (20:31.327)
since you talked about, and we live descriptive, prescriptive, predictive, and we have a fourth cog spoke in our wheel, and that's comparative analytics. Because we believe anything that you do descriptively, if you just share with a company, hey, you got this value from your sponsorship. Or an athlete, you ran this fast. Or here's your performance metric.

It is absolutely in our worldview, 20th century analytics, to live by a descriptive analytic today for most points of discussion, in most aspects of discussion. Without understanding its relative performance, whether it's to its own performance the year before, how they did in performance from a return on objectives in sponsorship of the same event sponsor, for athlete engaged, whatever it may have been, stadium sponsorship, or

How did they do against their cohorts if I'm a PGA title sponsor? I am truest, which took over from Omos Fargo $20 million signature event at Quail Hollow in Charlotte. I wanna know, well, how did BMW perform? The BMW championship, I wanna see its metrics and understand what can I learn from them? And there's so much to be gained in the comparative side of analytics.

because I can then improve my performance. And from a sponsorship standpoint, our whole mission in the world of sponsorship is we use the critical word, optimize choice, maximize what I chose to sponsor. And I've only got a few choices. That's athlete, team, league, association, stadium, venue, and event. And then not only measure,

but multiply what I measured, meaning I understand what I've got, but I've got to predictively understand how to improve my performance. When you look at the folk coming through your program, the students coming through your master's program,

Steve (22:36.567)
that wisdom that they have to be able to interpret the analytics that big data yields. What do you think is the incredible human nuance that is going to keep that human student of any age going into corporate or athletic department, brand sponsorship, whatever it may be, team analytics, that you see?

will not become obsolete and let's just give it a simple timeline, short timeline over the next 36 months.

David Peyton (23:10.954)
Very good question. let me just preface this by saying that it depends on the organization, depends on the data. Also, there is a gap. There's a huge gap. And I'm going to say this in a kind way between the geek and the executive. There's a huge gap. The geek knows how to manipulate computer data.

knows how to get the data that we need, but the gap is between the executive and the geek. Now, let me give an example here as a statistician, as an analyst. If a person, if a geek is trying to explain somebody, knows his data very well, knows how to do coding very well, but nine times, that's what they're trained on, but they're not trained on what is R-square.

when you're a progression analysis or regression analysis. They're not familiar with what is, how does that R-square value, what does that R-square value mean to the executive? And if you ask the executive, a lot of times executives, know, they're smart people too. They make great business decisions, but why is that R-square significant to me? And that gap is where my students, I want my students to be in that sweet spot so they have the ability.

to leverage the AI, to leverage the computer programmers, but also have the ability to have that communication effectively and to say, Mr. Smith, this is what that R-square means to you in our predictions. This is the paradigm. This is what you're facing with the least sum of squares, all those kinds of things. I want them to have that ability to communicate those values.

effectively so that the executive now has an educated response based upon the analytics that he's provided. Again, our job is to provide the optimal values, not necessarily the right values, and give them a set of alternatives. Now here's another one we do. We do what's called a sensitivity analysis. When we're doing some analysis based upon decisions, and this technology's been around since like 1910.

David Peyton (25:33.06)
our great grandparents have been using it based upon graphs and y-intercepts and x-intercepts and putting the constraints together and coming up with stuff. But there's feasible and optimal. We want the executive to have the ability to understand, here's what your optimal solutions are, but also here's where your flexibility is in the sensitivity. Here's where you can, you have the ability to move up. And also, last thing,

is there's also times when we do what we call unknowns and we have the ability to simulate unknowns. then, okay, for example, nobody knows what the price of gasoline is going to be tomorrow. We have an idea of approximately what it's going to be, but when you're talking about accuracy, we want to have that ability to simulate, you know, what the price of gasoline is going to be 10,000 times. That way the education to the executive is there and my students have the ability to give that interpretation.

to give that educated response to the executive and can you come manipulate going back.

Steve (26:37.774)
I think that's a very cogent point. You know, it's interesting and I haven't thought about this in five or six years. When I, it's funny that we're not so different in age. And when I wrote my application to this master's program at Northwestern, I had to write a few essays. And considering I was in my late 50s at the time, I thought it was adorable.

And what I wrote about, or what I was passionate about at the time, as the idea, having been in the business for 30 years at the time, in the business of sports, on the event ownership side, athlete representation, all aspects of broadcasting, concessions, security, operations, logistics, media, merchandising, et cetera.

I was very into, and I was very disturbed by it, David. I was disturbed by the monopoly that came about after Lotus 1-2-3. And I want to say the founder of that group was Mitch Kapoor, think it was, out of Boston, I think it was. I don't know if you remember Lotus 1-2-3, but, okay. So.

David Peyton (27:51.392)
Listen, I programmed aircraft on, I programmed aircraft with paper tape.

Steve (28:00.506)
So, but I was, they kind of fizzled out. I don't know whatever happened to them. And then Bill Gates with his brilliance in business had what seemed for eternity, basically something called Excel. And all our data visualizations.

David Peyton (28:19.699)
Yes.

Steve (28:22.998)
were ostensibly, as you said, the word geek. We used to in the old days call them mean counters, if you will. Although that's more, guess, accounting reference. But the bottom line is the representation was it was for folk who were very bright.

David Peyton (28:28.553)
Yeah.

Yeah.

Steve (28:38.228)
and could put together enormous amounts of analysis visually, but the people on the other side of running the business and marketing and public relations and management outside of the finance realm, usually they kind of didn't really take to those types of analytics. What's fascinating outside of Tableau, which was acquired by, I believe, Salesforce, I want to say, for $13 billion a few years back,

We, what I wrote about at the time, and I'm so interested to learn how you approach this today, was how disgruntled I was.

that the analytics from a data visualization standpoint, which is so vital. It's like building a foundation of a house, doing 90 % of the work, getting all the studs, joists, electric, plumbing, everything done, flooring, and now you gotta do the sheetrock and put on your, the sheetrock is there as well, and now you gotta put on your whatever design you have, wallpaper, paint, all the finishings. But we still had as a few years ago, and almost to this day,

and we'll segue to what is happening on the multimodal AI side, but the visualizations were still what they were back in the 80s. What can you share with us? Is there any change taking place at the academic level of deployment of knowledge, wisdom, that's helping those folk who are now going to communicate more effectively to C-suite? Is there any change that has taken place?

David Peyton (30:16.128)
You know...

David Peyton (30:20.392)
You've got a great point there and I've been making this point for quite some time and let me even tell my story. When I graduated college, I went to work for an energy company in Oklahoma City. And what I discovered is that what I learned in my college was useful, but the application in the industry was about four years ahead. So like for example, the industry that I was using, we were using the

company I went to, we were using SAS, which is a analytical software out of Raleigh, North Carolina. And what I discovered, and this is kind when I started going into teaching, is because I discovered there was a gap even in education. So what they were teaching in education, know, and academic, Steve, academic just for some inherent reason.

They just, they lag, even though they're doing research, sometimes their research takes precedence over and they don't see what the industry's actually doing out there. So there's a little bit of a lag. So in Clemson's standpoint, we have taken a, a reactive standpoint, but a proactive standpoint on this. So we are looking for those softwares that we can integrate into our, not just into our analytics program, but let's say for example, into human resources.

into supply chain, into information systems, into all aspects of our business education to help them, help the students know how to use them and leverage them, not abuse them because I mean, know, as well as I do, can get possibly for plagiarism in some cases, and it has happened before, but we want to provide them the tool set that they will be using in the real industrial world as a tool. Now,

I can remember when I first started in computer science and stuff like that, one of the things that we were taught was Moore's Law. I'm sure you're familiar with Moore's Law where it says, and Steve Moore, was one of the founders of Intel, he was noticing that they were doubling the amount of chips, or doubling the amount of transistors, and decreasing by half. And technology was supposed to double every 18 months. Well, here we are with the AI world, and technology's doubling every six months.

David Peyton (32:42.912)
So we're really on a rapid pace. So for Clemson, we're trying to be very proactive and get our students to look at all the different, and matter of fact, I teach an information systems course at a bachelor's level for the MBAs. And one of the things I forced them to do is, okay, in strategy, want your company to evaluate AI platforms. So I forced them to go out to the, go look at the platforms out in the industry.

come back and tell me why it's a strategic fit for your organization, for the organization that they're doing research for. So we're just, we're doing a proactive and I think there's so many AI platforms out there. I have a gentleman in Oklahoma City who's created his own AI platform for education and he's learned it to me and it's scary. It's so good, it's scary because it takes keynotes and stuff like that. And there's just stuff out there that, you know, the garage, the, you know, the garage business is doing.

We've just got to be open eyes and open ears to see what's out there and it's going to be useful. Excel was disruptive for us and because it's disruptive for us.

Steve (33:52.036)
Yes, that's a very good point. The only thing that I still, I guess commiserate with myself over, and I think we are as a company, you know, trying to remedy that with our visualizations in a very unique novel way. But it's the fact that for 40 years you had a, and even Tableau in many ways, had a, if you will, excel on...

know, exponential element to it that made it more user friendly. The reality again, to your point, and I'm gonna take a little bit of a alternative approach to our conversation, because you said something very important about Moore's law, if I remember correctly, it was somewhere in the 60s that Gordon Moore formulated that law. And if I'm correct, I don't remember if it was the 60s or 70s.

David Peyton (34:40.864)
I said Steve Moore.

Steve (34:47.641)
But if you look where we are now, and again, my mind's not what it used to be, but I do recall about a year ago or 10 months ago, watching a webinar with Microsoft and its quantum team. And they came through with a big breakthrough, which was one of the most remarkable presentations I had ever watched. And what this is going to do...

David Peyton (35:01.866)
Yes.

Steve (35:13.443)
that I don't even, I don't have the mindset that my pay grade is not that high to understand what does that mean from a Moore's law standpoint when you're getting a million cubits on a chip for another time. But what is interesting to me,

And maybe what we could do, because I know you're involved in so many use case elements of teaching in a way of the practical application. And I heard you speak on the net. came across, actually Dave shared with me a link last night, I believe. And I see that you're very into the practical application, which I think is brilliant. And when we look at the practical application,

Let's try to dissect when you talk to, because the performance side, yeah, we have 25 years of publicized, if you will, what was called at the time, Saber Metrics, which was very germane to baseball, and it has evolved into every sports club. What we learned from Saber Metrics, obviously, was as Billy Bean did it at the A's, once someone tried to replicate his approach, and it became duplicated by others, it then rendered his approach.

obsolete because it became too copycatted and his novelty of the way he was recruiting was no longer extant from his standpoint from a Saber metric deployment. There was not just one one size fits all. There was a law of diminishing returns when others started to adopt it. But it's all pervasive. We do see teams spending outspending 10 to 1 on others and not performing as well.

David Peyton (36:42.73)
exactly what trying

Steve (36:57.965)
So the analytics led them to believe something that they couldn't fulfill. We know that on the performance side, all aspects up until COVID, everything was about sports science in our world and technology, right? The big push.

was on athlete, sports tech, how to monitor, and primarily how to prevent injury and get a sense of, my God, this guy's arm is lagging or his oxygen level is not what it needs to be. He could be susceptible or she could be susceptible to injury. We've got a means of preventing it. Or one last comment on it.

where you had the famous front lineman signed a deal and now in practice they don't work out as hard. So now you know every day, every snap, is this athlete putting in, you know, putting in and paying his dues to be on the front line or is he coasting because he doesn't want to get hurt, et cetera, et cetera, et cetera, and just protect his non-guaranteed contract. When you look on the business side, let's see if we could identify the top

three. And if you don't mind, let's choose one stakeholder that I'll lead with and then I'll yield the other two to you. The stakeholder that I'm interested in is the corporate brand, the sponsor that puts forth $100 billion globally to align with, again, from athlete all the way to event, team to league, venue, stadium, et cetera. That's one stakeholder I would be grateful to hear a use case of what is the, in your

vision and what you're teaching. What is it that the stakeholder called corporate sponsor needs most from an analytics standpoint to optimize its sponsorship? And I'll yield to another two, it could be two of the same stakeholder or any stakeholders you would like of where analytics and what is it the express analytic that you're most coming up against?

Steve (38:58.189)
where you see we need to serve this very big pain point.

David Peyton (39:04.35)
Well, that's a very, very good point. And the challenge here is, the challenge reel here is a lot about, in my opinion, the marketing side. For example, you have two teams and I like to use an example. You have two great football teams that have histories, have a fan base that's immense in New York. You have the New York Giants and New York Jets.

It's a tier one market for TV. know, and TV drives a lot of the NFL budget, the NFL revenue. But yet you have a team in Kansas City. That's a tier three TV market. It's not even close to being, having the size market that New York has. Why does Kansas City continuously perform in that brand is becoming very

popular across the country when they don't have the TV revenue. And I think a lot of that has to do with, and I'm just going to throw something out here because I think it's really true, Americans love winners. So realistically, a lot of times the W's and L's make a huge difference with regard to where revenue is traveling. Now granted, Dallas Cowboys have had a TV market, it's probably the most

The Dallas Cowboys have the largest capitalization in the country for any sports team. But I mean, if you think about it, Kansas City's taken away fans from, let's say from New Jersey. They've taken away fans from New York because they're doing the W's compared to the L's. So I think that when it comes to revenue.

there is a very, very migration towards the teams that have a W. Americans love winners. mean, that's just the way it is here in our country. I won't say we don't lose it. I don't say we don't like losers. What I would say is we get frustrated with losers. The frustration and emotional frustration, why do I want to support this team? Because they don't seem to care. Does that make sense?

Steve (41:24.495)
Mmm.

David Peyton (41:24.52)
And so for me, that metric of W makes a huge difference and it transpires. Let me throw a college football scenario for you because my other than a Clemson fan, I'm a Mississippi State fan. Love my Bulldogs, but the expectations for Mississippi State football are not what they are compared to a team that's only 80 miles away called the University of Alabama.

And look at the amount of revenue that Alabama generates across the southeastern region compared to Mississippi State. You know, it's those W's that make a world of difference. You know what I mean? And so for me, lot of times, and here's, let me throw something different at Mississippi State. We expect, we've got a 15,000 seat baseball stadium. We expect every year to be in Omaha with baseball and the revenue. So a lot of the money.

Steve (42:08.973)
Yes.

David Peyton (42:23.956)
comes to Mississippi State baseball because of the winnings. Alabama, not so much. Duke, college basketball, North Carolina, college basketball, the revenue goes based upon those Ws, the TV revenue. everybody, whether you're a North Carolina fan or Duke fan, everybody hates, they'll watch the game because they hate, they either hate you or love you because you have so many Ws.

And I think that one of the biggest metrics is not necessarily the popularity or the name brand, but also the debits that go into those brands.

Steve (43:05.346)
You know, it's interesting you say that. I had once interviewed years back, Sal Galatiano, who is a big sports investment banker, won several rings. The teams gave him rings for, I believe in baseball, if I remember correctly. He's done a lot in the industry. He was quite a guy. Older statesman today, if you will. And it was interesting. Sale of asset when it came to acquisition.

was not predicated on the Ws or the Ls, which is interesting. To your point as well, when you look at Forbes and our friend Kurt Badenhausen is now over at Sportico, but he and Michael, I believe Ohanian were the ones who formulated Forbes valuation report. I don't wanna deviate too much on the subject, you're absolutely, what's so interesting and by the way, just for our viewers.

In the NFL, know, Roger Goodell few years back did $11 billion deal a year, $110 billion deal for terrestrial revenue broadcast rights, $11 billion a year. And those monies get distributed equally throughout the 32 franchise. So even though you have a small marketing like Kansas City versus a big marketing like Dallas or New York Giants.

Those monies do get distributed equally. What's interesting in the most recent report, if I read correctly, Kansas City is at a just under five billion in valuation. And I believe Dallas is still number one, as you said, at 10.1 billion market valuation, if Jones were to sell asset. What I think is interesting from an analytics standpoint, maybe I'll shift you my way on that and we'll go back to any other use case you'd like to discuss.

Is there any, with your finger on the pulse, if we were to take a sample size of 20 corporate sponsors in any of the ball sports? And by the way, I'm just randomly creating this use case. There's no rhyme or reason to it. But of course, this is where some of the bigger monies are spent. And you could certainly suggest, well, why don't we talk NASCAR, Formula One, and many other sports? And we can. So.

Steve (45:29.91)
With that said, and by the way, let alone FedEx's annual contribution of just in prize money, bonus prize money, 100 million to the PGA Tour, that in its own right is a mega deal. And we could of course wax poetic about the World Cup coming into the United States in a few months time, or actually a few months time, the Olympics, and then a few months time we go to the World Cup in the States. And then two years later, we go to the Summer Games in Los Angeles.

With that said, is there of the 20, if we just assembled and you were with your team at Clemson and we were just, we were in a panel together and the question was asked of you, if we had to take the paramount question that sponsors ask when it comes to understanding receiving analytics and understanding analytics of what's so important for them to optimize their sponsorship.

What would you respond to?

David Peyton (46:30.528)
Very, very good question. And I think a lot of it has to do is, for me, you have to see...

One thing I've learned in data is that you can't always assume. You can't always, and what you have to do is look at, okay, what are the primary drivers? You can put this into any kind of what I would call a monocrystal scenario and look at what is the primary driver. I would have to look at the data to say that.

because I think a lot of times even analysts, make an assessment that like I was saying a while ago, is it the W's or is it the name brand? Is it the location? Is it the amount of times they're on TV? So they're just always in your face. There's just so many data points and I would never say this is the exact one. I would always have to look at the data. What drives it is a data point.

Steve (47:32.472)
Sure.

Steve (47:36.979)
Let me take it to your own wonderful stadium, I believe, as I've learned that you have over 80,000 seats at your stadium. And it's a rare day that we're going to find any of those empty. And from what I've learned from Dave is that in the Southern charm of Clemson,

David Peyton (47:42.783)
Yes.

Steve (47:55.695)
In some ways, actually, it's got a little taste of a little taste, if you will, of the Kentucky Derby every game. That people actually come, fans in some capacity will come decked out. Sometimes women will have, you know, certain hats on, you know, with some flair in their own.

Again, that kind of Southern dalliance of there's a certain etiquette that seems to apply particularly for the old timers that were just raised, you know, true and orange with respect to their fidelity and loyalty to your school, which seems to run very, very deep. If we looked at the actual brands that are being exposed at the stadium for football,

What do you think motivates those brands to be a part of your football club?

David Peyton (48:51.488)
Well for Clemson, I can say this basically for most of the teams in the South, it's like a family. I mean, it's like a family reunion. So your identity is not necessarily with the football team that's playing on the field, but the family that you're around. Everybody's pulling for my family member. There's almost an ownership of kinship there.

So, know, can I say there's any specific brand? No. What I would say is there is an identity of family. Matter of fact, you know, one of Dabo's sayings is we're all in or we're family. You know, there was a movie, I can't think of the name of it right now, but it was based upon one of our safeties back during Balladins time where the whole team, you know,

help this kid get his college degree. And I think the movie was called Safety, if I'm not mistaken. And the thing about Clemson, Mississippi State, Ole Miss, Florida, Alabama, Auburn, that's why the South has got such heated rivalries in these kinds of sports programs is because it's family against family. Family member against family member.

It's almost a Civil War recreated except instead of guns we're playing with balls. it's definitely, it's family, it's family.

Steve (50:23.086)
So it's the half-field of McCoys, if you will. Yeah. Which, by the way, truly can be...

the worst of war. I mean, that internecine fighting that we all have maybe had a taste of can be as lethal as they come.

David Peyton (50:39.488)
I'm in this place.

David Peyton (50:46.144)
Well, one of the most fun games I've ever been to is I've been to a Boston Yankees game. It's one of my bucket lists. And it was so fun to go into the, you know, it went to the Yankees game in Boston there at Fenway. And it was so fun because you would have the Yankees bars on one side and you would have the Red Sox bars on the other. And they're just shouting at each other, you know, because they're families. it's like, you know, there's a

Identity, there's a family identity there, just fun stuff.

Steve (51:19.422)
I really, I really, I, you know, only because of my relationship with my partners here, and obviously Dave, am I really, have I really learned that over the last few years? And it absolutely is indisputable that 100 % that this is family, which begets the following question. You know, you had referenced,

Monte Carlo simulation earlier. So deploying certain forms of algorithms, computational algorithms that will be help, for example, in taking repeated random sampling to better understand performance and gain insights into what might occur. so let's take a step back now and try to match that, that

elite algorithmic AI deployment with the reality that brands that are deploying $100 billion globally in sports sponsorship at times can be making decision to join a family. And sometimes in a family, those metrics don't become so critical.

David Peyton (52:34.282)
Yes.

David Peyton (52:41.888)
Yes.

Steve (52:43.288)
How do we navigate those waters?

David Peyton (52:46.208)
Well, this is one of those, you know, you have to deploy continual analytics to say, okay, are we getting our value? Is there value here? Is there a return on that value? So if I'm investing X amount of dollars and I don't see any return, I mean, you need to have a jumping off point or have a decision that says this is not working. You have to have some kind of

ability to evaluate the return. So when I'm looking at, for example, State Farm, every time State Farm throws their banner up on a kick and they make a statement, what kind of metrics is State Farm using to say we're getting our value from this? And you have to evaluate, well, how many exposures do I get if

If I've got X amount of exposures from this game and I'm only seeing, you know, I went from 2 % return to a 1 % return, then I might have to reevaluate where I'm spending my money. It's just a constant. It's not a one size fits all. There's a constant evaluation that has to be deployed.

Steve (54:05.218)
And if we could in our waning moments, just probe into that a bit deeper. So if we're looking, you mentioned one is exposure. You I had as a guest one of your sponsors from the South Carolina Educational Lottery.

headed that campaign and spearheads that relationship with Clemson and was up for negotiation, I believe, and obviously with all aspects of NIL deployment and the increasing fees that are associated with an athletics department.

You know, their rates were going up quite a bit, but they were very happy with their sponsorship. And they got to deploy, actually in their lottery, a specific Clemson and University of South Carolina, their own proprietary draw, you know, if you will. they, yeah, thank you. So they integrate that scratch card. They integrate it into their point of sale. So point of sale, we understand, is a very strong metric.

David Peyton (54:36.276)
Yes.

David Peyton (54:51.562)
scratch offs.

Steve (55:05.004)
We understand that media exposure in all forms, whether it's legacy media, whether it's digital, social, all forms of measurement is critical in today's world. Are you teaching students today how to approach that measurement? And if you don't mind, could you give us a little deeper color to what is the novelty, what is your methodology with

David Peyton (55:17.909)
Yes.

Steve (55:33.496)
We're not here to get into the textbook-esque element of it. And that's my final element of the query. Is it textbook dictated? Meaning, are we finally will see the light?

David Peyton (55:48.232)
familiar with what the athletic department is doing with regard to these metrics but I can tell you that I do teach, I teach what's known as decision sciences and through decision sciences what we actually deploy is this thing called linear programming. Now linear programming has been around like I said since like the 1915's or early before World War I and back then they used graph paper and algebra today we're using Excel or

analytics solver, stuff like that. And what Python, yes, so what we're doing is we're doing what's called linear programming. Linear programming gives me a set of choices and basically allows me to see based upon the constraints that I have. For example, the constraint could be a budget. The budget could be, I'm just gonna throw a number out there just for fun, a million dollars. And what I can do is I can look at

Steve (56:19.041)
fun, right?

David Peyton (56:42.592)
all these, whether it's TV, whether it's radio, whether it's magazine, whether it's social media, and I can have an idea across the board what those exposure values are, and I can also say, okay, I've got a million dollars to spend, where do I need to spend that money at to get the biggest amount of exposure? We do teach that kind of, we do teach that, as matter of fact, I teach it to undergrads, just regular undergrads. That's one of part of our, one of our programs here for just teaching general management.

And that's a thing called linear programming. We also do teach them cost benefit trade-offs. You have a cost that you have to pay, what is the benefit that you receive out of it, what is the trade-off. So you're trying to minimize your cost, but at the same time you're trying to get the maximum amount of value out of your algorithm. So yes, we do actually teach that in our program here.

Steve (57:40.697)
So you know, I've never done this, but I'm going to conclude with this query. So when we look at Clemson and the Tigers, I understand that you have an incredible rivalry, that people are willing to lay down on the tracks and do anything to not lose to the Gamecocks over at South Carolina. I want you to, if you will, I want you to pitch.

Pretend I'm a brand and let's just say for the sake of discussion, I'm Lexus, okay? And no disrespect to Clemson and I didn't do my homework recently. I knew this before, but if there's an automotive sponsor, I mean no disrespect to the beautiful relationship that Clemson sports with one. But let's just pretend it's Lexus. And I were to ask you, we've gone through our cost-benefit analysis. We've determined

through frankly very advanced methodologies and both using Clemson and SportsBiz as our software solution. We use DeepSport solutions from SportsBiz and we had great consultative input from Dave Payton. What would you say to the Clemson as a spokesperson pitching me from Lexis?

as to why I should come the way of Clemson versus the way of the Gang Cocks over at South Carolina for my singular investment into college sports in football.

David Peyton (59:20.562)
Easy. the past, since Dabo Sweeney's been here in the past, 19 or so years, we've been to 11 championship type games. We've been to four national championship games. Now with the rivalry that's coming up, know, maybe we're not having such a great year this year, but still you have an alumni base of over 300,000 worldwide. The name brand of the PAW is...

totally recognizable throughout the entire country. And we all tilt our paw to one o'clock because that's game time. Everybody knows that. And so when you come...

Steve (59:59.093)
Can I ask you, as a northerner, when you say you tilt your paw, is that something you could demonstrate for us?

David Peyton (01:00:08.542)
See the paw right here? It is tilted to one o'clock.

My unclutched reaction.

Steve (01:00:15.566)
So you actually, so you take any logo.

David Peyton (01:00:20.23)
Well, we take our paw, which is the tiger paw, and we tilt it to one o'clock.

Steve (01:00:24.258)
Yeah.

David Peyton (01:00:28.724)
Because that's game time.

Steve (01:00:29.518)
So is it something you're wearing that you're tilting? Is that always something you wear?

David Peyton (01:00:33.472)
Yes, well for example, our brand is the Tilted Paw.

Steve (01:00:40.494)
Would it be someone wearing a cap would do it? Would someone have something they're holding?

David Peyton (01:00:44.574)
Everything that you see with Clemson, with a cap, with a jersey, with pants, with, I mean, you see them on cars, you see them on bumper stickers, you see them everywhere. Everybody in the South, whether you're in Georgia, whether you're in Atlanta, Georgia, Charlotte, North Carolina, Knoxville, Tennessee, Nashville, Tennessee, Birmingham, Alabama, Jacksonville, Florida, everybody knows the Tilted Paw. And we're all in it.

Steve (01:00:48.75)
You

Steve (01:01:11.276)
and it tilts towards 1 o'clock and that means... and you're tilting it just to go back to that so I get it that means the way you're standing the clock is ahead of you and you lean where are you like so you go towards that as if the clock was in front of you

David Peyton (01:01:14.076)
It kills towards 1 o'clock because that's game time.

David Peyton (01:01:26.976)
to the rock.

David Peyton (01:01:32.032)
is if the clock is in front of you like my hand is here and what we'll do is we'll tilt that paw to one o'clock.

Steve (01:01:35.905)
Nope.

Steve (01:01:42.294)
And so, so you mentioned people are driving. Do we have a very high rate on game day of accidents from Clemson fans? Because they moved the steering wheel.

David Peyton (01:01:51.2)
It's just the way our logo is putt-pussed. Everybody knows the Tilted Paw. When you're about selling this to Lexus or anybody else right here, the point of the matter is the name brand recognition of Clemson is very, very national, especially when it comes to football. Dabo Sweeney's name brings a lot of money.

It's just one of those name brands that, know, let me, let me tell you where it really started. The name brand really started. Clemson won a national championship in 1980. That's 45 years ago. But when Nick Saban was coaching at Alabama and he was just creating this dynasty, nobody could beat Bama. All of a sudden there's this little team from Clemson small town, small town, South Carolina.

goes into the national championship game not expected to do anything just whips the fire out of Nick Saban. People woke up and said, my gosh, what's going on down there at Clemson? Because it was not just a close game. It was a beat down over Nick Saban. And that just provided everybody a wake up call that there was something going on.

And then what the way down here, we call it in them their hills to collude to go to the colloquial statement. There's something going on in them their hills. So yes, the brand, the brand that we have here, the amount of, like I said, the family, the name gets across. We, we, we, love each other. And I would have to tell you when it comes to North Carolina game, it's good old fashioned hate. You know, we, live amongst, we live amongst South Carolina people.

Steve (01:03:46.52)
I'm

David Peyton (01:03:49.76)
But you have the same thing with Georgia fans. It's good old fashioned hate. Duke, when it comes to basketball, we just have that kind of rivalry that crosses state lines.

Steve (01:04:04.876)
Well, very solomantic. There's a time for war, a time for peace, a time to love and a time to hate, to invert it. And there it is. So I'll tell you, I could never wax poetic like this about my giants or any college team in the Metro New York area. Brilliant. David Payton, again, stewarding the Masters of Science in sports program over in analytics.

David Peyton (01:04:12.436)
Both are precious.

Steve (01:04:31.934)
at Clemson University. What a pleasure. It's clearly perhaps one of the most exciting vicarious feelings I have is just to know that you experience, I believe what we do every day as well, and that is this is a lifelong learning curve ahead. There's no finish line to the analytics world and what we're going through.

David Peyton (01:04:53.451)
yeah.

Steve (01:04:58.114)
We're going into a world over the next three to five years with artificial general intelligence up until super intelligence, which is speculative, but I think it's a foregone conclusion. And it makes your and my world at this stage of our careers just like a kid in a candy store. And it's a privilege to have you on the transaction report and thoroughly enjoyed our conversation today.

David Peyton (01:05:24.352)
Steve, I thank you for letting me come here. It's just, it's been an honor. I'm glad to see what you're doing is amazing and I look forward to collaborating with you possibly.

Steve (01:05:35.705)
Well done, well done. We're gonna wait for Ryan just one quick sec.