Last Updated on July 14, 2021
Interview with Dr. Darin White
Dr. Darin White, who founded and directs the groundbreaking Sports Analytics program at Samford University spoke with Sports Degrees Online staff writer, Bryan Haggerty about creating a rewarding career by merging his love of sport with his interest in marketing and analytics.
About Dr. Darin White
Professor Darin White is the the Executive Director of the Center for Sports Analytics at Samford University and the founding Coordinator of the Sports Marketing Program. He holds a PhD in Marketing Strategy from the University of Alabama, and he has extensive experience working, researching, and teaching about the business of sports and analytics.
Bryan: Professor White, you hold multiple titles as the executive director of the Samford University Center for Sports Analytics and the founding director of the sport marketing program. Can you give us some background in your career and how you have worked to get to this point?
Dr. White: Sure, my career is rather circuitous. Not what you would probably expect. I started out getting a PhD in marketing strategy and in the process taking a lot of analytics classes. I ended up coaching soccer for about a decade as well. So with the merging of those two, my love of coaching and background in strategy and analytics, I was able to discover the world of sports marketing and analytics. About a decade ago I took a job at Samford University in Birmingham. We built the first sports marketing program at an AACSB business school in the south east at the time. Six years later we launched the center for sports analytics and added that component to our program after a lot of advice from folks in the industry who serve on the advisory board.
Bryan: I want to ask you a few questions about The Center for Sports Analytics at Samford University because it seems that it’s not something you find at a lot of universities out there. Samford as far as I know still has the first and likely the only center for sports analytics on campus in the United States. Can you talk about the type of activities the center is involved in?
Dr. White: Sure, we are involved in all three phases where analytics intersect. What we are most well known for is sports business analytics. Since my PhD is in marketing strategy, we are physically located in the business school and we do quite a bit of work around helping sports teams take on data. Sports teams are buried in data on the business side of the organization. We’ll take ticketing data, social media data and help teams better determine how they can drive revenue from that data. How can they make better business decisions based on that data? That’s one part of it.
Another part is focusing on the sponsorship side. As you probably know this is an exploding channel in the marketing world. It’s in the seventy billion dollar range just for North America in terms of total sponsorship spends each year. We also find they work quite a bit with brands like Coca Cola, Chick-Fil-A, Transamerica, Phillips 66 and on and on. There are so many brands out there that leverage sports property so you have to achieve their marketing objectives as well. We are able to come in and help those brands understand the value they are actually getting from those partnerships. That is one area we work in, that business side of sports analytics.
The other side would be the more traditional side. When people hear sports analytics they think Moneyball, Billy Bean, and Oakland A’s because that’s where it all started. We have that as well. We have students on our campus going that path. While they are on our campus we actually embed them into about a dozen sports teams that we partner with. Some are on the pro level, others on the college level. Students have the opportunity to gain real-world experience while on campus, take classes and embed them into a team doing analytics.
The third, and this is the newer area really growing in the world of sports is what I like to call sports science analytics. There is a lot of health related data which is now collected on athletes through companies like Catapult, like heart rate data, distance covered and responses to weight training. There are all kinds of wearable devices which monitor these things as well as sleep and nutrition data. Just a lot of health related data. Teams and organizations want to be able to use that data to gain maximum performance so our center has that aspect as well. We really do all three phases.
Bryan: That’s impressive that the Center on campus at Samford functions in such depth in all three of those areas. I want to ask you a question to dig into the work that you do a bit more. There might be a lot of people out there who are interested or intrigued in the idea of using analytics to solve problems in all these different areas. I want you to ask you about how the approach varies – from the big picture perspective – when you encounter questions in these different areas. The first question I want to ask you is how do these really big sponsors like Coca Cola, Chick-Fil-A go about telling the story of how they get value from these sponsorships? Can you talk about how you help them begin to answer that question for them?
Dr. White: It varies. Every client and brand is different. Every brand has different objectives. When a company like Coca Cola, Pepsi or Verizon or whoever signs a partnership agreement to put their name on a stadium for example or their logo on the front of an NBA jersey, there are a lot of different ways this kind of plays out.
The market objective and the business objective of the brand is going to be different in almost every situation. Some are looking for awareness, others are looking to get into the consideration set. When you’re hungry and it’s lunch time and there’s three restaurants, you want to make sure your restaurant pops into their mind. Sponsorship is a great way for that to happen.
Some are looking for brand favorability, to grow people’s perception of certain attributes. For example here in Alabama we have the Alabama Grand Prix which is sponsored by Honda. One of Honda’s big objectives is for folks to feel like Honda automobiles are safe. So that is one of the ways they try to activate that. A good example of a company we were working with many years ago was Aon. They spent one hundred million dollars to be on the front of the Manchester United jersey. Everybody thought, ‘why would they do that?’ They wanted to increase the retention of their employees across the globe. They were finding that a relatively high percentage of their employees were being poached by their competitors so they wanted to create more pride in the organization on a global level hoping to increase retention.
Every single situation is different and therefore the way we measure it is different. We have to step in and see what are the marketing objectives, what are the business objectives of the partnership and based on that, how do we measure against that? On a broad level that’s what we consider.
Bryan: That is fascinating and I really appreciate that answer. Let’s pivot to the Moneyball aspect. Correct me if I am wrong, but it would seem that approaching Moneyball questions would be a lot more statistical based rather than the business approach and the sponsorship problem that you just mentioned. Is that true, and how does the Moneyball problem-solving compare to what you just described from the business point of view?
Dr. White: They are actually quite similar. On a Moneyball type of project, I’ll give you a real example. We do analytics for our pro soccer team here in Birmingham. There is a tremendous amount of data collected here when the team takes the field, every pass, tackle, event that happens in the game for every player is recorded. We are talking about thousands and thousands of events.
We then take that data and make sense of that because it’s not valuable unless someone can analyze it, make trends, and look for valuable insights that can be put into a format and fed back to the coaching staff or head of sports science. One example is the Moneyball side.
When a pro team is looking to buy a new player, they are out there hunting for their next player and there is a tremendous amount of analysis done to determine, “Should I take this player, this player, or this player?” But on the business side, as an example, we are working with an app company that has a prominent relationship with a pro sports team. We have app download data, number of sessions data and install data that is collected for the city where this particular partnership is happening and other cities. Again, we are talking about thousands of data points and we are trying to understand if it indicates an impact on that partnership. In this case, half way through the season there was a major marketing campaign happening in this city and we wanted to see if it had an impact on the number of installs. It’s big data and it’s really the same statistical approaches like regression to analyze it.
Bryan: That’s fascinating. I’m curious about something – when you were referencing the incredible volume of statistics from a soccer game, how much of that is automatically collected by software or manually input by someone watching the game? Can you give me an idea about how that data is collected?
Dr. White: It’s done through artificial intelligence. There are several companies that do that. It basically takes a video of the match and it’s able to produce that data from the video of the game itself. In this particular case the company we are using is called Instat. But there are multiple companies that provide that kind of service. Obviously you have to subscribe and pay for that but it’s pretty remarkable. We also have access to a database we can go into and watch, for example, a second division team in the Czech Republic from last week. Let’s say a team is thinking about buying their starting center mid, we can go in and watch that player’s performance, get all the stats from their last twenty to thirty games and analyze that against what we are looking for. They say if you were to take all the data that has ever been created since the world began and put it in one big warehouse, ninety percent of what is in that warehouse has been created in the last twenty four months.
Bryan: Oh my gosh.
Dr. White: Exactly. And it’s not slowing down. It’s exponentially increasing and there is so much technology around us all the time with our wearable devices and phones, it’s unbelievable just how much data is being generated. This isn’t just in sports, this is in all industries. They are literally drowning in data but they don’t know how to derive that data. So that’s where we come in, specifically the sports industry because it’s a unique industry.
Even on the Moneyball side, the analytics we run on a soccer game are very different than the ones we run on a baseball, volleyball or golf match. Each sport has its own unique metrics and ways of taking the data and looking at it. For example, baseball is much more advanced whereas a sport like soccer is more in the developmental stage. We are still trying to figure out the things we need to be measuring that are meaningful. There is a company that came out with a new statistical approach in the Bundesliga recently called the “Packing Stat”. It’s a new thing so everyone in the sports analytic world of soccer is all over it. There is another one called the “Expected Goals”. That’s another metric developed in the last couple years in the sport of soccer. The sport is really trying to develop what it should be measuring and it’s a very evolving field.
Bryan: I was listening to a podcast recently with an MIT professor named Ben Shields who said that – and I am paraphrasing here – because of the competition related to sports, analytics in sports are far ahead of analytics in other industries. Even though you said that some sports are behind others, as a whole, sports is essentially on the leading edge of analytics. Do you agree with that? Is that what you are seeing, and do you think that what you are doing in sport, we are going to start to see in other industries in the coming decades?
Dr. White: Absolutely, no doubt about it. He’s right, because of the competition of winning and losing is so much a part of that industry. Not that the cola and financial industry aren’t also competitive, they are, but sports are so public. There is also another side of it, the customers. Customers for a sports team demand analytics. They want analytics. Your current fan that watches the NFL Red Zone, they are hungry for data. You don’t see that [type of enthusiasm] so much from bank customers, for example.
So it’s being driven from that perspective as well. It’s a way for sports organizations to engage their fans deeper and better. There are a lot of reasons why you are seeing the sports industry as the leader pushing into this area. There is no doubt about it. In our university, not only do we have students pursuing careers in sports analytics, we have students pursuing careers in all sorts of analytics, but we have way more demand for students graduating than we have students to fill those positions. If you are mathematically minded at all and you go into business analytics in general, adding an analytic component into that, either a minor or major, you are going to be set from a career perspective.
Bryan: Wow, I believe it. I do want to ask about the career aspect but just another quick question about sports analytics generally. What are the myths and misconceptions out there, and do you think movies like Moneyball have helped or hurt the accuracy of people’s perception of sports analytics specifically?
Dr. White: No, I think Moneyball is fantastic. It was the book, then the movie that sparked the general public’s understanding that this was even a thing. At least when it comes to sports analytics, one of the misperceptions, students think they are going to get into this career to attend sporting events all day long. That’s actually not accurate.
When you work in the sports industry, it can be a difficult industry in some ways. If you think about it, it’s the entertainment industry so you are working a lot of nights and weekends because that’s when the games are going on. You spend a lot of time in front of a computer, about eighty to eighty five percent of your time looking at data, trying to get different data sets to talk to each other or transfer them between data software. You spend a huge amount of your time manipulating data which is not particularly exciting or sexy, but that’s just part of it.
Another challenge is that your client – the person you’re working for and who will be the consumer of what you are doing – won’t have a PhD in statistics or know data like you do. A huge part of your success will be not only running analytics and finding valuable insight but translating it to folks who aren’t involved in data all day. There’s still some sort of resistance in some circles in the sports world. There are two sides, the side really into analytics and the side who goes with their gut or more innate. You will meet some resistance so from the standpoint of the analytics executive, you have to be patient and develop strong visuals to show your analytics. Those are the two sides that a lot of people are surprised about when they get onto the field.
Bryan: I wanted to ask you about the new school versus the old school way of thinking as far as sports analytics is concerned. With the wide acceptance of statistics these days, going “with the gut feeling” is almost a dinosaur way of thinking because many organizations who have trusted in analytics have proven to be so successful. If you mention something like the “eye test” or “gut feeling” – what you see watching the game versus what statistics would tell you – how does analytics account for these factors in the story telling?
Dr. White: That’s a great question. The best approach is a combination of both. There was a football game recently played down here in our state of Alabama against Ole Miss. We are really into college football down here in our state and the coach from Ole Miss, Lane Kiffin is really into analytics. He made every single decision, the entire game based on it. He would look at his sheet, “It’s fourth and three, the score is this.” and that’s how he made his decisions. He almost beat Alabama but of course Alabama went on to win the national title and all that. But there was a point in the game where he went with analytics and people would say that probably wasn’t the right call at that moment given the circumstances that happened in the immediate moment of that game. You do have to have both.
The analytics is telling you what happens across a thousand teams and a thousand games and what your chances of success are. But any coach knows that in a specific game and moment, there is information there in the moment that you just can’t account for in the analytics. So gut still comes into it, that’s what I’m trying to say. You can’t just blindly follow the analytics. The best approach is one that blends the two. Famously there was the World Series game, I think it was the last World Series with the Dodgers. Well, the pitcher was just on fire in game five or game six and the analytics said that after pitch eighty-five, [the pitcher was likely to] fall off in terms of his performance. So they got him out and they ended up losing the ball game because of it. In that situation, it’s the World Series and you’ve got adrenaline going and it’s a completely different situation. You have to have some wisdom in the way you apply analytics, I guess that’s a good way to say it.
Bryan: That makes sense. For a prospective student going into sports analytics, you did say to expect to spend eighty to eighty five percent of your time in front of a computer trying to make sense of data. So in the world of business analytics and sports science analytics, would you say the screen time and the nature of the tasks and the projects is the same across all areas of analytics?
Dr. White: Absolutely. But it depends on which side you’re on. If you are working on the Moneyball side, you’re probably going out on the field every day making sure the Catapult devices are collecting the correct way. So you’re not just going to be in your office. You will interact with the team. Our students are at the games and a lot of them are out tracking data in real time. But if you are on the business side of analytics, you are going to be meeting with business and marketing executives of teams for brands.
This [business] side of sports analytics requires a deep understanding of business in general. So you need a business degree. Secondly you need to understand the business model of the sports industry. There are four revenue streams and you need to understand how that all works. And the fact that every single relationship in that partnership is unique, you need to be creative in the way you approach the measurement against the marketing objective.
On the Moneyball and sports science side, it’s a little more clear what you do in terms of procedure. You learn how to do it and then replicate it. You may need to think outside the box a little bit but it’s the business side that you really need to be creative in the way you apply analytics to these types of questions.
Bryan: Going on what you just said there, how important is mathematics in analytics generally speaking? If someone is not strong in math, does that mean these different paths: whether it be business, Moneyball or sports science side of analytics are just not a good career fit, regardless of how interested a student might be in the subject matter?
Dr. White: Yes I would say that. But when you say math, we aren’t talking calculus, geometry and trigonometry. We are talking about statistics, which is a different type of math. I know a lot of students who were not very good when they did advanced calculus but once they got into stats, they really flourished. It’s a very different kind of math than what you would take in high school. There you will take algebra, calculus, geometry and trigonometry. Some students in high school might have the chance to take a class in statistics, but certainly not everyone.
I ask two questions all the time to high school students applying to our undergraduate program – I want to know 1) how well they did in the statistics class, and 2) did they enjoy it. Because that’s what you are going to be doing all day every day. You are going to be running statistical analysis, regression, ANOVA and correlation data. A lot of people do like stats. Those who don’t like the other math subjects thrive in statistics because they feel like they can see the relationship to the real world easier. In my background I took a lot of different math, tons of calculus, differential equations and all that as an undergraduate. It wasn’t into I got into my PHD that I discovered statistics.
But [if you hope to find your way in sports analytics], you definitely need to be strong in [statistics] and enjoy it – because that is what you will be doing all day.
I have a student working on the MLB All Stars game and they want to know if the players who were snubbed, the ones that almost made it but didn’t quite get in, if they statistically performed better than those who did get in. He’s looking at the past fifteen years of data and he’s like a kid in a candy store. He thinks it’s awesome.
Another example. we had five of my freshmen [come up with a proposal] back in March. You may remember in college basketball that there were no fans in the stands. They were listening to the announcers on ESPN state, “It seems like the home court advantage just isn’t what it used to be in college basketball.” We heard that several times as we were watching college basketball games. So these freshmen came to me and said, “Dr. White, we want to know if we can prove that out.” So they spent many hours collecting data on college basketball games played over the last four years like shooting percentages, [and everything else].
We collected on twenty thousand games and dug into it and found that – sure enough – the home court winning advantage for college basketball was sixty eight percent. This past year, it dropped about five percent. So the fact that there were no crowds definitely impacted the performance of the home team. You could see that the shooting percentages and steals – just to name a couple – went down for the home team. Visiting teams performed at the same levels they normally do but the home teams did not perform nearly as well.
Something else we discovered, which became a pretty big news story, was that the number of fouls called on the visiting team with no fans in the stadium dropped dramatically. Home team fouls stayed the same, but the number of fouls called on the visiting team was roughly three thousand fewer compared to a typical year. Of course we thought, “Why did that happen?” It was because there are no crowds there to put pressure on the referee. We actually wrote up an article about it and got some really good national press from that study. The point is, to answer your question, the students just love doing that stuff. They had this interesting question that was out there and spent hundreds of hours collecting and cleaning the data. Who knew we were going to find anything? But you have to have that inquisitive sort of mind that is interested. Those are the students that will be successful, thinking of questions and answering them with data.
Bryan: That sounds exciting to me, and I would guess that there are a lot of young people out there who are going to get excited as well to hear you say these things. I think the future of analytics is incredibly bright, and that you, Dr. White, and your program at Samford are in an amazing position at an ideal moment. In the coming years, I think that every aspect of sports analytics that we have touched on will continue to become more popular.
Can you tell me a bit about the job market for graduates that you are currently seeing? You previously mentioned that you have more demand for your graduates than you can even fill, including that you have more companies recruiting Samford’s University’s graduates than actual graduates for those positions. Can you talk about some of the recent job opportunities that some of your graduates are being hired and considered for?
Dr. White: When I say that, I’m talking about analytics in general, not sports analytics. To get any job in sports is extraordinarily difficult. It is one of the most competitive industries out there because everybody wants to work in sports. You’ll see an entry level social media job position open for our minor league baseball team and they’ll get one thousand resumes, for example.
Having said that, it doesn’t mean you can’t get a job in sports. We’ve actually had really good success over the last decade. We’ve placed over ninety percent of our students that have graduated into the industry. It can be done but you have to be very intentional about your educational process. Number one, you need a business degree. Now, the world of sports is all about revenue generation so you need to be in a business school environment. You have to get real world sports industry experience on your resume and lots of it, not just one internship. You need multiple things on your resume to point to the fact that you have prepared yourself to work in this industry.
It helps if you are at a school that has a reputation in this industry because they bring all those relationships with it. Three of our faculty are connected to hundreds of people in the industry. Every single day we are talking to them and that’s a big help when it comes to getting a job in this field. We also have alums. When they have an opening, they prefer to hire someone from our school. So going to a school that specializes in this industry is very helpful.
Bryan: I think that’s great advice. Imagine that there is somebody that is lost in the shuffle for that hypothetical social media position where there might be one-thousand applicants. What should those younger, up-and-coming applicants do to differentiate themselves from the pack? What sorts of activities would you recommend to them that will help bolster their experience for them to be considered more qualified next time?
Dr. White: I have that conversation all the time. I have students that are at universities that don’t have a program in sports marketing, business or analytics. Maybe they went to a really great university and got a business degree and great GPA but have almost no sports stuff on their resume. So the first thing that a team is going to do is look at those thousand resumes and look for relevant sports industry experience. They don’t care about your school or GPA, if you don’t have the experience you are already gone. Knowing that, it’s why you’ve got to get it on your resume.
If you are a college student who is not at a school that specializes in this area, you need you do everything in your power to get an internship. You’re probably not going to get paid as most of these aren’t paid. And if you can’t get an internship, just volunteer. Here in Birmingham we just had the SEC Baseball tournament with students volunteering there. It doesn’t matter what you are doing, go and volunteer to get to know people. That’s another misnomer with my students. When they come in as freshmen and they want to do analytics, the first thing I do is put them over in our athletic department rolling up t-shirts and throwing them up in the stands during the game. It’s not analytics, but you need to get experience doing anything you can in the industry.
There is a website called teamworkonline.com. That’s sort of a clearinghouse for internships and a place I would point students. Finally, be creative in your class projects. If you are in a class and need to write a research project and have the ability to pick your own topic, pick a sports business topic. Look at trends in ticketing. How are teams using apps? The thing everyone’s talking about is legalized gambling so you could do a whole paper on that. Find projects and turn them into something that is sports industry related and then put that on your resume, that you did a research study on this. Finally, I would mention our (Samford University’s) website. We have the Center for Sports Analytics website at Samford and get a couple hundred thousand unique visitors a year to our website. As a part of this we have a blog with undergrad and graduate students from all over the world that will write for our blog.
We have an editorial review board and if their content is accepted it will be published. We’ve had all kinds of success stories being able to publish on our blog and get “discovered” for the research they did. We had a student who did a blog post about a new kind of baseball that came out called pitch logic where the technology is actually inside the baseball rather than a radar that is measuring it. He did this really cool research and published it in our blog. It got noticed by the top Sabremetrics podcast and talked about it on air for thirty minutes one day. The Vanderbilt coach heard it, contacted us asked us about the student. Long story short, he ended up getting to do analytics for Vanderbilt baseball and then about three months ago, he got hired by the Tampa Bay Rays organization.
But the way he got discovered was by doing research and publishing on our blog. That was his big break. If you go onto our website there is a little guest blogger area you can check out. We love research, on anything. You can talk sports business, about the upcoming Olympics, anything. It’s just got to be sports related and have an analytics component to it. We recently had a young lady who was into cricket analytics from Pakistan. She’s at a university in Pakistan and she recently published an article in our blog. So I would point students to our Samford sports analytics website and if they are interested, they can shoot me an email. They can tell me their idea and what they are interested in. Of course there has to be some data and as far as that goes, there is a resource on our page that lists over a hundred free data websites, organized by sport. There is a tremendous amount of data out there, you just have to know how to go out and find it.
Bryan: Two more questions. The first one. How do you see the field of sports business and analytics changing in the next five to ten years?
Dr. White: That’s a good question. I think it’s going to mature. If you look at baseball, for example, your typical pro team now has several dozen analytical executives that work for [professional teams] like the Braves, Marlins or whoever. You have a pretty large analytic staff whereas the NBA, pro soccer or NHL, the numbers are much smaller. Those numbers are about ten to fifteen years behind when it comes to analytics.
I think what you’ll find in the next ten years or so, those other sports are going to pick up the pace and do more hiring. Analytics are going to become more a part of the equation in a lot of those areas. As an example we had a student hired by the Carolina Panthers. I think his title is ‘business intelligence coordinator.’ They created the position for the organization and he ended up getting that position. That’s very common when you look at NBA, NFL and MLS teams. They are just now hiring their first folks in analytics in the business or sports side, whereas the MLB have full blown analytics departments.
Bryan: Are there any books, podcasts or other resources you might want to steer people towards who are interested learning about any of these fields?
Dr. White: Yes. Again, our website is a fantastic place. We try to be a clearing house for data sites and podcasts. We have students interning this summer that are building wiki pages for us on each sport. We will probably be publishing this in the next few months. Say you don’t know anything about baseball analytics, you’ll be able to go to our site and see the top metrics and how you calculate them. We’ll have videos showing you how to do the calculations, the history of the metric, what’s considered really good for the metric and how it’s used. We’re currently building that for each sport.
I’ve got a student from Michigan State for example working on the one for hockey right now. I’ve got a student from Clemson working on a different one. We are building those as we speak because right now it doesn’t exist. Almost like a Khan Academy, where you can go to each sport on our page with your own little mini course to get you going on different analytics. It isn’t out there yet but will be soon. Speaking of this, I haven’t mentioned it yet, if you are a college student out there wanting to do an internship at our center, we have summer, spring and fall internships that you can do remote. I’ve got five or six students this summer all over the US and one in Ireland doing analytics internships. There is information on our website about our internship process.
Bryan: These are amazing opportunities. Professor White, I have to give you a lot of credit, you’ve created something special at Samford University. As someone who’s combed the internet looking for people like yourself and universities on your level, there isn’t much out there. I don’t think there is any other university on the same level as Samford because of what you have built there. So kudos to you. It’s been a privilege to chat with you. You are really onto something and this is the future. It’s very clear to me that the future of business and sport all goes through analytics
Dr. White: Thank you. It’s been a pleasure talking to you guys. I feel like I’m the luckiest guy in the world, I’ve got the greatest job in the world. I’m a coach at heart and that’s still what I get to do, coach up the next generation of sports industry executives. It’s a lot of fun.
Bryan: It definitely seems like it. Thanks so much for your time, Professor White.