Data Economy

A job that lets you hang with professional sports stars

Grady Reese | E+ | Getty Images

Duke University business professor Otis Jennings likes tackling problems with data. Whether it's in the manufacturing or health-care sectors, or how to improve call center operations, Jennings is a disciple of the data analytics gospel. He's never met an industry that can't be made better by amassing data, sifting through the data and implementing changes based on what only the data can show a business manager.

One unexpected problem that arose: As a visiting scholar, Jennings was asked to teach first-year engineering students at Columbia University an introductory analytics course without making it overly complicated. It just so happened that Jennings had been thinking about analytics and bowling, and a bizarre anomaly in amateur bowling tournaments.

Handicaps in bowling tournaments are used to encourage people with various skill to participate, but there was a counterintuitive result of the handicap method. When you rank everyone from the best to the worst bowler, take the average score and develop a handicap based on the distance between the worst player and the average, it should help to level the playing field, and in theory, the winner could end up being anyone.

But it turns out that the winner more often than not comes from the top half of the rankings, even after leveling the playing field with the handicap, and the bias to the top half happens with unusually high frequency.

Thinking about a data problem in the context of something as "American" as bowling led Jennings to believe that sports might be a good topic for an introductory course on data analytics. Yet Jennings' "teaching tool" is an area he thinks will be a viable career option for MBA students.

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"You can make a career of this. Every organization has to have analytics personnel on board. Businesses will uncover that these people end up paying for their own salaries," Jennings said, and that includes professional sports leagues and teams.

Data analyst has already been dubbed the "sexiest job" of the 21st century. And professional sports leagues and teams, from Nascar to Major League Baseball and the NFL, are ramping up their data efforts. For MBAs looking to capitalize on the big data wave—and whose dreams of being a professional athlete petered out about the eighth grade—there is a way to athletic stardom yet.

Jennings's course ended up attracting a mix of engineering majors and MBAs, and the ideas that began flowing related to sports were all over the place. Jennings, though, said it was the MBA students who showed the greater creative, strategic flair in thinking about sports analytics, while the engineering students crunched the numbers.

One student created a fictitious golf course and worked their way backward to figure out every point on the course and the wind conditions and skills of each player so a tablet computer could tell the player exactly which club to use—the caddie of the future—half man, half machine. "If this was really refined, the software would generate on the fly the best clubs to use," Jennings said, which would probably make stodgy PGA officials shudder.

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Big data hits the jackpot at Caesars
Big data hits the jackpot at Caesars

For Scott Lewis, the director of consumer marketing for Major League Baseball's Washington Nationals, his "moonlighting" as a professor of sports analytics will begin in October, as baseball season ends and George Washington University offers its first ever sports analytics course to its business school students.

"One of the first things I did after George Washington University approached me to teach the course was to do a search on how many existed," Lewis said, who has previously taught sports marketing at Georgetown University, and he found that the discipline is just taking off. One of the existing programs is at Georgia Tech's Tennenbaum Institute, which has a primary focus on enterprise solutions for the health care and manufacturing sectors, but also offers a sports analytics track. Lewis believes the growth curve will accelerate.

Lewis explained that in the history of sports analystics, at least in its early days, candidates came from consulting and other industries where they already worked with analytics and could show how their expertise could be applied to sports. An oft-cited example goes all the way back to the 1980s when San Francisco 49ers guru Bill Walsh hired a Bain executive to bring data into the world of player analysis.

Lewis, who worked at the NBA league office before joining the Nationals, said that the basketball league was early among sports' head offices in disseminating data analytics and best practices, but other league offices have now emulated it, with data-driven decision-making tied to main revenue drivers. They include ticket pricing and sales, attendance, in-game purchases and sponsorships—not to mention the player analysis side.

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"The goal of the class is to show the contribution that can be made with analytics, making the best informed decisions," Lewis said. "On the player statistics side of the business, analytics has been fairly prominent and then there is the business side. That's the challenge of this class, to show the valuable contribution that can be made, if students do want to go down this route professionally," Lewis added.

In Lewis' previous sports marketing course, the focus was typically on how to message fans and advertise to them—building on traditional customer relationship management to grow individuals as fans and customers. Sports analytics, though related to that goal, focuses on hard data.

"You could have an entire course just on 'Moneyball,' " Lewis said, but the goal is to take what's already been done on the player management side of the business and create case studies for the consumer side of the business.

Customer profiling is something that has already paid for the Nationals—knowing who their fans are and knowing how they define value is not only critical, but it's something that changes faster than you think, Lewis said.

Analytics allows the team to know how and where to most effectively reach the fan. And ultimately, a person has to sift through numerous data points. Even though there are systems set up to recommend certain prices for a ticket—what is known as dynamic pricing—Lewis said it is still very important to look at a number of factors that tie into attendance and whether any price should be changed, and the variables change as a game approaches.

"We could have an entire discussion about games we were able to predict well and games that caught us by surprise attendance-wise," Lewis said.

In Las Vegas, profiling of the casino patron to increase their buying behavior is already a major focus of companies like Caesars Entertainment.

"We are the youngest professional franchise in the MLB and for us the emphasis is on building a fan base. We have grown our fan base at a double-digit percentage three straight years," Lewis said. A growing fan base translates into a growing revenue base.

Yet Lewis says one of the biggest challenges will be gauging the return on investment from sports analytics. If it is to become a serious discipline with business schools and a significant career opportunity for MBAs, the ROI question will have to be answered with, no surprise, hard data.

Nascar, for example, which has implemented what it dubs a "big data" initiative, says it is not even looking at the effort as a "bottom line" item yet. The bottom line will meet somewhere between the data and the human analyst interpreting it.

"The human element is still important. No system can run on its own," Lewis added.

"I believe this about all industries: There is a lack of appreciation for impact that people on 'the line' can have to the bottom line of the company," Jennings said. And on the sidelines of professional sports fields, too.

—By Eric Rosenbaum,