Triumph of the Nerds

Lately I’ve posted a number of items on the application of statistical analysis to sports such as baseball (Money Ball: the Movie), soccer (Liverpool), NFL football (Fun with Numbers NFL style) and the Yankee’s Mariano Rivera (A Brilliant Analysis). This story, by Cade Massey and Bob Tedeschi, provides an insight into how the book Moneyball and other applications of analysis to sports has helped to generally make the use of analytics in business more accepted and widespread. For example, at Yale this fall, Edward H. Kaplan is offering “Mathletics,” a course at the Graduate School of Management that focuses on data analysis in sports. Professor Kaplan is a world-class academic who researches subjects like terrorism and H.I.V. “I’m just using sports as a way to get students interested in modeling problems,” he says. Their article also has a great quote by Nobel laureate Max Planck:

“A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.”

Joshua Milberg has plenty of business cred: an M.B.A. from Yale, experience in the mayor’s office in Chicago, a job as a vice president for an energy consulting firm.

But all of that, Mr. Milberg says, matters less than his reputation as “the data guy” — someone who can offer insights through statistical analysis. And for that, he and a growing number of young executives can credit none other than “Moneyball: The Art of Winning an Unfair Game,” by Michael Lewis.

More than eight years after “Moneyball” was published, the book refuses to shuffle meekly to the remainder bin of public consciousness. Now, “Moneyball,” the movie starring Brad Pitt, could restore the title to best-seller lists.

But a generation of managers like Mr. Milberg, now 31, never really put down “Moneyball,” which examines how the Oakland Athletics achieved an amazing winning streak while having the smallest player payroll in Major League Baseball. (Short answer: creative use of data.)

These managers are savvier with data and more welcomed in business circles in part because of the book. Among other things, Mr. Milberg’s analysis has helped his clients understand when to prioritize high-volume, low-revenue sales over their sexier, high-revenue siblings.

“The book impacted the way I looked at data,” he says. “And it impacted those around me, allowing me to go farther afield with those data than usual.”

At its heart, of course, “Moneyball” isn’t about baseball. It’s not even about statistics. Rather, it’s about challenging conventional wisdom with data. By embedding this lesson in the story of Billy Beane and the Oakland A’s, the book has lured millions of readers into the realm of the geek. Along the way, it converted many into empirical evangelists.

This evangelism has created opportunities for the analytically minded. Julia Rozovsky is a Yale M.B.A. student who studied economics and math as an undergraduate, a background that prepared her for a traditional — and lucrative — consulting career. Instead, partly as a result of reading “Moneyball” and finding like-minded people, she pointed herself toward work in analytics. This summer, she interned at the People Analytics group at Google.

Not exactly the traditional human resources department, this group enthusiastically pursues the kind of creative empiricism that Mr. Lewis documents, Ms. Rozovsky says, and it hires M.B.A.’s and Ph.D.’s to help.

Granted, other forces have aided the rise of creative data analysis — Web analytics, behavioral economics, health care reform and technology, to name a few. But “Moneyball” dramatized the principles behind these forces: a reliance on data to exploit inefficiencies, allocate resources and challenge conventional wisdom — and thus broadened their appeal.

“Moneyball” traces Billy Beane’s use of unorthodox analytics to the work of Bill James. Working as a baseball outsider, Mr. James began self-publishing his analysis and commentary in 1977 and built a passionate following. As Mr. Lewis writes, Mr. James “made it so clear and interesting that it provoked a lot of intelligent people to join the conversation.”

This is also what “Moneyball” has done for analytics more generally. Once people see the value of a batter’s O.P.S. — on-base plus slugging percentage, a key measure in the book — it’s a short step to applying similar principles in their own organizations.

If graduate schools are any sign, the boardrooms of the future — especially compensation committees — may be in for a shake-up. Among business school students, there has been a surge in interest in analytics. Some of it is related to sports: today, M.B.A.’s are doing analysis in the N.B.A., the N.F.L. and other leagues. But many students are seeking analytics-related work outside the sports realm.

This surge is affecting even the curriculum. At Yale this fall, Edward H. Kaplan is offering “Mathletics,” a course at the Graduate School of Management that focuses on data analysis in sports. Professor Kaplan is a world-class academic, and a serious one. He researches subjects like terrorism and H.I.V. “I’m just using sports as a way to get students interested in modeling problems,” he says.

Unfortunately, the road from modeling problems to influencing organizational decisions is a long one. In many industries, analysts don’t even have a seat at the table. We have seen this battle up close in the professional football, which lags behind baseball and basketball in the use of analysis. Predictably, those in the N.F.L. who are most interested in analysis are the lieutenants rather than the generals.

That’s typical. In most industries, Generation Moneyball isn’t yet in charge. But as the Nobel laureate Max Planck once said, “A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.”

Still, the trend, if not exactly linear, is moving in the “Moneyball” direction. Brad Pitt can only help.