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Analytics Magazine

Analyze This: When geeks and jocks collide

January/February 2011

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Vijay MehrotraBy Vijay Mehrotra

During my first week as a professor, I received an e-mail from a San Francisco State undergraduate named Kevin Mello. “I have just completed a summer internship with the Oakland A’s,” he wrote, “and though I’ve taken a course in statistics, I’m interested in learning more.” In the world of sports, the Oakland A’s are viewed as one of its first analytics success stories, thanks in large part to Michael Lewis’ 2003 book, “Moneyball: The Art of Winning an Unfair Game” [1]. I wrote back enthusiastically, offering to do an independent study course with him. Our semester studying Jim Albert’s book, “Teaching Statistics Through Baseball,” [2] ultimately led to a research paper [3], while also helping Kevin earn some statistical credibility with the Oakland A’s. He has since embarked on a career with the A’s as a professional scout.

A brilliant and eclectic writer with a finance background and a passion for sports, Lewis has a keen eye for situations in which fresh paradigms and smart analysis are combined to create significant competitive advantages that conventional observers often simply cannot grasp. In “Moneyball,” he eloquently describes the analytic innovations deployed by the A’s front office to compete successfully with a payroll less than 25 percent of some of its competitors.

Ben Alamar is the Michael Lewis of the academic world. He has been publishing scholarly papers on professional baseball, football and basketball topics for a decade, and some of his research is presented by Lewis in his football book, “The Blind Side: Evolution of a Game” [4]. An economist by training, Alamar is the founder and editor in chief of the Journal of Quantitative Analysis in Sports [5]. In addition to his day job as a professor at Menlo College, he also serves as the team statistician for the NBA’s Oklahoma City Thunder.

From chatting with Alamar, my sense is that the adoption of analytical techniques in professional sports is evolving in a strange and disparate way. Some teams – for example, the Detroit Pistons and the California Angels – take great pride in not utilizing data analysis for any meaningful decision-making. On the other end of the spectrum, the Houston Rockets have an MIT MBA as their general manager, as well as two full-time statisticians and a data specialist on their payroll. In a 2009 New York Times Magazine article [6], Lewis revealed some of the fruits of their analysis actually make it into the team’s game strategies.

But the Rockets remain something of an outlier. By Alamar’s informal count, roughly 15 (about half of) NBA teams have somebody spending at least some of their time dealing with statistical models, though several of these are merely part-time folks or interns noodling around with some datasets. Moreover, even for most of these “enlightened” teams, the connection between data, analysis and decision-making is somewhere between tenuous and non-existent.

Like Lewis in “Moneyball,” Alamar attributes much of the resistance to the cultural clash between the “insiders” (who have spent their life immersed in a game’s customs and intricacies) and the “outsiders” (with their laptop computers who are often perceived as irrelevant). Still, there appears to be no turning back. Computers keep getting faster and cheaper; analysis and visualization tools keep improving; and many companies are now dedicated to extracting and delivering structured and increasingly detailed historical data for professional sports to young, data-literate professionals who are trying to figure out how to effectively make use of it. As the business of sport grows bigger, the constant quest for a competitive edge will continue to lead some coaches and executives to grasp for whatever advantages might possibly be obtained from data and algorithms.

As evidence, Alamar cites the growth of the MIT Sports Analytics Conference [7]. Launched in spring 2007 with less than 300 attendees, its 2011 organizers expect nearly 3,000 people to attend, with keynote presentations by team owners, general managers and player personnel directors. One of the feature panels from last year’s conference, moderated by Lewis, was entitled “What Geeks Don’t Get: the Limits of Moneyball” [8].

The challenges and opportunities associated with the successful application of analytics in professional sports seem strangely typical. Analytics professionals are almost always more familiar with their tools and techniques (what statistician Rob Easterly calls “the verbs”) than with the businesses in which analysts seek to apply them (“the nouns”). This too often limits our effectiveness by causing industry professionals, with their hard-earned domain knowledge, to distrust us.

What to do? First of all, if you are reading this column, I would encourage you attend the upcoming INFORMS Conference on Business Analytics and Operations Research, April 10-12 in Chicago (http://meetings2.informs.org/Analytics2011). Conference organizers have worked hard to create a forum to bring together business professionals seeking an edge with analytics professionals who might have the tools and tenacity to help them address vexing challenges. It is a great opportunity for all of us to learn more about what’s out there.

Secondly, a lesson from the world of sports. In his fine book “The Education of a Coach” [9], the late David Halberstam describes Bill Belichick as lowly assistant coach in his early 20s with no credibility with established players and coaches. Through years of hard work, Belichick ultimately finds that he can win trust, respect and loyalty in the locker room only by providing wisdom and insights that help his teams win on the field. For those who know Belichick as the Super Bowl-winning head coach, it is perhaps comforting to know that he started out just where we so often do: on the outside looking in, just wanting to find a way to use his talents to help.


Vijay Mehrotra (vmehrotra@usfca.edu) is an associate professor, Department of Finance and Quantitative Analytics, School of Business and Professional Studies, University of San Francisco. He is also an experienced analytics consultant and entrepreneur and an angel investor in several successful analytics companies.

REFERENCES

  1. www.amazon.com/Moneyball-Art-Winning-Unfair-Game/dp/0393057658
  2. www.amazon.com/Teaching-Statistics-Using-Baseball-Albert/dp/0883857278
  3. Mehrotra, V. and K. Mello, “A Different Game? Analyzing Management Strategies In Baseball’s World Series Using A Markov Chain Model,” California Journal of Operations Management, Vol. 3, No. 1, February 2005, pp. 92-98.
  4. www.amazon.com/Blind-Side-Evolution-Game/dp/039306123X
  5. www.bepress.com/jqas/
  6. www.nytimes.com/2009/02/15/magazine/15Battier-t.html
  7. www.sloansportsconference.com/
  8. www.sloansportsconference.com/panels-2/2010-2/what-geeks-dont-get-the-limits-of-moneyball/
  9. www.amazon.com/Education-Coach-David-Halberstam/dp/1401301541

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