Show me the ‘Moneyball’

Michael Lewis’ 2003 book, “Moneyball: The Art of Winning an Unfair Game,” did for sports analytics what Tom Davenport and Jeanne Harris’ 2007 book, “Competing on Analytics: The New Science of Winning” did for business analytics. They both made “analytics” a household name in the respective worlds of professional sports and corporate business (of course, professional sports is a subset of corporate business), and, as reflected in their titles, they both emphasized “winning” with data-driven decisions. In business as in sports, everyone loves a winner.

“Moneyball” ramped up the attention on sports analytics last year when the book was made into a movie starring Brad Pitt in the lead role of Oakland Athletics General Manager Billy Beane, who used an analytical, evidence-based approach dubbed “sabermetrics” to assemble a competitive Major League Baseball team on a tight budget.

Analytics has become a part of the sports culture as evidenced by “Moneyball’s” box office success and several Oscar nominations, the March MIT Sloan Sports Analytics Conference that drew another sell-out audience in Boston and the recent ESPN The Magazine’s special issue on the topic. ESPN’s magazine featured Oakland A’s starting pitcher Brandon McCarthey and his model wife, Amanda, on the cover under the headline: “Chicks Dig the Ground Ball.” The headline kicker was a classic: “After he discovered analytics, Brandon McCarthey changed his motion, saved his career, signed the big money deal and married this model. Score one for the nerds.”

While analytics has no doubt played a key role in the success of teams such as the Oakland A’s and players like McCarthey, it continues to run into acceptance barriers in many quarters of professional sports as authors Benjamin Alamar and Vijay Mehrotra point out in the final installment of their three-part series on sports and analytics (page 22).

“There is a deeper, ancient cultural conflict at play in the world of sports,” they write, noting that “proponents of increased use of data and models are often not considered credible by these traditionalists because of their lack of first-hand experience in the sport.”

Sound familiar?