When former Oakland Athletics’ general manager Billy Beane used data analytics to build a low budget winning baseball team in the early 2000s, it was so unusual it became the subject of a book and movie.
Sixteen years after author Michael Lewis wrote the book Moneyball, every Major League Baseball (MLB) team uses the technique. But a new study shows that while the tool can help a club create a stronger team – at a lower cost — it loses its edge once everyone’s on to it.
“It’s like having a secret sauce,” says study author Ramy Elitzur, the Edward J. Kernaghan Professor of Financial Analysis and associate professor of accounting at the University of Toronto’s Rotman School of Management. “When you have a secret sauce and nobody else knows about it, you have a competitive advantage. Once the secret sauce was outed, which was what happened with the book, everybody could imitate the Oakland A’s.”
Dr. Elitzur created a database for the study, inputting information from 1985 to 2013 about team payrolls, playoff success, the spread of data analytics use, and players’ overall contributions to their team, represented by a key statistic from Moneyball’s “sabermetrics,” — the type of data the Oakland A’s used to identify lower-priced, undervalued players through statistics such as how much time spent on base.
He found that between 1997 and 2001, there were only two “Moneyball” teams in the MLB. Another three teams had taken up the practice by 2002. By 2013, more than 75 percent of MLB teams were using it. Sabermetrics gave teams the strongest advantage up until 2003, the year Moneyball was published. By 2008, the comparative advantage was lost as more and more teams adopted sabermetrics. The practice of data analytics also spread beyond sports, to business and government.
The study illustrates that regardless of whether it’s sports or another enterprise, “if you have a built-in advantage, don’t ever talk about it,” says Dr. Elitzur. In the MLB case, “once Moneyball happened, you had a new arms race. There was no way out.” However, it did help teams better match their contracts to players’ true value, he says, by basing it on less biased information.