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484 Part Three Key System Applications for the Digital Age

Cleveland Indians, and Toronto Blue Jays have all hired full-time sabermetric
analysts.
Since all the major league teams use sabermetrics in one way or another to guide
their decisions, the A’s no longer have the competitive edge they once enjoyed
when they were the only ones with this knowledge. Even though Beane hasn’t
taken the A’s to the playoffs since 2006, he remains a highly sought after speaker
on the corporate management lecture circuit. It’s easy to see why. Moneyball isn’t
just about baseball—it’s about learning how to use data as a competitive weapon,
especially in environments where resources are scarce and innovation is essential.
Sources: Don Peppers, “Baseball, Business, and Big Data,” FastCompany.com, April 24, 2012;
Matthew Futterman, “Baseball after Moneyball,” The Wall Street Journal, September 22, 2011;
Adam Sternberge, “Billy Beane of ‘Moneyball’ Has Given Up on His Own Hollywood Ending,”
The New York Times, September 21, 2011; and Michael Lewis, Moneyball: The Art of Winning
an Unfair Game, 2003.
aseball has been, according to the subtitle of Moneyball, an “unfair game.”
BGiven the huge disparities in MLB team budgets, wealthier teams defi-
nitely have the advantage in recruiting the best players. But by using advanced
analytics to guide decisions about what players to recruit and cultivate, Billy
Beane was able to turn the underdog Oakland Athletics into a winning team.
Baseball is a business and this opening case has important lessons for other
businesses as well: You can be more efficient and competitive if, like Moneyball,
you know how to use data to drive your decisions.
The chapter-opening diagram calls attention to important points raised by this
case and this chapter. Managers at major league baseball teams were hamstrung
by earlier models of decision making that used the wrong metrics to predict
team performance. Teams with low budgets such as the Oakland A’s were stuck
in a rut because they could not afford the most highly skilled players, and the
advantage went to the teams with the biggest budgets. Beane and Paul DePodesta
ran sophisticated statistical analyses of player and game data to devise a better
set of metrics for predicting performance. Of course, an individual player’s skill
is still very important, but Beane showed that a team composed of less skilled
players could still win if it focused on players with high on-base percentages and
pitchers with large numbers of ground-outs. Beane was able forge a team that
delivered a first-rate performance much more cost effectively that competitors
because he paid attention to the data.
Here are some questions to think about: Some have said Moneyball isn’t really
about baseball. What are the implications of this statement? What can businesses
learn from Moneyball? What if all businesses were run like Moneyball?

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