Part 23 of our series on Important Moments in Team Building. See introduction, and up-to-date list.
In the spring of 2003 author Michael Lewis created a sensation in the baseball community with the release of Moneyball, a book that related the story of how Oakland A’s general manager Billy Beane kept his undercapitalized team competitive. A one-time financial trader turned writer who had unprecedented access to Beane’s activities, Lewis identified two related causes for the A’s success: Beane understood the concept of market inefficiencies and the analogous benefit of finding undervalued players; and Beane believed that these players could be better identified using statistical and analytical techniques than by traditional scouting methods.
The second issue caused a fair bit of controversy. Lewis is a terrific writer and much of the charm of his book came from the dichotomy he drew between the old-school scouts and the new-fangled statistical analysts. As Lewis put it, “Billy had his own idea about where to find future major league baseball players: inside [assistant general manager] Paul DePodesta’s computer. He flirted with the idea of firing all the scouts and just drafting kids straight from Paul’s laptop.” The two most prominent statistical insights of the A’s, as highlighted by Lewis, were that most organizations undervalued hitters with a high on-base-percentage who didn’t otherwise stand out, and that teams undervalued college players when compared to high school players in the amateur draft.
Baseball statistical analysis had been evolving and developing for roughly 50 years and had begun to find an audience with the writings of Bill James starting in the late 1970s, but this audience mainly consisted of independent researchers and a particular type of fan. Sabermetrics, a word coined by James, did not prescribe a set of formulas and answers as its critics might have thought. It is a process, a philosophy that teams should make decisions based on evidence and data. This was not a new idea – scouts had been using radar guns and stopwatches for decades rather than merely trusting their eyes – but sabermetrics suggested that baseball’s vast statistical record could tell a team which players were actually helping the team score or prevent runs, which strategies would increase the team’s chances of winning, which minor leaguers were likely to be good major leaguers, and more. Much more, in fact.
By the late 1990s sabermetrics had begun to creep into some of the more progressive baseball front offices. For example, Rockies general manager Dan O’Dowd and major league administrator (and current Twins GM) Thad Levine were making sophisticated mathematical evaluations of the effects of their high-altitude Coors Field in 1999. But most teams, before the publication of Moneyball, kept their analytical efforts out of the public eye. Not surprisingly, Lewis’s portrayal of a general manager who seemed to be rejecting 100 years of supposedly hide-bound traditionalist scouting in favor of novel statistical methods created a rift between the proponents of traditional scouting and statistical analysis.
Beyond player evaluation, statistical analysis was and is being used to evaluate in-game situations. The mountains of data that have recently become available allowed comprehensive analysis of on-field events like batter-pitcher matchups, strategic decisions such as bunting, and defensive positioning. As the front offices in some of the more statistically oriented organizations began to better understand these relationships, a natural tendency developed to impose some of this knowledge on the manager. Not surprisingly, this reset the line that had been observed between the front office and the field staff for nearly a century.
“You’re the manager and you’re going to get no interference or second-guessing from me,” Yankees general manager Ed Barrow told manager Miller Huggins in the 1920s. “Your job is to win, and my job is to see that you have the players to win with.”
Analytics has changed this relationship; the front office now had information that might contradict what a manager ordinarily would want to do. As one writer recently observed, overstating a little, “Teams don’t want a seasoned, master tactician anymore so much as they want a manager with a small ego and an open mind. At the root of this change is the proliferation of statistical analysis, which can make decisions for managers if they’re willing to embrace it.” Lewis described Beane’s preferred approach in Moneyball: “Beane ran the whole show. He wasn’t just making the trades and supervising scouts and getting his name in the papers and whatever else a GM did. He was deciding whether to bunt or steal; who played and who sat; who hit in which spot in the lineup; how the bullpen was used; even the manager’s subtle psychological tactics.”
The debate in the immediate aftermath of the book was between those who supported the traditional scouting model and those who thought, as Beane did in Lewis’s book, that sabermetrics could dramatically reduce the need for scouts. In fact, much of the acrimony was due to Lewis’s overstated caricaturization of scouts’ limitations. His unflattering portrayal of traditional scouts poisoned even his more compelling statistical arguments and encouraged an unnecessary choosing of sides. The smartest and most successful teams, as it turned out, grew their analytics staff to provide information that could enhance and augment what their scouts were telling them, and that, in the ideal environment, the scouts and analytics staffs could work together and learn from each other. On the field, even in the most analytically focused organizations, managers have remained critical to success given all the complexities in leading 25 men.
Back in 2003 Lewis thought that Beane’s advantage would eventually dissipate because other teams were going to start mimicking his strategies. “He [Beane] may feel pretty happy with himself now, because his team reflects inefficiencies exploited in the past, and looks pretty damned good. He might even get through this whole year without having to use the trade deadline, one of his favorite things. But two, three years down the road, he has problems.”
Beane thinks we have finally reached that point, though it took longer than two or three years. “Eventually, it was going to happen,” Beane recently acknowledged. “The big teams are run very wisely now. There are really smart guys who have capital. There’s no soft spots. They’re smart guys, and they’re surrounded by smart guys. It’s a very intelligent industry right now. In fact, one of the most intelligent [of any industry] … The big teams look like they’re going to be good for a long time.”