The Sabermetric Revolution Benjamin Baumer, Andrew Zimbalist Published by University of Pennsylvania Press Benjamin Baumer. and Andrew Zimbalist. The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball. Philadelphia: University of Pennsylvania Press, 2013. Project MUSE. Web. 21 Aug. 2015.http://muse.jhu.edu/. For additional information about this book http://muse.jhu.edu/books/9780812209129 Access provided by University of Michigan @ Ann Arbor (4 Dec 2015 21:10 GMT) PREFACE ichael Lewis wrote Moneyball because he fell in love with a story. Te Mstory is about how intelligent innovation (the creative use of statistical analysis) in the face of market inefciency (the failure of all other teams to use available information productively) can overcome the unfairness of baseball economics (rich teams can buy all the best players) to enable a poor team to slay the giants. Lewis is an engaging storyteller and, along the way, introduces us to intriguing characters who carry forward the rags-to-riches plot. By the end, the story of the Oakland A’s and their general manager, Billy Beane, is so well told that we believe its portrayal of baseball history, economics, and competitive success. Te result is a new Horatio Alger tale that reinforces a beloved American myth and, all the better, applies to our national pastime. Te appeal of Lewis’s Moneyball was sufciently strong that Hollywood wanted a piece of the action. With a compelling script, smart direction, and the handsome Brad Pitt as Beane, Moneyball became part of mass culture and its perceived validity—and its legend—only grew. Tis book will attempt to set the record straight on Moneyball and the role of “analytics” in baseball. Whether one believes Lewis’s account or not, it had a signifcant impact on baseball management. Following the book’s publication in , team afer team began to create their own analytics or sabermetric sub- departments within baseball operations. Today, over three-quarters of major league teams have individuals dedicated to performing these functions. Many teams have multiple stafers creatively parsing numbers. In a world where the average baseball team payroll exceeds $ million and the average team generates $ million in revenue each year, the hir- ing of one, two, or three sabermetricians, at salaries ranging from $, to $,, can practically be an aferthought. (Sabermetricians is what Bill x Preface James called individuals who statistically analyze baseball performance, named afer the Society for American Baseball Research, SABR.) Particu- larly, once the expectation of prospective insight and gain is in place and other teams join the movement, a team that does not hire a sabermetri- cian could be accused of malpractice. In baseball, much like the rest of the world, executives and managers are subject to loss aversion. Many of their actions are motivated not by which decision or investment ofers the high- est potential return, but by which decision will insulate them best from criticism for neglecting to follow the conventional wisdom. So, to some de- gree, the sabermetric wildfre in baseball is a product of group behavior or conformism. Meanwhile, the proliferation of data on baseball performance and its ex- tensive accessibility, as well as the emergence of myriad statistical services and practitioner websites, have imbued sabermetrics with the quality of a fad. Te fact that it is a fad, much like rotisserie baseball leagues, fantasy football leagues, and video games, does not mean that it doesn’t contain some under- lying validity and value. One of our tasks in this book will be to decipher what parts of baseball analytics are faddish and what parts are meritorious. Some of the new metrics, such as the one that purports to assess feld- ing ability accurately (UZR), are black boxes, wherein the authors hold their method to be proprietary and will not reveal how they are calculated. Te problem is that this makes the metric’s value much more difcult to evaluate. Of course, fads, like myths, are more easily perpetuated when it is not pos- sible to shed light on their inner workings. Here are some questions that need to be answered. What is the state of knowledge and insight that emanates from sabermetric research? How has it infuenced the competitive success of teams? Does the incorporation of sabermetric insight into player evaluation and on-the-feld strategy help to overcome the fnancial disadvantage of small market teams and, thereby, pro- mote competitive balance in the game? Lewis’s account in Moneyball exudes optimism on all counts. Beyond the rags-to-riches theme, Lewis’s story echoes another well-worn refrain in modern culture—the perception that quantifcation is scientifc. Given that our world is increasingly dominated by the TV, the computer, Preface xi the tablet, and the smartphone—all forms of electronic communication and dependent on binary signaling—it is perhaps understandable that society genufects before numbers and statistics. Yet the fetish of quantifcation well predates modern electronic communications. Consider, for instance, the school of industrial management that was spawned by Frederick Winslow Taylor over a hundred years ago. Taylor ar- gued that it was possible to improve worker productivity through a process that scientifcally evaluated each job. Tis evaluation entailed, among other components, the measurement of each worker’s physical movements in the production process and use of a stopwatch to assess the optimal length of time it should take to perform each movement. On this basis, an optimal output expectation could be set for each worker and the worker’s pay could be linked, via a piece rate system, to the worker’s output. Te Taylorist system was known as “scientifc management” and was promulgated widely during the frst decades of the twentieth century. Te purported benefts of scientifc management, however, proved to be spurious and the school was supplanted by another—one that emphasized the human relations of production. Tus, obsession with quantifcation at the expense of human relations met with failure. Baseball, much more than other team sports, lends itself to measurement. Te game unfolds in a restricted number of discrete plays and outcomes. When an inning begins, there are no outs and no one is on base. Afer one batter, there is either one out or no outs and a runner on frst, second or third base, or no outs and a run will have scored. In fact, at any point in time during a game, there are twenty-four possible discrete situations. Tere are eight pos- sible combinations of base runners: () no one on base; () a runner on frst; () a runner on second; () a runner on third; () runners on frst and second; () runners on frst and third; () runners on second and third; () runners on frst, second, and third. For each of these combinations of base runners, there can be either zero, one, or two outs. Eight runner alignments and three diferent out situations makes twenty-four discrete situations. (It is on this grid of possible situations that the run expectancy matrix, to be discussed in later chapters, is based.) Compare that to basketball. Tere are virtually an infnite number of xii Preface positions on the foor where the fve ofensive players can be standing (or moving across). Five diferent players can be handling the ball. Or, compare it to football. Each team has four downs to go ten yards. Te ofensive series can begin at any yard line (or half- or quarter-yard line) on the feld. Te eleven ofensive players can align themselves in a myriad of possible formations; likewise the defense. Afer one play, it can be second and ten yards to go, or second and nine and a half, or second and three, or second and twelve, and so on. Moreover, in baseball, performance is much less interdependent than it is in other team sports. A batter gets a hit, or a pitcher records a strikeout, largely on his own. He does not need a teammate to throw a precise pass or make a decisive block. If a batter in baseball gets on base percent of the time and hits home runs, he is going to be one of the leading batters in the game. If a quarterback completes percent of his passes, though, to assess his prowess we also to need to know something about his ofensive line and his receivers. So, while the measurement of a player’s performance is possible in all sports, its potential for more complete and accurate description is greater in baseball. It is, therefore, not surprising that since its early days, baseball has produced a copious quantitative record. Although one might not know it from either the book or the movie Moneyball, the keeping of complex records and the analytical processing of these records reaches back at least several decades prior to the machinations of Billy Beane and the Oakland A’s at the beginning of the twenty-frst century. Our book proceeds as follows. To clarify some matters of artistic license pre- sented as fact, Chapter 1 discusses the book and the movie Moneyball, what they get right, what they get wrong and various sins of omission. Chapter 2 traces the growing presence of statistical analysis in baseball front ofces. Chapters 3 and 4 introduce and survey the current state of sabermetric knowl- edge for ofense and defense, respectively. Chapter 5 sketches the Moneyball diaspora, that is, the growing application of statistical analysis to understand performance and strategy in other sports, principally basketball and football. Chapter 6 illustrates the use of statistical analysis to penetrate the business Preface xiii of baseball, particularly its efects on competitive balance. Chapter 7 assesses sabermetrics’ success, or lack thereof, in improving team performance.