From the Publisher
"Former Mets sabermetrician Benjamin Baumer and sports economist Andrew Zimbalist's The Sabermetric Revolution takes an expert look at the statistical analysis craze, debunking misconceptions and evaluating the role of sabermetrics in the future—no doubt of great interest to future general managers, both real and fantasy league."—The Daily Beast
"An ideal introduction to the topic of advanced statistics in baseball, The Sabermetric Revolution provides a thorough overview of the ways in which analytics has transformed the management and coaching of baseball. Demonstrating how the game has been changed by the evolving use of data over time, Baumer and Zimbalist also offer a tantalizing glimpse of how sabermetrics may continue to develop in the future."—Prozone Sports
"The Sabermetric Revolution is an excellent and well-written look at where sabermetric knowledge stands today. This is a very useful book."—Tyler Cowen, Marginal Revolution
"The Sabermetric Revolution truly is an engaging and succinct illumination of where the field is and how it got here. The book is ideal for a reader who wishes to tie together the importance of everything they have digested from sites like Fangraphs, Baseball Prospectus, Hardball Times, Beyond the Box Score, and, even, yes, Camden Depot. . . . Well worth the read."—Jon Shepherd, Camden Depot
"Leo Durocher once said that 'Baseball is like church; many attend, few understand.' The Sabermetric Revolution is a must-read for those in the baseball congregation seeking understanding of how objective analytics can be used to determine intrinsic value, identify undervalued and overvalued assets and dynamics, and create competitive advantage."—Tom Garfinkel, former president and CEO of the San Diego Padres
"Moneyball was a good read by Michael Lewis and a good part for Brad Pitt, but as Ben Baumer and Andrew Zimbalist show, it was primarily a good fairy tale. The Sabermetric Revolution doesn't just debunk, but has a high slugging average with all sorts of valuable new insights and baseball numbers. But, be on guard, stats freaks: it isn't doctrinaire."—Frank Deford, commentator for NPR and HBO Real Sports
"Moneyball played an important role in highlighting to mass culture the evolution of decision making in Major League Baseball front offices—but this was only a momentary reflection of a broader movement within the game. In The Sabermetric Revolution, Baumer and Zimbalist provide a much more accurate understanding of the exceptional work of the A's to overcome their expected outcomes and how other front offices continue to advance objective analysis and its role in player personnel decisions. A must read for anyone who wants a deeper understanding of why and how baseball continues to lead the way in the use of analytics."—Mark Shapiro, president of the Cleveland Indians
"Sabermetricians have developed new and important ways of measuring player performance. Baumer and Zimbalist turn the table on the sabermetricians and evaluate their performance. The result is an interesting and balanced portrayal of what the authors believe works and what doesn't, and of the challenges that lie ahead."—Bob Costas, broadcaster for NBC and MLBTV
"The Sabermetric Revolution is like the story behind the story. Michael Lewis's classic tugs at our heartstrings and opens our eyes, but Baumer and Zimbalist help us look behind the curtain. If you've ever wanted to understand what happens in the other offices around the general manager, this is a brilliant book."—Will Carroll, lead writer for sports medicine in Bleacher Report
"Andrew Zimbalist and Benjamin Baumer do the best job yet of evaluating the benefits and the myths of the ever-growing world of baseball analytics. This is a must-read for anyone interested in where sports metrics have been as well as where they're going."—Stan Kasten, CEO of the Los Angeles Dodgers
Most, or even all, baseball front offices now use varieties of statistical analysis, known as sabermetrics, brought to popular attention by Michael Lewis's Moneyball, in making decisions about their teams. Baumer and Zimbalist (mathematics and economics, respectively, Smith Coll.) here assess the value of the sabermetric approach—and they also assess Lewis's book. The term sabermetrics comes from the acronym for the Society for American Baseball Research. In the very spirit of baseball analytics, the authors analyze carefully and critically. The result is a debunking of most of what Lewis presented in his work and what audiences saw in the movie version. They argue that Lewis overstated the beneficial use of this method and that he misinterpreted its use by the Athletics with certain players. Yet the authors endorse sabermetrics, stating that its use can be helpful with the baseball draft, prospects, and building contending rosters. VERDICT This book is not for the casual baseball fan. However, it is highly recommended for the serious student of baseball or of professional team use of sports analytics.—SKS
Read an Excerpt
Michael Lewis wrote Moneyball because he fell in love with a story. The story is about how intelligent innovation (the creative use of statistical analysis) in the face of market inefficiency (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 2002 Oakland A's and their manager, Billy Beane, is so well told that we believe its portrayal of baseball history, economics, and competitive success. The result is a new Horatio Alger tale that reinforces a beloved American myth and, all the better, applies to our national pastime.
The appeal of Lewis's Moneyball was sufficiently 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.
This 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 significant impact on baseball management. Following the book's publication in 2003, team after team began to create their own statistical analytics or sabermetric subdepartments within baseball operations. Today, over three-quarters of major league teams have individuals dedicated to performing these functions. Many teams have multiple staffers creatively parsing numbers.
In a world where the average baseball team payroll exceeds $100 million and the average team generates $250 million in revenue each year, the hiring of one, two, or three sabermetricians, at salaries ranging from $30,000 to $125,000, can practically be an afterthought. (Sabermetricians is what Bill James called individuals who do statistical analysis of baseball performance, named after the Society for American Baseball Research, SABR.) Particularly, once the expectation of prospective insight and gain is in place and other teams join the movement, a team that does not hire a sabermetrician 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 offers the highest potential return, but by which decision will insulate them best from criticism for neglecting to follow the conventional wisdom. So, to some degree, the sabermetric wildfire in baseball is a product of group behavior or conformism.
Meanwhile, the proliferation of data on baseball performance and its extensive accessibility, as well as the emergence of myriad statistical services and practitioner websites, have imbued sabermetrics with the quality of a fad. The 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 underlying 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 fielding ability accurately (UZR), are black boxes, wherein the authors hold their method to be proprietary and will not reveal how they are calculated. The problem is that this makes the metric's value much more difficult to evaluate. Of course, fads, like myths, are more easily perpetuated when it is not possible 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 influenced the competitive success of teams? Does the incorporation of sabermetric insight into player evaluation and on-the-field strategy help to overcome the financial disadvantage of small market teams and, thereby, promote 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 quantification is scientific. Given that our world is increasingly dominated by the TV, the computer, the tablet, and the smartphone—all forms of electronic communication and dependent on binary signaling—it is perhaps understandable that society genuflects before numbers and statistics. Yet the fetish of quantification 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 argued that it was possible to improve worker productivity through a process that scientifically evaluated each job. This 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. The Taylorist system was known as "scientific management" and was promulgated widely during the first decades of the twentieth century. The purported benefits of scientific management, however, proved to be spurious and the school was supplanted by another—one that emphasized the human relations of production. Thus, obsession with quantification at the expense of human relations met with failure.
Baseball, much more than other team sports, lends itself to measurement. The 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. After one batter, there is either one out or no outs and a runner on first, 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. There are eight possible combinations of base runners: (1) no one on base; (2) a runner on first; (3) a runner on second; (4) a runner on third; (5) runners on first and second; (6) runners on first and third; (7) runners on second and third; (8) runners on first, second, and third. For each of these combinations of base runners, there can be either none, one, or two outs. Eight runner alignments and three different 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. There are virtually an infinite number of positions on the floor where the five offensive players can be standing (or moving across). Five different players can be handling the ball.
Or, compare it to football. Each team has four downs to go ten yards. The offensive series can begin at any yard line (or half- or quarter-yard line) on the field. The eleven offensive players can align themselves in a myriad of possible formations; likewise the defense. After 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, 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 40 percent of the time and hits 30 home runs, he is going to be one of the leading batters in the game. If a quarterback completes 55 percent of his passes, though, to assess his prowess we also to need to know something about his offensive 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 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-first century.
Our book proceeds as follows. To clarify some matters of artistic license presented 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 offices. Chapters 3 and 4 introduce and survey the current state of sabermetric knowledge for offense 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 of baseball, particularly its effects on competitive balance. Chapter 7 assesses sabermetrics' success, or lack thereof, in improving team performance.
Finally, it is useful to clarify some vocabulary before proceeding. Sabermetrics means the use of statistical methods to analyze player performance and game strategy. Baseball analytics also means the use of statistical methods to assess player performance and game strategy, but it further involves the use of statistical methods to evaluate team and league business decisions. The term analytics as applied to sports has also come to include the interpretation of digital video images, often with associated quantity metrics. We use moneyball (with the lowercase m) to mean the application of sabermetrics with the goal of identifying player skills and players that the market undervalues.