One of the most remarkable and unique aspects of baseball that differentiates it from almost all other sports is its compatibility with statistics. Unlike most team sports, in which every player works together to achieve a common goal, baseball is individualistic; each play starts with two people, the pitcher and the hitter, and ends in an easy to measure outcome. For this reason, statistics have always been a central aspect of baseball. Even when the game began in the 1800s, records were kept of batting average and earned run average for every player. As the game evolved, so did the complexity and intricacy of how fans, analysts, and management measured all aspects of the game.
From this increasing usage of statistics came sabermetrics, a term derived from SABR, an acronym for the Society of American Baseball Research. Since Bill James coined it in the 1980s, the term has come to acquire many different connotations and uses, but all broadly depict it as the analysis of baseball through the use of empirical data and evidence. The recent film adaptation of Michael Lewis’ book Moneyball has brought sabermetrics into the public eye. It tells the story of Billy Beane, the general manager of the A’s who revolutionized baseball management by using sabermetrics to exploit market inefficiencies. By relying largely on advanced statistics rather than scouting and traditional assumptions about what makes a player valuable, Beane was able to build a high-performing team with very little money.
Despite Beane’s relative success and its objective, scientific nature, sabermetrics carries a very negative stigma among many baseball enthusiasts. The reasons for this, as I see it, are twofold. Firstly, sabermetrics questions many of the traditional assumptions about baseball that have existed since its conception: that batting average, home runs, and RBIs determine a hitter’s prowess; that ERA and wins determine a pitcher’s; that there is such a thing as clutch hitting, that sacrifice bunts are useful, and so on. Sabermetrics uses statistical data, research, and analysis to determine what truly makes a player valuable, how to predict performance and the correct strategy for certain situations, but in the process it comes to conclusions that go against the status quo. This is why it took so long for managers and general managers to use more advanced statistics; on the one hand, they wanted to do whatever necessary to win, but on the other hand, they wanted to keep fans interested. Running a baseball team is a business, and the risks of throwing away deeply held intuitions about how to evaluate players could be detrimental to the fan base of the team.
The second reason that sabermetrics often carries a negative stigma comes from inaccurate representations of its purpose and methods. I have heard people say that “stats people” don’t watch the games, think all players are robots, claim to be able to predict every event perfectly, don’t take scouting or emotions into account, and so on. Though these depictions may describe the beliefs of a small number of people, they absolutely do not represent the purpose of sabermetrics as a whole. Bill James originally called sabermetrics “the search for objective knowledge about baseball,” without mention of statistics, intangibles, or scouting. James did believe that the use of more advanced statistics could more effectively discover “objective knowledge” about baseball, but that does not mean that other methods are not still useful or that his method was foolproof.
Part of the problem with Moneyball was that it gave the impression that Billy Beane disregarded the advice of scouts or that he completely ignored the mental and emotional aspects of the game. This was simply not true. Beane could not have created such a strong core of homegrown players without good scouting. What Beane did was realize that there were certain biases present in not only scouting evaluations, but in the entire free agent market. By being willing to question assumptions and use all methods available to gain a better understanding of players, Beane was able to exploit inefficiencies in the market and gain an advantage over other teams.
As a philosophy major, I have realized that there are a lot of similarities between philosophy and sabermetrics. Both carry a negative stigma because of their willingness to question assumptions. Both try to search for objective knowledge about their relative domain of study (for sabermetrics, this is baseball; for philosophy, this is, well, everything). Because of these two things, both lead us to change the way we understand certain questions, issues, and the way we view the world and baseball. Nevertheless, neither philosophy nor sabermetrics is the be-all and end-all of how to understand their respective fields. We cannot learn everything about baseball through statistics and objective research, and more importantly, we must admit that there is more to baseball then the search for objective truth within it, just as we must understand that there is more to experiencing life than rational thought and Socratic dialogue.
There are many ways to be a fan of baseball. I use sabermetrics as a way to understand and appreciate the game, but many other prefer to hold on to the traditional beliefs that have been a part of baseball for ages. This is completely understandable, and appropriate in the sense that baseball is not a science, but a game in which tradition plays a central role. What people should understand, however, is that sabermetrics does not take away meaning from baseball, but adds it, bringing more complexity, analysis, and intricacy to the game. “Stats nerds” like myself don’t want to take the fun out of baseball, but have more fun following it by being able to gain a deeper understanding of the game.