America’s Pastime: Revolutionized; Sabermetrics Analysis
2000: $107,588,459. 2001: $112,287,143. 2002: $125,928,583. 2010: $213,359,389. Those numbers are clearly astonishing. If you guessed that those are what the U.S. Military spends annually, then you weren’t far off. The above numbers represent the Opening Day payroll of the New York Yankees in 2000, 2001, 2002, and 2010. The New York Yankees have dominated baseball since the 1920s, winning 27 World Series titles during this time. Like many fans of the game, opposing teams have also grown tired of the Yankees’ success. So tired, in fact, that Billy Beane, general manager of the Oakland A’s, along with his assistant, Paul DePodesta, revolutionized baseball by developing a new style of winning dubbed “Moneyball,” which allows low-budget teams like the Oakland A’s to have a chance at winning (Lewis). See, the main reason people are irked by the New York Yankees is because they have seemingly unlimited funds, and have enjoyed success because of their ability to simply buy the best players in baseball. Well, Billy Beane had had enough of that. Using a theory called sabermetrics, which points out that talent evaluators have been analyzing the wrong statistics for decades, Beane turned the low-budget 2002 Oakland A’s into the best team in baseball for a while. But despite its early successes, the theory of sabermetrics, or “Moneyball,” hasn’t worked recently due to too many teams adopting this style, and the fact that sabermetrics mostly relies on statistics, often ignoring certain human elements.
The Oakland A’s, after enduring multiple losing seasons and cries from fans to leave the city, adopted the theory of sabermetrics and finished the 2002 season tied for the best record in the MLB at 103-59 (MLB.com). However, unlike the New York Yankees, the Oakland A’s had the third lowest payroll, just in front of the Montreal Expos and Tampa Bay Devil Rays (Baseball Prospectus). The A’s used the main element of sabermetrics by focusing on a statistic that was widely overlooked, OBP (On Base Percentage) (Lewis, Moneyball, 127). At that time, the payroll of the Oakland A’s was a mere $40,004,167 compared to the Yankees’ payroll of $125,928,583 (Baseball Prospectus). This was, in essence, “Moneyball.” And it worked. Beane assembled misfits, burnouts, and other unorthodox players, at least that’s how other teams saw them, and challenged the mighty New York Yankees. But the success was short-lived. James’ theories definitely had merit, as was evidenced by the Oakland A’s success. James’ findings, such as using OBP as the main indicator and stating that batting average, the most popular statistic in baseball, was overrated, were indeed different (James n. pag). But for multiple reasons, sabermetrics was also inherently flawed.
Common knowledge indicates that once a practice becomes popular, and everyone starts doing it, it becomes less-efficient. Bob Costas, one of the most prominent sportscasters in America, points out that sports evolve quickly and are ever-changing (“The Future of Sports”). Even Beane, whose practice has become more and more popular, said, “With the advent of technology, there’s been a greater interest in it (sabermetrics)” (Sports Analytics TV). One reason why sabermetrics is flawed is the fact that many teams are running their clubs like this now. Over the years, the theory picked up steam, with the Tampa Bay Rays being the most glaring example of another team adopting “Moneyball” (“’Moneyball’: Tracking Down How Stats Win Games”). In 2008, the Rays’ payroll was approximately $43,745,597 (Baseball Prospectus). That’s just a little higher than the payroll of the Oakland A’s in 2002. However, the A’s miraculous season was six years before, and generally, the average payroll has been increasing over the last decade. So technically, they are almost equivalent. The Rays faced a tougher challenge because they played in the same division as the Boston Red Sox and New York Yankees. This model was almost an exact replica of the Oakland A’s model. In 2008, the Rays won the AL East division with a record of 97-65 (MLB.com). Meanwhile, the Oakland A’s finished with a 75-86 record and missed the playoffs. The popularity of “Moneyball” and sabermetrics had an immediate effect on the team that started it.
The Rays weren’t the only team in 2008. The Minnesota Twins posted an outstanding record of 88-75 in 2008, and the Twins are another example of a low-budget team. The snowball effect this had on the A’s is evident. More teams were getting better and the competition was increasing. Bill James wouldn’t have predicted that multiple teams would begin using analytics to win baseball games. However, teams are always looking for a competitive edge, and this was it. Furthermore, it’s extremely likely that teams like the Rays developed “Moneyball” and made it more sophisticated (Henderson). The strategies used were similar, but they weren’t identical. For one, the Oakland A’s didn’t steal bases at all and they didn’t use the bunt either (Lewis, Moneyball, 85). On the other hand, the 2008 Tampa Bay Rays had Carl Crawford, one of the best base-stealers in the game, on their team and chose to steal frequently. Thus, an example of how teams were using their low-cost “resources” differently. Many executives had integrated sabermetrics into their system somehow. As Tony La Russa, one of the most successful managers of all-time, posits, “A lot of baseball owners and front-office people know there’s a place for the analytics, but you can’t make all your decisions based on that. That’s foolish” (SFgate.com). La Russa’s point illustrates that after the early success of the A’s, using sabermetrics became popular on different levels, and thus, became largely ineffective. The theory has evolved and the implementation of sabermetrics differs from team to team, but as La Russa stated, sabermetrics is integrated somehow into basically every Major League team. After all, the goal of “Moneyball” is to win as many games as possible, while spending as little as possible, and instead relying on statistics to find cheap, effective players, which is why sabermetrics has been widely used around Major League Baseball.
Moreover, as great a statistician and thinker as Bill James was, he ignored another key element that makes sabermetrics flawed – the human element. The fact is that “Moneyball” is based largely on statistics and the analysis of statistics (James n.pag). But most of the times certain human elements, such as margin of error and injuries, aren’t taken into account (Barra). If a player sustains an injury, his effectiveness is significantly reduced, thus eliminating his production. And while an injured player can be replaced, it can only be done by spending money, which seems, or actually is, counterproductive in the “Moneyball” system. James, along with many other sabermetricians, failed to recognize the impact that injuries can have on a ball club. Also, it’s impossible to predict injuries and injuries are an essential part of baseball. The premise of “Moneyball” is essentially destroyed when one of the key players, perhaps one with a high OBP, gets injured. Also ignored by James and Beane is the fact that statistics can be flawed. One could argue that sabermetrics was successful, and it did work for the Oakland A’s, but there’s always an outside shot of statistics being inaccurate or even erroneous. In business, many companies have adopted the Six Sigma method, which is essentially the same as sabermetrics in that it seeks to improve the quality of outputs. However, the Six Sigma system, much like the theory of sabermetrics, is also flawed because of over-reliance on certain methods and processes. The same goes for sabermetrics and its over-reliance on statistics without accepting a margin of error, or simply the fact that statistics can often be incorrect.
Another inconsistency included in the reliance on statistics is the dependency on one specific statistic, On Base Percentage. As Cork Gaines, the baseball analyst for Business Insider, notes, “At the time the book was being written (2001-2002), it just so happened that players with a strong affinity for getting on base (high OBP) were overlooked by most teams” (Gaines). Gaines’ point, in essence, is that relying on OBP might have worked for the A’s, and we now know it did, but it probably won’t work for other teams. At the time of the Oakland A’s success, other teams were overlooking, probably accidently, the one statistic that the Oakland A’s found useful. Moving forward, teams will always look for a certain stat that their counterparts are ignoring in order to gain a competitive edge. It’s fairly certain that players with a high OBP such as Alex Rodriguez and Albert Pujols were no longer overlooked after the 2002 season, and now they are some of the highest-paid players in the game. This is because most teams began to narrow in on OBP simply because it worked. Gaines, then, contends that the type of statistic doesn’t matter, but “what is important is that those players have a certain amount of value that may not be recognized by most other teams” (Gaines). This simply means that there is no set list of statistics to look at, and that certain stats won’t guarantee success. One could even make the argument that finding the overlooked statistic involves a bit of luck. Luck is involved in the process of using a statistic to one’s advantage, and also in avoiding the wrath of human elements such as injuries. Thus, one can conclude that James and Beane ignored that there’s a significant amount of luck involved in sabermetrics as well. The combination of having a possibility of human error, and the fact that “Moneyball” relies mostly on statistics, opens the door to skewed data.
Another misconception that many “Moneyball” believers have illuminated is the reduction of the role of baseball scouts. Talent evaluators, however, have been involved in the game for decades, and are an integral part of any front office (Barra). Bill James and other sabermetricians were effectively attempting to eliminate the role that scouts play in finding talented players. But fact is that key players on the 2002 Oakland A’s team such as Barry Zito, Tim Hudson, and Mark Mulder were all discovered by area scouts in the A’s organization (Barra). Most people would assume that set data trumps baseball scouts at any time, but this is actually not always the case. Baseball is a unique game in that every player has a talent that could be valuable, which is why many of the so-called “busts” that occur in the National Football League are practically non-existent in baseball. Baseball scouts have a trained eye for talent, which is why they remain an integral part of the game, regardless of how many statistics were used to analyze a certain player. This is why Allen Barra of The Wall Street Journal hints that Zito, Mulder, and Hudson wouldn’t have been discovered if the A’s had relied solely on sabermetrics and the scouts were the ones who saw the talent in them. Baseball’s an interesting game because of the distinct eye for talent that scouts have and it’s likely that statistics would not have seen the potential in these three players, who turned out to be stars.
There’s no doubt that the 2002 Oakland A’s had one of the most successful seasons of any team in MLB history. The team, led by Billy Beane, competed with the Goliaths of the league such as the Boston Red Sox and New York Yankees, while spending about 30% of what these two teams spent on players. However, the A’s haven’t enjoyed the same success in recent years as they did in the early 2000s. Most notably, the Oakland A’s finished with a miserable 74-88 record in 2011, and currently sit in last place in 2012. With these poor finishes, the A’s have shown that the “Moneyball” theory has lost steam and the competition, especially in the A’s own division, has improved immensely. It remains to be seen how Oakland will finish out the 2012 season, but it doesn’t look like sabermetrics have been in the team’s favor recently. But the theory is still in place in Oakland and Billy Beane remains the general manager. Just last season, the A’s have added many young, talented players, who could end up being the next coming of “Moneyball” players. But there’s no guarantee of that, and unless baseball is revolutionized again, the fast-changing theory of sabermetrics in the form of “Moneyball” looks to be a staple that will become more and more popular, despite a few glaring issues.
You can follow Max Luckan on Twitter @MaxLuckan, or e-mail him at MLuckan@aol.com