Before the first pitch of every game, Gabriel Chandler, professor of statistics and assistant coach of the Pomona-Pitzer baseball team, tapes a sheet of the opponents’ stats on the dugout wall. Coach Chandler prefers to see someone play in person, but numbers undoubtedly help him strategize during games.
Missing from the coach’s sheet, however, are statistics like Defense Independent Pitching Statistics (DIPS) that measures a pitcher’s effectiveness based only on plays that do not involve fielders, or Value Over Replacement Player (VORP) that demonstrates how much a batter or a pitcher contributes to the team in comparison to a fictitious average replacement player.
These unfamiliar terms are actual metrics, known as sabermetrics, which people produce and analyze and which might offer the P-P team a slight advantage, if only we could take and compute such measurements.
“Statistically, we’re pretty in the dark,” said Coach Chandler, when asked how he incorporates statistics into baseball. “We play a lot fewer games than at the professional level, so there is less data available. Plus, we only get to see guys for at most four years.”
High schools and colleges are inherently equipped with less data due to the nature of the league, but what about the professional organizations? For major league teams, there are cameras at just about every corner of the stadium to collect data. They record, for example, where the ball landed, at what speed, and where the defender was as the batter made contact.
Until recently, teams often wallowed in the large pool of data. Franchises ignored the idea of using sabermetrics, which theoretically allow you to objectively evaluate players. Baseball has always been treated as an art, a romantic sport. It wasn’t an area that was supposed to be overtaken by mathematical computation and statistical analysis. That is, until the Oakland Athletics started winning games back in 2002.
The Card Counters
“We are card counters at the blackjack table,” declares Billy Beane in a room full of veteran scouts. He adds, “If we pull this off, we change the game. We change the game for good.”
This is how Billy Beane defines the concept behind Moneyball. He is the general manger who turned a woefully funded team into a legitimate contender. The success of the Athletics has shocked and awed the baseball community. The Athletics spent less than $40 million to win as many games as the Yankees, who were paying in excess of $120 million.
Beane was influenced by the baseball philosophy of Bill James. In a series of books called the “Bill James Baseball Abstracts,” James created the foundation of sabermetrics and argued that teams were not asking the right questions and not analyzing the right data. He formed new methods of evaluating players and teams, and helped inspire Beane to creatively use the wealth of data available. The Athletics found that teams could essentially become more ‘efficient’ by having players that know how to get on base. On-base-percentage, which measures how often the player gets on base, will lead to runs, and runs end up in wins.
The genius of sabermetrics has been popularized in other sports clubs as well. The Boston Red Sox hired Theo Epstein, a savvy front office manager from Yale, who brought the Red Sox two championships by using sabermetrics. In basketball, the Houston Rockets hired Daryl Morey, an MIT graduate, to incorporate statistics in their player development process. Liverpool, a Premier League team that consults with Billy Beane, increasingly employs different statistical methods to evaluate player movements and rate their efficiencies. In effect, sabermetrics has not only helped organizations put together better teams but fundamentally changed the way we think about sports. Nowadays, money doesn’t guarantee you wins—but statistics can.
The Academic Fervor
Paul DePodesta is the stat guru who helped Billy Beane revolutionize the Athletics. He studied economics and statistics at Harvard, so in a world where former athletes mostly run the show, DePodesta is outlier. Unfazed, DePodesta intellectualized the game by using advanced statistical methods to take advantage of “market inefficiencies” and paved the way for future generation of academics getting involved in the field.
Professor Jo Hardin, who teaches statistics at Pomona, is likewise fascinated by such a notion. “I strongly believe that humans are exceedingly bad judges of observable quantitative information,” Hardin said. She taught an ID1 course last year called Statistics in the Real World and used Moneyball as a part of the class curriculum. While she is more interested in the idea of randomness in sports statistics, she certainly appreciates the advent of sabermetrics in baseball.
“Without statistics, our subjective perception of ability will invariably mislead our judgment of the players,” Hardin said. “It is hard to differentiate a .300 hitter and a .350 hitter across an entire season, let’s say you only see the home games. Consider a .300 hitter who gets 1000 at-bats in a season, 500 at-bats at home. In an average season, he’ll get 150 hits with a range of 111-186 hits. The .350 hitter will get 175 hits on average with a range of 127-216 hits. But it is completely possible for the .350 hitter to get fewer runs than the .300 hitter!”
According to Billy Beane, runs produce wins. In his formula, it didn’t matter if you hit .350 or .300 as long as you produce runs. Such an idea seems counterintuitive to fans who are trained to laud players with high averages. The economists and the statisticians of the world thought otherwise.
A growing interest in sports statistics at schools across the country has helped to bolster the movement. People are starting to take sabermetrics as a serious academic discipline. Tufts University offers a course called Sabermetrics 101, Williams College’s Math 399 course focuses exclusively on sabermetrics, and Yale School of Management teaches sabermetrics to future MBA-holders.
Professor Chandler has also embarked on research projects at Pomona. He has taught sports statistics before and is intrigued at the prospect of translating statistical ideas into baseball. Chandler is working with Guy Stevens PO ’13 to figure out which data might predict major league success and how evaluation is done at various levels. He’s also helping Evan Watts PO ’12 on his senior thesis, which is looking at trying to establish patterns in pitch selection and location.
“I read a Nature paper on predicting splicing patterns for exons in RNA based on a few hundred features,” professor Chandler said, “And I realized their technique was exactly what we needed for the baseball project.”
Sabermetrics is now somewhat of a discourse. People are asking serious questions and answering them through different theories. While people are still tentative about the realistic effects of Moneyball, scholars recognize the practical benefits.
The Statistical Fan
Nick Gentili PO ’13 plays for the baseball team and thinks that Moneyball gives fans a deeper appreciation for the numbers in baseball.
“I play fantasy baseball, so I am hugely into stats. But more than that, Moneyball made people take the statistical side of baseball more seriously. It helps to get a better overall evaluation of players.”
There is a mixed reaction among fans regarding Moneyball. For people like Nick, analytical statistics has definitely helped fans appreciate the game on a different level, looking for subtleties and praising contributions of undervalued players. But for others, numbers may simply be a distraction.
“From a fan’s standpoint, it’s questionable whether it’s made the game better,” said coach and professor Chandler. “It’s easy to like a guy that hits the ball a mile, it’s harder to get excited about watching someone take ball four on a pitch just off the plate. But that turns out to be an incredibly valuable skill.”
More heated reactions to sabermetrics have also taken place. A couple of years ago, Shane Jenson, a statistics professor at the Wharton School, argued through his newly devised statistical analysis that Derek Jeter was the worst defensive shortstop in the game. Jenson’s analysis created a fury among New Yorkers, who dismissed the argument as farcical. Derek Jeter is more than a number to New York fans. They see him making clutch plays and churning out Gold Gloves to the tune of five in his career. There is a sentiment among casual fans that numbers are taking away the excitement of watching athletes who, more than anything, should be adored for their athleticism.
To most fans, however, Moneyball is already history. It happened a decade ago and has become a story to be enjoyed on screen. But the sabermetrics saga didn’t end with Billy Beane; it just started. Sabermetrics has altered the way the public watches baseball and changed the way debates will be framed. And as teams struggle to find more efficient means of managing players, the market simply keeps on growing.