On summer mornings when I was 6 or 7, I used to slurp noisily on cereal while poring over the box scores from the previous night's baseball games. My days were defined by how well my hometown Oakland Athletics were doing. When the free-spending New York Yankees ousted my shoestring-budget A's from the playoffs for the second straight year in 2001, I didn't speak to anyone for two days. When those same Yankees lured our star player, hero-turned-mercenary Jason Giambi, for $120 million, I cursed the injustice of a world that had been pretty good to me till then.
In my lifetime, A's fans have gotten used to end-of-season disappointment. But what the team has lacked in playoff wins, it has made up tenfold in giving me the most important intellectual philosophy of my life: sabermetrics.
Graphic: Jean-Francois PodevinIn essence, sabermetrics is the use of context-driven, objective statistics to analyze baseball. Using the work of sabermetric godfather Bill James as a guide, A's general manager Billy Beane and a small staff of stats nerds threw out the book of conventional wisdom—mainly baseball lore from generations of scouts, themselves often former players. Routinely given the lowest payroll in all of Major League Baseball, Beane used stats to figure out which skills the conventional wisdom over- and undervalued. (The tactic was famously documented in Michael Lewis's book, Moneyball, and the movie, starring Brad Pitt, based on it.)
In 2001, the A's had a payroll of $34 million and won 102 games; the Yankees paid their players $110 million and won 95. And although every other team has caught on to the sabermetrics revolution, Beane is still the king: Last year the A's, thought by many analysts to have the worst (and cheapest) team in the league, had a better record than all but three teams (out of 30) and finished first in their division.
But at the end of the day, baseball is still a bunch of guys hitting a ball with a stick. I find sabermetrics inspiring because people who look like me—bespectacled, scrawny folks who struck out a lot more than they hit in seventh grade—are applying its kind of analysis to crucial issues.
Take Ben Bernanke, chairman of the Federal Reserve, who wrote a piece in the Wall Street Journal praising the Washington Nationals baseball team for making wise decisions based on data-driven, rigorous analytical tests. His conclusion: "Many of us in Washington could learn a thing or two from the Nationals' approach."
Or Nate Silver, who, before becoming America's luminary on election forecasting, was a baseball stats guy. If his bestseller, The Signal and the Noise: Why Most Predictions Fail but Some Don't, is a harbinger, we could see Silver turning his attention to the statistical models of climate change.
Sabermetrics is all about a worldview of attacking conventional wisdom. If the answer for why our society does something is, "Because we've always done it that way," it's essential to reexamine that logic. And so sabermetrics can be applied to just about any big issue of our age. Isolating a pitcher's performance from the influence of his team's defense and the ballpark he plays in? Crucial. The sacrifice bunt? Almost always a bad move. Defining education as rows of desks, a teacher lecturing at the front of the classroom, common curriculum, standardized tests, kids sorted by age group and not ability? An outmoded, centuries-old pedagogical model—running schools a lot like factories—that my generation is going to revolutionize.
I still haven't quite figured out what I want to do in life. But I've learned that being the Billy Beane or Nate Silver of you name it—questioning how it's always been done and finding an intellectually rigorous paradigm that works better—is how my generation can fix our world's enormous problems.
Benjy Mercer-Golden, '15, is a political science major.
