The Real Lessons of Moneyball

The efficient market hypothesis in Michael Lewis’s book essentially holds fast, but there are still inefficiencies to be exploited

Bryan Dalton

By Justin Fox

We are deep into October. The Yankees and Red Sox have been sent home, but the Oakland Athletics are still thrilling fans on a nightly basis. It’s the 2002 A’s, not 2011, and their exploits can be seen only on a dwindling number of movie screens. But we A’s fans (I’ve been one since the early 1970s) take what we can get.

What we get is a movie, Moneyball, in which Brad Pitt, as A’s General Manager Billy Beane, throws a few chairs and craftily rebuilds his team into a 103-game winner after the departure of three stars Oakland can’t afford to keep. We also get a seemingly unending debate over what the A’s run of success-but-not-quite-triumph from 2000 through 2006, and the team’s subsequent failure to put together a winning season, really means. Is Beane an innovative genius? A fraud? Just lucky? Is scientific analysis the key to success in sports and in life? Is baseball fair? My A’s are no longer just a baseball team; they are a heavily freighted metaphor.

As recounted in the 2003 Michael Lewis book upon which the movie Moneyball was based, Beane was among the first high-level baseball executives (the first was his predecessor and mentor, Sandy Alderson) to openly embrace what baseball geeks call sabermetrics, commonly known in the business world as data analytics. Through the unsentimental use of statistics, Beane was able, in Lewis’s telling, to exploit inefficiencies in the market for baseball talent and build a low-budget team that triumphed over lavishly funded foes.

Moneyball quickly became a business classic, and deservedly so. It’s a lot more fun to read than your average business book, making brainy iconoclasm seem heroic. Plus, baseball possesses two characteristics that in business exist only in the abstract: a level playing field (except for the pitcher’s mound) and truly reliable performance metrics. As a result, cause and effect are clearer. You can isolate successful behaviors and counterproductive ones. All this makes baseball a great demonstration ground for concepts with application elsewhere. But the most useful lessons to be drawn from the recent history of the Oakland A’s—and, for that matter, from sports in general—may not be the ones celebrated in Moneyball.

The concept at the heart of Moneyball is the efficient market hypothesis. As originally formulated in the 1960s, this theory held that on a big, transparent market such as the New York Stock Exchange, hard-nosed speculators will quickly sniff out discrepancies between asset prices and fundamental value. Through what’s called arbitrage—buying underpriced assets and short-selling overpriced ones—these geniuses get rich and the discrepancies disappear. Meanwhile, the goofballs who peruse market charts in search of secret meanings, or buy a stock because they just read about it in the Wall Street Journal, lose their money. The result: an efficient market.

In recent years, scholars have poked lots of holes in the belief that real-world financial markets approach this ideal. Emotion and shortsightedness can prevail for long periods. Arbitrageurs who are correct in their assessment of fundamental values can still lose all their money if they get the timing wrong. Goofballs sometimes get rich.

In sports, unlike finance, the fundamentals are there for everyone to see. Earnings per share can be manipulated; earned run average cannot. So you might think it would be harder for inefficiencies to persist. Yet they do.

The inefficiency at the heart of Moneyball had to do with on-base percentage. It’s a better metric of offensive value than batting average, but in the early 2000s a team could hire a player with a high on-base percentage but a middling batting average (that is, somebody who walks a lot) at a discount. When Clemson University economists Jahn K. Hakes and Raymond D. Sauer set out to test what they called “the Moneyball hypothesis” a few years ago, they found that on-base percentage was deeply undervalued through the 2002 season. The price began to go up in 2003, as other teams—notably the Boston Red Sox—began to emulate Beane’s approach. By 2004, after Lewis’s book had topped the best-seller lists, players with high on-base percentage were no longer a bargain.