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The Moneyball Myth

By now, everyone in the sporting world has heard of “Moneyball,” Michael Lewis’s account of how the 2002 Oakland A’s won an astounding 102 games with an unorthodox roster built through statistical analysis. The term has spilled into the popular zeitgeist, becoming shorthand for the triumph of statistics over old methods of subjective analysis. However, the secret of the A’s success is more complex than Lewis portrays: Statistics are meaningless without context, and the true lesson of “Moneyball” is that we should learn to temper our faith in numbers.

Lewis’s account begins in 2002, when Oakland’s General Manager Billy Beane realized that the A’s, one of baseball’s poorest teams, could not compete by playing by the same rules as big-budget behemoths like the Yankees. If the A’s looked for players with the same traditional scouting methods as rich teams, the rich teams would beat them with their wallets. With no other options, Beane abandoned traditional scouting models and adopted advanced statistical analysis as a tool to find players who had skills overlooked by the fickle eyes of traditional scouts.

Lewis describes the deliberations of baseball’s scouts with barely concealed contempt. According to the arcane wisdom of the scouts, if a player had an ugly girlfriend, he lacked confidence and wouldn’t cut it in the big leagues; if he had an odd body type, he was off the shortlist altogether. These were articles of baseball faith—in Lewis’s words, the old scouts were “a Greek chorus [whose job was to] underscore the eternal themes of baseball.”

By contrast, Lewis, ever the Wall Street quant, recounts with glee how Beane and his team discovered useful assets that the scouting market undervalued—older players, pitchers with injury histories, hitters who didn’t do anything flashy but knew how to take a walk. The common thread connecting these choices was hard stats—objective metrics like on-base percentage and defense-independent pitching that spoke of a player’s skill free from the biases of the human eye. With this roster selected from what one observer called the “island of misfit toys”, the A’s amazed baseball by winning 102 games.

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There is much truth to Lewis’s account of the A’s success. Just a decade ago, baseball’s valuation of players was indeed highly flawed—Beane’s discovery of market inefficiencies through statistical analysis gave the A’s an edge over teams stuck in a century-old mode of subjective appraisal. But Lewis also severely undersells the importance of scouting. Oakland’s success came not from abandoning scouting, but from its ability to combine subjective scouting information with sophisticated statistical analysis.

For example, by most modern statistical metrics like Wins Above Replacement, the real strength of the 2002 Oakland A’s was not in Beane’s assortment of undervalued castaways, but in three members of its traditionally constructed rotation—Barry Zito, Tim Hudson, and Mark Mulder. These three carried the A’s to the playoffs—yet all three pitchers were drafted within the subjective scouting system that Lewis derides. By contrast, Jeremy Brown, a pudgy catcher lionized in the book for his great stats but poor scouting profile, ended up dropping out of baseball after playing in only 5 major-league games.

True, talking about a player’s girlfriend as a bellwether of his confidence is silly—but so is ignoring substantial information about his athletic ability, his work ethic, and his playing style.

The counterexample to the opposing examples of Brown and the A’s rotation is Nick Swisher, an outfielder who was drafted by the A’s in 2002 with Brown. With an athletic build and solid performance across numerous statistical categories, Swisher was touted by scouts and number crunchers alike. Over 9 seasons, Swisher has gone on to slug over 230 home runs, demonstrating the success of a synthesis between statistical analysis and subjective scouting.

Indeed, Paul DePodesta ’95, one of Beane’s former lieutenants, has repeatedly argued that the conflict that Lewis describes between scouting and stats is “fantastical.”

“When I go to the park,” DePodesta says, “I try not to look at the stats until after the fact, and then see whether or not what I saw with my eyes agrees with what the page says.”

DePodesta would agree that the true lesson of “Moneyball” is that a clear and empirical view of the facts cannot be entirely dependent on numbers. As any statistician will tell you, statistics are meaningless without context—ignoring the context provided by scouting means discarding valuable data.

Michael Lewis’s writing is a valid critique of the blind faith of baseball’s old guard—those who believed that subjective scouting told the whole story of a player’s value. Perhaps it’s time that the faith of a new generation—those beholden to the absolute truth of numbers—be questioned.

Oliver W. Kim ’16, a Crimson editorial writer, is an economics concentrator living in Leverett House.

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