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MONEYBALL: DATA-DRIVEN BASEBALL

O n September 23, 2011, the film Moneyball opened in theaters across the United States,
starring Brad Pitt as Billy Beane, the iconoclastic general manager of the Oakland
Athletics. The film was based on the bestselling book by Michael Lewis that described
how Beane led the underdog A’s, with one of the tiniest budgets in Major League
baseball, to win 103 games in 2002. Under Beane’s watch, the A’s made the playoffs five times in
the next eight seasons.
At the opening of the 2002 baseball season, the wealthiest team was the New York Yankees, with
a payroll of $126 million; the Oakland A’s and Tampa Bay Devil Rays, each with payrolls of about
$41 million, were the poorest. These disparities meant that only the wealthiest teams could afford
the best players. A poor team, such as the A’s, could only afford what the “better” teams rejected,
and thus was almost certain to fail. That is, until Billy Beane and Moneyball entered the picture.
How did Beane do it? He took a close look at the data. Conventional baseball wisdom main-
tained that big-name highly athletic hitters and skillful young pitchers were the main ingredients
for winning. Beane and his assistant general manager Paul DePodesta used advanced statistical
analysis of player and team data to prove that wrong. The prevailing metrics for predicting wins,
losses, and player performance, such as batting averages, runs batted in, and stolen bases, were
vestiges of the early years of baseball and the statistics that were available at that time. Baseball
talent scouts used these metrics, as well as their gut intuition, to size up talent for their teams.
Beane and DePodesta found that a different set of metrics, namely, the percentage of time
a hitter was on base or forced opposing pitchers to throw a high number of pitches, was more
predictive of a team’s chances of winning a game. So Beane sought out affordable players who
met these criteria (including those who drew lots of “walks”) and had been overlooked or rejected
by the well-funded teams. He didn’t care if a player was overweight or seemed past his prime-he
only focused on the numbers. Beane was able to field a consistently winning team by using
advanced analytics to gain insights into each player’s value and contribution to team success that
other richer teams had overlooked.
Beane and his data-driven approach to baseball had a seismic impact on the game. After
observing the A’s phenomenal success in 2002, the Boston Red Sox used the talents of baseball
statistician Bill James and adopted Beane’s strategy, only with more money. Two years later, they
won the World Series.
Although many experts continue to believe that traditional methods of player evaluation,
along with gut instinct, money, an
d luck, are still the key ingre-
dients for winning teams, the
major league teams acknowl-
edge that statistical analysis
has a place in baseball. To some
degree, most major league
teams have embraced saber-
metrics, the application of
statistical analysis to baseball
records to evaluate the perfor-
mance of individual players.
The New York Yankees, New
York Mets, San Diego Padres,
St. Louis Cardinals, Boston Red
Sox, Washington Nationals,
Arizona Diamondbacks,
© ZUMA Press, Inc. / Alamy
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