MONEYBALL: DATA-DRIVEN BASEBALL on september 23, 2011, the film Moneyball opened in theaters across the United States starring Brad Pitt as Billy Beane, the iconoclastic general manager of Athletics. The film was based on the bestselling book by Michael Lewis that described baseball, how Beane led the underdog As, with one of the t est budgets in Major League the next to win 103 games in 2002. Under Beane's watch, the As made the playoffs five times in eight seasons. a At the opening of the 2002 baseball season, the wealthiest team was the New York with payroll $126 million; the oakland As and Tampa Bay Devil Rays, each with payrolls of $41 million, were the poorest These meant that only the wealthiest teams could afford the best players A poor team, such as the As, could only afford what "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 tained that big-name highly a and were main ingredients for winning. Beane and his assistant general manager Paul DePodesta used advanced statistical analysis of player and to prove that wrong. The prevailing metrics for predicting win 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 a their gut intuition, to size up talent for their team 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