Posted on April 01, 2014
On the evening of Sunday, October 17, 2004, Boston Red Sox slugger David Ortiz stepped up to Fenway Park’s storied home plate. It was the bottom of the 12th inning in Game 4 of the American League Championship Series; and the hometown team, as in so many years prior, stood with its back against the wall, down three games to none against its archrival, the New York Yankees. As most baseball fans, and every Bostonian will recall, the Red Sox rallied back that night and went on to beat the historic “curse of the Bambino,” bringing the World Series Championship title back to the city for the first time in 86 years.
The 2004 Boston Red Sox were an experiment in statistics – general manager Theo Epstein had selected his rag-tag team of players by relying on predictive analytics. The self-proclaimed “Idiots” didn’t look like World Series Champions, but the numbers didn’t lie. Ortiz’s walk-off homerun that cold, autumn night in Boston not only served as the catalyst for his team’s curse-breaking run to the World Series but further supported a growing movement in Major League baseball. Consider this: just two years earlier, the Minnesota Twins had released Ortiz after being unable to find a team willing to trade for the slow, 6’4”, 230-pound first baseman. He had performed well at the plate but didn’t fit the league’s traditional view of a premier infielder. In Boston, he became a star.
Two years before, the Oakland Athletics finished first in the American League West despite losing three, key free agents to larger market teams in the offseason. As depicted in the movie Moneyball, general manager Billy Beane constructed a winning team through statistical analysis. Baseball, a game that for so long had been dictated solely by intuition, superstition and old-fashioned blood, sweat and tears, was changing – analytics finally had a seat in dugouts around the league.
In recent years, almost every industry – including sports – has begun to realize the value of big data analytics in decision-making. As data becomes increasingly easier to collect, organizations are turning to the numbers to influence strategy, and professional athletics has taken notice. Booz Allen’s Strategic Innovation Group is exploring data opportunities in the sports arena – our work is highlighted by recent coverage in All Analytics, SportTechie and Gigaom. One of our team’s first efforts was to develop analytics that can predict the next pitch in an MLB game with more than 90 percent accuracy. We have also experimented with using data science to mitigate in-game injuries, examined spatial analytics and predictive models to assist NBA teams with identifying optimal shooting zones and provided the MLB with our expertise in developing command centers.
As we celebrate the commencement of professional baseball’s 145th season, it’ll be interesting to see how data will continue to change the playing, and even the viewing, of America’s pastime. In Moneyball, statistics whiz Peter Brand – played by Jonah Hill – said, “Using the stats the way we read them, we’ll find value in players that no one else can see.” Who knows, maybe this could be another year in which a previously overlooked player has the opportunity to lift an entire city – and perhaps even end a curse.
Let's play ball.