For sports teams, analyzing and visualizing data — on-field and in the stands — is still an emerging discipline. In the past year, we’ve highlighted a few examples from leading-edge practices at the Boston Red Sox, a pioneer in analyzing video, and various teams using data analytics and predictive tools to plan ticket pricing . Just before the NBA All-Star Game, one analyst found that the 18 teams that employ analytics departments are winning more games than those without — as much as 7 percent more.
Ben Heller at the ESPN-affiliated “Red94″ blog noticed the Rockets and Trail Blazers were winning often against sub-.500 teams, but losing more often against teams above .500 with analytics departments in their organizations. Heller reviewed won-loss records and showed a higher winning percentage, but there were other explanations, like “sometimes that’s just the way the ball bounces.” And unlike other sports where plays are aimed at a particular person, basketball has more chance elements (A bad bounce, a personal foul, poor officiating are frequent complaints this year).
Sure luck and timing affect the game and its outcome. But using data visualization tools makes it easier than ever before to see the impact time-of-possession, shot selection and even player habits. There are barfront arguments over teams and players and perhaps people are debating the role of analytics. For example, Kobe Bryant is viewed as a last-shot superstar largely because his highlights get TV airtime frequently and the Lakers wins are high-profile. Statistically, he sinks game-winning shots at about the same rate (30 percent or so) as other buzzer-beaters.
Chicago Now sports blogger Alex Sonty explores how coaching strategies of some teams — particularly the Utah Jazz and Chicago Bulls – depend on strict, consistent plays that force mistakes by the opposing team. Combined with an analytical view of how to key on specific players or locations on the court, these can also have a game-changing impact.
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The bloggers agree that basketball metrics are an imperfect and still-evolving science compared to those employed in baseball. highly imperfect relative to baseball’s statistical revolution, but that’s an indicator that we need to work harder to discover ways to better understand the roles of efficiency, value and performance stats.
Expect these and other discussions at the annual MIT Sloan Sports Analytics Conference, in Boston March 4-5. This growing annual event explores the many ways teams and players are turning to business intelligence and predictive tools for everything from roster building to front-office contracts and game-time or financial strategy.
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David Wallace
Spotfire Blogging Team
