How Big Data Is Changing the Future of Basketball

Analytics has affected most major American sports immensely. Detailed statistics have allowed teams to analyze their players with fresh perspectives, and incorporate such findings into the pricing of player contracts. Baseball was the trailblazer, due to Billy Beane’s “Moneyball” revolution, as depicted in the feature film. Beane and his analytics staff saw that some traditional statistics were often over-valued because they often had to do with factors out of the player’s control. RBIs (runs batted in), for example, are clearly as much a product of how many men are on base as they are of how the hitter bats. Beane recognized that by tweaking and broadening traditional statistics, he could create more objective and comprehensive measures of a player’s skill. Later on, others would take this a step further and analyze areas that statistics hadn’t traditionally penetrated, like defense.

However, the most effective use of sports analytics might be in basketball, because of the low number of variables. There are only 10 players on the court, much fewer than in any other major American sport. Furthermore, there are no weather or park conditions to worry about, because basketball has a standard court size and is indoors. Because of this, basketball analytics has taken huge leaps forward in the last couple of years, as the mechanics of the game become increasingly well examined.

SportVU cameras that the NBA has installed in all 30 arenas have helped usher in a new era of analytics. These cameras share the same technology as cutting edge missile trackers, and can record data points on all 10 players and the ball over 20 times per second. This tracking has allowed teams to sift through mounds of data that was never before thought possible, and what has ensued is a more thorough appreciation of what constitutes an effective player. For example, Enes Kanter averaged 15.5 points and 9 rebounds a game, while Draymond Green averaged almost 12 and 8. Traditional statistics point to Kanter as having had the better season, but data that via SportsVU and other sources shows Kanter as a massive defensive detriment and Green as an extraordinarily defense, positioning, and passing asset. It should not come as a surprise that Kanter was traded midseason, and teams on either side of the trade had a better record without him this season. Nor should it come as a surprise that Green started for the NBA champion Golden State Warriors. As advanced stats continue to proliferate the game, inefficient volume scorers will see less minutes, and teams will become more efficient in their lineup selections.

However, perhaps the most interesting field of basketball analytics is currently in wearable technology. Several teams have begun to wear these in practice to track fatigue levels in players. When combined with SportsVU camera data, this technology can objectively measure fatigue and how a body is holding up to a season. One team that uses the technology is the aforementioned champions. While they controversially rested players several times after the All-Star Break, their coach insisted that his players were fatigued. He was proven right in resting them when the Warriors went on to claim the title. This technology will ideally lead to a drop in player injury rate and in turn elongate careers. The future of technology’s role win basketball has a bright future, as do the players who will the beneficiaries.

By Sam Minter CMC ‘17

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