The Mixed Blessing of Big DataThe NBA is in the midst of a data-explosion that at times feels paralysis-inducing. We're spoiled for choice, with literally hundreds of thousands of data points, per game, begging to de dissected and analyzed. And yet, progress has been made. Kirk Goldsberry of Grantland, an early pioneer of shot location data, last year introduced a new SportVU-based stat called EPV, or Expected Point Value. EPV evaluates a team's expected points on a real time basis, as the possession evolves, accounting for shot clock, ball location, and the position of all ten players on the court. A new "microeconomics" for the NBA, as Goldsberry and his coauthors described it in their Sloan Analytics paper.
The NBA itself, in addition to funding and supporting the addition of the cameras, has also developed a whole host of new stats from this "big data" they helped create: Catch and Shoot, Defense at the Rim, Pull Up Shooting, among many, many others. There is also the work of the smart people at Nylon Calculus, making sense of the SportVU data with simple metrics built from common sense understanding of the game of basketball (as opposed to inscrutable mathematical black boxes).
In this post, I begin my own foray into the NBA's big data world, but with a different focus. While it's common knowledge that SportVU data provides location in two dimensions for every player on the court, what may not be widely appreciated is that the ball itself is tracked in all three dimensions. When developing EPV with his students, Kirk Goldsberry code named the work "XY Hoops". Consider the work below an "XYZ Hoops" project of sorts.