Neural Network Model for Predicting NBA Shot Outcome

As the game of basketball evolved, analysis of the game has also grown from taking average of field goal percentage to more complex analytics. In the 2013-2014 season, the NBA has installed the SportVU Player Tracking technology in every NBA arena. SportVU collects 25 frames of data per second, each frame containing the (x,y) coordinates of each of the 10 players and the (x,y,z) coordinates of the basketball. The goal of this research is to understand how much better we can predict the outcome of shot given this massive amount of newly available information, and what the important factors are in contributing to a made shot. This understanding will assist the Toronto Raptors, and even the general basketball community, on many levels. For example, this could provide some guidance to players (shot location and time-of-game selection) and to coaches (which player/game situations tend to be most successful. The system could also help quantify the quality of performances of players by the shots that they took, instead of only looking at the outcome, which is inherently probabilistic.

Faculty Supervisor:

Dr. Richard Zemel

Student:

Kuan-Chieh Wang

Partner:

Toronto Raptors

Discipline:

Computer science

Sector:

Sports and recreation

University:

University of Toronto