Analyzing NBA basketball data with R

For about three years now, telemetry has been gathered for professional basketball games in the US by SportVU for the NBA. Six cameras track the on-court position of the players and the ball, with a resolution of 25 samples per second.

Combine this movement data with NBA play-by-play data (players, plays, fouls, and points scored — data sadly no longer made available by the NBA), and you have a rich data set for analysis. Naturally, you can read these data files into R, and Rajiv Shah provides several R scripts to facilitate the process. These include functions to import the motion and play-by-play data files and merge them into a data frame, exploration of the movement data, extract and analyze player trajectories and calculate metrics on player motion (such as speed and 'jerk').

James Curley used the same data and extended those scripts to animate NBA plays, such as this basket scored during a December 2015 game between the San Antonio Spurs and the Minnesota Timberwolves. The orange polygon is a measure of player spacing on the court. (Pop a taut rubber band around the players and let go: that's a convex hull.) It would be interesting to extract the area of this convex hull over time as a series, and see if the value relates to scoring opportunities, but that's a task for another time.

James used simple ggplot2 functions to plot the positions of the players and the ball on top of a geom extension to draw the court. Each frame was animated from records in the SportVU data, and then assempled into an animated GIF using the gg_animate function. (Many thanks to James for providing the GIF itself.) You can see further details, including the complete R code, at the blog post linked below.

Curley Lab: Animating NBA Play by Play using R

Comments

[Alex]

This is very interesting. Perhaps we could exchange ideas, I have a lot of basketball data in our archives and also lots of data from other sports. Perhaps there are ways some trends can be found or some other useful patterns?

Drop me a line so we can discuss.