Introduction
This post shares some of the code that I have created for analyzing NBA SportVu data.
For background, the NBA SportVu data is motion data for the basketball and players taken 25 times a second. For a typical NBA game, this means about 2 million rows of data. The data for over 600 NBA games (first half of the 2015-2016 season) is available. This is over a billion rows of telematics (iOT) type data. This is a gold mine and here are some early pieces from studying that data.
EDA
The first is basic EDA on the movement data. This code allows you to start analyzing the ball and player movement. 
PBP
The next markdown, PBP, shows how to merge play by play data with the SportVu movement data. This allows using the annotated data which contains information on the type of play, score, and home/visitor info.
Chull
The next set of documents start analyzing the data. The first measures player spacing using convex hulls. The next shows how to calculate player velocity, acceleration, and jerk. (I really wanted to do a post on the biggest jerk in the NBA, but unfortunately the jerk data is way too noisy.) 
The third document offers a few different ways for analyzing player and ball trajectories.
You can find all these files at my SportVu Github repo.