This page shares a few different ways to analyzing player and ball trajectories. I have been exploring these methods as I build new features on the SportVu data. I start by visualizing trajectories, then simplifying trajectories, and finally consider some similarity measures.

This page shows how to calculate player velocity, acceleration, and jerk using the NBA SportVu data.

This page shows how to measure the spacing distance using the concept of a convex hull measurement. Stephen Shea and Chris Baker explain this in their article. Take the players’ positions and create a convex hull around them. The area of the defensive polygon is termed Convex Hull Area of the Defense (CHAD) and the area of the offense is called the Convex Hull Area of the Offense (CHAO). Shea and Baker argue and show with limited data that the lineups that typically stretched the defense (CHAO much greater than CHAD) were very successful and efficient.

This page shows how to combine NBA play by play data with SportVu data. The play by play dramatically increases the usefulness of the SportVu data by allowing the identification of plays that are misses and makes as well as the type of shot, e.g., layup or dunk.

Shows how to download and perform basic EDA on the NBA SportVU movement data.