Monday, May 29, 2017

Free Throw Deep Dives: Accuracy Scatter Plots

In an effort to fill the void during this year's Superbowl-like interlude before the NBA Finals, I have added one more tool to my series of free throw deep dives: Accuracy Scatter Plots

This tool is essentially an interactive version of the charts I originally published two years ago in my first post analyzing SportVU motion tracking data. For those of you familiar with MLB's PitchF/x system, which can track each ball's placement relative to the strike zone, this is a similar view, but for free throw shooting. By analyzing the raw SportVU data on ball motion and applying a simple physics model, I can chart where a player's free throw shots land relative to the center of the hoop.

Here is a sample PitchF/x chart:
And here is a sample "ShotF/x" chart for Kevin Durant:

The blue dots show made free throws and the red dots are misses. My code does its best to make sense of the SportVU data, but some anomalies remain (e.g. the blue dots that fall well outside the hoop).

Saturday, May 6, 2017

Free Throw Deep Dives: The Windup and the Release

Part three of an infrequent series. Click here to go straight to the interactive tool.

In previous free throw deep dives, I used SportVU ball tracking data to examine how launch angle and release spot affect free throw accuracy. In this post, we back things up a bit, one second to be precise, and dive into the specific mechanics of each player's free throw shot.

For this free throw analysis I focused on the motion of the ball (in all three dimensions) for the second prior to the ball being released. One second, while somewhat arbitrary, was chosen so that I'm capturing the natural shooting motion of the player after any pre-shot routine has been completed (e.g. one dribble, two dribbles, Klay Thompson's weird arm tap thing, etc.).

The ball tracking data is messy, and shooting motion will vary from shot to shot, so I built a simple LOESS model for each player, with the goal of teasing out a player's typical shooting motion in all three dimensions. LOESS models are nice because they don't force you to shoehorn your data into a pre-determined type of curve (e.g. polynomial, exponential, etc.).

Here are the results for Kevin Durant's typical shooting form: