Kaggle Author notes: This dataset is composed of positional tracking data collected through the NBA SportVU system during the 2013-2016 NBA seasons. This isn't raw data, but is a composition by the original author (Michael Beuoy) meant to support the visualization of player shooting motions. It contains the 190 most prolific 3 point shooters over this time period. I have downloaded and assembled what the original author freely shared on their website for use here on Kaggle.

Source: https://www.inpredictable.com/2021/01/nba-player-shooting-motions-data-dump.html

Original Author notes: " The dataset has two main csv files:

player_metrics: A player level data set with a variety of metrics characterizing their three point shot motion

path_detail: This file contains detailed trajectory data (in all three dimensions) for each player. This is the "typical" shooting motion, derived from all available player three point shots (minimum of 200).

January 30 Update: I have added separate sets of files with shot trajectories trained on the following subsets of shots: Open shots: nearest defender is 4 feet away or greater at time of shot Defended shots: nearest defender within 4 feet at time of shot Made shots Missed shots

And a few more technical details: The data is a bit stale at this point, as it is derived from raw tracking data from 2013-2016 (the NBA took the data down publicly on January 23, 2016, a date firmly etched in my mind) The shot trajectories were modeled using standard LOESS techniques (the locfit package in R, specifically). The x, y, and z trajectories were each modeled separately as a function of time, with a "span" of 0.05 The trickiest part is figuring out when shooting motion begins. There is no perfect answer here, but the definition I am using is as follows: "When the horizontal velocity of the ball towards the basket reaches a maximum". The idea here is that is the point when the player has decided to start bringing the ball back towards their body. "