  One of the emerging trends for sports analytics is the growing use of player
and ball tracking data. A parallel development is deep learning predictive
approaches that use vast quantities of data with less reliance on feature
engineering. This paper applies recurrent neural networks in the form of
sequence modeling to predict whether a three-point shot is successful. The
models are capable of learning the trajectory of a basketball without any
knowledge of physics. For comparison, a baseline static machine learning model
with a full set of features, such as angle and velocity, in addition to the
positional data is also tested. Using a dataset of over 20,000 three pointers
from NBA SportVu data, the models based simply on sequential positional data
outperform a static feature rich machine learning model in predicting whether a
three-point shot is successful. This suggests deep learning models may offer an
improvement to traditional feature based machine learning methods for tracking
data.

Neural and Evolutionary Computing;Computer Vision and Pattern Recognition;Machine Learning