Applying Deep Learning to Basketball Trajectories

TL;DR

This paper demonstrates that deep learning models, specifically recurrent neural networks, can effectively predict basketball shot success from trajectory data, outperforming traditional feature-based models.

Contribution

It introduces a sequence modeling approach using RNNs to predict shot success directly from trajectory data, reducing reliance on manual feature engineering.

Findings

Deep learning models outperform static feature-based models in shot prediction.

Trajectory data alone can effectively predict shot success.

Recurrent neural networks learn basketball trajectories without physics knowledge.

Abstract

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.