Applying Deep Learning to Basketball Trajectories
This is an incremental improvement for sports analytics, offering a potential enhancement over traditional feature-based methods for tracking data.
The paper tackled the problem of predicting three-point shot success in basketball using deep learning on trajectory data, and found that recurrent neural networks outperformed a static feature-rich machine learning model on a dataset of over 20,000 shots.
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.