Applying Deep Learning to Basketball Trajectories
By Rajiv Ratn Shah, Rob Romijnders. 2016
59 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Economics and Econometrics, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ala Al-Fuqaha
- Mohsen Guizani
- Sameh Sorour
- M. Mohammadi
- Ekram Hossain
- Sanjay Chawla
- Raghavendra Chalapathy
- Syed Ali Hassan
In Economics and Econometrics
In Signal Processing
Citations in the corpus
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “Similarly, [156] used an RNN with LSTM units over the same dataset to predict the success rates of three-point shots, and reported better classification accuracy compared to gradient boosted machine (GBM) and generalized linear model (GLM).”