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2016

Applying Deep Learning to Basketball Trajectories

Shah, Rajiv, Romijnders, Rob

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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.

Applying Deep Learning to Basketball Trajectories · Around

Built on

Generating Text with Recurrent Neural Networks
J. Martens · 2011
Earlier work this paper cites.
Generating sequences with recurrent neural networks
Original
A. Graves · 2013
Earlier work this paper cites.
Bach in 2014: Music Composition with Recurrent Neural Network
Original
I.-T. Liu and B. Ramakrishnan · 2014
Earlier work this paper cites.
Introducing ShArc: Shot Arc analysis
M. Beuoy · 2015
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