A Survey of Deep Learning in Sports Applications: Perception,
  Comprehension, and Decision
Zhonghan Zhao Wenhao Chai Shengyu Hao Wenhao Hu Guanhong Wang Shidong Cao Mingli Song Jenq-Neng Hwang Gaoang Wang
Deep learning has the potential to revolutionize sports performance, with
applications ranging from perception and comprehension to decision. This paper
presents a comprehensive survey of deep learning in sports performance,
focusing on three main aspects: algorithms, datasets and virtual environments,
and challenges. Firstly, we discuss the hierarchical structure of deep learning
algorithms in sports performance which includes perception, comprehension and
decision while comparing their strengths and weaknesses. Secondly, we list
widely used existing datasets in sports and highlight their characteristics and
limitations. Finally, we summarize current challenges and point out future
trends of deep learning in sports. Our survey provides valuable reference
material for researchers interested in deep learning in sports applications.