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.

翻译：深度学习具有革新体育表现的潜力，其应用范围涵盖从感知、理解到决策的各个环节。本文对深度学习在体育表现中的应用进行了全面综述，重点关注三个主要方面：算法、数据集与虚拟环境，以及挑战。首先，我们讨论了体育表现中深度学习算法的层次结构，包括感知、理解与决策，并比较了各自的优势与不足。其次，我们列举了体育领域常用的现有数据集，并强调了它们的特点与局限性。最后，我们总结了当前面临的挑战，并指出了深度学习在体育领域的未来趋势。本综述为从事体育应用深度学习研究的学者提供了有价值的参考资料。