<![CDATA[We explore theories and applications of Computer Vision (CV) in sports. We use the method proposed included: object, research question, search process, inclusion and exclusion, quality assessment, data collection, data analysis, and characteristics of the article. We review it based on problem, methods, interpretation, finding, and future work. We analyze it based on categories: recognition, motion, detection, classification, identification, and automation. Process CV in sports included computing technology, capture motion, multi-scenarios, application of statistical sports, output prediction, object measurement, performance, and object adjudication. We found that Machine Learning (ML) and Deep Learning (DL) were widely used on CV in sports. DL approach has more advantages than the ML approach because the DL approach is supported by high-performance computing and high-quality image datasets. The implication of this research is an artificial feature-based, multi-scenarios, syntaxis method, rapid prototype, indoor localization, and gaze method as big challenge and new potential research for CV in sports. ]]>
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