Purpose: The purpose of this study is to map the body of scholarly literature at the intersection of artificial intelligence (AI), analytics and sports and thereafter, leverage the insights generated to chart guideposts for future research. Design/methodology/approach: The study carries out systematic literature review (SLR). Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol is leveraged to identify 204 journal articles pertaining to utilization of AI and analytics in sports published during 2002 to 2024. We follow it up with extraction of the latent topics from sampled articles by leveraging the topic modelling technique of BERTopic. Findings: The study identifies the following as predominant areas of extant research on usage of AI and analytics in sports: performance modelling, physical and mental health, social media sentiment analysis, and tactical tracking. Each extracted topic is further examined in terms of its relative prominence, representative studies, and key term associations. Drawing on these insights, the study delineates promising avenues for future inquiry. Research limitations/implications: The study offers insights to academicians and sports administrators on transformational impact of AI and analytics in sports. Originality/value: The study introduces BERTopic as a novel approach for extracting latent structures in sports research, thereby advancing both scholarly understanding and the methodological toolkit of the field.

翻译：目的：本研究旨在梳理人工智能（AI）、数据分析与体育交叉领域的学术文献，并利用生成的洞见为未来研究规划方向。设计/方法论/方法：本研究采用系统性文献综述（SLR）方法，依据系统综述与荟萃分析优先报告条目（PRISMA）协议，筛选出2002年至2024年间发表的204篇关于AI与数据分析在体育中应用的期刊论文。随后，通过主题建模技术BERTopic从样本文献中提取潜在主题。研究发现：研究识别出当前AI与数据分析在体育应用中的主要研究领域包括：运动表现建模、身心健康、社交媒体情感分析以及战术追踪。每个提取的主题均从其相对重要性、代表性研究和关键术语关联性等方面进行了深入分析。基于这些发现，本研究进一步勾勒出未来研究的潜在方向。研究局限性/启示：本研究为学术界和体育管理者提供了关于AI与数据分析对体育转型影响的深刻见解。原创性/价值：本研究首次将BERTopic作为提取体育研究潜在结构的新方法，既推进了学术理解，也拓展了该领域的方法论工具箱。