AI and analytics in sports: Leveraging BERTopic to map the past and chart the future
It provides a methodological advance for academicians and sports administrators by applying BERTopic to structure sports research, but it is incremental as it adapts an existing tool to a new domain.
This study conducted a systematic literature review of 204 articles from 2002 to 2024 to map research on AI and analytics in sports, identifying key areas like performance modelling and health, and used BERTopic for topic modeling to guide future research.
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.