Emerging Trends in Sports and Artificial Intelligence: A Scientometric Analysis in Citespace

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Yue Ren, Shaowei Wang, Xiangyu Wang, Jun Chu


This study endeavors to utilize CiteSpace software to construct and examine co-citation networks of references within the domains of sports and artificial intelligence (AI), gathered from the Web of Science. The aim is to delineate the progression of AI applications in sports and to pinpoint current research focal points. Analyzing a corpus of 2,493 papers sourced from the Web of Science via CiteSpace, the findings reveal a year-on-year escalation in the application of AI within sports over the last twelve years (2010 to 2021). Machine learning has found extensive application in sports, notably in the analysis of athlete behavior, prediction of match outcomes, and physiological monitoring, among other areas. The evolution of sports and AI has witnessed two significant fluctuations within the past twelve years, specifically in 2011 and 2014. The modularity shift in 2011 indicated an uptick in the analysis of tasks and the identification of human activities. The year 2014 was marked as a pivotal moment with the advent of visualization utilization in team sports and wearable technologies. The study underscores the presence of numerous unexplored intellectual avenues within the sports and AI domain, which warrant further exploration in future research endeavors. Key milestones spanning from 2010 to 2021 are elucidated through visual analysis.

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