Sampaio Tatiana, Oliveira João P, Marinho Daniel A, Neiva Henrique P, Morais Jorge E
Department of Sports Sciences, University of Beira Interior, Covilhã, Portugal.
Research Centre in Sports, Health and Human Development (CIDESD), Covilhã, Portugal.
Front Sports Act Living. 2024 Dec 23;6:1456998. doi: 10.3389/fspor.2024.1456998. eCollection 2024.
The aim of this study was to conduct a scoping and bibliometric review of articles using artificial intelligence (AI) in tennis. The analysis covered various aspects of tennis, including performance, health, match results, physiological data, tennis expenditure, and prize amounts. Articles on AI in tennis published until 2024 were retrieved from the Web of Science database. A total of 389 records were screened, and 108 articles were retained for analysis. The analysis identified intermittent gaps in publication output during certain intervals, notably in the years 2007-2008 and 2012-2013. From 2012 onward, there was a clear upward trend in publications and citations, peaking in 2022. The theme was led by China, the United States, and Australia. These countries maintain their status as the top contributors in terms of publications. The analysis of author collaborations revealed multiple clusters, with notable contributions from researchers in China, Australia, Japan, and the United States. This bibliometric review has elucidated the evolution of AI research in tennis, highlighting the countries and authors that have significantly contributed to this field over the years. The prediction model suggests that the number of articles and citations on this topic will continue to increase over the next decade (until 2034).
本研究的目的是对网球领域中使用人工智能（AI）的文章进行范围界定和文献计量学综述。分析涵盖了网球的各个方面，包括表现、健康、比赛结果、生理数据、网球支出和奖金数额。从科学网数据库中检索了截至2024年发表的关于网球领域人工智能的文章。共筛选了389条记录，保留108篇文章进行分析。分析发现，在某些时间段内，特别是2007 - 2008年和2012 - 2013年，发表量存在间歇性差距。从2012年起，发表量和被引次数呈明显上升趋势，在2022年达到峰值。该主题由中国、美国和澳大利亚引领。这些国家在发表量方面保持着顶级贡献者的地位。对作者合作的分析揭示了多个聚类，中国、澳大利亚、日本和美国的研究人员做出了显著贡献。这篇文献计量学综述阐明了网球领域人工智能研究的发展历程，突出了多年来对该领域做出重大贡献的国家和作者。预测模型表明，在未来十年（至2034年），关于该主题的文章数量和被引次数将持续增加。
