# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
###### Abstract  
(1) Background: Tennis has changed toward power-driven gameplay, demanding a nuanced understanding of performance factors. This review explores the role of machine learning in enhancing tennis performance. (2) Methods: A systematic search identified articles utilizing machine learning in tennis performance analysis. (3) Results: Machine learning applications show promise in psychological state monitoring, talent identification, match outcome prediction, spatial and tactical analysis, and injury prevention. Coaches can leverage wearable technologies for personalized psychological state monitoring, data-driven talent identification, and tactical insights for informed decision-making. (4) Conclusions: Machine learning offers coaches insights to refine coaching methodologies and optimize player performance in tennis. By integrating these insights, coaches can adapt to the demands of the sport by improving the players' outcomes. As technology progresses, continued exploration of machine learning's potential in tennis is warranted for further advancements in performance optimization. machine learning; performance; tennis; artificial intelligence (AI) +
Footnote †: journal: Medical Intelligence  
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Footnote 1: email: _Applied_  
coaches, and trainers. Artificial intelligence (AI) emerges as a potential tool to address this challenge, offering new techniques for analyzing and optimizing performance in tennis. Tennis generates large volumes of data that capture nuanced aspects of performance. These characteristics make tennis particularly suitable for machine learning applications, as AI algorithms can efficiently process these complex datasets to extract meaningful insights. In recent years, computational intelligence has emerged as a powerful tool for optimizing athletic performance across various sports [15], and research interest concerning AI and its subcategories is growing exponentially, with considerable potential for further growth in the coming years [16]. In this dynamic and data-rich environment, machine learning emerges as a powerful tool for applied scientists [14; 17]. Machine learning, a subfield of artificial intelligence, investigates the realm of knowledge discovery within data [18]. Additionally, neural networks are used in deep learning, an additional branch of machine learning, to accomplish the same purpose. Once the data have been collected, a considerable amount of time is usually spent formatting and preparing the data for analysis [18]. This includes standardizing the data for analysis, removing or interpreting variables with an excessive number of missing values, and performing common statistical tests to evaluate relationships, such as collinearity [18]. Machine learning has been applied in several fields of investigation for talent identification in tennis [19], as well as in sports injury prevention in soccer [20; 21], skiing [22], baseball pitchers [23], basketball [24], and volleyball [25]. Additionally, machine learning has also been applied to predict match results [26; 27; 28]. These advancements across diverse sports suggest the potential of machine learning to change performance optimization in tennis as well. Although machine learning has changed various aspects of sports science, including talent identification, injury prevention, match outcome prediction, and training optimization, its application in optimizing tennis performance remains an under-explored topic. While several systematic reviews have explored various applications of machine learning in tennis [29; 30; 31], a comprehensive understanding of how machine learning can be applied across various aspects of tennis performance is lacking in the literature.