# The Role of Artificial Intelligence in Sports Analytics:
## 2 Materials and Methods  
### Eligibility Criteria and Search Strategy  
This systematic review and meta-analysis was conducted in accordance with the PRISMA 2020 guidelines. The review protocol was prospectively registered in the PROS-PERO database (registration number: CRD4201018603). A comprehensive systematic literature search was conducted across multiple high-impact databases, including Semantic Scholar, PubMed, IEEE Xplore, Web of Science, and Scopus. The search strategy incorporated a combination of controlled vocabulary (MeSH terms) and free-text keywords, ensuring a broad yet precise retrieval of relevant studies. Search terms included \"artificial intelligence\", \"machine learning\", \"deep learning\", \"computer vision\", \"reinforcement learning\", \"sports performance\", \"action recognition\", and \"predictive analytics\". Boolean operators (AND, OR) and truncation techniques were applied to refine search queries and maximize relevant retrieval. Only peer-reviewed journal articles and rigorously reviewed conference proceedings were eligible for inclusion in the quantitative synthesis. Preprints, dissertations, or non-archival grey literature sources were excluded unless they had undergone formal peer-review. To ensure the inclusion of the most recent and methodologically robust evidence, the search was restricted to peer-reviewed articles or conference papers published in English between 2015 and 2025. Previous systematic reviews in sports analytics and AI applications underscore the necessity of a structured and transparent search strategy, particularly given the heterogeneity of AI methodologies and the multidisciplinary nature of sports science research [24; 25]. Given the multidisciplinary nature of AI applications in sports, a comprehensive review of both domain-specific and general AI literature is essential to accurately capture methodological advancements and practical applications. Accordingly, our search strategy deliberately encompassed both specialized sports science databases and broader AI-focused repositories to ensure an inclusive and methodologically sound synthesis [3; 18]. \begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline
**Component** & **Description** \\ \hline Population (P) & Competitive athletes and team sports as well as individual sports (e.g., soccer, basketball, tennis) at both amateur and professional levels. \\ \hline Intervention (I) & Application of artificial intelligence techniques, such as machine learning, deep learning, computer vision, and reinforcement learning, to analyze and enhance sports performance (e.g., real-time feedback, predictive analytics, automated motion analysis). \\ \hline Comparison (C) & Traditional performance analysis methods (e.g., manual assessments, conventional statistical techniques) or comparisons among different AI-based methodologies. \\ \hline Outcomes (O) & Quantitative metrics (e.g., accuracy, precision, recall, F1-score, mean absolute error) and qualitative outcomes (e.g., tactical decision support, improved training strategies, enhanced performance monitoring). \\ \hline Study Design (S) & Empirical studies including experimental, observational, and quasi-experimental designs, as well as systematic reviews and meta-analyses that report on AI applications in sports performance analysis. \\ \hline \end{tabular}
\end{table} 
Table 1: PICOS framework. Only those investigations that explicitly examined the application of artificial intelligence encompassing machine learning, deep learning, computer vision, and reinforcement learning in the realm of sports performance analysis were considered. Eligible studies were required to involve competitive athletes or teams across a range of sports disciplines (e.g., soccer, basketball, tennis) and to report empirical findings using quantifiable performance metrics such as accuracy, precision, recall, or F1-score.