# The Role of Artificial Intelligence in Sports Analytics:
###### Abstract  
This systematic review and meta-analysis investigates the application of artificial intelligence (AI) in sports performance analysis. Sixteen peer-reviewed studies spanning 13 distinct sports disciplines were included, employing a variety of AI techniques--from classical machine learning algorithms to advanced deep learning and computer vision models. Methods applied encompassed Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, reinforcement learning, and predictive modeling architectures. The pooled average classification accuracy was 87.78% (95% CI: 82.66-92.90), although substantial heterogeneity was observed across studies (I\({}^{2}\) = 93.75%). Computer vision and deep learning-based approaches were associated with higher performance metrics in several studies, particularly in movement-intensive sports such as tennis and basketball. Nevertheless, several challenges were identified, including lack of standardization in model evaluation, limited algorithmic transparency, and difficulties in generalizing findings from controlled laboratory environments to real-world competitive settings. The results underscore the promising role of AI in optimizing training protocols, supporting tactical decisions, and enhancing injury prevention strategies. Further research is warranted to address the ethical, methodological, and practical considerations surrounding the deployment of AI in sports contexts. artificial intelligence (AI); sports performance analysis; machine learning; deep learning Permission from IEEE must be found at the IEEE  
AI advancements now enable automated data extraction from wearables, GPS, and video--providing high-dimensional inputs for performance analysis through ML, DL, RL, and computer vision techniques [6,7]. These AI-driven approaches have been successfully applied across multiple disciplines, such as integrating GPS and physiological data from soccer and other sports for enhanced player tracking [8] and leveraging AI-embedded inertial measurement units (IMUs) for advanced gymnastics performance evaluation [9]. Additionally, AI-based play sequence analysis has demonstrated significant promise in skill development and tactical decision-making, particularly in sports like volleyball [10]. These technologies have been reported to achieve higher levels of accuracy and predictive capability in some studies, when compared with traditional methods, leading injury prevention [11,12]. A growing body of literature suggests that hybrid deep learning models, such as CNN-LSTM architectures, are particularly effective in motion recognition and performance prediction across different sporting disciplines [13]. The fusion of AI techniques with domain-specific knowledge allows for a more refined and adaptable approach to sports analytics, as demonstrated by AI-driven basketball tactical analysis [14]. Recent studies highlight AI's ability to extract key performance metrics, classify movement patterns, and predict athletic outcomes across various sports. For instance, deep learning architectures--such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks demonstrate superior accuracy in action recognition and predictive modeling, with reported performance metrics often exceeding 90% in sports such as basketball, tennis, and soccer [9,15]. In soccer, AI-based motion tracking has revolutionized player behavior analysis, with predictive models enabling more precise assessment of tactical positioning and decision-making [16]. Similarly, AI-assisted combat sports analysis has seen major advancements, with real-time computer vision applications improving athlete evaluation and automated action recognition [12]. Moreover, AI-driven methodologies, particularly in computer vision, have been instrumental in tactical analysis, skill evaluation, and game strategy optimization [17,18].