# The Role of Artificial Intelligence in Sports Analytics:
## 4 Discussion
### Implementation Contexts Across Sports  
Several overarching technological trends emerge from the reviewed literature. One key advancement is multimodal data integration, which enhances sports performance analysis by combining multiple data sources. GPS tracking, inertial measurement units (IMUs), and physiological data collectively create a more comprehensive monitoring system, allowing for a deeper understanding of athletic movement and performance [8]. AI-embedded IMUs combined with computer vision techniques have further expanded real-time movement analysis capabilities, particularly in gymnastics, where precision and execution play a critical role [9]. Another significant trend is the evolution of advanced machine learning architectures, which have improved the accuracy and efficiency of motion recognition and predictive modeling. Hybrid architectures, such as CNN-LSTM models, have been particularly effective in capturing both spatial and temporal patterns in athlete movements, thereby enhancing the predictive capabilities of AI systems [14]. Additionally, deep reinforcement learning is increasingly explored as a means to develop adaptive training environments, allowing AI models to optimize training regimens based on real-time performance feedback [33]. The importance of explainable AI and model transparency has also gained prominence. In soccer analytics, expected goals models are now incorporating explainabillity metrics, ensuring that AI-generated insights are interpretable for coaches, analysts, and players. This trend reflects a growing emphasis on AI systems that not only generate accurate predictions but also provide transparent and actionable recommendations [3]. A final major trend involves real-time sensor integration for performance monitoring. Wearable AI systems, such as smart sportswear and embedded biometric sensors, are now being developed to provide instant feedback on athlete's movements, physiological condition, and performance. These innovations have been shown to improve training outcomes by delivering precise, real-time data that enables more targeted coaching strategies [30; 32]. Despite these advancements, AI-based sports analysis still faces several challenges. One of the primary obstacles is the lack of standardization in AI model evaluation. Different studies employ varying datasets, performance metrics, and validation methodologies, making it difficult to draw direct comparisons between findings. This inconsistency limits the reproducibility of research and highlights the need for universal benchmarking frameworks [10; 18]. To further validate our findings, we conducted a leave-one-out sensitivity analysis, which demonstrated that the pooled classification accuracy remained stable (86.9-88.5%), indicating that no single study disproportionately affected the overall estimate despite the high I\({}^{2}\) value. Another challenge is data availability and quality constraints, particularly in computer vision applications. Occlusion effects, motion blur, and suboptimal camera positioning can significantly degrade the accuracy of AI models, leading to inconsistencies in action recognition and movement tracking [12]. Additionally, real-world applicability issues persist, as many AI models are trained and tested in controlled laboratory conditions. While this ensures methodological rigor, it limits the generalizability of findings to dynamic, competitive sports environments, where external variables such as crowd movement, environmental conditions, and athlete fatigue can influence AI performance [20]. Moving forward, several areas warrant further research and development. Establishing standardized AI benchmarking frameworks for sports analytics would enhance the comparability of findings across different studies and improve the reliability of AI applications. Expanding AI applications in underrepresented sports, such as rowing, ice hockey, and track cycling, could provide new insights into training methodologies and performance optimization.