## 4 Discussion  
The integration of technology into sports is rapidly transforming how athletes train, compete, and improve their performance. The use of AI, in particular, has been widespread across various areas of sports [35; 36; 37]. This trend is particularly evident in sports like table tennis, where precision, speed, and technique are crucial. Our system's distinctive architecture combines binocular vision with an optimized YOLOv5 deep learning framework, achieving unprecedented performance metrics--including 100% ball detection accuracy and >98% positional precision under normal training conditions. These results are comparable to and slightly surpass the 97% achieved with other machine learning methods [8]. Additionally, the implementation of a MediaPipe-based kinematic analysis enables a robust quantification of player form through frame-by-frame comparison against biomechanically optimal stroke patterns. Notably, our DTW classification algorithm demonstrates statistically significant discriminative power between skill levels (25 beginner- and 25 intermediate-level players), with a \(p\) value of 0.004 and Cohen's d (effect size) of 0.86, representing a significant advancement in automated sports pedagogy. With a total implementation cost under USD 800, this solution establishes a new benchmark for cost-effective, scalable AI training systems. Our system enables detailed load monitoring of players' physical activities during table tennis training, such as tracking the number of rallies and the speed of the ball. This functionality supports training load optimization and injury prevention by providing actionable insights into player performance [35]. Beyond sports training, the system's adaptability allows it to be applied in various racket sports settings for tactical advancement. By quantifying match play dynamics, it provides coaches and analysts with enhanced insights derived from a comprehensive data analysis [36], fostering better decision-making and strategy development. Equally important is the system's ability to analyze and assess the player's hitting pose in real time. By integrating the DTW algorithm, the system quantitatively calculates the difference in the hitting pose angles between players and professional athletes. This allows teachers and coaches to provide personalized feedback for correcting posture, adjusting grip, and refining swing mechanics, helping players refine their technique. Such immediate and precise feedback contributes to more powerful, accurate, and consistent shots. With a total cost of less than USD 800, the system is highly accessible and can be deployed in diverse settings, such as schools or training facilities. The system's high accuracy in ball tracking enables teachers or coaches to gain deep insights into patterns of play, strengths, and areas for improvement. These data can then be used to individualize training sessions, focusing on specific skills or strategies, thereby making practice more efficient and targeted [36]. Additionally, the cost-effectiveness of the system could foster the widespread proliferation and adoption of AI in sports professions, transforming the role and knowledge requirements for coaches [37]. Rather than relying solely on subjective observation and experience, coaches can now leverage precise, data-driven insights to guide their training strategies. This shift empowers coaches to focus on interpreting analytics, designing personalized programs, and fostering strategic thinking among players, ultimately elevating the quality of coaching and athlete development. While promising, our findings have limitations. First, the system's performance was validated under controlled lighting and camera angles; variable environments (e.g., outdoor settings or uneven illumination) may reduce tracking accuracy. Second, the dataset, though balanced (25 beginners vs. 25 intermediates), lacks elite athletes and players with diverse body types, potentially limiting generalizability.