Computational Analysis and Statistics of Table Tennis Games

Abstract

This paper presents ChatPPG prototype, which is an innovative system that combines large language models (LLMs) fine-tuned with Low-Rank Adaptation (LoRA) and computer vision for real-time data analysis and coaching for table tennis games. By integrating multi-camera 3D reconstruction, visual object detection and object tracking, ChatPPG processes match data such as player speed, ball trajectories, and service legality, transforming raw metrics into actionable insights. The fine-tuned model achieved a Q/A accuracy 92.3 %, surpassing the baseline model 83.7 %, with sub-second response times enabled by 8-bit quantization. Practical applications demonstrated its ability to deliver personalized training plans and tactical recommendations tailored to individual player profiles. User feedback from professional coaches and athletes rated tactical suggestions at 9.3/10 and training recommendations at 8.9/10. Integrating structured CV outputs with LLM capabilities enhanced transparency and interpretability, allowing users to trace recommendations to data-driven decisions. Despite dataset limitations and the need for advanced query handling, ChatPPG bridges the gap between data analysis and decision-making, setting a new standard for integrating LLMs and CV technologies in fast-paced sports analytics.