Tennis Player Performance Analytics with Roboflow

Professional tennis players often utilize advanced technologies like Hawk-Eye and dedicated analytics teams, but most players rely on memory and basic recordings, missing out on detailed analysis of their play. This text outlines a tutorial for building an automated tennis player performance analysis system using Roboflow's RF-DETR model and OpenAI's GPT-5.1 for tactical reasoning. The system involves training a custom detection model to analyze tennis match footage, identifying player and ball positions, and providing tactical insights through AI-generated commentary. By deploying this model through Roboflow Workflows, users can transform match images into annotated visuals that include bounding boxes, class labels, and concise tactical observations, offering coaches valuable insights into player positioning and strategy. The process includes preparing a dataset, training the model, evaluating performance metrics, deploying the workflow, and configuring a Vision Language Model to generate and format tactical commentary, ultimately providing a comprehensive tool for analyzing tennis performance.