Sports Data Annotation: How to Label Sports Data for Computer Vision Models

Sports data annotation is crucial for developing effective computer vision models, especially in sports where objects like balls and players are often small, fast-moving, and partially obscured. This guide highlights the importance of labeling with tight bounding boxes, labeling occluded objects as if they were fully visible, and using clear and consistent class names to ensure data usability. It emphasizes the need for training data that closely resembles the production environment to optimize model performance, and introduces Roboflow's Outsource Labeling service, which connects users with professional labelers for efficient dataset curation. The guide also touches on the benefits of documenting instructions clearly, providing feedback, and scaling labeling efforts with expert guidance to achieve high-quality annotations. Additionally, it mentions the use of AI in various sports applications, such as basketball and soccer, and introduces an open-source tracking library for enhanced sports analytics.