Sports analytics is about observing, understanding and describing the game in an intelligent manner. In practice, this requires a fully automated, robust end-to-end pipeline, spanning from visual input, to player and group activities, to player and team evaluation to planning. Despite major advancements in computer vision and machine learning, today sports analytics solutions are limited to top leagues and are not widely available for downmarket leagues and youth sports. In this talk, we explain how we have developed scalable and robust computer vision solutions to democratize sport analytics and offer pro-league-level insights to leagues with modest resources, including youth leagues. We highlight key challenges—such as the requirement for low-cost, low-latency processing and the need for robustness despite variations in venues. We discuss the approaches we tried and how we ultimately overcame these challenges, including the use of transformers and fusion of multiple type of data streams to maximize accuracy.