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—and explain how we solved some of these challenges.
Computer Vision in Sports: Scalable Solutions for Downmarket Leagues
Session Speakers
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Mehrsan Javan
Chief Technology Officer, SportLogiq
Mehrsan Javan, Ph.D., M.B.A., is the co-founder and CTO of SPORTLOGiQ, the AI platform for sport analytics that provides advanced game insights to teams, leagues, media organizations, betting operators and rights holders of all sizes. He holds a Ph.D. in computer vision and machine learning and has over a decade of experience in building intelligent systems. His passion is new technologies; he has particular interest in intelligent systems and their positive impacts on our daily lives. He is also an adjunct faculty member in the ECE department at McGill University. Mehrsan has published numerous research articles in top-tier journals and conferences and has authored several patents and pending patent applications.
