Reinforcement Learning for Predictive Sports Analytics

Our project develops novel machine learning algorithms for interpreting complex, multi-agent scenarios in sports analytics. The collaboration with our industrial partner SPORTLOGiQ will tackle open problems in deep reinforcement learning to build novel capabilities in sports analytics for ice hockey. Deep reinforcement learning is a breakthrough technology with prominent successes in games such as Go (AlphaGo) and Chess (AlphaZero). We will develop fundamental algorithmic advances and apply them to tasks including: – player evaluation – event predictions (match outcomes, next action, expected scores) – recognizing types of players, teams, play sequences, and tactics – identifying characteristic strengths and weaknesses of players and teams Montreal-based SPORTLOGiQ uses advanced computer vision to extract information about events from video of sports matches. Their information is more detailed than that provided by any other company or organization. This project will build significant Canadian capacity in sports analytics, support academic research, advance commercialization in the sports industry, and train highly qualified personnel.

Faculty Supervisor:

Oliver Schulte;Pascal Poupart


Guiliang Liu;Yu-dong Luo;Michael (Mike) Rudd;Xiangyu (Shawn) Sun;Amur Ghose;Michael John (Jack) Davis





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