Sketching plays is a universal way for coaches to communicate what they want their players to do. What if a coach didn’t have to rely solely on intuition, but could instead foresee how the defending team is likely to respond to the intended play? For the 2018 MIT Sloan Sports Conference Thomas Seidl, Aditya Cherukumudi, Andrew Hartnett, Peter Carr and Patrick Lucey did just that.
In our work, we considered play sketching from a data-driven perspective. We combined a powerful analytics framework built on deep-imitation learning with an intelligent and highly intuitive user interface. Users freehand sketch plays or modify existing tracking data. Our software then inferred the equivalent animation and synthesised realistic “ghost” defenders. Users tested their plays against different teams and game contexts, and fine-tune sketches to maximise the expected points in a given situation.
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