Algorithms developed in Cornell’s Laboratory for Intelligent Systems and Controls can predict the in-game actions of volleyball players with more than 80% accuracy, and now the lab is collaborating with the Big Red hockey .
October 5, 2022
Algorithms developed in Cornell’s Laboratory for Intelligent Systems and Controls can predict the in-game actions of volleyball players with more than 80% accuracy, and now the lab is collaborating with the Big Red hockey team to expand the research project’s applications.
The algorithms are unique in that they take a holistic approach to action anticipation, combining visual data—for example, where an athlete is located on the court—with information that is more implicit, like an athlete’s specific role on the team.
«Computer vision can interpret visual information such as jersey color and a player’s position or body posture,» said Silvia Ferrari, the John Brancaccio Professor of Mechanical and Aerospace Engineering, who led the research. «We still use that real-time information, but integrate hidden variables such as team strategy and player roles, things we as humans are able to infer because we’re experts at that particular context.»
Ferrari and doctoral students Junyi Dong and Qingze Huo trained the algorithms to infer hidden variables the same way humans gain their sports knowledge—by watching games. The algorithms used machine learning to extract data from videos of volleyball games, and then used that data to help make predictions when shown a new set of games.
