The best offense has always been a good defense. Until now.
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Thanks to a team of researchers at Disney Research, California Institute of Technology and STATS, we may soon find ourselves saying something more along the lines of, “The best offense is a defense informed by advanced machine learning technologies.” In a paper titled, “ Data Driven Ghosting using Deep Imitation Learning ,” a team of researchers introduced a machine learning approach that can help sports teams better prepare for specific opponents by calculating that opponent’s most likely response to a hypothetical attack.
Machine learning in sports?
Yisong Yue , an assistant professor at CalTech and coauthor of the paper in question, says the approach begins by asking a simple question: “Given that this is the location and speed and acceleration or whatever of the current players, can we predict where all these players will go?” Yue asks. To do so the approach takes demonstrated behavioral data for every player, builds individual models of fine-grain behavior for each, uses deep imitation learning to predict a player’s most likely response to a proposed game situation and visually renders that most likely response as a “ghost.”Thus the title of the research paper. Let’s break that down.The colloquial definition of artificial intelligence (AI) describes any system that in some way mimics the cognitive behavior of a human being.Automated assistants like Siri, Google Home and Alexa, Google’s AlphaGo and self-driving cars are all examples of modern AI. They behave like we would expect another human to behave.
Making predictions about behavior
If a particular player tends to back away from an attacker rather than confront him nine out of 10 times, the supervised learning approach will most likely determine that in the future that player will step back rather than forward when an attacker approaches. Run that approach tens of thousands of times, take into account the movement of other players on the field and their data, combine the results and you will end up with a detailed pattern of movements for each player. “The hope is that well over time using this algorithm and leveraging some very powerful deep learning techniques underneath I’m going to be able to learn some sort of decision making mechanisms,” said Hoang Le, a PhD candidate at CalTech and coauthor of the paper […]
The best offense has always been a good defense. Until now.
copyright by www.sporttechie.com
Thanks to a team of researchers at Disney Research, California Institute of Technology and STATS, we may soon find ourselves saying something more along the lines of, “The best offense is a defense informed by advanced machine learning technologies.” In a paper titled, “ Data Driven Ghosting using Deep Imitation Learning ,” a team of researchers introduced a machine learning approach that can help sports teams better prepare for specific opponents by calculating that opponent’s most likely response to a hypothetical attack.
Machine learning in sports?
Yisong Yue , an assistant professor at CalTech and coauthor of the paper in question, says the approach begins by asking a simple question: “Given that this is the location and speed and acceleration or whatever of the current players, can we predict where all these players will go?” Yue asks. To do so the approach takes demonstrated behavioral data for every player, builds individual models of fine-grain behavior for each, uses deep imitation learning to predict a player’s most likely response to a proposed game situation and visually renders that most likely response as a “ghost.”Thus the title of the research paper. Let’s break that down.The colloquial definition of artificial intelligence (AI) describes any system that in some way mimics the cognitive behavior of a human being.Automated assistants like Siri, Google Home and Alexa, Google’s AlphaGo and self-driving cars are all examples of modern AI. They behave like we would expect another human to behave.
If a particular player tends to back away from an attacker rather than confront him nine out of 10 times, the supervised learning approach will most likely determine that in the future that player will step back rather than forward when an attacker approaches. Run that approach tens of thousands of times, take into account the movement of other players on the field and their data, combine the results and you will end up with a detailed pattern of movements for each player. “The hope is that well over time using this algorithm and leveraging some very powerful deep learning techniques underneath I’m going to be able to learn some sort of decision making mechanisms,” said Hoang Le, a PhD candidate at CalTech and coauthor of the paper […]
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