Interactive sports analytics
Imagine watching sports and being able to immediately find all plays that are similar to what just happened. Better still, imagine being able to draw a play with the Xs and Os on an interface like a coach draws on a chalkboard and instantaneously finding all the similar plays and conduct analytics on those plays (i.e., when those plays occur, how many points a team expects from that play). Additionally, imagine having the ability to evaluate the performance of a player or a team in a given situation and compare it against another player in exactly the same situation.
This approach is called interactive sports analytics, and Patrick Lucey explains methods to find play similarity using multi-agent trajectory data, as well as predicting fine-grain plays. You’ll see examples using tracking data amassed from the Stats Perform massive sports data archives. And you’ll learn how you can go beyond center-of-mass tracking (i.e., dots) and capture body-pose information from broadcast video to take analysis to the next level.
Patrick Lucey is the chief scientist of artificial intelligence at Stats Perform, where his goal is to maximize the value of the 35+ years worth of sports data. His main research interests are in artificial intelligence and interactive machine learning in sporting domains. Previously, Patrick spent five years at Disney Research, where he conducted research into automatic sports broadcasting using large amounts of spatiotemporal tracking data, and was a postdoctoral researcher at the Robotics Institute at Carnegie Mellon University and the Department of Psychology at the University of Pittsburgh, conducting research on automatic facial expression recognition. He holds a BEng (EE) from the University of Southern Queensland and a PhD from QUT, Australia. He was a coauthor of the best paper at the 2016 MIT Sloan Sports Analytics Conference and in 2017 and 2018 was coauthor of best paper runner-up at the same conference. Patrick has also won best paper awards at the INTERSPEECH (2007) and the Winter Conference on Applications of Computer Vision (WACV) (2014) international conferences.
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