STATS’s Patrick Lucey presents “Interactive Sports Analytics: Going Beyond Spreadsheets” as part of the IRIM Robotics Seminar Series. The event will be held in the Marcus Nanotechnology Bldg., Rooms 1116-1118, from 12-1 p.m. and is open to the public.
Imagine watching a sports game live and having the ability to find immediately all plays that are similar to what just happened. Better still, imagine having the ability to draw a play with X’s and O’s on an interface—like a coach draws on a chalkboard—and being able to find instantaneously all the plays like that and then 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 in a given situation and compare it to another player in exactly the same position. We call this approach “Interactive Sports Analytics,” and in this talk, I will describe methods to find play similarity using multi-agent trajectory data, as well as predicting fine-grain plays. I will show examples using STATS SportVU data in basketball, Prozone data in soccer, and Hawk-Eye in tennis.
Patrick Lucey is currently the director of Data Science at STATS. He maximizes the value of fine-grained player tracking data currently captured in high-performance sports. Previously, Lucey worked at Disney Research for 5 years, where he conducted research on automatic sports broadcasting using large amounts of spatiotemporal tracking data. Prior to that, he was a postdoctoral researcher at the Robotics Institute at Carnegie Mellon University, where he conducted research on automatic facial expression recognition. Lucey received his BEng(EE) from USQ and his Ph.D. from QUT, Australia in 2003 and 2008, respectively. He has won best paper awards at INTERSPEECH (2007) and WACV (2014) international conferences. He has also been a finalist in the research track at the MIT Sloan Sports Analytics Conference for the past four years. His main research interests are in artificial intelligence and interactive machine learning in sporting domains.