Bio:

​Since October 2015, I have been at Stats Perform and currently serve as Chief Scientist. My goal is to maximize the value of the 35+ years worth of sports data we have.  Previously, I was at ​Disney Research for 5 years, where I conducted research into automatic sports broadcasting using large amounts of spatiotemporal tracking data. Previous to that, I was a Postdoctoral Researcher at the Robotics Institute at Carnegie Mellon University/Department of Psychology at University of Pittsburgh conducting research on automatic facial expression recognition. I  received my BEng(EE) from USQ and my PhD from QUT, Australia in 2003 and 2008 respectively. I was a co-author of the best paper at the 2016 MIT Sloan Sports Analytics Conference and in 2017 & 2018 was co-author of best-paper runner-up at the same conference.  Additionally, I have won best paper awards at INTERSPEECH (2007) and WACV (2014) international conferences.  My main research interests are in artificial intelligence and interactive machine learning in sporting domains.

Recent Press:

• MIT Sloan Sports Analytics Panel - "The Next Generation of Tracking Data", (March 2019), https://www.youtube.com/watch?v=QcRBn7ILANY&t=1501s
• Featured in Chicago Magazine: “Numbers Game by Jake Malooley” https://www.chicagomag.com/Chicago-Magazine/October-2018/Sports-Stats/
• Appeared in FourFourTwo Documentary: "The Numbers Game: How Data is Changing Football" (Dec 22nd, 2017)
• Pasadena Now: Engineers at Caltech Train a Machine to Watch Soccer (August 25th, 2017)
• Daily Telegraph: 'Expected goals' (xG): What is it, and how does it show Man City should win the Premier League this season? (July 25th, 2017)
• ADTMag: How AI-Based Sports Analytics Is Changing the Game (July 18th, 2017)
• DataNami: Deep Learning Is About to Revolutionize Sports Analytics. Here’s How (May 26th, 2017)
• CIODive: How Big Data is helping sports teams find the winning edge (May 10th, 2017)
• Search Business Analytics: Deep learning algorithms demand nearly limitless supplies of data (May, 2017)
• Sport Techie: The Future Of Match Preparation Lies In Artificial Intelligence (March 22, 2017)
• Nylon Calculus: "Progressing toward personalized analytics – A Q&A with Patrick Lucey of STATS" (March 13th, 2017)
• Silicon UK Magazine: "Machine Learning Is The Future Of Sports Data" (March 8th, 2017)
• SI.com: "Eight things we learned from the 2017 MIT Sloan Sports Analytics Conference" (March 8th, 2017)
• Engadget: "AI predicts how athletes will react in certain situations" (March 6th, 2017)
• MITSSAC: "Q&A: Patrick Lucey, 2016 Research Paper Competition Winner" (Oct 19th, 2016)

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Datasets:

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• Basketball dataset: Go to this link for more details on how to obtain the basketball tracking dataset
• Soccer dataset: Go to this link for more details on how to obtain the soccer tracking dataset