Bio:

​I'm currently the VP of Artificial Intelligence at STATS, where 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 News:

• March 2018: Speaking on MIT SSAC panel "Is AI the  Answer?"
• March 2018: My group has 4 papers accepted to the MIT SSAC Research Track. 
• October 2017: Invited Talk at Northwestern University, Chicago USA
• September 2017: Keynote at IMPAC 2017 in Chicago, USA
• September 2017: Keynote at IACSS 2017, Konstantz, Germany
• August 2017: 2 papers at KDD 2017 (Leicester City Analysis and Estimating Pass Quality in Football)
• August 2017: Paper at ICML 2017 (Coordinated Multi-Agent Imitation Learning)
• July 2017: Keynote at CVSports at CVPR 2017, Hawaii, USA
• June 2017: Invited Talk at ECSS 2017, Essen, Germany 
• March 2017: Best Paper Runner-up at MIT SSAC 2017
• March 2017: 2 Papers Accepted in Final of Research Track at MIT SSAC 2017
• March 2017: Speaking at SXSW Panel on "Interactive Sports Analytics"

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Recent Press:

• 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)

Datasets:

• 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