Sports Data Science

New technology is starting to enable the capture of game play at unprecedented levels of detail, including the tracking of positions of all players and game events at all times. This data deluge requires the development of novel visualization and machine learning tools and is leading to major new developments in sports data science. In this talk, we will review recent developments in this area and the enabling technologies. We will also cover our recent work, including the development of the Statcast Baseball Metrics Engine (BME) and related data science tools and techniques.

Cláudio T. Silva is Professor of Computer Science and Data Science at New York University. His research has focused on data science, visualization, graphics, and geometry processing. Recently he has been particularly interested in urban and sports applications. He received his BS in mathematics from Universidade Federal do Ceará, and his MS and PhD in computer science at SUNY-Stony Brook.

Claudio is a Fellow of the IEEE and has received the IEEE Visualization Technical Achievement Award. The MLB.com’s Statcast player tracking system, which he helped develop, won the Alpha Award for Best Analytics Innovation/Technology at the 2015 MIT Sloan Sports Analytics Conference, and more recently a 2018 Technology & Engineering Emmy Award from the National Academy of Television Arts & Sciences. Our lab’s work has been covered in The New York Times, The Economist, ESPN, and other major news media. 

Claudio’s work has been funded by MLB Advanced Media, the National Science Foundation, NASA, NVIDIA, Capital One, DOE, the Moore-Sloan Data Science Environment at NYU, Labex DigiCosme, and an NYU Provost’s Global Research Institute (GRI) fellowship