XY - Basketball meets Big Data
Wednesday, April 23, 2014
12:30 pm - 1:30 pm
Gross Hall 330
Luke Bornn, Dept Statistics, Harvard University
12:30pm lunch, 1pm learner lecture 3:30pm lecture, 4:30pm reception Abstract: In this talk, I will explore the state of the art in the analysis and modeling of player tracking data in the NBA. In the past, player tracking data has been used primarily for visualization, such as understanding the spatial distribution of a player¿s shooting characteristics, or to extract summary statistics, such as the distance traveled by a player in a given game. In this talk, I will present how we're using advanced statistics and machine learning tools to answer previously unanswerable questions about the NBA. Examples include ¿How should teams configure their defensive matchups to minimize a player¿s effectiveness?¿, ¿Who are the best decision makers in the NBA?¿, and ¿Who was responsible for the most points against in the NBA last season?¿ Bio: I am an Assistant Professor in the Department of Statistics at Harvard University. My research focuses on computational statistics and machine learning applied to large-scale spatial and dynamic data. Applications include structural health monitoring, climate informatics, and sports analytics. I¿m also interested in the corresponding computational issues, mainly in the form of stochastic computation (Markov chain Monte Carlo, sequential Monte Carlo, and deterministic variants).