Date: 2013-10-25

Time: 15:30-16:30

Location: HEC Montréal Salle CIBC 1er étage

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?”

Speaker

Luke Bronn is an Assistant Professor of Statistics at Harvard University, Boston, MA. He is also the winner of the 2012 Pierre Robillard Award for the best PhD thesis awarded in a Canadian university in a given year.