Can motion tracking data tell whether a basketball player is at increased risk of injury? What are the latest techniques that build toward an "optimal" NBA draft model? Can analytics measure a player's toughness?

Data and analytics are transforming every industry and basketball is no exception. The opportunities for analytics to contribute to wins on the court are limitless. Developments in hardware provide increasingly richer data sets about player movement, wellness, and ability while advancements in algorithms and statistical techniques provide increasingly insightful descriptive and predictive models for player and team performance. These topics and others are among those that have captured the interest of NBA teams like the Orlando Magic.

Ryan Chen is a Basketball Data Scientist in his second season with the Orlando Magic. He graduated from the University of Michigan in 2014 with a B.S.E. in Industrial & Operations Engineering (and B.M. in Music Performance) and received a M.S. in Management Science & Engineering from Stanford University in 2017. While at Michigan, Ryan was a member of Tau Beta Pi (F13 President) and conducted research in aviation and healthcare operations with Profs. Amy Cohn and Mark Daskin and the Center for Healthcare Engineering and Patient Safety. While at Stanford, he was a key member of the Stanford Sports Analytics Club, contributing to prize-winning teams at the Graphicacy Major League Data Challenge, the UNC Basketball Analytics Summit Case Competition, and the NBA Hackathon.

Food will be provided by Noodles n' Company.

When: Sun. Oct. 28, 2018 5 p.m.–7 p.m.
Where: Ross 2240 (on campus)