Statistics Professor authors sports analytics paper on performance of players as they age.

Charles A. Dana Professor of Statistics Michael Schuckers recently published a paper entitled “Estimation of player aging curves using regression and imputation” in the Annals of Operation Research (https://www.springer.com/journal/10479).  The paper discusses the best ways to estimate changing player performance as players age in the presence of unobserved performances.  Using data from the National Hockey League to illustrate the methods proposed in the paper, this work evaluates which methodologies seem to best estimate average aging performance.  It is joint work with Michael Lopez, Senior Director of Football Data and Analytics at the National Football League, and Brian Macdonald, Senior Lecturer and Research Scientist at Yale University.  A view-only version of the paper can be found here: https://rdcu.be/c3o4c. 

Professor Schuckers is a leader in the field of sports data science having published dozens of papers on analyzing data in sports, especially ice hockey.  Six of his former St. Lawrence University students have gone on to work in analytics for professional sports teams or leagues.  He has been named Significant Contributor by the Section on Statistics in Sports of the American Statistical Association.  He is also one of the prominent figures in developing statistical methods for bioauthentication devices such as facial recognition and fingerprint readers.  A Fulbright Scholar at the VTT Technical Research Centre of Finland in 2013, his research has been funded by the National Science Foundation, the Department of Homeland Security, the Department of Defense and the Center for Identification Technology Research.