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Columbia University’s Data Science Institute Presents:
DATA SCIENCE DAY

Authors/Collaborators are listed in alphabetical order.



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Wednesday, April 6 • 2:00pm - 4:30pm
The Hidden Advantage of Lefties in Sports [P29]

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In the modern era of professional tennis, there have been many great left-handed players. Although only 11% of the world's population is left-handed, left-handers comprise roughly 15% of professional tennis players. In sports as varied as boxing, baseball and fencing, the contrast is even more stark. Here we present a method for extracting the advantage of being left-handed in sports (as well as the inherent skill of each player) from match results, applying it to data from men’s professional tennis. Unlike previous point estimate approaches to this problem, our formulation is Bayesian and uses induced order statistics to address the truncated nature of the data set. We further demonstrate how the question can be approached in the absence of explicit match result data, outlining a methodology that can be used to determine the latent advantage of specific factors wherever there exists notions of ranking and competition.

Demo/Poster Presenter
avatar for Francois Fagan

Francois Fagan

PhD Candidate in Industrial Engineering and Operations Reasearch, Columbia Engineering
Francois Fagan started his PhD in the IEOR department in September 2013. He is currently working with Professors Martin Haugh and Garud Iyengar in the field of machine learning. Before coming to Columbia University, he attended the University of Cape Town in South Africa where he received a BSc (Honours) in Mathematics and Applied Mathematics in 2010. In 2012-2013 he completed a masters at Stellenbosch University, also in South Africa, in... Read More →
avatar for Hal Cooper

Hal Cooper

PhD Candidate in Industrial Engineering and Operations Reasearch, Columbia Engineering
Hal Cooper is an IEOR Ph.D. Candidate who began his Ph.D. in the fall of 2013 under adviser Garud Iyengar. Hal works on a variety of machine learning problems, particularly Bayesian inference and deep learning. His research focuses on the development of new modeling techniques for these frameworks, and their application to network science and computational neuroscience.    Hal holds a Master of Science in Operations Research from... Read More →

Demo/Poster Collaborator
avatar for Martin Haugh

Martin Haugh

Associate Professor of Professional Practice in Industrial Engineering and Operations Research, Columbia Engineering
Martin Haugh originally joined the Industrial Engineering and Operations Research Department in January 2002 after completing his PhD in Operations Research at MIT. He was a faculty member in the IEOR department until June 2005 and during this time his teaching and research focused on financial engineering. Between 2005 and 2009 he worked as a quant in the hedge fund industry in New York and London. He then returned to academia and the... Read More →
GI

Garud Iyengar

Professor of Industrial Engineering and Operations Research and Department Chair, Columbia Engineering
Professor Garud Iyengar joined Columbia University’s Industrial Engineering and Operations Research Department in 1998. Professor Iyengar teaches courses in simulation and optimization. Professor Garud Iyengar’s research interests include convex optimization, robust optimization, queuing networks, combinatorial optimization, mathematical and computational finance, communication and information theory. He has published in... Read More →


Wednesday April 6, 2016 2:00pm - 4:30pm
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Attendees (3)