Data Science Day @ Columbia University

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.