The advantage of lefties in one-on-one sports

Fagan Francois (), Haugh Martin () and Cooper Hal ()
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Fagan Francois: Department of Industrial Engineering and Operations Research, Columbia University, 500 W. 120th Street, Mudd 315, New York, NY, USA
Haugh Martin: Department of Industrial Engineering and Operations Research, Columbia University, 500 W. 120th Street, Mudd 315, New York, NY, USA
Cooper Hal: Imperial College Business School, Imperial College, London, UK

Journal of Quantitative Analysis in Sports, 2019, vol. 15, issue 1, 1-25

Abstract: Left-handers comprise approximately 15% of professional tennis players, but only 11% of the general population. In boxing, baseball, fencing, table-tennis and specialist batting positions in cricket the contrast is even starker, with 30% or more of top players often being left-handed. In this paper we propose a model for identifying the advantage of being left-handed in one-on-one interactive sports (as well as the inherent skill of each player). We construct a Bayesian latent ability model in the spirit of the classic Glicko model but with the additional complication of having a latent factor, i.e. the advantage of left-handedness, that we need to estimate. Inference is further complicated by the truncated nature of data-sets that arise from only having data of the top players. We show how to infer the advantage of left-handedness when only the proportion of top left-handed players is available. We use this result to develop a simple dynamic model for inferring how the advantage of left-handedness varies through time. We also extend the model to cases where we have ranking or match-play data. We test these models on 2014 match-play data from top male professional tennis players, and the dynamic model on data from 1985 to 2016.

Keywords: Bayesian; latent ability models; left-handedness (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1515/jqas-2017-0076

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