The Conversion of Money Lines into Win Probabilities: Reconciliations and Simplifications

45 Pages Posted: 9 Sep 2015 Last revised: 13 Feb 2018

See all articles by Jason P. Berkowitz

Jason P. Berkowitz

St. John's University - Department of Economics and Finance

Craig A. Depken

University of North Carolina at Charlotte - The Belk College of Business Administration - Department of Economics

John Gandar

University of North Carolina (UNC) at Charlotte

Date Written: August 1, 2016

Abstract

We contribute to the literature on money line betting markets by investigating the relationships between the various methods used to derive subjective win probabilities from money lines. We show that, although the seven methods described appear to be unique, they actually share many common assumptions and that, surprisingly, they reduce to three distinct estimates of bookmaker commission and subjective win probabilities. We also show that among the three distinct estimates, one is biased when money lines suggest a very heavy favorite in a particular sporting event. Thus, it is important to consider the assumptions for each method when deciding which to use in a particular context. Two empirical examples demonstrate how a market inefficiency, such as a long-shot favorite bias, should influence the choice of methodology.

Online appendix is available at: https://ssrn.com/abstract=2919597

Keywords: betting markets, subjective probabilities, market efficiency

JEL Classification: Z23, L83

Suggested Citation

Berkowitz, Jason P. and Depken, Craig A. and Gandar, John, The Conversion of Money Lines into Win Probabilities: Reconciliations and Simplifications (August 1, 2016). Available at SSRN: https://ssrn.com/abstract=2658335 or http://dx.doi.org/10.2139/ssrn.2658335

Jason P. Berkowitz

St. John's University - Department of Economics and Finance ( email )

Jamaica, NY 11439
United States

Craig A. Depken (Contact Author)

University of North Carolina at Charlotte - The Belk College of Business Administration - Department of Economics ( email )

Charlotte, NC 28223
United States

John Gandar

University of North Carolina (UNC) at Charlotte ( email )

9201 University City Boulevard
Charlotte, NC 28223
United States

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