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Disequilibrium Play in Tennis

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Abstract

Are the serves of the world’s best tennis pros consistent with the theoretical prediction of Nash equilibrium in mixed strategies? We analyze their serve direction choices (to the returner’s left, right or body) with data from an online database called the Match Charting Project. Using a new methodology, we test and decisively reject a key implication of a mixed strategy Nash equilibrium, namely, that the probability of winning a service game is the same for all serve directions. We also use dynamic programming (DP) to numerically solve for the best-response serve strategies to probability models of service game outcomes estimated for individual server-returner pairs, such as Novak Djokovic serving to Rafael Nadal. We show that for most elite pro servers, the DP serve strategy significantly increases their service game win probability compared to the mixed strategies they actually use, which we estimate using flexible reduced-form logit models. Stochastic simulations verify that our results are robust to estimation error. Classification- C61, C73, L21

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  • Axel Anderson & Jeremy Rosen & John Rust & Kin-Ping Wong, 2021. "Disequilibrium Play in Tennis," Working Papers gueconwpa~21-21-07, Georgetown University, Department of Economics.
    • Handle: RePEc:geo:guwopa:gueconwpa~21-21-07
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    1. Walker, Mark & Wooders, John & Amir, Rabah, 2011. "Equilibrium play in matches: Binary Markov games," Games and Economic Behavior, Elsevier, vol. 71(2), pages 487-502, March.
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    4. Romain Gauriot & Lionel Page & John Wooders, 2016. "Nash at Wimbledon: Evidence from Half a Million Serves," QuBE Working Papers 046, QUT Business School.
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    Cited by:

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    More about this item

    Keywords

    tennis; games; Nash equilibrium; Minimax theorem; constant sum games; mixed strategies; dynamic directional games; binary Markov games; dynamic programming; structural estimation; muscle memory;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm

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