Standard Article

An Overview of Operations Research in Tennis

  1. Geoff Pollard1,2,
  2. Denny Meyer2

Published Online: 15 JUN 2010

DOI: 10.1002/9780470400531.eorms0606

Wiley Encyclopedia of Operations Research and Management Science

Wiley Encyclopedia of Operations Research and Management Science

How to Cite

Pollard, G. and Meyer, D. 2010. An Overview of Operations Research in Tennis. Wiley Encyclopedia of Operations Research and Management Science. .

Author Information

  1. 1

    Swinburne University of Technology, Faculty of Life and Social Science, Melbourne, Victoria, Australia

  2. 2

    Tennis Australia, Melbourne, Victoria, Australia

Publication History

  1. Published Online: 15 JUN 2010

Abstract

For nearly 100 years, the equipment and the rules of tennis hardly changed. But since the opening of the game to professionals as well as amateurs in 1968, and the expansion of the game worldwide and in the media, scientific and technological developments have created both problems and opportunities for tennis. This article looks at the use and opportunities for operations research and management science methods to analyze the unique tennis scoring system and the optimal strategies and techniques players might employ. It also considers the use of these methods for the optimization of tennis equipment, including court surface and line calling, and coaching while minimizing medical risk. Efficient tournament management and the accuracy of player ranking are also addressed. This article describes how modern developments allow a scientific approach to measuring and categorizing while suggesting that it may be necessary to limit developments in order to maintain the character of the game.

Keywords:

  • scoring;
  • tennis equipment;
  • surface;
  • coaching;
  • medicine;
  • biomechanics;
  • tournament management;
  • psychology;
  • computer rankings;
  • electronic line calling;
  • stochastic optimization;
  • risk taking;
  • dynamic programming;
  • simulation;
  • Markov chain;
  • game theory;
  • multi-criteria optimization;
  • Monte Carlo simulation;
  • Kalman filter;
  • nonlinear optimization