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Optimal challenges in tennis

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Journal of the Operational Research Society

Abstract

The use of technology in sport to assist umpires has been gradually introduced into several sports. This has now been extended to allow players to call upon technology to arbitrate when they disagree with the umpire's decision. Both tennis and cricket now allow the players to challenge a doubtful decision, which is reversed if the evidence shows it to be incorrect. However, the number of challenges is limited, and players must balance any possible immediate gain with the loss of a future right to challenge. With similar challenge rules expected to be introduced in other sports, this situation has been a motivation to consider challenges more widely. We use Dynamic Programming to investigate the optimal challenge strategy and obtain some general rules. In a traditional set of tennis, players should be more aggressive in challenging in the latter stages of the games and sets, and when their opponent is ahead. Optimal challenge strategy can increase a player's chance of winning an otherwise even five-set match to 59%.

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References

  • Barnett T and Clarke SR (2002). Using Microsoft Excel to model a tennis match. In: Cohen G and Langtry T (eds). Sixth Australian Conference on Mathematics and Computers in Sport. University of Technology Sydney: Sydney, Australia, pp 63–68.

    Google Scholar 

  • Barnett T, Brown A and Clarke S (2004). Optimal use of tennis resources. In: Morton H and Ganesalingam S (eds). Seventh Australasian Conference on Mathematics and Computers in Sport. Massey University: Palmerston Nth, New Zealand, pp 57–65.

    Google Scholar 

  • Barnett T, Brown A and Clarke SR (2006). Developing a tennis model that reflects outcome of tennis matches. In: Hammond J and de Mestre N (eds). Eighth Conference on Mathematics and Computer Science in Sport. Coolangatta: Australia, pp 178–188.

    Google Scholar 

  • Clarke SR (1988). Dynamic programming in one-day cricket—Optimal scoring rates. Journal of the Operational Research Society 39: 331–337.

    Google Scholar 

  • Clarke SR and Norman JM (1999). To run or not?: Some dynamic programming models in cricket. Journal of the Operational Research Society 50: 536–545.

    Article  Google Scholar 

  • Croucher JS (1998). Developing strategies in tennis. In: Bennett J (ed). Statistics in Sport. Arnold: London, pp 157–170.

    Google Scholar 

  • George SL (1973). Optimal strategy in tennis: A simple probabilistic model. Applied Statistics 22: 97–104.

    Article  Google Scholar 

  • Klaassen FJGM and Magnus JR (2003). Forecasting the winner of a tennis match. European Journal of Operational Research 148: 257–267.

    Article  Google Scholar 

  • Miles RE (1984). Symmetric sequential analysis: The efficiencies of sports scoring systems (with particular reference to those of tennis). Journal of the Royal Statistical Society, Series B 46: 93–108.

    Google Scholar 

  • Morris C (1977). The most important points in tennis. In: Ladany SP and Machol RE (eds). Optimal Strategies in Sports. North Holland: Amsterdam, pp 131–140.

    Google Scholar 

  • Norman JM (1985). Dynamic programming in tennis: When to use a fast serve. Journal of the Operational Research Society 36: 75–77.

    Google Scholar 

  • Norman JM (1995). Dynamic programming in sport: A survey of applications. IMA Journal of Mathematics Applied in Business and Industry 6 (December): 171–176.

    Google Scholar 

  • Pollard G, Pollard G, Barnett T and Zeleznikow J (2010). Applying strategies to the tennis challenge system. Journal of Medicine and Science in Tennis 15 (1): 12–15.

    Google Scholar 

  • Preston I and Thomas J (2000). Batting strategy in limited overs cricket. The Statistician 49: 95–106.

    Google Scholar 

  • Riddle LH (1988). Probability models for tennis scoring systems. Applied Statistics 37: 63–75.

    Article  Google Scholar 

  • Schutz RW (1970). A mathematical model for evaluating scoring systems with specific reference to tennis. Research Quarterly 41: 552–561.

    Google Scholar 

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Correspondence to S R Clarke.

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Clarke, S., Norman, J. Optimal challenges in tennis. J Oper Res Soc 63, 1765–1772 (2012). https://doi.org/10.1057/jors.2011.147

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  • DOI: https://doi.org/10.1057/jors.2011.147

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