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Shot-by-shot stochastic modeling of individual tennis points

Author

Listed:
  • Floyd Calvin Michael

    (Rochester Institute of Technology, Department of School of Mathematical Sciences, Rochester, NY 14623, USA)

  • Hoffman Matthew

    (Rochester Institute of Technology, Department of School of Mathematical Sciences, Rochester, NY, USA)

  • Fokoue Ernest

    (Rochester Institute of Technology, Department of School of Mathematical Sciences, Rochester, NY, USA)

Abstract

Individual tennis points evolve over time and space, as each of the two opposing players are constantly reacting and positioning themselves in response to strikes of the ball. However, these reactions are diminished into simple tally statistics such as the amount of winners or unforced errors a player has. In this paper, a new way is proposed to evaluate how an individual tennis point is evolving, by measuring how many points a player can expect from each shot, given who struck the shot and where both players are located. This measurement, named “Expected Shot Value” (ESV), derives from stochastically modeling each shot of individual tennis points. The modeling will take place on multiple resolutions, differentiating between the continuous player movement and discrete events such as strikes occurring and duration of shots ending. Multi-resolution stochastic modeling allows for the incorporation of information-rich spatiotemporal player-tracking data, while allowing for computational tractability on large amounts of data. In addition to estimating ESV, this methodology will be able to identify the strengths and weaknesses of specific players, which will have the ability to guide a player’s in-match strategy.

Suggested Citation

  • Floyd Calvin Michael & Hoffman Matthew & Fokoue Ernest, 2020. "Shot-by-shot stochastic modeling of individual tennis points," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 57-71, March.
    • Handle: RePEc:bpj:jqsprt:v:16:y:2020:i:1:p:57-71:n:1
    DOI: 10.1515/jqas-2018-0036
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    References listed on IDEAS

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    1. Daniel Cervone & Alex D’Amour & Luke Bornn & Kirk Goldsberry, 2016. "A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 585-599, April.
    2. Brillinger David R, 2007. "A Potential Function Approach to the Flow of Play in Soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(1), pages 1-21, January.
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