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A new metric for pitch control based on an intuitive motion model

Author

Listed:
  • Lucas Wu

    (Simon Fraser University)

  • Tim B. Swartz

    (Simon Fraser University)

Abstract

With the availability of tracking data, the determination of pitch control (field ownership) is an increasingly important topic in sports analytics. This paper reviews various approaches for the determination of pitch control and introduces a new field ownership metric that takes into account associated sporting dynamics. The methods that are proposed utilize the movement of the ball and players. Specifically, physical characteristics such as current velocity, acceleration and maximum velocity are considered. The determination of pitch control is based on the time that it takes the ball and the players to reach a given location. The main result of our investigation concerns the validation of the resultant pitch control diagram. Based on a sample of 5887 passes, the team identified as having pitch control was the observed recipient of the pass with 91% accuracy. The approach is generally applicable to invasion sports and is illustrated in the context of soccer. Various parameters are introduced that allow a user to modify the methods to alternative sports and to introduce player-specific maximum velocities and player-specific accelerations.

Suggested Citation

  • Lucas Wu & Tim B. Swartz, 2025. "A new metric for pitch control based on an intuitive motion model," Computational Statistics, Springer, vol. 40(4), pages 1713-1730, April.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01512-2
    DOI: 10.1007/s00180-024-01512-2
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    References listed on IDEAS

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    1. Matthew Reyers & Tim B. Swartz, 2023. "Quarterback evaluation in the national football league using tracking data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 327-342, March.
    2. 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.
    3. Daniel Link & Steffen Lang & Philipp Seidenschwarz, 2016. "Real Time Quantification of Dangerousity in Football Using Spatiotemporal Tracking Data," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-16, December.
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