Miss it like Messi: Extracting value from off-target shots in soccer

Baron Ethan (), Sandholtz Nathan (), Pleuler Devin () and Chan Timothy C. Y. ()
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Baron Ethan: University of Toronto, Toronto, ON, Canada
Sandholtz Nathan: Department of Statistics, Brigham Young University, Provo, UT, USA
Pleuler Devin: Maple Leaf Sports & Entertainment, Toronto, ON, Canada
Chan Timothy C. Y.: Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada

Journal of Quantitative Analysis in Sports, 2024, vol. 20, issue 1, 37-50

Abstract: Measuring soccer shooting skill is a challenging analytics problem due to the scarcity and highly contextual nature of scoring events. The introduction of more advanced data surrounding soccer shots has given rise to model-based metrics which better cope with these challenges. Specifically, metrics such as expected goals added, goals above expectation, and post-shot expected goals all use advanced data to offer an improvement over the classical conversion rate. However, all metrics developed to date assign a value of zero to off-target shots, which account for almost two-thirds of all shots, since these shots have no probability of scoring. We posit that there is non-negligible shooting skill signal contained in the trajectories of off-target shots and propose two shooting skill metrics that incorporate the signal contained in off-target shots. Specifically, we develop a player-specific generative model for shot trajectories based on a mixture of truncated bivariate Gaussian distributions. We use this generative model to compute metrics that allow us to attach non-zero value to off-target shots. We demonstrate that our proposed metrics are more stable than current state-of-the-art metrics and have increased predictive power.

Keywords: generative model; mixture model; shot trajectories; player valuation; Bayesian hierarchical model; spatial data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/jqas-2022-0107

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