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An empirical Bayes approach for estimating skill models for professional darts players

Author

Listed:
  • Haugh Martin B.

    (Department of Analytics, Marketing & Operations, Imperial College Business School, Imperial College, London, UK)

  • Wang Chun

    (Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Beijing, China)

Abstract

We perform an exploratory data analysis on a data-set for the top 16 professional darts players from the 2019 season. We use this data-set to fit player skill models which can then be used in dynamic zero-sum games (ZSGs) that model real-world matches between players. We propose an empirical Bayesian approach based on the Dirichlet-Multinomial (DM) model that overcomes limitations in the data. Specifically we introduce two DM-based skill models where the first model borrows strength from other darts players and the second model borrows strength from other regions of the dartboard. We find these DM-based models outperform simpler benchmark models with respect to Brier and Spherical scores, both of which are proper scoring rules. We also show in ZSGs settings that the difference between DM-based skill models and the simpler benchmark models is practically significant. Finally, we use our DM-based model to analyze specific situations that arose in real-world darts matches during the 2019 season.

Suggested Citation

  • Haugh Martin B. & Wang Chun, 2024. "An empirical Bayes approach for estimating skill models for professional darts players," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 20(4), pages 385-404.
    • Handle: RePEc:bpj:jqsprt:v:20:y:2024:i:4:p:385-404:n:1001
    DOI: 10.1515/jqas-2023-0084
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