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Predicting the outcome of professional darts tournaments

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  • Steffen Liebscher
  • Thomas Kirschstein

Abstract

Sports statistics are one of the oldest applications of data analysis techniques in order to visualize and understand the outcome of sports events. One particularly interesting application is the analysis of darts data as darts has become tremendously popular and, hence, professional in recent years. In this paper we analyse a data-set consisting of more than 800 professional darts matches. We propose and test various player specific and match specific performance measures in order to identify variables that allow us to assess the players’ winning probabilities in a (single-set) darts match. We find that in about 80% of the analysed single-set matches the winner could be identified correctly by the fitted generalized linear regression models. Afterwards we show how these results can be used in a negative Poisson binomial model to assess the winning probabilities for a multi-set match. Finally, the results of multi-set matches are used in a tournament framework in order to predict the probabilities of all potential outcomes of a darts tournament. The approach is illustrated by analysing the latest World Darts Championship.

Suggested Citation

  • Steffen Liebscher & Thomas Kirschstein, 2017. "Predicting the outcome of professional darts tournaments," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(5), pages 666-683, September.
    • Handle: RePEc:taf:rpanxx:v:17:y:2017:i:5:p:666-683
    DOI: 10.1080/24748668.2017.1372162
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    Cited by:

    1. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    2. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.

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