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Binary segmentation procedures using the bivariate binomial distribution for detecting streakiness in sports data

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Listed:
  • Seong W. Kim

    (Hanyang University)

  • Sabina Shahin

    (Karakoram International University)

  • Hon Keung Tony Ng

    (Southern Methodist University)

  • Jinheum Kim

    (University of Suwon)

Abstract

Streakiness is an important measure in many sports data for individual players or teams in which the success rate is not a constant over time. That is, there are many successes/failures during some periods and few or no successes/failures during other periods. In this paper we propose a Bayesian binary segmentation procedure using a bivariate binomial distribution to locate the changepoints and estimate the associated success rates. The proposed method consists of a series of nested hypothesis tests based on the Bayes factors or posterior probabilities. At each stage, we compare three different changepoint models to the constant success rate model using the bivariate binary data. The proposed method is applied to analyze real sports datasets on baseball and basketball players as illustration. Extensive simulation studies are performed to demonstrate the usefulness of the proposed methodologies.

Suggested Citation

  • Seong W. Kim & Sabina Shahin & Hon Keung Tony Ng & Jinheum Kim, 2021. "Binary segmentation procedures using the bivariate binomial distribution for detecting streakiness in sports data," Computational Statistics, Springer, vol. 36(3), pages 1821-1843, September.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-020-00992-2
    DOI: 10.1007/s00180-020-00992-2
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    References listed on IDEAS

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    1. D. A. Stephens, 1994. "Bayesian Retrospective Multiple‐Changepoint Identification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 159-178, March.
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    7. Tae Young Yang, 2004. "Bayesian binary segmentation procedure for detecting streakiness in sports," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(4), pages 627-637, November.
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    Cited by:

    1. Vangelis Sarlis & George Papageorgiou & Christos Tjortjis, 2024. "Leveraging Sports Analytics and Association Rule Mining to Uncover Recovery and Economic Impacts in NBA Basketball," Data, MDPI, vol. 9(7), pages 1-20, June.

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