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A functional data approach to model score difference process in professional basketball games

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  • Tao Chen
  • Qingliang Fan

Abstract

In this paper, we investigate the progress of score difference (between home and away teams) in professional basketball games employing functional data analysis (FDA). The observed score difference is viewed as the realization of the latent intensity process, which is assumed to be continuous. There are two major advantages of modeling the latent score difference intensity process using FDA: (1) it allows for arbitrary dependent structure among score change increments. This removes potential model mis-specifications and accommodates momentum which is often observed in sports games. (2) further statistical inferences using FDA estimates will not suffer from inconsistency due to the issue of having a continuous model yet discretely sampled data. Based on the FDA estimates, we define and numerically characterize momentum in basketball games and demonstrate its importance in predicting game outcomes.

Suggested Citation

  • Tao Chen & Qingliang Fan, 2018. "A functional data approach to model score difference process in professional basketball games," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 112-127, January.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:112-127
    DOI: 10.1080/02664763.2016.1268106
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    Cited by:

    1. Claus Thorn Ekstrøm & Andreas Kryger Jensen, 2023. "Having a ball: evaluating scoring streaks and game excitement using in-match trend estimation," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 295-311, March.
    2. Song, Kai & Gao, Yiran & Shi, Jian, 2020. "Making real-time predictions for NBA basketball games by combining the historical data and bookmaker’s betting line," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    3. Chen, Yaqing & Dawson, Matthew & Müller, Hans-Georg, 2020. "Rank dynamics for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    4. Jeon, Gyuhyeon & Park, Juyong, 2021. "Characterizing patterns of scoring and ties in competitive sports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    5. Song, Kai & Shi, Jian, 2020. "A gamma process based in-play prediction model for National Basketball Association games," European Journal of Operational Research, Elsevier, vol. 283(2), pages 706-713.

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