Scoring probability maps in the basketball court with Indicator Kriging estimation

Abstract Measuring players’ and teams’ shooting performance in the basketball court can give important information aimed to the definition of both game strategies and personalized training programs. From a methodological point of view, the estimation of the scoring probability can be faced by resorting to different tools in the field of statistical or algorithmic modelling. As a matter of fact, the most natural theoretical framework for this problem is that of spatial statistics, with the particularity that the analysis is based on the binary measurement variable informing about whether a shot is made or missed. In this paper we propose the use of spatial statistics tools suited to this specific context, namely lorelograms to investigate the spatial correlation and Indicator Kriging to draw scoring probability maps. A structured case study is presented, dealing with all the teams of the Italian Basketball First League, based on a non-public dataset containing substantive additional information, that allows interesting insights about assisted and uncontested shots.

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Publication Details

Journal
Computational Statistics
Published
2024-11-07
DOI
https://doi.org/10.1007/s00180-024-01564-4
Citations
1
Primary Topic
Sports Analytics and Performance
Type
article
Field-Weighted Citation Impact
1.13

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article

Scoring probability maps in the basketball court with Indicator Kriging estimation

Marica Manisera, Andrea Cappozzo, Paola Zuccolotto, Mirko Luigi Carlesso
1 citations
Computational Statistics
Sports Analytics and Performance
1.13
article

Scoring probability maps in the basketball court with Indicator Kriging estimation

Marica Manisera, Andrea Cappozzo, Paola Zuccolotto, Mirko Luigi Carlesso
article en
1 citations

Abstract

Abstract Measuring players’ and teams’ shooting performance in the basketball court can give important information aimed to the definition of both game strategies and personalized training programs. From a methodological point of view, the estimation of the scoring probability can be faced by resorting to different tools in the field of statistical or algorithmic modelling. As a matter of fact, the most natural theoretical framework for this problem is that of spatial statistics, with the particularity that the analysis is based on the binary measurement variable informing about whether a shot is made or missed. In this paper we propose the use of spatial statistics tools suited to this specific context, namely lorelograms to investigate the spatial correlation and Indicator Kriging to draw scoring probability maps. A structured case study is presented, dealing with all the teams of the Italian Basketball First League, based on a non-public dataset containing substantive additional information, that allows interesting insights about assisted and uncontested shots.

Computational StatisticsVol. 40(4)
University of Milan (IT), University of Brescia (IT)
Università degli Studi di Milano, Dipartimenti di Eccellenza, European Commission, Ministero dell'Università e della Ricerca
Openalex Percentile: Top 11%
Sports Analytics and Performance
1.13
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