Kendall correlations and radar charts to include goals for and goals against in soccer rankings

Abstract This paper deals with the challenging themes of the way sporting teams and athletes are ranked in sports competitions. Starting from the paradigmatic case of soccer, we advance a new method for ranking teams in the official national championships through computational statistics methods based on Kendall correlations and radar charts. In detail, we consider the goals for and against the teams in the individual matches as a further source of score assignment beyond the usual win-tie-lose trichotomy. Our approach overcomes some biases in the scoring rules that are currently employed. The methodological proposal is tested over the relevant case of the Italian “Serie A” championships played during 1930–2023.

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Publication Details

Journal
Computational Statistics
Published
2024-09-17
DOI
https://doi.org/10.1007/s00180-024-01542-w
Citations
2
Primary Topic
Sports Analytics and Performance
Type
article
Field-Weighted Citation Impact
2.26
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article

Kendall correlations and radar charts to include goals for and goals against in soccer rankings

Raffaele Mattera, Valerio Ficcadenti, Roy Cerqueti
2 citations
Computational Statistics
Sports Analytics and Performance
2.26
article

Kendall correlations and radar charts to include goals for and goals against in soccer rankings

Raffaele Mattera, Valerio Ficcadenti, Roy Cerqueti
article en
2 citations

Abstract

Abstract This paper deals with the challenging themes of the way sporting teams and athletes are ranked in sports competitions. Starting from the paradigmatic case of soccer, we advance a new method for ranking teams in the official national championships through computational statistics methods based on Kendall correlations and radar charts. In detail, we consider the goals for and against the teams in the individual matches as a further source of score assignment beyond the usual win-tie-lose trichotomy. Our approach overcomes some biases in the scoring rules that are currently employed. The methodological proposal is tested over the relevant case of the Italian “Serie A” championships played during 1930–2023.

Computational StatisticsVol. 40(4)
London South Bank University (GB), Université d'Angers (FR), Sapienza University of Rome (IT)
Openalex Percentile: Top 8%
Sports Analytics and Performance
2.26
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