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Analysis Of Sports Performances Using Machine Learning And Statistical Models - A General Analysis Of The Literature

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  • COJOCARIU Irina-Cristina

    (Alexandru Ioan Cuza University of Iasi)

Abstract

The attendance of football fans at the matches played in the big city stadiums has a significant impact on the incomes of football clubs, an aspect studied more in recent years in the specialized literature, but with the summary presentation of related analysis techniques. Machine Learning remains certainly one of the preferred methodologies that has shown gratifying results in the fields of sports classification and prediction. One of the ever-growing fields that require good accuracy continues to be sports prediction, due to the huge amounts of money involved (player transactions, betting market, etc). These predictive models created for application within various clubs become a starting point for creating revenue maximization strategies. Taking into account these aspects, I will start by presenting the necessary steps in cleaning the data sets, continuing with the data preparation and their exploratory analysis by presenting the techniques offered by CRAN for the use of the R language and by non-programmers. Therefore, after the data set is prepared, we can start formulating the research questions, and this paper aims to present an objective analysis of the sports prediction models presented in specialized papers and the directions that can be followed in future research in the sports field, especially football.

Suggested Citation

  • COJOCARIU Irina-Cristina, 2023. "Analysis Of Sports Performances Using Machine Learning And Statistical Models - A General Analysis Of The Literature," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 75(2), pages 34-39, June.
    • Handle: RePEc:blg:reveco:v:75:y:2023:i:2:p:34-39
    DOI: 10.56043/reveco-2023-0013
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    References listed on IDEAS

    as
    1. Bradley Efron, 2020. "Prediction, Estimation, and Attribution," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 636-655, April.
    2. Bradley Efron, 2020. "Prediction, Estimation, and Attribution," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 28-59, December.
    3. Jaume García & Plácido Rodríguez, 2002. "The Determinants of Football Match Attendance Revisited," Journal of Sports Economics, , vol. 3(1), pages 18-38, February.
    4. R. A. Hart & J. Hutton & T. Sharot, 1975. "A Statistical Analysis of Association Football Attendances," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 24(1), pages 17-27, March.
    5. Leif Brandes & Egon Franck & Stephan Nüesch, 2008. "Local Heroes and Superstars," Journal of Sports Economics, , vol. 9(3), pages 266-286, June.
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    More about this item

    Keywords

    Sports Performances; Statistical Models; Machine Learning;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • Z20 - Other Special Topics - - Sports Economics - - - General

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