Model to Predict the Result of a Soccer Match Based on the Number of Goals Scored by a Single Team

Authors

  • Alba Maribel Sánchez Gálvez Benemérita Universidad Autónoma de Puebla
  • Ricardo Álvarez González Benemérita Universidad Autónoma de Puebla
  • Sully Sánchez Gálvez Benemérita Universidad Autónoma de Puebla
  • Mario Anzures García Benemérita Universidad Autónoma de Puebla

DOI:

https://doi.org/10.13053/cys-26-1-4172

Keywords:

Supervised learning, machine learning algorithms, assessment metric

Abstract

Soccer is a very popular sport; it is a fine subject of study given the large amount of data it generates. This article presents a model that through Machine Learning algorithms predicts the victory or defeat of a soccer team, based on the number of goals scored. This model applies four machine learning classifiers: Linear Regression, Support Vector Machines, Naive Bayes and Decision Trees. The proposal is supported with data from the Mexican football league from 2012 to March 2020, the study has been divided into two sections: in the first draws are considered and in the second aren’t, with the purpose of discovering the influence of draw in analysis. With the proposal model accuracy in the range of 81% to 84% was achieved without draws and considering ties the accuracy was in the range of 72% to 75%.

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Published

2022-03-26

Issue

Vol. 26 No. 1 (2022): Recent Advances in Language & Knowledge Engineering (Guest editors: D. Pinto, B. Beltrán, A. Vázquez, D. Vilariño)

Section

Articles of the Thematic Issue

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