Técnicas y algoritmos para predecir el resultado de los partidos de fútbol utilizando la minería de datos, una revisión de la literatura

Translated title of the contribution: Techniques and algorithms to predict the outcome of soccer matches using data mining, a review of the literature

Antonio Araujo-Ahon, Brayan Cardenas-Mayta, Orlando Iparraguirre-Villanueva, Joselyn Zapata-Paulini, Michael Cabanillas-Carbonell

Research output: Contribution to journalOriginal Articlepeer-review

1 Scopus citations

Abstract

The outcome of a sport has become a necessity for competitors as well as for fans following their favorite teams. However, the prediction of the outcome of a soccer match (PSMR) is very varied due to the various existing models. The research is a systematic literature review (SLR) based on manuscripts published in IEEE Xplore, Scopus, Science Direct, and Springer. Prisma methodology was used for analysis and systematization. The objective of this research is to provide a guide for using machine learning (ML) techniques. The results showed that the most frequently used ML techniques are supervised learning (SL) and unsupervised learning (UL) and the most frequent ML algorithm for predicting the outcome of a soccer match is Random Forest (RF), considering its great contribution in prediction accuracy. In addition, a novel and efficient model for predicting the outcome of soccer matches, supported with Data Mining (DM) and focused on ML, is proposed after the study.

Translated title of the contributionTechniques and algorithms to predict the outcome of soccer matches using data mining, a review of the literature
Original languageSpanish
Pages (from-to)245-263
Number of pages19
JournalRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
Volume2023
Issue numberSpecial Issue E55
StateIndexed - 2023

Bibliographical note

Publisher Copyright:
© 2023, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

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