Bayesian Based Approach Learning for Outcome Prediction of Soccer Matches

Laura Hervert-Escobar, Neil Hernandez-Gress, Timothy I. Matis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


In the current world, sports produce considerable data such as players skills, game results, season matches, leagues management, etc. The big challenge in sports science is to analyze this data to gain a competitive advantage. The analysis can be done using several techniques and statistical methods in order to produce valuable information. The problem of modeling soccer data has become increasingly popular in the last few years, with the prediction of results being the most popular topic. In this paper, we propose a Bayesian Model based on rank position and shared history that predicts the outcome of future soccer matches. The model was tested using a data set containing the results of over 200,000 soccer matches from different soccer leagues around the world.

Original languageEnglish
Title of host publicationComputational Science – ICCS 2018 - 18th International Conference, Proceedings
EditorsJack Dongarra, Haohuan Fu, Valeria V. Krzhizhanovskaya, Michael Harold Lees, Peter M. Sloot, Yong Shi, Yingjie Tian
Number of pages11
ISBN (Print)9783319937120
StatePublished - 2018
Event18th International Conference on Computational Science, ICCS 2018 - Wuxi, China
Duration: Jun 11 2018Jun 13 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10862 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference on Computational Science, ICCS 2018


  • Bayesian models
  • Machine learning
  • Prediction
  • Soccer
  • Sport matches


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