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Bayesian Based Approach Learning for Outcome Prediction of Soccer Matches

  • Laura Hervert-EscobarEmail author
  • Neil Hernandez-Gress
  • Timothy I. Matis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10862)

Abstract

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.

Keywords

Machine learning Soccer Bayesian models Sport matches Prediction 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Laura Hervert-Escobar
    • 1
    Email author
  • Neil Hernandez-Gress
    • 1
  • Timothy I. Matis
    • 2
  1. 1.Instituto Tecnológico y de Estudios Superiores de MonterreyMonterreyMexico
  2. 2.Texas Tech UniversityLubbockUSA

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