Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/5128
Title: Machine learning : football results prediction model
Authors: Hindieh, Jad
Advisors: Mokbel, Chafic 
Keywords: Football, Machine Learning, Convolution Neural Network, Dataset, Prediction rate for football results
Issue Date: 2021
Abstract: 
Being the world’s most popular sports, predicting the results of Football games has always been an interest worldwide. With the increase in availability of football games statistics, using machine learning became more relevant and feasible. This study aims to use previous football matches statistics to predict the future result by applying machine learning on specific subsets of data. The hypothesis is that using machine learning we will be able to have a high prediction percentage given the correct features for the model. Using the convolution neural network machine learning model, we built multiple models to predict football results. We tested the models on a dataset gathered from eleven European countries and spanning across eight football seasons with our main focus being the English Premier League. The results showed that the more data features we were able to provide, the higher the prediction rate for football results. The obtained results showed that by using machine learning we can predict football games results by knowing the participating players in the future matches. These results can open up further research topics such as creating a model to assist managers in team selection for
optimizing results.
Description: 
Includes bibliographical references (p. 60-64)
URI: https://scholarhub.balamand.edu.lb/handle/uob/5128
Rights: This object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the personal and educational use exceptions must be obtained from the copyright holder
Type: Thesis
Appears in Collections:UOB Theses and Projects

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