NORMA eResearch @NCI Library

Artificial Neural Network for Betting Rate In Football

Kumar, Sumeet (2020) Artificial Neural Network for Betting Rate In Football. Masters thesis, Dublin, National College of Ireland.

[thumbnail of Master of Science]
PDF (Master of Science)
Download (817kB) | Preview
[thumbnail of Configuration manual]
PDF (Configuration manual)
Download (367kB) | Preview


Prediction has been an integral part of human lives, no matter it is weather, flipping a coin, or a match between opponent. Introduction of machine learning has brought about a massive change to how people can do prediction using different attribute which they never thought of. Today prediction is almost a part of everything, be it election to president or a game between opponents. Football has massive following and during a match a lot of data is generated. Betting prediction in football is very interesting but also a very challenging task. It’s challenging because a lot of factors need to be identified before using machine learning models. This challenge has been taken up for this thesis and we would be predicting betting rates for a team at home and away. For this, two different datasets from opensource have been identified for this: one – European Soccer Database and second- Complete Football Dataset. A lot of research has been done in the domain of football, but the use of neural network in predicting betting hate has not been seen. Neural Network is a developing field of Artificial Intelligence, in this research it will be put to test with Ridge Regression, Lasso Regression, Random Forest Regression and XGBoost for the prediction of Betting Rates in football. Also, after doing a thorough research it was found certain footballing parameters are vital for any prediction in football. Since these parameters were not available in the data taken, they had to be built for which Ms Excel was used. It would be interesting to see how the machine learning models and neural network model perform when these parameters are used in our research.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 20 Jan 2021 16:42
Last Modified: 20 Jan 2021 16:42

Actions (login required)

View Item View Item