Rodrigo Augusto da Silva Alves, Ph.D.

Theses

Bachelor theses

Machine Learning-Based Prediction of Football Match Statistics

Author
Ondřej Herman
Year
2023
Type
Bachelor thesis
Supervisor
Rodrigo Augusto da Silva Alves, Ph.D.
Reviewers
Ing. Petr Kasalický
Summary
Football, the most widely played and followed sport globally, captivates billions of fans worldwide. The significance of predicting match outcomes has garnered attention from statisticians, machine learning researchers, and avid bettors alike. However, while substantial progress has been made in machine learning for outcome prediction, relatively little focus has been placed on forecasting the statistical aspects of the sport. This study aims to address this gap by exploring machine learning methods to analyze and estimate various match statistics as regression problems. Specifically, I investigate six statistics: corners, shots, shots on target, fouls, yellow cards, and red cards. By conducting experiments on four datasets from different football leagues, I evaluate the performance of eight models. My findings reveal that different methods adapt better to certain statistics, and also that some statistics exhibit different behaviors across leagues. Additionally, I observe that certain features, such as the number of corners or shots, are more predictable due to their higher occurrence rates during matches compared to the number of cards.

Master theses

Football outcomes prediction with tensor completion embeddings

Author
Martin Kostrubanič
Year
2023
Type
Master thesis
Supervisor
Rodrigo Augusto da Silva Alves, Ph.D.
Reviewers
Ing. Karel Klouda, Ph.D.
Summary
Football is a hugely popular sport with over 3.5 billion fans worldwide, and predicting the outcome of matches has become increasingly important. While several machine learning methods have been used for this purpose, personalized machine learning methods like matrix completion have been neglected. In this thesis, I introduce tensor completion techniques for predicting football match outcomes, using two experimental strands: (1) tensor completion as a prediction method; and (2) tensor completion embeddings extraction. I consider data from five different leagues, four from Europe and one from South America. The results show that tensor completion matches or outperforms other state-of-the-art prediction methods and is capable of improving the performance of Artificial Neural Networks in this task.