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Weir, Christopher (2023) Beyond The Beautiful Game: Leveraging Clustering & Generative Adversarial Network Techniques for Europe's Top Football Leagues. Undergraduate thesis, Dublin, National College of Ireland.
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Abstract
This project is based on Europe's highest level of football. Throughout this report, multiple techniques and tools will be used to classify teams at the highest level, into a certain level of playing style. Based on multiple statistics scraped from a footballing data website named FBref1, the aim is to extract data regarding the top 5 European football leagues to build a classification model based on the performance and the strength of each team. Once the teams are successfully classified, a Generative Adversarial Network (GAN) model is built to generate synthetic data given a team’s input to push them to compete with Europe’s elites. Along with this, for ease of comprehensibility, the terminology involved in the data will be discussed and how data, along with these terms, have become more prevalent in football now than ever before.
By the end of this project, the goal is to build an accurate classification and GAN model that can compare team’s data and groups teams together by the level of football instilled into the club by the manager, owners, and culture of the football club itself, and then generate new data to help improve the team and gain them a step in the right direction. This project should also allow for any person, whether a die-hard football fan or a mere casual fan of the game, to understand football playing styles and the impact they have on the biggest sport in the world. With this GAN model, there is a desire to create an interactive dashboard with filters for different data metrics to highlight different data points and where a team’s data sits on any given metric in comparison to the data points they should be striving to reach.
Overall, the main aim is to analyse teams’ playing styles, taking data from the past five years in their respective leagues, and to provide a complete report, along with a dashboard, a classification model, and a GAN model on what is internationally known as “The Beautiful Game” to allow for any reader to gain a little more knowledge and interest on the wonderful sport, and to acknowledge the impact statistics, and data has on the biggest game in the world.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Supervisors: | Name Email Bradford, Michael UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science G Geography. Anthropology. Recreation > GV Recreation Leisure > Sports > Soccer Q Science > QA Mathematics > Electronic computers. Computer science > Computer Systems > Computers > Electronic data processing T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science > Computer Systems > Computers > Electronic data processing |
| Divisions: | School of Computing > Bachelor of Science (Honours) in Computing |
| Depositing User: | Tamara Malone |
| Date Deposited: | 16 Jan 2024 16:57 |
| Last Modified: | 16 Jan 2024 16:57 |
| URI: | https://norma.ncirl.ie/id/eprint/6926 |
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