Knowledge Discovery in Databases for a Football Match Result

• Published in: Electronics (Basel) Vol. 12; no. 12; p. 2712
• Main Authors: Głowania, Szymon, Kozak, Jan, Juszczuk, Przemysław
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Algorithms; Artificial intelligence; Competitions; Datasets; Football; Knowledge discovery; Knowledge-based systems; Machine learning; Neural networks; Poland; Popularity; Prediction theory; Professional soccer; Regression analysis; Soccer; Support vector machines; Poland
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12122712

The analysis of sports data and the possibility of using machine learning in the prediction of sports results is an increasingly popular topic of research and application. The main problem, apart from choosing the right algorithm, is to obtain data that allow for effective prediction. The article presents a comprehensive KDD (Knowledge Discovery in Databases) approach that allows for the appropriate preparation of data for sports prediction on sports data. The first part of the article covers the subject of KDD and sports data. The next section presents an approach to developing a dataset on top football leagues. The developed datasets are the main purpose of the article and have been made publicly available to the research community. In the latter part of the article, an experiment with the results based on heterogeneous groups of classifiers and the developed datasets is presented.