The "Beautiful Game", also known as football, remains an important sport in the world, with followers generated across different social divides and strata. Interestingly, researchers are becoming more interested in analyzing football match results in order to aid prediction and match forecasting using a single variable or a large number of variables.

In a recent study, an M.Sc postgraduate student at Covenant University, Iyiola, Tomilayo Promise, used a feature selection method to reduce sixteen selected independent variables (football related) to six variables in the classification of the outcome variable (home win, away win, and draw) of five seasons of English premier league matches.

Iyiola’s study discovered that five performance metrics attest that the Machine Learning models are good in the classification, while Cross-Validation ensured that the issues of over-fitting were adequately addressed. Bookmakers may find this research interesting as some variables were identified as key to the classification of outcomes of football matches.

For more on this study, visit

http://eprints.covenantuniversity.edu.ng/id/eprint/16178