Machine Learning for Football Matches and Tournaments

The excitement of predicting the result of a football game and support for their favourite clubs has brought all the sports enthusiasts together to develop platforms around it. Given the access to data, football analytics has emerged as a huge industry not limited to the players and teams. This research manipulates data available for different football leagues and concepts of classification models to predict the result of a match at half time with 80% accuracy (using ensemble techniques) for a match being played in the English Premier League and the winner of the same league using the historical and current performance of the club and the players in the league at a match week level with up to 95% accuracy. The research uses a dynamic dataset and proposes an application that requires minimum user input for prediction. Further, simple features and visualization of results make the research understandable and reusable.