Comparison of Football Results Using Machine Learning Algorithms

Many of the predictions for a result of a football match have primarily taken only the number of goals scored into account while giving out their predictions. One of the major drawbacks in this type of prediction is the inconsistency while basing the results using only just goals. Thus, the main objective of this project is to explore different Machine learning techniques to predict the score and outcome of football matches using previous game match events rather than the number of goals scored by each team. The project sees us develop a set of custom generated features which helps us evaluate a team’s full time result, instead of using the actual number of goals scored. These features are trained using the selected algorithms and the results are then compared to the models trained using basic in game data obtained after conclusion of the match.