This study aimed to compare the accuracy of machine learning models with the probabilities generatedby sports betting companies in predicting the outcome of football matches. The study also investigatedthe impact of different feature combinations on the performance of machine learning models for predicting football match outcomes. The study used data from various sources of the Swedish football leaguebetween the seasons 2018-2022. The comparison between the model’s predictions and the probabilitiesgenerated by sports betting companies showed that the model’s predictions were more accurate. SupportVector Machines(SVM) performed the best with an accuracy of 52.4 percent compared to the bettingcompanies at 40.4 percent. The results also showed that different feature combinations can have a significant impact on the performance of machine learning models for predicting football match outcomes butthe importance of features varied depending on the selection method used. The study used four different feature selection approaches: filter methods, Lasso, Ridge, and PCA, to identify the most importantfeatures for prediction.

Overall, the results of this study suggest that machine learning models can outperform sports bettingcompanies in predicting football match outcomes and that the choice of feature combination can have asignificant impact on model performance. Further research is needed to explore these findings in moredetail and to investigate the usefulness of different feature selection techniques at different points in theseason.