The study investigates the use of machine learning to predict the results of football matches, with its main goal being to enhance the effectiveness of sports betting techniques.An assessment of diverse machine learning methods was carried out by analyzing a comprehensive dataset that included European league games spanning 2008 to 2016, such as Random Forest, Gaussian Process Regression, Logistic Regression, K-Nearest Neighbors, AdaBoost, XGBoost, and LightGBM. Our results revealed that the LightGBM and Ada model exhibited great performance, achieving an accuracy of 52.6% and 52.8% in predicting match outcomes.Moreover, we incorporated the concept of double chances into our analysis and a simulation-based betting strategy was used in our model, demonstrating a 3% profit margin. This study demonstrates a hopeful potential of machine learning. It is very useful in sports analysis and betting. At the same time, the study talks about some limitations. It also suggests directions for future research.
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