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Machine learning models for predicting football results in Veikkausliiga
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School of Business |
Bachelor's thesis
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en
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19
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Artificial intelligence and machine learning models have evolved at a rapid pace in the recent years and decades, leading to an expansion of potential use cases for these technologies. The use of machine learning models has also spread to the world of sports. These models are used for a variety of tasks, including player talent identification, injury prevention, player valuation and match result forecasting. This study focuses on the appliance of machine learning models on football result forecasting in the Finnish top-flight football league, Veikkausliiga. A comparison is made between two popular machine learning models, random forests, and XGBoost. The study revealed that random forests performed better, achieving better overall accuracy, precision, recall and F1-scores. The results of this study indicate that these models could be used to predict results in Veikkausliiga, with a better accuracy than simple strategies, such as always betting for the home win.