Comparison of Machine Learning Approaches Applied to Predicting Football Players Performance
Comparison of Machine Learning Approaches Applied to Predicting Football Players Performance Master’s thesis in Computer science and engineering ADRIAN LINDBERG DAVID SÖDERBERG Department of Computer Science and Engineering CHALMERS UNIVERSITY OF TECHNOLOGY UNIVERSITY OF GOTHENBURG Gothenburg, Sweden 2020 Master’s thesis 2020 Comparison of Machine Learning Approaches Applied to Predicting Football Players Performance ADRIAN LINDBERG DAVID SÖDERBERG Department of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg, Sweden 2020 Comparison of Machine Learning Approaches Applied to Predicting Football Play- ers Performance ADRIAN LINDBERG DAVID SÖDERBERG © ADRIAN LINDBERG, DAVID SÖDERBERG, 2020. Supervisor: Carl Seger, Research Professor, Functional Programming division, Com- puter Science and Engineering. Supervisor: Yinan Yu, Postdoc, Functional Programming division, Department of Computer Science and Engineering. Examiner: Andreas Abel, Senior Lecturer, Logic and Types division, Department of of Computer Science and Engineering. Master’s Thesis 2020 Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg SE-412 96 Gothenburg Telephone +46 31 772 1000 Typeset in LATEX Gothenburg, Sweden 2020 iv Comparison of Machine Learning Approaches Applied to Predicting Football Players Performance ADRIAN LINDBERG DAVID SÖDERBERG Department of Computer Science and Engineering Chalmers University of Technology Abstract This thesis investigates three machine learning approaches: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) on predicting the performance of an upcoming match for a football player in the English Premier League. Each approach is applied to two problems: regression and classifi- cation. The last four seasons of English Premier League is collected and analyzed. Each approach and problem is tested several times with different hyperparameters in order to find the best performance.
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