Comparison of Machine Learning Approaches Applied to Predicting Football Players Performance
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Lindberg, Adrian
Söderberg, David
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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 classification.
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. We evaluate on five game weeks by picking
a lineup for each model that is then measured by its collective score. The results
indicate that regression outperforms classification, with LSTM being the best performing
model. The score ends up outperforming the average of all managers during
the evaluated period in the online football game, Fantasy Premier League. The findings
could be used to assist in providing insight from historical data that might be
too complex to find for humans.
Beskrivning
Ämne/nyckelord
SVM , SVR , MLP , LSTM , Predicting Athletic Performance , Computer Science