Machine learning Model for footballer position classification

Artificial intelligence is widely applied in sports science, especially in football, including match result prediction, and analysis of the performance of footballer. In team sports, the classification of footballer position can provide guidance for the selection of talents. However, there is still a lack of work in determining the position of the footballer, which is one of the leading problems for coaches in football. At present, it mainly relies on the preferences of footballers and arrangements of coaches. Therefore, the aim of this study is to predict the position of footballer using the dataset of football matches. In this study, we used web crawler to receive match dataset from the football analysis website whoscored. We tried to analyse the position of footballer and the factors affecting their positions. To achieve this aim, a two-stage application is followed. In the first stage, feature engineering was established to select important features. In the second stage, machine learning model was established. The result shows that compared with other models, the model based on GRU-attention has the highest accuracy (92.16%), the lowest loss (0.0144) and the fastest time. The AUC values of forwards, midfielders and defenders are 98%, 92% and 92%. The result verifies the effectiveness of the model.