Optimization and comparison of machine learning algorithms for the prediction of the performance of football players
Author(s)
Date Issued
2024
Type
article
Volume
36
Issue
31
Start Page
19653
End Page
19666
Journal
Abstract
Athletes’ performance evaluation is a critical step for the assessment of the skills and the quality of training of the athletes.
This is even more important in team sports such as football, in which different roles, and hence different skills, are
required. In light of this, the presented work aims at forecasting above team-average performance of football players by
using supervised machine learning algorithms. Such algorithms were trained and tested on four biometric parameters as
input and seven performance indicators as labels. The algorithms have been optimized using three optimization techniques:
the gridsearch, and two versions of the whale-optimization algorithm, the standard one and another, proposed by us, in
which Euclidean distance is used. The analyses were conducted by dividing the players by their role: the strikers, the
midfielders and the defenders, to take into account the different skills required for each task. The obtained results show that
the Random Forest, trained with specific combinations of biometric parameters is capable of predicting the selected
performance indicators with an accuracy higher than 90%, for all the considered players’ roles and that the proposed
optimization technique outperforms the existing ones. In light of these results, the proposed method could be used
profitably by football teams’ staff to monitor their players, offering the possibility to take tactical decisions, design
customized training sessions and orient market choices.