Computational Mathematics and Computer Modeling with Applications (CMCMA)

Predicting football player features through hierarchical clustering and representative selection

https://doi.org/10.48308/CMCMA.4.1.43
Volume 4, Issue 1
April 2025
Pages 43-55
Computational Mathematics and Computer Modeling with Applications (CMCMA)

Document Type : Regular paper

Authors

Mahdi Nouraie 1
Meysam Agah 2
Changiz Eslahchi 3
Arnold Baca 4

1 Department of Statistics, Shahid Beheshti University, Tehran, Iran

2 Department of Mathematics, Shahid Beheshti University, Tehran, Iran

3 Department of Computer and Data Sciences, Shahid Beheshti University, Tehran, Iran

4 Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria

Abstract
This study offers a comprehensive analysis of both classic and advanced features of football players, aiming to enhance player evaluation and feature prediction. We applied a three-step methodology: first, hierarchical clustering was used to reveal the group structure of features in each position. Second, representative features were identified within clusters leveraging the correlation matrix of features, reducing the dimensionality of the dataset while retaining critical information. Third, several regression models were employed to predict other player features using the representatives. This approach was applied across the distinct positions of goalkeeper, defender, midfielder, and forward. Bootstrap resampling confirms the robustness of the results obtained, revealing consistent clusters against random data variations. The findings indicate that representative features effectively encapsulate the entire feature space for each position, allowing other features to be predicted accurately with minimal errors. This study contributes to football analytics by providing a robust method for feature selection and prediction, ultimately improving the accuracy and efficiency of player performance analysis.

Keywords

Football tactical analysis
Advanced football analysis
Feature selection
Machine learning
  • Receive Date 06 March 2025
  • Revise Date 21 July 2025
  • Accept Date 05 August 2025

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