Optimizing Football Player Selection Using Random Forest for Criterion Weighting and TOPSIS for Ranking

DOI

10.2991/978-94-6463-654-3_6How to use a DOI?

Keywords

football player selection; random forest; TOPSIS; multicriteria decision-making; machine learning

Abstract

In professional football, selecting players involves evaluating multiple criteria to ensure optimal team performance. This paper introduces a novel approach for optimizing player selection by combining the Random Forest algorithm for criterion weighting with the multi-criteria decision-making method TOPSIS for ranking players. Experimental results highlight the effectiveness of this approach in accurately ranking players, providing valuable insights for team managers and scouts. For this study, we analyzed sports data from thirty-three players of Paris Saint-Germain (PSG) for the 2021-2022 season, considering thirty-five performance criteria across various positions: goalkeeper, defender, midfielder, and forward.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Abdessatar Ati
AU  - Patrick Bouchet
AU  - Roukaya Ben Jeddou
PY  - 2025
DA  - 2025/02/24
TI  - Optimizing Football Player Selection Using Random Forest for Criterion Weighting and TOPSIS for Ranking
BT  - Proceedings of the  International Conference on Decision Aid and Artificial Intelligence (ICODAI 2024)
PB  - Atlantis Press
SP  - 62
EP  - 77
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-654-3_6
DO  - 10.2991/978-94-6463-654-3_6
ID  - Ati2025
ER  -