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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

In professional soccer, the choices made in forming a team lineup are crucial for achieving good results. Players are characterized by different skills and their relevance depends on the position that they occupy on the pitch. Experts can recognize similarities between players and their styles, but the procedures adopted are often subjective and prone to misclassification. The automatic recognition of players’ styles based on their diversity of skills can help coaches and technical directors to prepare a team for a competition, to substitute injured players during a season, or to hire players to fill gaps created by teammates that leave. The paper adopts dimensionality reduction, clustering and computer visualization tools to compare soccer players based on a set of attributes. The players are characterized by numerical vectors embedding their particular skills and these objects are then compared by means of suitable distances. The intermediate data is processed to generate meaningful representations of the original dataset according to the (dis)similarities between the objects. The results show that the adoption of dimensionality reduction, clustering and visualization tools for processing complex datasets is a key modeling option with current computational resources.

Details

Title
Uniform Manifold Approximation and Projection Analysis of Soccer Players
Author
Lopes, António M 1   VIAFID ORCID Logo  ; Tenreiro Machado, José A 2   VIAFID ORCID Logo 

 INEGI, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal 
 Institute of Engineering, Polytechnic of Porto, Dept. of Electrical Engineering, 4249-015 Porto, Portugal; [email protected] 
First page
793
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
10994300
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2554507667
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.