In-game behaviour analysis of football players using machine learning techniques based on player statistics

García Aliaga, Abraham and Marquina Nieto, Moisés and Coterón López, Javier and Rodríguez González, Asier and Luengo Sánchez, Sergio (2020). In-game behaviour analysis of football players using machine learning techniques based on player statistics. "International Journal of Sports Science & Coaching", v. 16 (n. 1); pp. 148-157. ISSN 1747-9541. https://doi.org/10.1177/1747954120959762.

Description

Title: In-game behaviour analysis of football players using machine learning techniques based on player statistics
Author/s:
  • García Aliaga, Abraham
  • Marquina Nieto, Moisés
  • Coterón López, Javier
  • Rodríguez González, Asier
  • Luengo Sánchez, Sergio
Item Type: Article
Título de Revista/Publicación: International Journal of Sports Science & Coaching
Date: September 2020
ISSN: 1747-9541
Volume: 16
Subjects:
Freetext Keywords: Association football; performance analysis; soccer; sport analytics
Faculty: Facultad de Ciencias de la Actividad Física y del Deporte (INEF) (UPM)
Department: Deportes
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The purpose of this research was to determine the on-field playing positions of a group of football players based on their technical-tactical behaviour using machine learning algorithms. Each player was characterized according to a set of 52 non-spatiotemporal descriptors including offensive, defensive and build-up variables that were computed from OPTA’s on-ball event records of the matches for 18 national leagues between the 2012 and 2019 seasons. To test whether positions could be identified from the statistical performance of the players, the dimensionality reduction techniques were used. To better understand the differences between the player positions, the most discriminatory variables for each group were obtained as a set of rules discovered by RIPPER, a machine learning algorithm. From the combination of both techniques, we obtained useful conclusions to enhance the performance of players and to identify positions on the field. The study demonstrates the suitability and potential of artificial intelligence to characterize players' positions according to their technical-tactical behaviour, providing valuable information to the professionals of this sport.

More information

Item ID: 65731
DC Identifier: https://oa.upm.es/65731/
OAI Identifier: oai:oa.upm.es:65731
DOI: 10.1177/1747954120959762
Official URL: https://journals.sagepub.com/doi/10.1177/1747954120959762
Deposited by: Memoria Investigacion
Deposited on: 30 Sep 2021 08:25
Last Modified: 30 Sep 2021 08:25
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