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Predicting soccer matches with complex networks and machine learning

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Author(s):
Baratela, Eduardo Alves ; Xavier, Felipe Jordao ; Peron, Thomas ; Ribeiro Villas-Boas, Paulino ; Rodrigues, Francisco Aparecido
Total Authors: 5
Document type: Journal article
Source: JOURNAL OF COMPLEX NETWORKS; v. 12, n. 6, p. 16-pg., 2024-11-05.
Abstract

Soccer attracts the attention of many researchers and professionals in the sports industry. Therefore, the incorporation of science into the sport is constantly growing, with increasing investments in performance analysis and sports prediction industries. This study aims to (i) highlight the use of complex networks as an alternative tool for predicting soccer match outcomes and (ii) show how the combination of structural analysis of passing networks with match statistical data can provide deeper insights into the game patterns and strategies used by teams. In order to do so, complex network metrics and match statistics were used to build machine learning models that predict the wins and losses of soccer teams in different leagues. The results showed that models based on passing networks were as effective as 'traditional' models, which use general match statistics. Another finding was that by combining both approaches, more accurate models were obtained than when they were used separately, demonstrating that the fusion of such approaches can offer a deeper understanding of game patterns, allowing the comprehension of tactics employed by teams relationships between players, their positions and interactions during matches. It is worth mentioning that both network metrics and match statistics were important and impactful for the mixed model. Furthermore, the use of networks with a lower granularity of temporal evolution (such as creating a network for each half of the match) performed better than a single network for the entire game. (AU)

FAPESP's process: 20/09835-1 - IARA - Artificial Intelligence in the Remaking of Urban Environments
Grantee:André Carlos Ponce de Leon Ferreira de Carvalho
Support Opportunities: Research Grants - Research Centers in Engineering Program
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC