Predicting soccer matches with complex networks and machine learning

TL;DR

This paper explores how complex network analysis combined with machine learning can predict soccer match outcomes and provide insights into team strategies, showing that integrated models outperform individual approaches.

Contribution

It introduces a novel approach combining complex network metrics with match statistics for soccer prediction, demonstrating improved accuracy and deeper tactical understanding.

Findings

Models based on passing networks are as effective as traditional models.

Combining network metrics with match statistics yields more accurate predictions.

Segmenting matches into halves improves model performance.

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.