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.

Eduardo Alves Baratela

Felipe Jordão Xavier

Thomas Peron

Paulino Ribeiro Villas-Boas

Francisco Aparecido Rodrigues

Journal of Complex Networks

Journal of Complex Networks publishes original articles and reviews with a significant contribution to the analysis and understanding of complex networks and its applications in diverse fields. Complex networks are loosely defined as networks with nontrivial topology and dynamics, which appear as the skeletons of complex systems in the real-world. The journal covers everything from the basic mathematical, physical and computational principles needed for studying complex networks to their applications leading to predictive models in molecular, biological, ecological, informational, engineering, social, technological and other systems.     It includes, but is not limited to, the following topics:     - Mathematical and numerical analysis of networks   - Network theory and computer sciences   - Structural analysis of networks   - Dynamics on networks   - Physical models on networks   - Networks and epidemiology   - Social, socio-economic and political networks   - Ecological networks   - Technological and infrastructural networks   - Brain and tissue networks   - Biological and molecular networks   - Spatial networks   - Techno-social networks i.e. online social networks, social networking sites, social media   - Other applications of networks   - Evolving networks   - Multilayer networks   - Game theory on networks   - Biomedicine related networks   - Animal social networks   - Climate networks   - Cognitive, language and informational network

The Journal of Complex Networks publishes original articles and reviews that significantly contribute to the analysis and understanding of complex networks and their applications across diverse fields. Complex networks are broadly defined as networks with nontrivial topology and dynamics, which serve as the skeletons of complex systems in the real world. The journal covers everything from the fundamental mathematical, physical, and computational principles necessary for studying complex networks to their applications that lead to predictive models in molecular, biological, ecological, informational, engineering, social, technological, and other systems. It includes, but is not limited to, the following topics: - Mathematical and numerical analysis of networks - Network theory and computer sciences - Structural analysis of networks - Dynamics on networks - Physical models on networks - Networks and epidemiology - Social, socio-economic, and political networks - Ecological networks - Technological and infrastructural networks - Brain and tissue networks - Biological and molecular networks - Spatial networks - Techno-social networks, i.e., online social networks, social networking sites, social media - Other applications of networks - Evolving networks - Multilayer networks - Game theory on networks - Biomedicine-related networks - Animal social networks - Climate networks - Cognitive, language, and informational networks.

《Journal of Complex Networks》期刊发表对复杂网络及其在各个领域应用的分析与理解有重要贡献的原创文章和综述。复杂网络被宽泛定义为具有非平凡拓扑和动态的网络，这些网络在现实世界中作为复杂系统的骨架。期刊涵盖了从研究复杂网络所需的基本数学、物理和计算原理，到其在分子、生物、生态、信息、工程、社会、技术等系统中应用的预测模型。具体包括但不限于以下主题：- 网络的数学和数值分析 - 网络理论与计算机科学 - 网络的结构分析 - 网络上的动态 - 网络的物理模型 - 网络与流行病学 - 社会、社会经济和政治网络 - 生态网络 - 技术和基础设施网络 - 大脑和组织网络 - 生物和分子网络 - 空间网络 - 技术社会网络（如在线社交网络、社交媒体） - 网络的其他应用 - 发展中的网络 - 多层网络 - 网络上的博弈论 - 生物医学相关网络 - 动物社会网络 - 气候网络 - 认知、语言和信息网络

Category	Quartile
数学	4
数学, 数学跨学科应用	4

Predicting soccer matches with complex networks and machine learning

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
MATHEMATICS, INTERDISCIPLINARY APPLICATIONS	2