Passes are undoubtedly the more frequent events in football and other team sports. Passing networks and their structural features can be useful to evaluate the style of play in terms of passing behavior, analyzing and quantifying interactions among players. The present paper aims to show how information retrieved from passing networks can have a relevant impact on predicting the match outcome. In particular, we focus on modeling both the scored goals by two competing teams and the goal difference between them. With this purpose, we fit these outcomes using Bayesian hierarchical models, including both in-match and network-based covariates to cover many aspects of the offensive actions on the pitch. Furthermore, we review and compare different approaches to include covariates in modeling football outcomes. The presented methodology is applied to a real dataset containing information on 125 matches of the 2016–2017 UEFA Champions League, involving 32 among the best European teams. From our results, shots on target, corners, and such passing network indicators are the main determinants of the considered football outcomes.

传球无疑是足球和其他团队运动中较为频繁的事件。传球网络及其结构特征可用于评估传球行为、分析和量化球员之间的互动方面的比赛风格。本文旨在展示从传递网络中检索到的信息如何对预测比赛结果产生相关影响。特别是，我们专注于对两支竞争球队的进球数和他们之间的进球差进行建模。为此，我们使用贝叶斯分层模型拟合这些结果，包括比赛中和基于网络的协变量，以涵盖球场上进攻动作的许多方面。此外，我们回顾并比较了不同的方法，以在足球比赛结果建模中包含协变量。所提出的方法应用于包含 2016-2017 年欧洲冠军联赛 125 场比赛信息的真实数据集，其中包括 32 支欧洲最佳球队。从我们的结果来看，射门、角球和此类传球网络指标是所考虑的足球结果的主要决定因素。