We propose using Network Science as a complementary tool to analyze player and team behavior during a football match. Specifically, we introduce four kinds of networks based on different ways of interaction between players. Our approach's main novelty is to use tracking datasets to create football tracking networks, instead of constructing and analyzing the traditional networks based on events. In this way, we are able to capture player interactions that go beyond passes and introduce the concepts of (a) Ball Flow Networks, (b) Marking Networks, (c) Signed Proximity Networks and (d) Functional Coordination Networks. After defining the methodology for creating each kind of network, we show some examples using tracking datasets from four different matches of LaLiga Santander. Finally, we discuss some of the applications, limitations, and further improvements of football tracking networks.

中文翻译：

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足球跟踪网络：超越基于事件的连接

我们建议使用网络科学作为补充工具来分析足球比赛期间的球员和球队行为。具体来说，我们根据玩家之间不同的交互方式介绍了四种网络。我们的方法的主要新颖之处在于使用跟踪数据集来创建足球跟踪网络，而不是基于事件构建和分析传统网络。通过这种方式，我们能够捕捉超越传球的球员互动，并引入 (a) 球流网络、(b) 标记网络、(c) 签名接近网络和 (d) 功能协调网络的概念。在定义了创建每种网络的方法之后，我们展示了一些使用来自 LaLiga Santander 的四个不同比赛的跟踪数据集的示例。最后，我们讨论一些应用、限制、