In professional soccer, nowadays almost every team employs tracking technology to monitor performance during trainings and matches. Over the recent years, there has been a rapid increase in both the quality and quantity of data collected in soccer resulting in large amounts of data collected by teams every single day. The sheer amount of available data provides opportunities as well as challenges to both science and practice. Traditional experimental and statistical methods used in sport science do not seem fully capable to exploit the possibilities of the large amounts of data in modern soccer. As a result, tracking data are mainly used to monitor player loading and physical performance. However, an interesting opportunity exists at the intersection of data science and sport science. By means of tracking data, we could gain valuable insights in the how and why of tactical performance during a soccer match. One of the most interesting and most frequently occurring elements of tactical performance is the pass. Every team has around 500 passing interactions during a single game. Yet, we mainly judge the quality and effectiveness of a pass by means of observational analysis, and whether the pass reaches a teammate. In this article, we present a new approach to quantify pass effectiveness by means of tracking data. We introduce two new measures that quantify the effectiveness of a pass by means of how well a pass disrupts the opposing defense. We demonstrate that our measures are sensitive and valid in the differentiation between effective and less effective passes, as well as between the effective and less effective players. Furthermore, we use this method to study the characteristics of the most effective passes in our data set. The presented approach is the first quantitative model to measure pass effectiveness based on tracking data that are not linked directly to goal-scoring opportunities. As a result, this is the first model that does not overvalue forward passes. Therefore, our model can be used to study the complex dynamics of build-up and space creation in soccer.

中文翻译：

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并非每个通行证都可以提供帮助：一种数据驱动的模型来衡量职业足球比赛中的通行效率。

如今，在职业足球中，几乎每个团队都使用跟踪技术来监视训练和比赛中的表现。近年来，足球中收集的数据的质量和数量都在迅速增加，导致团队每天收集大量数据。大量可用数据为科学和实践提供了机遇，也带来了挑战。体育科学中使用的传统实验和统计方法似乎并不完全能够利用现代足球中大量数据的可能性。结果，跟踪数据主要用于监视玩家的负荷和身体表现。但是，在数据科学和运动科学的交叉领域中存在着一个有趣的机会。通过跟踪数据，我们将获得关于足球比赛中战术表现的方式和原因的宝贵见解。通过是战术表现中最有趣，最频繁出现的要素之一。每个团队在一场比赛中大约进行500次传球互动。但是，我们主要通过观察分析来判断传球的质量和有效性，以及传球是否到达队友。在本文中，我们提出了一种通过跟踪数据来量化通过效率的新方法。我们引入了两种新方法，通过传球破坏对方防守的程度来量化传球的有效性。我们证明了我们的措施对于区分有效传球和不太有效传球以及有效球手和不太有效球手之间的敏感性和有效性。此外，我们使用这种方法来研究数据集中最有效的传球的特征。提出的方法是第一个基于不与目标得分机会直接相关的跟踪数据来衡量通过效率的定量模型。结果，这是第一个不会高估正向传递的模型。因此，我们的模型可用于研究足球中空间的积累和创造的复杂动力。