Decision-makers in soccer routinely assess the tactical behaviour of a team and its opponents both during and after the game to optimize performance. Currently, this assessment is typically driven by notational analysis and observation. Therefore, potential high-impact decisions are often made based on limited or even biased information. With the current study, we aimed to quantitatively assess tactical performance by abstracting a set of spatiotemporal features from the general offensive principles of play in soccer using position tracking data, and to train a machine learning classifier to predict match outcome based on these features computed over the full game as well as only parts of the game. Based on the results of these analyses, we describe a proof of concept of a decision support system for coaches and managers. In an analysis of 302 professional Dutch Eredivisie matches, we were able to train a Linear Discriminant Analysis model to predict match outcome with fair to good (74.1%) accuracy with features computed over the full match, and 67.9% accuracy with features computed over only 1/4th of the match. We therefore conclude that using only position tracking data, we can provide valuable feedback to coaches about how their team is executing the various principles of play, and how these principles are contributing to overall performance.

中文翻译：

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使用源自位置跟踪数据的战术特征对足球中的球队表现进行建模

足球决策者会在比赛期间和比赛后定期评估球队及其对手的战术行为，以优化表现。目前，这种评估通常由符号分析和观察驱动。因此，潜在的高影响决策通常是基于有限甚至有偏见的信息做出的。在目前的研究中，我们旨在通过使用位置跟踪数据从足球比赛的一般进攻原则中抽象出一组时空特征来定量评估战术表现，并训练机器学习分类器根据这些计算出的特征来预测比赛结果。完整的游戏以及游戏的一部分。基于这些分析的结果，我们为教练和经理描述了一个决策支持系统的概念证明。在对 302 场荷兰荷甲职业比赛的分析中，我们能够训练一个线性判别分析模型来预测比赛结果，在完整比赛中计算出的特征具有一般到良好 (74.1%) 的准确度，而在仅计算特征的情况下准确度为 67.9%比赛的1/4。因此，我们得出结论，仅使用位置跟踪数据，我们可以向教练提供有价值的反馈，说明他们的团队如何执行各种比赛原则，以及这些原则如何促进整体表现。