The tactics of successful attacks in professional association football: large-scale spatiotemporal analysis of dynamic subgroups using position tracking data.

Association football teams can be considered complex dynamical systems of individuals grouped in subgroups (defenders, midfielders and attackers), coordinating their behaviour to achieve a shared goal. As research often focusses on collective behaviour, or on static subgroups, the current study aims to analyse spatiotemporal behaviour of dynamic subgroups in relation to successful attacks. We collected position tracking data of 118 Dutch Eredivisie matches, containing 12424 attacks. Attacks were classified as successful (N = 1237) or non-successful (N = 11187) based on the potential of creating a scoring opportunity. Using unsupervised machine learning, we automatically identified dynamic formations based on position tracking data, and identified dynamic subgroups for every timeframe in a match. We then compared the subgroup centroids to assess the intra- and inter-team spatiotemporal synchronisation during successful and non-successful attacks, using circular statistics. Our results indicated subgroup-level variables provided more information, and were more sensitive to disruption, in comparison to team-level variables. When comparing successful and non-successful attacks, we found decreases (p < .01) in longitudinal inter- and intra-team synchrony of interactions involving the defenders of the attacking team during successful attacks. This study provides the first large-scale dynamic subgroup analysis and reveals additional insights to team-level analyses.

Not Every Pass Can Be an Assist: A Data-Driven Model to Measure Pass Effectiveness in Professional Soccer Matches.

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.