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

Abstract

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.

Move it or lose it: Exploring the relation of defensive disruptiveness and team success

“…Both performance indicators value passes higher if the induce a higher amount of total movement of defending players (I-Mov) or result in a larger change in defensive alignment and distance and space between team subunits (D-Def). In a validation study we could 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 (Goes et al, 2018). In addition, we could show in a second study that I-Mov relates to classic individual pass performance parameters like passing accuracy of key passes (passes that create goals or shots on goal) ).…”

“…The disruption of the defensive organization as result of a pass was quantified using our previously published Defensive Disruptiveness (Def-D) feature (Goes et al, 2018). This feature is constructed based on the change in the average position of the attacking, midfield, and defensive line, the change in the average team position, and the change in team surface area and team spread.…”

“…[4, 3, 3]), we then automatically identified, for example, the defensive line based on the 4 last players (excluding the goalkeeper) in every timeframe, creating a much more robust and representative feature. For further details we refer to our previous publications (Goes et al, 2018;Kempe et al, 2018).…”

“…In a previous study, we were able to combine several of those spatio-temporal features in an new approach to measure pass performance of soccer players (Goes, Kempe, Meerhoff, & Lemmink, 2018). The evaluation of passes is one of the most common ways to asses' tactical performance at individual and team levels in (scientific) performance analysis.…”

Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review

“…A potential example could be applying the work of Bialkowski et al (2014aBialkowski et al ( , 2014bBialkowski et al ( , 2014cBialkowski et al ( , 2016, that has resulted in methodology to automatically and dynamically identify formations and positional roles. Applying this method in sports science research like that of Memmert et al (2017), Goes et al (2019) or Siegle and Lames (2013), who all use line centroids in which the lines are based on manual annotation of fixed positional roles, could lead to different answers and new insights. The other way around, applying the theoretical framework of dynamical systems theory that is presented in for example the sports science work by Frencken et al (2012Frencken et al ( , 2013, to feature construction in computer science work like that on quantifying pressure by Andrienko et al (2017), could lead to advanced methods that use coupling between features and movement synchrony of players to quantify pressure, defensive strategies and off-ball performance of offensive players.…”