Modelling team performance in soccer using tactical features derived from position tracking data

Abstract

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.

A risk-reward assessment of passing decisions: comparison between positional roles using tracking data from professional men’s soccer

“…Reward is typically modelled based on metrics related to goal-scoring (Power et al, 2017;Link et al, 2016), thereby typically attributing success to the offensive players. Although scoring is essential in soccer, not every pass can be an assist, and most passes not even intend to be one (Goes et al, 2019). As has been demonstrated in previous work, the two football teams in a match can be considered as two tightly coupled interacting complex dynamical systems (Low et al, 2020;Balague et al, 2013;Goes et al, 2021b).…”

“…A typical example of tactical decision-making in soccer is passing. Passes are amongst the most frequent actions in a soccer game (Goes et al, 2019), and are the preferred way of a team to move the ball up the field, disrupt the opponent, and create scoring opportunities (Goes et al, 2021a). Traditional notational analysis only assesses basic attributes like accuracy or number of assists.…”

“…As has been demonstrated in previous work, the two football teams in a match can be considered as two tightly coupled interacting complex dynamical systems (Low et al, 2020;Balague et al, 2013;Goes et al, 2021b). While the ultimate goal of these teams is to create a scoring opportunity, doing so first requires them to create space by disrupting the coupling with the opponent (Goes et al, 2019;Goes et al, 2021b). This disruption can be assessed on the level of individual players making a dribble or passing action (Goes et al, 2019;Corrêa, 2016), or on the level of subgroups of players responding to an on ball action (Goes et al, 2019;Goes et al, 2021a), and has been shown to be a key factor in ultimately gaining offensive success.…”

“…While the ultimate goal of these teams is to create a scoring opportunity, doing so first requires them to create space by disrupting the coupling with the opponent (Goes et al, 2019;Goes et al, 2021b). This disruption can be assessed on the level of individual players making a dribble or passing action (Goes et al, 2019;Corrêa, 2016), or on the level of subgroups of players responding to an on ball action (Goes et al, 2019;Goes et al, 2021a), and has been shown to be a key factor in ultimately gaining offensive success. Therefore, one could argue that reward is pluriform and context-dependent, and that as goal scoring is not the only crucial type of reward to be gained from passing, players of different positional roles can seek different types of rewards (Goes et al, 2019).…”

“…This disruption can be assessed on the level of individual players making a dribble or passing action (Goes et al, 2019;Corrêa, 2016), or on the level of subgroups of players responding to an on ball action (Goes et al, 2019;Goes et al, 2021a), and has been shown to be a key factor in ultimately gaining offensive success. Therefore, one could argue that reward is pluriform and context-dependent, and that as goal scoring is not the only crucial type of reward to be gained from passing, players of different positional roles can seek different types of rewards (Goes et al, 2019). Recent work has resulted in an increased ability to quantify various aspects of risk and reward in more detail, and with greater accuracy, using position-tracking data (Goes et al, 2020;Goes et al, 2019;Herold et al, 2019).…”