Soccer, the most watched sport in the world, is a dynamic game where a team’s success relies on both team strategy and individual player contributions. Passing is a cardinal soccer skill and a key factor in strategy development; it helps the team to keep the ball in its possession, move it across the ield, and outmaneuver the opposing team in order to score a goal. From a defensive perspective, however, it is just as important to stop passes fromhappening, thereby disrupting the opposing team’s low of play. Ourmain contribution utilizes this fundamental observation to deine and learn a spatial map of each team’s defensive weaknesses and strengths. Moreover, as a byproduct of this approach we also obtain a team speciic offensive control surface, which describes a team’s ability to retain possession in different regions of the ield. Our results can be used to distinguish between different defensive strategies, such as pressing high up the ield or sitting back, aswell as speciic player contributions and the impact of a manager.
[1]
H. Rue,et al.
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
,
2009
.
[2]
P. Diggle,et al.
Model‐based geostatistics
,
2007
.
[3]
Sw. Banerjee,et al.
Hierarchical Modeling and Analysis for Spatial Data
,
2003
.
[4]
H. Rue,et al.
An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach
,
2011
.
[5]
David Sally,et al.
The Numbers Game: Why Everything You Know About Soccer Is Wrong
,
2013
.
[6]
Carl E. Rasmussen,et al.
Gaussian processes for machine learning
,
2005,
Adaptive computation and machine learning.