Authors:
Thijs Overmeer
1
;
2
;
Tim Janssen
3
and
Wim Nuijten
2
;
1
Affiliations:
1
Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
;
2
Eindhoven AI Systems Institute, Eindhoven University of Technology, Eindhoven, The Netherlands
;
3
Royal Dutch Football Association (KNVB), Zeist, The Netherlands
Keyword(s):
Expected Possession Value, U-Net Architecture, Football Analytics, Pass Analysis, Risk-Reward Decomposition, Machine Learning in Sports.
Abstract:
This paper presents an Expected Possession Value (EPV) model for football with three main new components: a U-Net-inspired convolutional neural network architecture, ball height as a feature, and a dual-component pass value model that analyzes reward and risk. We furthermore introduce the Overmeer–Janssen–Nuijten Pass Expected Possession Value benchmark (OJN-Pass-EPV benchmark), which enables a quantitative evaluation of EPV models by using pairs of game states with given relative EPVs. The presented EPV model achieves good results in model loss and Expected Calibration Error on a dataset containing Dutch Eredivisie and 2022 FIFA Men’s World Cup matches and correctly identifies the higher value state in 78% of the game state pairs in the OJN-Pass-EPV benchmark, demonstrating its ability to accurately assess goal-scoring potential. Our findings enable more precise EPV estimations, support risk-reward analysis for passing decisions, and establish quality control standards for EPV models.