Actions Speak Louder Than Goals: Valuing Player Actions in Soccer

Tom Decroos, Lotte Bransen, Jan Van Haaren, Jesse Davis
2/18/2018
stat.APstat.ML

Official code: ML-KULeuven/socceraction (821 stars, MIT, last commit 2026-10-18).

Abstract

Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in which the actions occurred. This paper introduces (1) a new language for describing individual player actions on the pitch and (2) a framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. By aggregating soccer players' action values, their total offensive and defensive contributions to their team can be quantified. We show how our approach considers relevant contextual information that traditional player evaluation metrics ignore and present a number of use cases related to scouting and playing style characterization in the 2016/2017 and 2017/2018 seasons in Europe's top competitions.

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Code Implementations(1)

ML-KULeuven/socceractionOfficial100%
MIT

Convert soccer event stream data to SPADL and value player actions using VAEP or xT

821159Python, MakefileJul 2, 20193 days agoMIT
soccersoccer-analyticssoccer-datasports-analytics

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Cite this paper

@article{decroos2018actions,
  title  = {Actions Speak Louder Than Goals: Valuing Player Actions in Soccer},
  author = {Tom Decroos and Lotte Bransen and Jan Van Haaren and Jesse Davis},
  year   = {2018},
  eprint = {1802.07127},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1802.07127}
}

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