What Happened Next? Using Deep Learning to Value Defensive Actions in
  Football Event-Data
  Objectively quantifying the value of player actions in football (soccer) is a
challenging problem. To date, studies in football analytics have mainly focused
on the attacking side of the game, while there has been less work on
event-driven metrics for valuing defensive actions (e.g., tackles and
interceptions). Therefore in this paper, we use deep learning techniques to
define a novel metric that values such defensive actions by studying the threat
of passages of play that preceded them. By doing so, we are able to value
defensive actions based on what they prevented from happening in the game. Our
Defensive Action Expected Threat (DAxT) model has been validated using
real-world event-data from the 2017/2018 and 2018/2019 English Premier League
seasons, and we combine our model outputs with additional features to derive an
overall rating of defensive ability for players. Overall, we find that our
model is able to predict the impact of defensive actions allowing us to better
value defenders using event-data.
