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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Actions Speak Louder than Goals:Valuing Player Actions in Soccer
Assessing the impact of the individual actions performed by soccerplayers during games is a crucial aspect of the player recruitmentprocess. Unfortunately, most traditional metrics fall short in ad-dressing this task as …
Football Action ValuationActions Speak Louder Than Goals: Valuing Player Actions in Soccer
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…
Football Action ValuationModeling Complex Event Scenarios via Simple Entity-focused Questions
Event scenarios are often complex and involve multiple event sequences connected through different entity participants. Exploring such complex scenarios requires an ability to branch through different sequences, somethin…
Language ModelingLanguage ModellingDid that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show
While popular Television (TV) shows are airing, some users interested in these shows publish social media posts about the show. Analyzing social media posts related to a TV show can be beneficial for gaining insights abo…
Better Prevent than Tackle: Valuing Defense in Soccer Based on Graph Neural Networks
Evaluating defensive performance in soccer remains challenging, as effective defending is often expressed not through visible on-ball actions such as interceptions and tackles, but through preventing dangerous opportunit…