{"type":"ScholarlyArticle","title":"VAEP: An Objective Approach to Valuing On-the-Ball Actions in Soccer (Extended Abstract)","authors":[{"name":"Tom Decroos","slug":"tom-decroos","given":"Tom","family":"Decroos","affiliation":"KU Leuven , Department of Computer Science & Leuven.AI , Belgium"},{"name":"Lotte Bransen","slug":"lotte-bransen","given":"Lotte","family":"Bransen","affiliation":"SciSports , Netherlands"},{"name":"Jan Van Haaren","slug":"jan-van-haaren","given":"Jan","family":"Van Haaren","affiliation":"KU Leuven , Department of Computer Science & Leuven.AI , Belgium; SciSports , Netherlands"},{"name":"Jesse Davis","slug":"jesse-davis","given":"Jesse","family":"Davis","affiliation":"KU Leuven , Department of Computer Science & Leuven.AI , Belgium"}],"abstract":"Despite the fact that objectively assessing the impact of the individual actions performed by soccer players during games is a crucial task, most traditional metrics have substantial shortcomings. First, many metrics only consider rare actions like shots and goals which account for less than 2% of all on-the-ball actions. Second, they fail to account for the context in which the actions occurred. This work summarizes several important contributions.  First, we describe a language for representing individual player actions on the pitch. This language unifies several existing formats which greatly simplifies automated analysis and this language is becoming widely used in the soccer analytics community. Second, we describe our 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. This framework enables giving a broad overview of a player's performance, including quantifying a player's total offensive and defensive contributions to their team. Third, we provide illustrative use cases that highlight the working and benefits of our framework.","doi":"10.24963/ijcai.2020/648","url":"https://ijcai.org/proceedings/2020/648","pdf":"https://ijcai.org/proceedings/2020/0648.pdf","markdown":"https://ijcai.org/proceedings/2020/648.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/648","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":648,"pages":{"first":4696,"last":4700},"keywords":["Multidisciplinary Topics and Applications: Other","Machine Learning Applications: Applications of Supervised Learning","Machine Learning: Time-series;Data Streams","Data Mining: Classification, Semi-Supervised Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25153/file_25153.m3u8","length_sec":299,"provider":"hls"}],"references":[{"position":1,"raw":"[Altman, 2015] Daniel Altman. 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