Name
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 events like shots and goals alone or fail to account for the context in which the actions occurred. This paper introduces a novel advanced soccer metric for valuing any type of individual player action on the pitch, be it with or without the ball. Our metric values each player action based on its impact on the game outcome while accounting for the circumstances under which the action happened. When applied to on-the-ball actions like passes, dribbles, and shots alone, our metric identifies Argentine forward Lionel Messi, French teenage star Kylian Mbappu0027e, and Belgian winger Eden Hazard as the most effective players during the 2016/2017 season. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3292500.3330758 | knowledge discovery and data mining |
Keywords | Field | DocType |
event stream data, probabilistic classification, soccer match data, sports analytics, valuing actions | Sports analytics,Data science,Contextual information,Use case,Computer science,Artificial intelligence,Constructed language,Probabilistic classification,Machine learning,Offensive | Conference |
ISBN | Citations | PageRank |
978-1-4503-6201-6 | 7 | 0.48 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tom Decroos | 1 | 13 | 3.31 |
| Lotte Bransen | 2 | 7 | 1.15 |
| Jan Van Haaren | 3 | 60 | 7.76 |
| Jesse Davis | 4 | 1425 | 78.27 |