Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes

Abstract:The two main traditional player statistics used in soccer are goals scored and assists, with an assist credited when a player's pass immediately leads to a teammate scoring. Attributing credit solely to the player who makes the final pass before a goal fails to account for the contributions of multiple players in the buildup to the goal. Consequently, traditional statistics provide only a limited view of player involvement. In this paper, we use multivariate Hawkes processes (MHPs) to model timestamped sequences of passes and shots, which form continuous-time networks. MHPs provide a branching mechanism where an event (a pass or shot) can be generated as a child of a previous event, which allows us to quantify each pass's contribution to a subsequent pass or shot. This approach distributes credit across multiple passes within an attacking sequence, making it applicable across a wide range of situations. Building on this framework, we propose expected goal involvements (xGI), a metric that measures a player's involvement in creating goal scoring opportunities based on their passes. This metric identifies players who play key roles in creating goal scoring opportunities even if they do not make the final pass, offering a more comprehensive measure of attacking involvement and player performance.

Social and Information Networks (cs.SI); Machine Learning (cs.LG); Physics and Society (physics.soc-ph); Applications (stat.AP)

arXiv:2610.12775 [cs.SI]

(or arXiv:2610.12775v1 [cs.SI] for this version)

https://doi.org/10.48550/arXiv.2610.12775 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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