Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes

Key points

• 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.1
• In this paper, we use multivariate Hawkes processes (MHPs) to model timestamped sequences of passes and shots, which form continuous-time networks.1
• 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.1
• 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.1

Sources (1)

• [1]Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 10:34 PM

  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.

Extractive summary: sentences quoted from the sources.