Capturing Player Influence in Football Matches

Abstract

Sports analytics explores various interconnected dimensions of sports, including team dynamics, player interactions, and strategic decision-making. This work proposes a methodology based on network analytics to model and study how specific in-game events influence match dynamics and outcomes. Representing players as nodes and their interactions as links, we capture individual and collective influences within the game over time to model sports analytics as sequence-based problems. As a proof of concept, this paper uses the 2022 FIFA World Cup final between Argentina and France to construct the temporal network of the match to examine its progression and compute each player’s instantaneous and total influence. We compare our results with the post-match subjective evaluations of expert journalists, analyzing the reasons behind any discrepancies between our method and their assessments.