Crafting a Player Impact Metric through analysis of football match event data

Abstract

The evaluation of football players remains a challenging task due to the limitations of existing rating models as well as the diverse nature of in-game actions and their varying impact on outcome of the matches, which often emphasize offensive actions while overlooking key defensive and strategic contributions. While some player impact metrics exist for football, their effectiveness, complete in-depth analysis, and relationship with match outcomes (win, loss, draw) has not been studied well. In this study, we have developed a Player Impact Metric (PIM) that provides a more comprehensive and data-driven assessment of player performance by incorporating match event data, Expected Goals (xG), Expected Threat (xT), and defensive contributions. The PIM framework assigns weighted scores to player actions using ordinal logistic regression based on their influence on match outcomes. The model evaluates player contributions using event-level data, integrating both offensive and defensive actions. The dataset is sourced from WhoScored, with structured data processing in PostgreSQL and analytical modeling techniques applied to derive impact scores. The PIM was tested against WhoScored Ratings, revealing notable differences in player rankings, particularly for defensive players. Our findings show that PIM provides a more balanced assessment, capturing critical non-scoring contributions that traditional rating systems tend to undervalue. We have introduced PIM as an advanced evaluation metric for football analytics, offering a data-driven, context-aware, and holistic approach to player performance assessment in this study. We show that the PIM can serve as a valuable tool for coaches, analysts, and scouts, enabling more accurate talent identification and match analysis.