Estimating Player Performance in Different Contexts Using Fine-tuned Large Events Models

TL;DR

This paper applies fine-tuned Large Event Models to soccer analytics, enabling match simulation and player performance prediction across contexts, with insights into transfer impacts and team strategies.

Contribution

It introduces a novel application of Large Event Models in soccer, fine-tuned on real match data to evaluate player contributions and simulate hypothetical transfer scenarios.

Findings

LEMs effectively forecast team standings and transfer impacts.

Contextual analysis reveals narrower performance gaps between players.

Model limitations highlight the importance of context in player evaluation.

Abstract

This paper introduces an innovative application of Large Event Models (LEMs), akin to Large Language Models, to the domain of soccer analytics. By learning the language of soccer - predicting variables for subsequent events rather than words - LEMs facilitate the simulation of matches and offer various applications, including player performance prediction across different team contexts. We focus on fine-tuning LEMs with the WyScout dataset for the 2017-2018 Premier League season to derive specific insights into player contributions and team strategies. Our methodology involves adapting these models to reflect the nuanced dynamics of soccer, enabling the evaluation of hypothetical transfers. Our findings confirm the effectiveness and limitations of LEMs in soccer analytics, highlighting the model's capability to forecast teams' expected standings and explore high-profile scenarios, such as the potential effects of transferring Cristiano Ronaldo or Lionel Messi to different teams in the Premier League. This analysis underscores the importance of context in evaluating player quality. While general metrics may suggest significant differences between players, contextual analyses reveal narrower gaps in performance within specific team frameworks.