Estimating Player Performance in Different Contexts Using Fine-tuned
  Large Events Models
Tiago Mendes-Neves Luís Meireles João Mendes-Moreira
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.