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.

该研究介绍了大型事件模型（LEMs）在足球分析领域的创新应用，通过学习 “足球语言”，LEM 模型能够模拟比赛、预测球员表现以及探索不同球队情境下的应用，研究方法涉及微调 LEM 模型以反映足球的微妙动态，结果确认了 LEM 模型在足球分析中的有效性和局限性，以及衡量球员素质时上下文的重要性。

使用精调的大型事件模型评估不同情境下的玩家表现

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

Player performance prediction is a serious problem in every sport since it
brings valuable future information for managers to make important decisions. In
baseball industries, there already existed variable prediction systems and many
types of researches that attempt to provide accurate predictions and help
domain users. However, it is a lack of studies about the predicting method or
systems based on deep learning. Deep learning models had proven to be the
greatest solutions in different fields nowadays, so we believe they could be
tried and applied to the prediction problem in baseball. Hence, the predicting
abilities of deep learning models are set to be our research problem in this
paper. As a beginning, we select numbers of home runs as the target because it
is one of the most critical indexes to understand the power and the talent of
baseball hitters. Moreover, we use the sequential model Long Short-Term Memory
as our main method to solve the home run prediction problem in Major League
Baseball. We compare models' ability with several machine learning models and a
widely used baseball projection system, sZymborski Projection System. Our
results show that Long Short-Term Memory has better performance than others and
has the ability to make more exact predictions. We conclude that Long
Short-Term Memory is a feasible way for performance prediction problems in
baseball and could bring valuable information to fit users' needs.

本研究采用深度学习模型中的长短时记忆（LSTM）模型，以全垒打数为目标，与多个机器学习模型及广泛应用的棒球投影系统 SZymborski 投影系统进行比较，结果表明 LSTM 模型的表现优于其他模型，可以用于棒球表现预测问题，并能提供有价值的信息。