This paper introduces an approach to predicting the next event in a soccer
match, a challenge bearing remarkable similarities to the problem faced by
Large Language Models (LLMs). Unlike other methods that severely limit event
dynamics in soccer, often abstracting from many variables or relying on a mix
of sequential models, our research proposes a novel technique inspired by the
methodologies used in LLMs. These models predict a complete chain of variables
that compose an event, significantly simplifying the construction of Large
Event Models (LEMs) for soccer. Utilizing deep learning on the publicly
available WyScout dataset, the proposed approach notably surpasses the
performance of previous LEM proposals in critical areas, such as the prediction
accuracy of the next event type. This paper highlights the utility of LEMs in
various applications, including betting and match analytics. Moreover, we show
that LEMs provide a simulation backbone on which many analytics pipelines can
be built, an approach opposite to the current specialized single-purpose
models. LEMs represent a pivotal advancement in soccer analytics, establishing
a foundational framework for multifaceted analytics pipelines through a
singular machine-learning model.

该论文介绍了一种预测足球比赛中下一个事件的方法，该方法受到大型语言模型的启发，通过深度学习在公开可用的 WyScout 数据集上提供了新颖的技术，显著改进了以前的大事件模型在关键领域（如下一个事件类型的预测准确性）的性能，同时提供了用于多方面分析的模拟背景，奠定了多元分析管道的基础框架。

通过语言预测足球比赛中的事件

Forecasting Events in Soccer Matches Through Language

In this paper, we define a novel census signal temporal logic (CensusSTL)
that focuses on the number of agents in different subsets of a group that
complete a certain task specified by the signal temporal logic (STL). CensusSTL
consists of an "inner logic" STL formula and an "outer logic" STL formula. We
present a new inference algorithm to infer CensusSTL formulae from the
trajectory data of a group of agents. We first identify the "inner logic" STL
formula and then infer the subgroups based on whether the agents' behaviors
satisfy the "inner logic" formula at each time point. We use two different
approaches to infer the subgroups based on similarity and complementarity,
respectively. The "outer logic" CensusSTL formula is inferred from the census
trajectories of different subgroups. We apply the algorithm in analyzing data
from a soccer match by inferring the CensusSTL formula for different subgroups
of a soccer team.

本文定义了一种新的基于人口普查的时态逻辑（CensusSTL），并提出了一种新的推理算法来从一组代理的轨迹数据中推断 CensusSTL 公式，并将算法应用于足球比赛数据的分析中，以推断足球队的不同子组的 CensusSTL 公式。