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

Continuously-observed event occurrences, often exhibit self- and
mutually-exciting effects, which can be well modeled using temporal point
processes. Beyond that, these event dynamics may also change over time, with
certain periodic trends. We propose a novel variational auto-encoder to capture
such a mixture of temporal dynamics. More specifically, the whole time interval
of the input sequence is partitioned into a set of sub-intervals. The event
dynamics are assumed to be stationary within each sub-interval, but could be
changing across those sub-intervals. In particular, we use a sequential latent
variable model to learn a dependency graph between the observed dimensions, for
each sub-interval. The model predicts the future event times, by using the
learned dependency graph to remove the noncontributing influences of past
events. By doing so, the proposed model demonstrates its higher accuracy in
predicting inter-event times and event types for several real-world event
sequences, compared with existing state of the art neural point processes.

提出了一种新颖的变分自编码器来捕捉时间动态的混合，通过使用学习到的依赖图来预测未来事件时间和事件类型，在预测真实世界事件序列中表现出更高的准确性，相较于现有先进的神经点过程。