Forecasting Events in Soccer Matches Through Language
Tiago Mendes-Neves Luís Meireles João Mendes-Moreira
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.