Forecasting Events in Soccer Matches Through Language

TL;DR

This paper presents a novel deep learning approach inspired by Large Language Models to predict sequences of soccer match events, significantly improving prediction accuracy and enabling versatile analytics pipelines.

Contribution

Introduces a new Large Event Model (LEM) framework for soccer event prediction, simplifying event sequence modeling and surpassing previous methods in accuracy.

Findings

Outperforms previous models in next event prediction accuracy

Enables development of multifaceted analytics pipelines

Provides a simulation backbone for match analysis

Abstract

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 match prediction and analytics. Moreover, we show that LEMs provide a simulation backbone for users to build many analytics pipelines, 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.