Forecasting Events in Soccer Matches Through Language
Tiago Mendes-Neves, Lu\'is Meireles, Jo\~ao Mendes-Moreira

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…
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Taxonomy
TopicsNatural Language Processing Techniques · Sports Analytics and Performance
