References & Citations
Computer Science > Machine Learning
Title: Forecasting Events in Soccer Matches Through Language
(Submitted on 9 Feb 2024 (v1), last revised 26 Apr 2024 (this version, v2))
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.
Submission history
From: Tiago Mendes-Neves [view email][v1] Fri, 9 Feb 2024 23:02:57 GMT (1183kb,D)
[v2] Fri, 26 Apr 2024 11:45:02 GMT (161kb,D)
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