A Scalable Approach for Unified Large Events Models in Soccer
Abstract
Large Events Models (LEMs) are a class of models designed to predict and analyze the sequence of events in soccer matches, capturing the complex dynamics of the game. The original LEM framework, based on a chain of classifiers, faced challenges such as synchronization, scalability issues, and limited context utilization. This paper proposes a unified and scalable approach to model soccer events using a tabular autoregressive model. Our models demonstrate significant improvements over the original LEM, achieving higher accuracy in event prediction and better simulation quality, while also offering greater flexibility and scalability. The unified LEM framework enables a wide range of applications in soccer analytics that we display in this paper, including real-time match outcome prediction, player performance analysis, and game simulation, serving as a general solution for many problems in the field.
Cite
Text
Mendes-Neves et al. "A Scalable Approach for Unified Large Events Models in Soccer." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025. doi:10.1007/978-3-032-06129-4_21Markdown
[Mendes-Neves et al. "A Scalable Approach for Unified Large Events Models in Soccer." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025.](https://mlanthology.org/ecmlpkdd/2025/mendesneves2025ecmlpkdd-scalable/) doi:10.1007/978-3-032-06129-4_21BibTeX
@inproceedings{mendesneves2025ecmlpkdd-scalable,
title = {{A Scalable Approach for Unified Large Events Models in Soccer}},
author = {Mendes-Neves, Tiago and Meireles, Luís and Mendes-Moreira, João},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2025},
pages = {354-371},
doi = {10.1007/978-3-032-06129-4_21},
url = {https://mlanthology.org/ecmlpkdd/2025/mendesneves2025ecmlpkdd-scalable/}
}