Summary of Forecasting Events in Soccer Matches Through Language, by Tiago Mendes-neves et al.
Forecasting Events in Soccer Matches Through Language
by Tiago Mendes-Neves, Luís Meireles, João Mendes-Moreira
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to predicting the next event in a soccer match, drawing inspiration from Large Language Models (LLMs). The proposed method uses deep learning on the WyScout dataset and outperforms previous approaches in terms of prediction accuracy. The research highlights the utility of Large Event Models (LEMs) for various applications, including match prediction and analytics. LEMs provide a simulation backbone for building multiple analytics pipelines, unlike current specialized models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Soccer fans want to know what will happen next! This paper shows how to use computers to predict what’s going to happen in a soccer game. It’s like trying to guess what your favorite team will do next. The researchers used special computer programs and a big dataset of past games to make predictions. They found that their method was better than others at guessing the type of event, like a goal or a foul. This can help teams and fans make better decisions during games. |
Keywords
* Artificial intelligence * Deep learning
