Predicting sport event outcomes using deep learning

Predicting the outcomes of sports events is inherently difficult due to the unpredictable nature of gameplay and the complex interplay of numerous influencing factors. In this study, we present a deep learning framework that combines a one-dimensional convolutional neural network (1D CNN) with a Tra...

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Bibliographic Details
Published inPeerJ. Computer science Vol. 11; p. e3011
Main Authors Gao, Jianxiong, Cheng, Yi, Gao, Jianwei
Format Journal Article
LanguageEnglish
Published PeerJ. Ltd 21.07.2025
PeerJ Inc
Subjects
CNN
Computational linguistics
Deep learning
Language processing
Liu, Timothy
Machine learning
Natural language interfaces
Neural networks
Sports analytics
Sports outcome prediction
Transformers
Video games
Online AccessGet full text
ISSN2376-5992
2376-5992
DOI10.7717/peerj-cs.3011

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Summary:Predicting the outcomes of sports events is inherently difficult due to the unpredictable nature of gameplay and the complex interplay of numerous influencing factors. In this study, we present a deep learning framework that combines a one-dimensional convolutional neural network (1D CNN) with a Transformer architecture to improve prediction accuracy. The 1D CNN effectively captures local spatial patterns in structured match data, while the Transformer leverages self-attention mechanisms to model long-range dependencies. This hybrid design enables the model to uncover nuanced feature interactions critical to outcome prediction. We evaluate our approach on a benchmark sports dataset, where it outperforms traditional machine learning methods and standard deep learning models in both accuracy and robustness. Our results demonstrate the promise of integrating convolutional and attention-based mechanisms for enhanced performance in sports analytics and predictive modeling.
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.3011