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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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-07-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-3011.pdf |
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| _version_ | 1849713061699518464 |
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| author | Jianxiong Gao Yi Cheng Jianwei Gao |
| author_facet | Jianxiong Gao Yi Cheng Jianwei Gao |
| author_sort | Jianxiong Gao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e182eb0ecade4ca3982349cf010fac85 |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-e182eb0ecade4ca3982349cf010fac852025-08-20T03:14:05ZengPeerJ Inc.PeerJ Computer Science2376-59922025-07-0111e301110.7717/peerj-cs.3011Predicting sport event outcomes using deep learningJianxiong Gao0Yi Cheng1Jianwei Gao2Department of Physical Education, Chengdu University of Information Technology, Chengdu, ChinaDepartment of Physical Education, Chengdu University of Information Technology, Chengdu, ChinaSchool of International Education, Sichuan University of Media and Communications, Chengdu, ChinaPredicting 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.https://peerj.com/articles/cs-3011.pdfSports outcome predictionSports analyticsDeep learningCNNTransformers |
| spellingShingle | Jianxiong Gao Yi Cheng Jianwei Gao Predicting sport event outcomes using deep learning PeerJ Computer Science Sports outcome prediction Sports analytics Deep learning CNN Transformers |
| title | Predicting sport event outcomes using deep learning |
| title_full | Predicting sport event outcomes using deep learning |
| title_fullStr | Predicting sport event outcomes using deep learning |
| title_full_unstemmed | Predicting sport event outcomes using deep learning |
| title_short | Predicting sport event outcomes using deep learning |
| title_sort | predicting sport event outcomes using deep learning |
| topic | Sports outcome prediction Sports analytics Deep learning CNN Transformers |
| url | https://peerj.com/articles/cs-3011.pdf |
| work_keys_str_mv | AT jianxionggao predictingsporteventoutcomesusingdeeplearning AT yicheng predictingsporteventoutcomesusingdeeplearning AT jianweigao predictingsporteventoutcomesusingdeeplearning |