Real-time forecasting within soccer matches through a Bayesian lens
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866914974278877184 |
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| author | Divekar, Chinmay Deb, Soudeep Roy, Rishideep |
| author_facet | Divekar, Chinmay Deb, Soudeep Roy, Rishideep |
| contents | This paper employs a Bayesian methodology to predict the results of soccer matches in real-time. Using sequential data of various events throughout the match, we utilize a multinomial probit regression in a novel framework to estimate the time-varying impact of covariates and to forecast the outcome. English Premier League data from eight seasons are used to evaluate the efficacy of our method. Different evaluation metrics establish that the proposed model outperforms potential competitors inspired by existing statistical or machine learning algorithms. Additionally, we apply robustness checks to demonstrate the model's accuracy across various scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_12401 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Real-time forecasting within soccer matches through a Bayesian lens Divekar, Chinmay Deb, Soudeep Roy, Rishideep Applications Methodology This paper employs a Bayesian methodology to predict the results of soccer matches in real-time. Using sequential data of various events throughout the match, we utilize a multinomial probit regression in a novel framework to estimate the time-varying impact of covariates and to forecast the outcome. English Premier League data from eight seasons are used to evaluate the efficacy of our method. Different evaluation metrics establish that the proposed model outperforms potential competitors inspired by existing statistical or machine learning algorithms. Additionally, we apply robustness checks to demonstrate the model's accuracy across various scenarios. |
| title | Real-time forecasting within soccer matches through a Bayesian lens |
| topic | Applications Methodology |
| url | https://arxiv.org/abs/2303.12401 |