| アイテムタイプ |
itemtype_ver1(1) |
| 公開日 |
2024-12-10 |
| タイトル |
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タイトル |
Evaluating soccer match prediction models: a deep learning approach and feature optimization for gradient-boosted trees |
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言語 |
en |
| 著者 |
Yeung, Calvin
Bunker, Rory
Umemoto, Rikuhei
Fujii, Keisuke
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| アクセス権 |
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アクセス権 |
open access |
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アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利 |
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|
権利情報 |
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10994-024-06608-w |
|
言語 |
en |
| 内容記述 |
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内容記述タイプ |
Abstract |
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内容記述 |
Machine learning models have become increasingly popular for predicting the results of soccer matches, however, the lack of publicly-available benchmark datasets has made model evaluation challenging. The 2023 Soccer Prediction Challenge required the prediction of match results first in terms of the exact goals scored by each team, and second, in terms of the probabilities for a win, draw, and loss. The original training set of matches and features, which was provided for the competition, was augmented with additional matches that were played between 4 April and 13 April 2023, representing the period after which the training set ended, but prior to the first matches that were to be predicted (upon which the performance was evaluated). A CatBoost model was employed using pi-ratings as the features, which were initially identified as the optimal choice for calculating the win/draw/loss probabilities. Notably, deep learning models have frequently been disregarded in this particular task. Therefore, in this study, we aimed to assess the performance of a deep learning model and determine the optimal feature set for a gradient-boosted tree model. The model was trained using the most recent 5 years of data, and three training and validation sets were used in a hyperparameter grid search. The results from the validation sets show that our model had strong performance and stability compared to previously published models from the 2017 Soccer Prediction Challenge for win/draw/loss prediction. Our model ranked 16th in the 2023 Soccer Prediction Challenge with RPS 0.2195. |
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言語 |
en |
| 出版者 |
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出版者 |
Springer |
|
言語 |
en |
| 言語 |
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|
言語 |
eng |
| 資源タイプ |
|
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資源タイプresource |
http://purl.org/coar/resource_type/c_6501 |
|
タイプ |
journal article |
| 出版タイプ |
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|
出版タイプ |
AM |
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出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 関連情報 |
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関連タイプ |
isVersionOf |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
https://doi.org/10.1007/s10994-024-06608-w |
| 収録物識別子 |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
0885-6125 |
| 書誌情報 |
en : Machine Learning
巻 113,
号 10,
p. 7541-7564,
発行日 2024-10
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| ファイル公開日 |
|
|
日付 |
2025-10-01 |
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日付タイプ |
Available |
Machine_Learning_in_Soccer_Competition.pdf (558 KB)
