Explore Machine Learning's Prediction of Football Games
The aim of this study is to predict the outcome and score of football matches. To achieve this goal, this paper employs a variety of machine learning models, including Random Forest, support vector classifiers (SVC), and Logistic Regression, and conducts in-depth analysis of the data. The results sh...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04005.pdf |
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| _version_ | 1825206565032951808 |
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| author | Pan Bomao |
| author_facet | Pan Bomao |
| author_sort | Pan Bomao |
| collection | DOAJ |
| description | The aim of this study is to predict the outcome and score of football matches. To achieve this goal, this paper employs a variety of machine learning models, including Random Forest, support vector classifiers (SVC), and Logistic Regression, and conducts in-depth analysis of the data. The results show that home teams have a significantly higher win rate than away teams. In addition, the score changes show a high degree of randomness, reflecting that the game is affected by a variety of factors. The prediction performance of these models is different, and the prediction accuracy of the random forest model is better than the other two models. Through the prediction of the winning rate, this paper aims to provide more scientific reference for the majority of fans and deepen the understanding of the strength of each team and the influence of external factors on the result of the game. This study not only helps to improve the analysis ability of football matches, but also provides a theoretical basis for the optimization of game strategies. |
| format | Article |
| id | doaj-art-64e338872a3643d693f8cf600c99afc8 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-64e338872a3643d693f8cf600c99afc82025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700400510.1051/itmconf/20257004005itmconf_dai2024_04005Explore Machine Learning's Prediction of Football GamesPan Bomao0Chongqing Bashu Ivy SchoolThe aim of this study is to predict the outcome and score of football matches. To achieve this goal, this paper employs a variety of machine learning models, including Random Forest, support vector classifiers (SVC), and Logistic Regression, and conducts in-depth analysis of the data. The results show that home teams have a significantly higher win rate than away teams. In addition, the score changes show a high degree of randomness, reflecting that the game is affected by a variety of factors. The prediction performance of these models is different, and the prediction accuracy of the random forest model is better than the other two models. Through the prediction of the winning rate, this paper aims to provide more scientific reference for the majority of fans and deepen the understanding of the strength of each team and the influence of external factors on the result of the game. This study not only helps to improve the analysis ability of football matches, but also provides a theoretical basis for the optimization of game strategies.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04005.pdf |
| spellingShingle | Pan Bomao Explore Machine Learning's Prediction of Football Games ITM Web of Conferences |
| title | Explore Machine Learning's Prediction of Football Games |
| title_full | Explore Machine Learning's Prediction of Football Games |
| title_fullStr | Explore Machine Learning's Prediction of Football Games |
| title_full_unstemmed | Explore Machine Learning's Prediction of Football Games |
| title_short | Explore Machine Learning's Prediction of Football Games |
| title_sort | explore machine learning s prediction of football games |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04005.pdf |
| work_keys_str_mv | AT panbomao exploremachinelearningspredictionoffootballgames |