Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
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Article Number | 04005 | |
Number of page(s) | 7 | |
Section | AI and Advanced Applications | |
DOI | https://doi.org/10.1051/itmconf/20257004005 | |
Published online | 23 January 2025 |
Explore Machine Learning's Prediction of Football Games
Chongqing Bashu Ivy School, Chongqing, 400000, China
Corresponding author: gary.panbomao@biacademy.cn
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.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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