通过融合图形卷积网络和随机森林算法提高篮球比赛结果预测
Kai Zhao1, Chunjie Du1, Guangxin Tan1
1School of Physical Education and Sports Science, South China Normal University, Guangzhou 510006, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
图形神经网络通过分析团队互动来增强篮球比赛结果预测. 将图形卷积网络与随机森林特征提取相结合,精度提高到71.54%.
科学领域:
- 运动分析 运动分析
- 机器学习 机器学习
- 网络科学 网络科学
背景情况:
- 传统的机器学习模型在体育预测中往往忽视了复杂的团队动态.
- 基于矢量模型无法捕捉联盟内的空间结构和团队间的关系.
研究的目的:
- 应用图形神经网络 (GNN) 来预测篮球比赛结果.
- 用基于图形的数据转换来表示团队互动和联赛结构.
- 通过结合关系数据来提高现有的预测准确性.
主要方法:
- 结构化篮球数据 (2012-2018年NBA赛季) 被转换为非定向图表.
- 在构建的团队表示图上使用了图形卷积网络 (GCN).
- 使用随机森林算法的特征提取被整合到增强GCN模型中.
主要成果:
- 最初的GCN模型实现了66.90%的平均预测成功率.
- 合并模型,将GCN与随机森林特征结合起来,提高了预测准确率至71.54%.
- 与基线模型和先前的研究相比,提出的基于GNN的方法显示出更高的性能.
结论:
- 图形神经网络有效地模拟团队互动和空间联盟结构,以改善预测.
- 随机森林特征提取的整合进一步提高了GNN在体育分析中的预测能力.
- 这项研究为篮球比赛结果预测提供了一种新的方法,突出了网络科学在体育研究中的潜力.
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