用组合融合分析提升篮球锦标赛预测准确率
NCAA Bracket Prediction Using Machine Learning and Combinatorial Fusion Analysis
- 通过排名特征函数与认知多样性融合多种评分体系
- 2024年数据预测准确率达74.60%，优于10种主流排名系统
- 适合关注体育赛事预测与多源信息融合的读者
近年来，机器学习模型在体育比赛预测中表现出色，通常将体育预测视为分类任务。本文引入新视角分析体育数据，以更精准预测结果。我们利用排名生成2024年数据集的球队排名，采用组合融合分析（CFA）这一新范式，通过排名-得分特征函数（RSC）和认知多样性（CD）整合多个评分系统。基于团队排名的组合结果准确率达到74.60%，高于十种主流公开排名系统的最佳表现（73.02%），验证了CFA通过多视角融合提升体育预测精度的有效性。
原文摘要 · Abstract (English)
Machine learning models have demonstrated remarkable success in sports prediction in the past years, often treating sports prediction as a classification task within the field. This paper introduces new perspectives for analyzing sports data to predict outcomes more accurately. We leverage rankings to generate team rankings for the 2024 dataset using Combinatorial Fusion Analysis (CFA), a new paradigm for combining multiple scoring systems through the rank-score characteristic (RSC) function and cognitive diversity (CD). Our result based on rank combination with respect to team ranking has an accuracy rate of $74.60\%$, which is higher than the best of the ten popular public ranking systems ($73.02\%$). This exhibits the efficacy of CFA in enhancing the precision of sports prediction through different lens.
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