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.

翻译：近年来，机器学习模型在体育预测领域取得了显著成功，通常将体育预测视为该领域内的分类任务。本文提出了分析体育数据以更准确预测结果的新视角。我们利用排名系统，通过组合融合分析（CFA）这一新范式，结合秩-分特征（RSC）函数和认知多样性（CD），为2024年数据集生成球队排名。我们基于球队排名的秩组合方法取得了$74.60\%$的准确率，优于十个常用公开排名系统中最佳结果的$73.02\%$。这展示了CFA通过不同视角提升体育预测精度的有效性。