基于堆叠集成模型的NBA比赛结果预测分析。

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-08-16 DOI:10.1038/s41598-025-13657-1

Guangsen He, Hyun Soo Choi

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引用次数: 0

摘要

这项研究提出了一种采用人工智能（AI）技术来预测NBA比赛结果的堆叠集成方法。使用了几种机器学习算法，包括Naïve Bayes， AdaBoost，多层感知器（MLP）， k -近邻（KNN）， XGBoost，决策树和逻辑回归。选择性能最好的模型作为集成体系结构中的基础学习器。为了提高模型的可解释性和透明度，我们使用SHAP来阐明其决策过程。该模型使用2021-2022年、2022-2023年和2023-2024年的公开NBA数据集进行训练和评估。实验结果表明，该方法在预测博弈结果方面是可行的。此外，SHAP分析为潜在的预测机制提供了有价值的见解，为教练和分析师提供了可操作的信息。

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Stacked ensemble model for NBA game outcome prediction analysis.

This research presents a stacked ensemble approach that employs artificial intelligence (AI) techniques to predict the outcomes of NBA games. Several machine learning algorithms were utilized, including Naïve Bayes, AdaBoost, Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), XGBoost, Decision Tree, and Logistic Regression. The best-performing models were selected to serve as the base learners in the ensemble architecture. To improve the model's interpretability and transparency, SHAP was used to clarify its decision-making process. The model was trained and evaluated using publicly available NBA datasets from 2021-2022,2022-2023, and 2023-2024. Experimental results indicate that the proposed ensemble approach is practical in predicting game outcomes. Furthermore, the SHAP analysis provides valuable insights into the underlying predictive mechanisms, offering actionable information for coaches and analysts.

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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