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<p>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.</p>

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Stacked ensemble model for NBA game outcome prediction analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月16日

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  • Guangsen He,
  • Hyun Soo Choi

摘要

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.