Stacked ensemble model for NBA game outcome prediction analysis
Abstract 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,...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-13657-1 |
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| author | Guangsen He Hyun Soo Choi |
| author_facet | Guangsen He Hyun Soo Choi |
| author_sort | Guangsen He |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-011e3e3702cf4910a08ce410f86f00a8 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-011e3e3702cf4910a08ce410f86f00a82025-08-20T03:07:20ZengNature PortfolioScientific Reports2045-23222025-08-0115111710.1038/s41598-025-13657-1Stacked ensemble model for NBA game outcome prediction analysisGuangsen He0Hyun Soo Choi1Department of Physical Education, Hanyang UniversityDepartment of Physical Education, Hanyang UniversityAbstract 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.https://doi.org/10.1038/s41598-025-13657-1Machine learningStacked ensembleShapley additive explanation (SHAP)Features selectionData mining |
| spellingShingle | Guangsen He Hyun Soo Choi Stacked ensemble model for NBA game outcome prediction analysis Scientific Reports Machine learning Stacked ensemble Shapley additive explanation (SHAP) Features selection Data mining |
| title | Stacked ensemble model for NBA game outcome prediction analysis |
| title_full | Stacked ensemble model for NBA game outcome prediction analysis |
| title_fullStr | Stacked ensemble model for NBA game outcome prediction analysis |
| title_full_unstemmed | Stacked ensemble model for NBA game outcome prediction analysis |
| title_short | Stacked ensemble model for NBA game outcome prediction analysis |
| title_sort | stacked ensemble model for nba game outcome prediction analysis |
| topic | Machine learning Stacked ensemble Shapley additive explanation (SHAP) Features selection Data mining |
| url | https://doi.org/10.1038/s41598-025-13657-1 |
| work_keys_str_mv | AT guangsenhe stackedensemblemodelfornbagameoutcomepredictionanalysis AT hyunsoochoi stackedensemblemodelfornbagameoutcomepredictionanalysis |