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Stacked ensemble model for NBA game outcome prediction analysis.

Guangsen He¹, Hyun Soo Choi²

¹Department of Physical Education, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul, Republic of Korea.

Scientific Reports

|August 16, 2025

Summary

This study uses artificial intelligence (AI) and ensemble machine learning models to accurately predict National Basketball Association (NBA) game outcomes. SHAP analysis offers insights into the AI

Keywords:

Data mining Features selection Machine learning Shapley additive explanation (SHAP)Stacked ensemble

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Area of Science:

Sports Analytics
Machine Learning
Artificial Intelligence

Background:

Predicting NBA game outcomes is complex due to numerous variables.
Traditional statistical methods often fall short in capturing game dynamics.
AI and machine learning offer advanced capabilities for sports prediction.

Purpose of the Study:

To develop and evaluate a stacked ensemble AI model for predicting NBA game results.
To enhance model interpretability using SHAP (SHapley Additive exPlanations) analysis.
To provide actionable insights for coaches and analysts based on predictive mechanisms.

Main Methods:

Utilized a stacked ensemble approach combining multiple machine learning algorithms: Naïve Bayes, AdaBoost, MLP, KNN, XGBoost, Decision Tree, and Logistic Regression.
Selected top-performing models as base learners for the ensemble.
Trained and validated the model on NBA datasets spanning the 2021-2022, 2022-2023, and 2023-2024 seasons.
Employed SHAP for model interpretability and transparency.

Main Results:

The proposed stacked ensemble AI model demonstrated practical effectiveness in predicting NBA game outcomes.
Experimental results confirmed the model's predictive capabilities across multiple seasons.
SHAP analysis successfully clarified the decision-making process of the ensemble model.

Conclusions:

The AI-driven stacked ensemble approach is a viable tool for NBA game outcome prediction.
SHAP analysis provides crucial insights into the factors influencing predictions, aiding strategic decision-making.
The research offers valuable, data-driven intelligence for sports analytics professionals.