Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction - Yeung Manho (ITM Web of Conferences)
With the rapid development of data analytics in sports, it is vital to use machine learning methods to make decisions and predictions. This study focuses on predicting NBA playoff qualifications using machine learning techniques. By utilizing team-level statistics from 1947 to 2024, the paper implemented models such as Logistic Regression, K-Nearest Neighbors, Random Forest, and Elastic Net Regression. The data was preprocessed by scaling, centering, and handling missing values, followed by rigorous 5-fold cross-validation to ensure robust evaluation. Among the models, Random Forest outperformed the others, achieving the highest ROC-AUC score of 0.841. Its ensemble approach allowed for the effective capture of complex feature interactions, making it the most accurate model for predicting whether a team would qualify for the playoffs based on team performance. The research demonstrates the power of machine learning in improving prediction accuracy, providing insights for future sports analytics, and offering a foundation for integrating more complex data like player metrics or strategic factors. This work contributes to advancing predictive modeling in sports.