Predicting NBA Playoffs Qualification Using Machine Learning Techniques

Sports data analysis has become an essential aspect of modern sports management, leveraging machine learning (ML) techniques to gain valuable insights into team and player performance. This study proposes a novel ML-based framework to predict NBA teams' qualification for the playoffs, utilizing comprehensive per-game data from the 2013–2014 to 2023–2024 seasons. Key features were selected using SHAP analysis, and model performance was evaluated using a rigorous 5-fold cross-validation approach. Among the models tested, the Support Vector Machine (SVM) achieved the highest prediction accuracy (96%), followed by the Multi-Layer Perceptron (MLP) with an 88% accuracy. These results highlight the potential of ML in providing actionable insights for team owners, coaches, and stakeholders to make data-driven decisions. Furthermore, the study addresses challenges such as data imbalance and provides a comparative analysis of model performance, offering a valuable contribution to the growing field of sports analytics