NBA Results Forecast: From League Dynamics Analysis to Predictive Model Implementation

This study presents a machine learning-based approach to predicting the outcosmes of NBA games, with the aim of enhancing decision-making in sports betting and performance analysis. Using a dataset spanning 20 NBA seasons (2003–2023), we incorporated key features such as team statistics, player performance metrics, and external factors like team fatigue and rankings. The methodology followed the CRISP-DM process, involving data preprocessing, feature selection, and model evaluation.

We experimented with multiple classification algorithms, including Logistic Regression, Random Forest, Gradient Boosting, and ensemble methods, to identify the best-performing models. Feature selection techniques such as LASSO and decision tree-based methods were employed to optimize model performance. Our best model, combining team rankings, statistics, and fatigue factors, achieved an accuracy rate of 64.1% and an F1 score of 72.4%, reflecting the complexity of NBA game outcome prediction.

The study highlights the importance of key features like team rankings and the challenges posed by the dynamic nature of the NBA. Future research will explore additional qualitative factors, such as emotional states and team dynamics, and employ more advanced machine learning techniques like deep learning to further improve prediction accuracy.