Machine learning-based analysis of defensive strategies in basketball using player movement data

The analysis of basketball strategies has traditionally relied on manual observation and limited data. As tracking technology progresses, there is potential for applying Artificial Intelligence specifically to strategy, delivering insights into defensive techniques to the teams. This research aims to develop a hybrid machine learning model combining Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to classify and analyze defensive basketball strategies, specifically identifying switch and trap plays. The study uses large-scale tracking data to understand the team's defensive behavior better. The proposed hybrid model integrates LSTM to identify temporal features of players' motion and CNN to recognize spatial patterns across the court. The model was trained and evaluated on the National Basketball Association's (NBA) SportVU tracking data, which includes over 32,000 possessions. Basic operations included standardizing the player's position and reformatting half-court representations into grids. Automatic annotations were evaluated based on accuracy, precision, recall, and F1 score and compared with manual annotations. When implemented independently, the hybrid model decided switches and traps with an overall accuracy of 91.4%, higher than basic methods such as LSTM and CNN. The spatial density approach and temporal sequence showed the deformations in defensive structures, and the hybrid model had immense benefits in distinguishing the situations that called for switches and traps. The proposed hybrid model successfully categorizes and identifies defensive basketball moves and provides coaches and players with a strong tool to evaluate defensive tactics. This investigation has demonstrated the importance of using supervised machine learning models for real-time tactical analysis in sports, and future research in automated strategy evaluation and game planning has been laid out.