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

Area of Science:

• Sports Analytics
• Artificial Intelligence in Sports
• Machine Learning for Strategy Analysis

Background:

• Traditional basketball strategy analysis relies on manual observation and limited data.
• Advancements in tracking technology enable AI applications for deeper strategic insights.
• Understanding team defensive behavior is crucial for performance improvement.

Purpose of the Study:

• To develop a hybrid AI model combining LSTM and CNN for classifying and analyzing defensive basketball strategies.
• To specifically identify switch and trap plays using large-scale tracking data.
• To provide coaches and players with a tool for evaluating defensive tactics.

Main Methods:

• Developed a hybrid Long-Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) model.
• Utilized National Basketball Association (NBA) SportVU tracking data (over 32,000 possessions).
• Integrated LSTM for temporal motion features and CNN for spatial court patterns; standardized player positions and reformatted half-court data into grids.

Main Results:

• The hybrid model achieved 91.4% accuracy in identifying switch and trap plays, outperforming independent LSTM and CNN models.
• Analysis of spatial density and temporal sequences revealed deformations in defensive structures.
• The model demonstrated significant benefits in distinguishing situations requiring switches and traps.

Conclusions:

• The hybrid LSTM-CNN model successfully categorizes defensive basketball moves, offering a powerful tool for tactical evaluation.
• Supervised machine learning models are vital for real-time tactical analysis in sports.
• This research lays the groundwork for automated strategy evaluation and game planning in basketball.