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HoopTransformer: Advancing NBA Offensive Play Recognition with Self-Supervised Learning from Player Trajectories.

Xing Wang¹, Zitian Tang^2,3, Jianchong Shao²

¹Facultad de Ciencias de la Actividad Física y del Deporte, Universidad Politécnica de Madrid, Madrid, Spain. xing.w@alumnos.upm.es.

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Summary

This study introduces an AI model using self-supervised learning to automatically recognize basketball offensive plays. The model achieved high accuracy in classification and similarity search tasks, demonstrating its effectiveness in understanding player movements.

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Area of Science:

Artificial Intelligence
Machine Learning
Sports Analytics

Background:

Recognizing basketball offensive plays is complex due to intricate player interactions.
Existing methods struggle with automatic recognition of these plays.

Purpose of the Study:

To propose an artificial intelligence (AI) model for automatic recognition of basketball offensive plays.
To utilize a novel self-supervised learning approach for this task.

Main Methods:

A dataset of 90,524 NBA possessions from the 2015-2016 season was used.
An axial-attention transformer-based multi-agent motion prediction model was developed.
Trained with motion prediction (MP), motion reconstruction (MR), and a joint MP + MR strategy.

Main Results:

The MP + MR joint masking strategy outperformed individual strategies.
Achieved 81.5% top-1 accuracy and 97.5% top-3 accuracy in play classification.
Attained 76% top-5 and 59% top-10 accuracy in similarity search.

Conclusions:

The self-supervised learning model effectively comprehends player movements and interactions.
Demonstrated potential in accurately recognizing complex offensive plays.
The HoopTransformer model showed superior performance over baselines.