Biomechanical Validation of Skeletal Tracking Data and Developing Action Recognition Models for Basketball: A Baseline for NBA Officiating Tools

Abstract

Optical tracking technology in sports has advanced rapidly in recent years, enabling new opportunities for data-driven analysis and tools to enhance the game. This study presents a framework for processing and analyzing a new skeletal tracking dataset collected from NBA basketball games. The methodology includes biomechanical joint validation, anomaly detection, and region-based consistency analysis to assess the integrity of player motion data. Joint movement anomalies are used to detect tracking errors, while court region and stadium-level evaluations help identify where the optical tracking system may be underperforming. These patterns can guide data providers toward specific areas that require refinement, offering a clearer starting point for improving system accuracy. After cleaning the dataset of 117 NBA games, two action recognition models—a transformer-based model and a temporal graph neural network—are implemented to classify player actions, specifically dribbling, passing, shooting, and rebounding, from sequences of skeletal tracking frames. The objective is to establish a baseline for developing tools to support officiating decisions in the NBA. By leveraging spatiotemporal representations of joint motion, this work improves the reliability of skeletal tracking data and contributes to the advancement of automated decision support in professional sports officiating.