This study aims to systematically review, synthesize and critically evaluate the expansive body of literature concerning the application of inertial sensor technology for performance evaluation in team sports. Also, this review aims to bridge the gap between sensor engineering, data processing methodologies and applied sports science.
A comprehensive narrative review of peer-reviewed literature was conducted. The analysis focuses on the technological underpinnings of Inertial Measurement Units (IMUs), the validation of commercial systems, the critical data processing pipeline from raw signals to actionable metrics and evidence-based applications in key team sports. This review further explores the integration of machine learning and addresses the prevailing challenges and future trajectories of the field.
Inertial sensors provide high-resolution data on athlete movement, enabling the quantification of external load beyond traditional time-motion analysis. However, their efficacy is critically dependent on robust data processing, including sensor fusion algorithms to mitigate errors like drift and accurately estimate orientation. While metrics like PlayerLoad are widely adopted, their interpretation is complex and lacks intersystem standardization. Applications in soccer, basketball and rugby demonstrate the technology’s utility in quantifying physical demands, analyzing technique and monitoring high-impact events. Key challenges remain in data validity, standardization and ethical data governance.
This paper provides a consolidated, multidisciplinary overview that connects the technical specifications of IMU hardware with the complexities of data processing and the practical realities of in-field application. It offers a critical framework for practitioners to evaluate and implement these technologies and for researchers to identify key areas for future investigation, particularly in the realms of advanced analytics and methodological standardization.
