Machine Learning in Sports and Exercise Analytics: Optimizing Training Load, Technique, and Injury Prevention Through Performance and Biomechanical Data

Abstract

Introduction and Objective. The rapid expansion of wearable sensing, biomechanical measurement and 
automated movement analysis has created athlete datasets that are high-frequency, multimodal and strongly 
individualized. This narrative review examines how machine-learning (ML) methods can be integrated with 
force-plate, motion-capture and wearable-sensor data to support training-load management, 
movement-technique analysis and injury-risk assessment. Material and method. The review was organized 
around three data modalities commonly used in applied sport science and the ML approaches paired with 
them, including classification, regression, ensemble learning and temporal models. Particular attention was 
given to the relationship between internal and external load, model validation, interpretability and translation 
to coaching or clinical decisions. Results. Across the reviewed applications, ML is most useful when it 
combines complementary physiological, mechanical and contextual features rather than relying on a single 
summary metric. Wearable data support longitudinal load profiling; force-time and kinematic data support 
technique and neuromuscular assessment; and multivariable models can identify patterns that warrant closer 
review. The main constraints are small athlete cohorts, heterogeneous acquisition procedures, overfitting, 
limited external validation and insufficiently transparent outputs. Conclusions. ML can extend conventional 
sports analytics by modelling complex interactions and temporal patterns, but predictive performance alone 
is insufficient. Robust applied use requires standardized data pipelines, appropriate validation, interpretable 
reporting and integration of model output with practitioner expertise.