TITLE:
Machine Learning Methods in Competitive Swimming Analysis: Paradigm Evolution, Architectural Framework, and Future Prospects
AUTHORS:
Sujing Su, Houwei Zhu
KEYWORDS:
Swimming Performance, Machine Learning, Biomechanics, Feature Engineering, Explainable AI
JOURNAL NAME:
Open Access Library Journal,
Vol.13 No.1,
January
14,
2026
ABSTRACT: In competitive swimming, victory is decided by hundredths of a second, yet traditional biomechanical analysis often struggles with data complexity and delayed feedback. This review addresses the current research fragmentation by proposing an innovative four-dimensional framework (Data, Feature, Model, and Application layers) to systematically categorize the machine learning (ML) landscape in swimming analysis. We deconstruct the racing process into macro-phases (start, swim-through, turns, and finish) and micro-stroke cycles, highlighting how deep learning architectures like Transformers are replacing manual annotation with automated action segmentation. Furthermore, the study identifies key performance predictors—such as Intra-cyclic Velocity Fluctuations (IVV), Stroke Index (SI), and Countermovement Jump (CMJ) impulse—as critical inputs for robust feature engineering. By integrating Explainable AI (XAI), this study bridges the gap between complex “black box” models and actionable coaching insights. Ultimately, we provide a methodological roadmap for building “digital twins” of athletes, shifting swimming science toward a synergy of biomechanical depth and computational precision.