Race-Performance Parameters Differentiating World-Best From National-Level Swimmers: A Race Video Analysis and Machine-Learning Approach.

Abstract

Background: Elite swimming performance is determined by a complex interplay of anthropometric, physiological, biomechanical, and technical factors. Previous research highlights how the 100-m freestyle demands explosive power, technical proficiency, and tactical acumen, yet factors that distinguish world-class swimmers from their closely performing (inter)national-level counterparts remain elusive.

Purpose: To identify race-performance factors differentiating world-class swimmers in the 100-m freestyle.

Methods: World-best to national-level (N = 204) male swimmers competing at long-course events between 2019 and 2024 were analyzed using high-definition video and race-analysis software. Key performance metrics including stroke rate and length, turn efficiency, underwater phase duration, and velocity at 5-m intervals were extracted. Using a machine-learning random forest algorithm, the most salient factors distinguishing between world-class (0%-2.5% off world record), international-level (2.5%-5% off), and national-level (5%-10% off) performance categories were identified.

Results: Analyses revealed a model classification accuracy of 89.5% with swim velocities at 65- to 70- and 70- to 75-m race segments most strongly associated with performance-level differentiation. These 2 race segments scored twice as high as all the other top 10 features. Shapley additive explanations (SHAP) analysis confirmed the importance of midrace velocities, while partial dependence plots identified the necessary velocity range values likely associated with national- to world-class performance levels.

Conclusions: The combination of race analysis and machine learning creates the opportunity for targeted intervention for coaches and sport scientists working with high-performing 100-m male swimmers.