BACKGROUND: How swimming performance develops from load-velocity (L-V) profiles remain unclear. Linking individual biomechanical profiles to race outcome may, however, guide training priorities to optimize performance. This study identified sex- and distance-specific hydrodynamic determinants of performance by mapping interplays between L-V outputs and 50-200m race speed; METHODS: 208 profiles (63 females), from local to world-class swimmers, were collected during semi-tethered front-crawl trials against four external loads. Theoretical maximal velocity (V0), maximal load normalized to body mass (BM) (rL0), and Slope were derived and paired with official long-course race speeds recorded within ±1month. Normative benchmarks were obtained via V0-rL0 quantile regression. Race speed predictions across V0-rL0 combinations were modelled at population and individual levels using 2D Multi-tAsk Gaussian processes with common MeAn (MAGMA); RESULTS: Logarithmic V0-rL0 relationships emerged across sex and distance with inflection points around 30%BM in males and 20%BM in females, reflecting higher V0 variance for a given rL0 increase. Beyond these thresholds, higher rL0 increased Slope faster than V0, indicating a shift in performance drivers. No optimal V0-rL0 interplays emerged; instead, multiple high-performance combinations highlighted swimmer-specific optimal hydrodynamic strategies. Swimmer positions within the V0-rL0 plane, relative to quantile curves and MAGMA mean processes, together with swimmer-specific race speed predictions support individual benchmarking; CONCLUSIONS: Above the identified rL0 thresholds, training should preferentially prioritize refining effective force application through the water while minimizing drag, rather than further strength development. Data-driven modelling provides population benchmarks and personalized performance drivers, offering coaches actionable insights into interactions between velocity, force-generation and drag-minimizing capabilities.
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