Real method, synthetic data. No person was measured. A population of 50,000 synthetic competitors is generated from a three-factor model (physical, cognitive, socio-emotional) with domain-specific loadings, then shifted by age and sex effect sizes set as stated parameters below. The scoring, normalization and aggregation code is the real thing; the inputs are invented.
The effect sizes are assumptions, not findings. They are set to plausible magnitudes so the mechanics are visible. The physical sex gap in particular is a single composite standin for measures that in reality range from near-parity to well over 2 SD depending on whether you test relative endurance or upper-body power. Do not read any number here as an empirical claim.
What is real is the structural result. Under any factor model where one domain has a large group-level mean difference and the aggregation is additive, that domain dominates the composite and the eligibility rule quietly determines the podium. That conclusion does not depend on the specific constants.
Age curves are directional. Physical and fluid-reasoning peaks are placed in the twenties; decision quality, social and emotional capacity peak decades later. This is the standard shape in the literature, rendered here as smooth quadratics rather than fitted curves.
Sex effect is signed male-minus-female in standard deviations, applied as ±half to each competitor. Loadings are on orthogonal standard-normal factors; unique variance fills the remainder.