Decision architecture quality in elite sport development systems: a framework for managing structural uncertainty

Research question Elite sport development systems exhibit persistent decision inconsistency despite increasing investment in predictive analytics and talent identification technologies. This article asks: what organizational and governance structures enable elite sport systems to make, revise, and coordinate high-stakes developmental decisions effectively under conditions of structural (Knightian) uncertainty? We argue that decision inconsistency reflects flawed decision architecture rather than insufficient measurement, and that existing sport governance frameworks have not directly integrated these concerns.Research methods This article develops a conceptual framework. Drawing on organizational decision theory, sport governance scholarship, and longitudinal developmental evidence, we synthesize empirical findings across multiple sports and governance contexts to identify the mechanisms through which architectural features influence decision quality under structural uncertainty.Results and Findings We advance Decision Architecture Quality (DAQ) as a mid-range theory specifying six interdependent architectural dimensions – longitudinal inference, reversibility, transparency, bias correction, separation of measurement and judgment, and accountability – distributed across four organizational layers. We introduce the optimization–optionality paradox: systems that intensify predictive optimization through early filtering and resource concentration simultaneously reduce developmental optionality, amplifying long-term decision error. We articulate five testable propositions and propose an indicative governance audit grid with measurable indicators.Implications DAQ provides sport managers, national sport organizations, and federation governance bodies with a diagnostic and reform framework for redesigning decision architecture to manage structural uncertainty. By reframing elite sport development as an architectural design problem rather than a prediction problem, DAQ advances a research agenda for sport management governance under irreducible uncertainty.