Towards a dynamic state-change assessment model of talent development

Despite growing recognition of the non-linear and multidimensional nature of athletic development, talent identification and development (TID) models remain largely grounded in static, trait-based assumptions. This study introduces the Dynamic State-Change Assessment Model (DSCAM), a conceptual assessment framework derived from 23 qualitative interviews with expert practitioners in elite football. DSCAM reconceptualizes talent development as an iterative process involving transitions between two primary dimensions: State Content, the athlete's current performability and capability, and Change Potential, their development needs and developability. The model integrates five assessment types (performative, diagnostic, formative, summative, and ipsative) and delineates both feedback and feedforward mechanisms that shape individualized developmental trajectories. Using constant comparative analysis, the study demonstrates how assessments simultaneously inform present evaluations and future projections, thereby challenging reductionist tendencies such as early selection and reliance on fixed metrics. By incorporating cognitive, physical, psychological, and contextual factors, DSCAM offers a more nuanced and adaptive framework for guiding long-term athlete development. The findings contribute to ongoing debates in sport science by advancing a theory-driven, evidence-informed model for improving assessment practices within applied talent development pathways.