
The article provides a scientific rationale for the adaptive training model for world-class sprinters 9.9X, conceptualized based on the author's elite-level competitive experience and an analysis of contemporary preparation systems. The relevance of the study is defined by the fact that traditional rigidly deterministic training schemes demonstrate limited efficacy in accommodating individual variability, often provoking overtraining and non-linear deteriorations in form. The novelty lies in integrating a phase architecture of preparation with weekly autoregulation and the introduction of targeted stressors–adaptive dynamic triggers (ADT)–that titrate the load in accordance with the athlete’s current capacities. The model comprises four sequentially organized phases–from foundational formation of functional prerequisites to peaking–and rests on principles of autoregulation based on a combination of objective and subjective feedback indicators. Special emphasis is placed on neuromotor adaptation, Local Isolated Stabilization (LIS), and special strength preparation (SSP) as mechanisms that ensure technical robustness and the economy of speed–strength work. The aim of the study is to demonstrate that the adaptive logic of the 9.9X model represents a functionally superior alternative to classical periodization; to this end, methods of comparative analysis and synthesis of contemporary scientific literature are employed. In conclusion, it is shown that the 9.9X model ensures steady, sustainable progress while simultaneously reducing the likelihood of injury. The findings presented will be of interest to coaches, other researchers, and athletes focused on optimizing the training process.
Periodization, Neuromotor Adaptation, Sporting Results, Elite Athletes, Adaptive Dynamic Trigger (ADT), Performance Optimization, Sprinter Training, Autoregulation, Phase-Based Preparation, Adaptive Model
Periodization, Neuromotor Adaptation, Sporting Results, Elite Athletes, Adaptive Dynamic Trigger (ADT), Performance Optimization, Sprinter Training, Autoregulation, Phase-Based Preparation, Adaptive Model
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