The application of a holistic methodological approach centred on the interactive principles of connectionism, remains largely unexplored within the field of sports expertise. Expertise research typically examines a narrow pool of features hypothesized as predictive, such as birthdate, or practice quantity. These features are analysed in isolation, using linear analysis techniques, and the results are generalised to produce theories of expertise development e.g., deliberate practice. However, emerging research has demonstrated that deliberate practice does not sufficiently explain the attainment of expertise. Such inconsistencies suggest the development of expertise is multifaceted, requiring a holistically-driven research approach. The present study adopted non-linear pattern recognition (machine learning) techniques to examine a multitude of features, across theoretical frameworks, and model the development experiences of super-elite performers. We tested the underlying assumptions of existing theory relating to practice quantity, as well as unexplored domains, including the nature and structure of practice, in a comparison of super-elite and elite cricket batsmen. We identify a subset of 18 features, from 658, that discriminate between the super-elite and elite batsmen with 96.25% classification accuracy. Results demonstrate that prospective super-elite batsmen undertake a greater volume of skills-based practice that is both more randomly sequenced and more varied in nature, aged 16. They subsequently adapt to, and transition across, levels of senior competition quicker. Thus optimising challenge at both a psychological and technical level relatively early, is a catalyst for the development of super-elite expertise. Application of this holistically-driven, non-linear, methodological approach to other domains of expertise would prove productive.