This thesis aimed to extend the existing body of research that has deployed machine learning to conduct multidimensional investigations of expertise development (e.g. Güllich et al., 2019; Jones et al., 2019); and was the first to provide a detailed account of the pathway to elite performance in Olympic weightlifting. The thesis contains seven chapters, four of which are empirical studies. Chapter 1 critically reviews current literature on expertise development and talent identification research. The review discusses research contributing the to the following talent development themes: (1) demographics and family sport participation, (2) physiological and (3) psychosocial characteristics, (4) sport participation history, and (5) sport specific practice activities. Empirical limitations of the current literature are also discussed which are centred around the need for research to accurately capture the dynamic nature of expertise development; as well as for sport specific frameworks of talent development to incorporate the relative importance of the developmental themes discussed. Chapter 2 presents a study that examined the degree with which future performance could be accurately represented from historic performance data. Advanced data handling and machine learning techniques were used to both prospectively and retrospectively examine the pathway to elite senior performance at each competitive age group classification. Predictive models were able to correctly classify elite performance at each stage in the pathway with 79-92% accuracy. The earliest age from which performance could be accurately predicted gave rise to the discussion of specialization in weightlifting as a developmental theme. Evidence for the role of NGB’s in effectively enabling talented athletes to transition between stages in the pathway were also discussed (Sotiriadou, Shilbury, & Quick, 2008). Chapter 3 investigated the discriminatory features in the biographical development of current and past senior weightlifting athletes. Semi-structured interviews reported the demographics, sporting history, competitive milestones, and weightlifting specific practice activities in sixteen weightlifting athletes. Logical attributes provided a detailed description of the discriminatory features of performance in each developmental theme. Qualitative accounts of the athlete’s experiences at competitive milestones also detailed the athlete’s transition throughout the competitive pathway. The final predictive model classified the groups with 85% classification accuracy. Chapter 4 presents a multidisciplinary observation of the development of youth and junior weightlifting athletes. The holistic profiles of 29 junior weightlifting athletes were observed longitudinally over a 10-month period. This holistic profile captured the developmental themes discussed in chapter 1. Odds ratio calculations uncovered both common and discriminating features in the profiles of high performing relative to low performing athletes, from which empirically derived logical statements could inform the description of high-performance attainment. A summary predictive model successfully differentiated the groups with 91% accuracy. In a three-part investigation, Chapter 5 comprehensively examines the prevalence of the relative age effect at the highest level of representation in weightlifting. The historic performance data from all youth, junior, and senior Olympic, world, commonwealth and continental championships was examined in order to determine the influence of the relative age effect and subsequent medal attainment. The findings provide evidence for an interactive influence of bodyweight category classification and relative age on subsequent medallist status. This chapter also explored the psychosocial characteristics that likely emerge as a result of the relative age effect. In chapter 6, the theoretical implications of the current thesis are discussed, the need for future research to continue to explore the dynamic development of expertise with state-of-the-art analytics are also emphasised.