The literature base regarding the development of sporting talent is extensive, and includes empirical articles, reviews, position papers, academic books, governing body documents, popular books, unpublished theses and anecdotal evidence, and contains numerous models of talent development. With such a varied body of work, the task for researchers, practitioners and policy makers of generating a clear understanding of what is known and what is thought to be true regarding the development of sporting talent is particularly challenging. Drawing on a wide array of expertise, we address this challenge by avoiding adherence to any specific model or area and by providing a reasoned review across three key overarching topics: (a) the performer; (b) the environment; and (c) practice and training. Within each topic sub-section, we review and calibrate evidence by performance level of the samples. We then conclude each sub-section with a brief summary, a rating of the quality of evidence, a recommendation for practice and suggestions for future research. These serve to highlight both our current level of understanding and our level of confidence in providing practice recommendations, but also point to a need for future studies that could offer evidence regarding the complex interactions that almost certainly exist across domains.

Lack of consensus regarding the nature and conceptual definition of the social support construct has led to a plethora of different forms of measurement of this psychosocial variable, many with psychometric limitations. Beyond the psychometric limitations of some measures, in sport there is also a need for measures to be relevant to the specific experiences of sports performers. In order to gain a greater understanding of the social support experiences of sports people, 10 high-level sports performers were interviewed regarding their experiences of social support. Principles of the grounded theory (Glaser & Strauss, 1967) approach were adopted for analysis of their responses and insights. Four dimensions of support were generated, within each of which were comments relating to sport-specific support and comments relating to support not directly concerning the sport itself. The dimensions were labeled emotional, esteem, informational, and tangible. Example quotes are given to highlight each dimension of support, and implications for intervention are derived.

This article provides initial evidence for the construct validity of the Perceived Available Support in Sport Questionnaire (PASS-Q), which assesses emotional, esteem, informational, and tangible support. In Study 1, confirmatory factor analyses provided evidence for a four-dimension factor structure. Correlations supported hypothesized relationships between the PASS-Q dimensions and the Social Support Survey questions (Richman, Rosenfeld, & Hardy, 1993). In Study 2, the four-dimension factor structure was supported in an independent sample. Further, higher levels of perceived available emotional, esteem, informational, and tangible support were associated with higher levels of self-confidence and lower levels of burnout. Researchers are encouraged to use the PASS-Q to examine the effects of perceived available support in sport contexts.

Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment—such as response to data imbalance, model fitting, and a lack of multi-season data—limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data.