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Elite sport practitioners increasingly use data to support training process decisions related to athletes’ health and performance. A careful application of data analytics is essential to gain valuable insights and recommendations that can guide decision making. In business organizations, data analytics are developed based on conceptual data analytics frameworks. The translation of such a framework to elite sport may benefit the use of data to support training process decisions. Purpose: The authors aim to present and discuss a conceptual data analytics framework, based on a taxonomy used in business analytics literature to help develop data analytics within elite sport organizations. Conclusions: The presented framework consists of 4 analytical steps structured by value and difficulty/complexity. While descriptive (step 1) and diagnostic analytics (step 2) focus on understanding the past training process, predictive (step 3) and prescriptive analytics (step 4) provide more guidance in planning the future. Although descriptive, diagnostic, and predictive analytics generate insights to inform decisions, prescriptive analytics can be used to drive decisions. However, the application of this type of advanced analytics is still challenging in elite sport. Thus, the current use of data in elite sport is more focused on informing decisions rather than driving them. The presented conceptual framework may help practitioners develop their analytical reasoning by providing new insights and guidance and may stimulate future collaborations between practitioners, researchers, and analytics experts.

Since its publication in 2012, the W′ balance model has become an important tool in the scientific armamentarium for understanding and predicting human physiology and performance during high-intensity intermittent exercise. Indeed, publications featuring the model are accumulating, and it has been adapted for popular use both in desktop computer software and on wrist-worn devices. Despite the model’s intuitive appeal, it has achieved mixed results thus far, in part due to a lack of clarity in its basis and calculation. Purpose: This review examines the theoretical basis, assumptions, calculation methods, and the strengths and limitations of the integral and differential forms of the W′ balance model. In particular, the authors emphasize that the formulations are based on distinct assumptions about the depletion and reconstitution of W′ during intermittent exercise; understanding the distinctions between the 2 forms will enable practitioners to correctly implement the models and interpret their results. The authors then discuss foundational issues affecting the validity and utility of the model, followed by evaluating potential modifications and suggesting avenues for further research. Conclusions: The W′ balance model has served as a valuable conceptual and computational tool. Improved versions may better predict performance and further advance the physiology of high-intensity intermittent exercise.

Purpose: The broad aim of sport-science research is to enhance the performance of coaches and athletes. Despite decades of such research, it is well documented that sport-science research lacks empirical evidence, and critics have questioned its scientific methods. Moreover, many have pointed to a research–practice gap, whereby the work undertaken by researchers is not readily applied by practitioners. The aim of this study was to use a systems thinking analysis method, causal loop diagrams, to understand the systemic issues that interact to influence the quality of sport-science research. Methods: A group model-building process was utilized to develop the causal loop diagram based on data obtained from relevant peer-reviewed literature and subject-matter experts. Results: The findings demonstrate the panoply of systemic influences associated with sport-science research, including the existence of silos, a focus on quantitative research, archaic practices, and an academic system that is incongruous with what it actually purports to achieve. Conclusions: The emergent outcome of the interacting components is the creation of an underperforming sport-science research system, as indicated by a lack of ecological validity, translation to practice, and, ultimately, a research–practice gap.