Developing Decision-Making Expertise in Professional Sports Staff: What We Can Learn from the Good Judgement Project

被引:0
作者
P. J. Wilson
John Kiely
机构
[1] Setanta College,Department of PE and Sports Sciences
[2] University of Limerick,undefined
来源
Sports Medicine - Open | / 9卷
关键词
Professional sport; Decision-making; Forecasting; Judgement; Uncertainty; Intuition; Experience; Expertise;
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中图分类号
学科分类号
摘要
Success within performance sports is heavily dependent upon the quality of the decisions taken by educated and experienced staff. Multi-disciplinary teams (MDTs) typically collate voluminous data, and staff typically undergo extensive and rigorous technical and domain-specific training. Although sports professionals operate in sometimes volatile, uncertain, complex and ambiguous decision-making environments, a common assumption seems to be that education and experience will automatically lead to enhanced and effective decision-making capabilities. Accordingly, there are few formal curriculums, in coaching or sports science contexts, focussed on translating the extensive research on judgement and decision-making expertise to professional sports staff. This article aims to draw on key research findings to offer insights and practical recommendations to support staff working within professional performance contexts. Through this distillation, we hope to enhance understanding of the factors underpinning effective decision-making in dynamic, high-stakes professional sporting environments. Broadly, the conclusions of this research demonstrate that decision-making efficacy is enhanced through application of three specific strategies: (i) Design of more engaging professional cultures harnessing the power of collectives encouraging diverse opinions and perspectives, and fostering and promoting collaborative teamwork, (ii) education specifically targeting debiasing training, designed to counter the most common cognitive pitfalls and biases and, (iii) the implementation of evaluation strategies integrating rigorous testing and real-time feedback.
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