![]() ![]() Current approaches range from subjective preferences and intuition ( Williams and Reilly, 2000 Christensen, 2009 Lund and Söderström, 2017) and the use of standardized testing batteries (e.g., 40 meter sprint, vertical jump, Gabbett, 2009 Wells et al., 2009) to hypothesis-free machine-learning approaches 2 (e.g., Güllich et al., 2019). To date, there does not appear to be a “gold standard” approach to talent selection rather, there appears to be a high degree of variability in the techniques, protocols, and processes used for assessment and selection. Following this assessment period, talent selectors make decisions regarding which athletes should be included (selected) or excluded (de-selected) from the team. ![]() Often a series of tests (primarily focused on the physical or physiological attributes of an athlete) combined with coach observations ( Christensen, 2009 Schorer et al., 2017) are used to assess performance ( Lidor et al., 2009). The talent selection process typically takes place early in an athlete’s life and involves administrative personnel (such as a coach, scout, or talent identifier 1), who are tasked with identifying and predicting future athletic success. ![]() Although there is some evidence to show improvements in the identification and selection of athletes ( Tetlock, 2016), research suggests that accuracy rates for predicting athlete potential remain quite low ( Abbott and Collins, 2004 Vaeyens et al., 2008 Koz et al., 2012 Schorer et al., 2017 Johnston et al., 2018). In an effort to predict and select the next athletic superstar, substantial resources (e.g., time, money, and energy) are invested with the hope of gaining an edge over the competition. ![]()
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