A Machine Learning Approach to “Revisit†Specialization and Sampling in Institutionalized Practice

Michael Barth, Eike Emrich and Arne Güllich

SAGE Open, 2019, vol. 9, issue 2, 2158244019840554

Abstract: The question which nature and scope of developmental participation patterns lead to international senior-level success has been controversially discussed in the literature for many years. The present article aimed to extend existing literature in two respects. First, we reviewed studies comparing developmental sport activities of international-level and national-level athletes. The results indicated that comparisons among the highest success levels are infrequent, findings partly varied across studies, while the practice volume in other sports, but not in the athlete’s main sport, mostly distinguished international-level from national-level athletes. Second, a new methodical approach combining decision trees and gradient boosting (conducted under the R environment) was applied to data from a previously published study. It allowed for multivariate, interactive, and nonlinear analysis and was promising to achieve relatively better explanation than earlier, traditional procedures. The results indicate that some formerly found differences between international and national-level athletes in the volume of main-sport and other-sports practice may represent artifacts of uncontrolled age effects, rather than variables factually differentiating success. In the context of the specialization–diversification debate, the present findings suggest that the debate addresses a “production function,†the structure of which is still unknown. Practice-related recommendations on developmental participation patterns are apparently expressions of highly rationalized myths, rather than evidence-based efficient norms.

Keywords: talent development; international sporting success; machine learning; extreme gradient boosting; deliberate practice (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:9:y:2019:i:2:p:2158244019840554

DOI: 10.1177/2158244019840554

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