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

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.

The path to international medals: A supervised machine learning approach to explore the impact of coach-led sport-specific and non-specific practice

“…Furthermore, these procedures allow controlling for confounding variables, an issue that has scarcely been adequately considered (cf. [11]). Supervised machine learning has very recently been introduced into the investigation of athletes' developmental participation patterns using Gradient Boosting Machine (GBM) (basically [22]; application [23]) and an advancement of GBM called "eXtreme gradient boosting" (XGBoost) [11,24].…”

“…Second, it examined a data set of matched pairs of international medallists and non-medallists matched on type of sport, sex and age of athletes' present career peak performance, thereby controlling for potential confounds of these variables (cf. [11]). The data set was first presented by [18], then only conducting traditional group comparison statistics.…”

“…[35]) or contextual factors (e.g., socioeconomic factors [36]). Furthermore, we should also consider "situative factors" (e.g., management of competition anxiety; [11]).…”

“…A recent article [11] reviewed studies addressing the relevance of coach-led practice for international success in the highest, open-age category (i.e., senior success). Table 1 summarises available empirical results regarding effects of the volume of coach-led practice in the athlete's respective main sport (henceforth: main-sport practice) and in other sports (henceforth: other-sports practice).…”

“…Table 1. Relevance of the volume of coach-led practice in an athlete's main sport and in other sports for the differentiation between senior international and national-level success (adapted and updated from [11]). x/y: the majority of the results in this category correspond to x, but y was also found…”