Abstract
Research investigating the nature and scope of developmental participation patterns leading to international senior-level success is mainly explorative up to date. One of the criticisms of earlier research was its typical multiple testing for many individual participation variables using bivariate, linear analyses. Here, we applied state-of-the-art supervised machine learning to investigate potential non-linear and multivariate effects of coach-led practice in the athlete's respective main sport and in other sports on the achievement of international medals. Participants were matched pairs (sport, sex, age) of adult international medallists and non-medallists (n = 166). Comparison of several non-ensemble and tree-based ensemble binary classification algorithms identified "eXtreme gradient boosting" as the best-performing algorithm for our classification problem. The model showed fair discrimination power between the international medallists and non-medallists. The results indicate that coach-led other-sports practice until age 14 years was the most important feature. Furthermore, both main-sport and other-sports practice were non-linearly related to international success. The amount of main-sport practice displayed a parabolic pattern while the amount of other-sports practice displayed a saturation pattern. The findings question excess involvement in specialised coach-led main-sport practice at an early age and call for childhood/adolescent engagement in coach-led practice in various sports. In data analyses, combining traditional statistics with advanced supervised machine learning may improve both testing of the robustness of findings and new discovery of patterns among multivariate relationships of variables, and thereby of new hypotheses.