The development of talent in sports: A dynamic network approach

Abstract

Understanding the development of talent has been a major challenge across the arts, education, and particularly sports. Here, we show that a dynamic network model predicts typical individual developmental patterns, which for a few athletes result in exceptional achievements. We first validated the model on individual trajectories of famous athletes (Roger Federer, Serena Williams, Sidney Crosby, and Lionel Messi). Second, we fitted the model on athletic achievements across sports, geographical scale, and gender. We show that the model provides good predictions for the distributions of grand slam victories in tennis (male players, n = 1528; female players, n = 1274), major wins in golf (male players, n = 1011; female players, n = 1183), and goals scored in the NHL (ice hockey, n = 6677) and in FC Barcelona (soccer, n = 585). The dynamic network model offers a new avenue toward understanding talent development in sports and other achievement domains.