Research materials for: The development of talent in sports: A dynamic network approach
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Understanding the development of talent has been a major challenge across the arts, education, and particularly sports. In the article, 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), goals scored in the NHL (ice hockey, n = 6677) and in FC Barcelona (soccer, n = 585). The res' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Complexity","Dynamic Network","Sports","Talent Development"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Understanding the development of talent has been a major challenge across the arts, education, and particularly sports. In the article, 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), goals scored in the NHL (ice hockey, n = 6677) and in FC Barcelona (soccer, n = 585). The res' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Understanding the development of talent has been a major challenge across the arts, education, and particularly sports. In the article, 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), goals scored in the NHL (ice hockey, n = 6677) and in FC Barcelona (soccer, n = 585). The res
Complexity; Dynamic Network; Sports; Talent Development