What the Coaching Science Literature Has to Say about the Roles of Coaches in the Development of Elite Athletes
Bibliographic record
Abstract
The story of John Naber about his progression to the Olympics Games is a good illustration that the road to expertise is long and has many unpredictable events. In the last 20 years, sport researchers have tried to understand what it takes to become the best among the best. For any one interested in sport expertise, I recommend the book edited by Starkes and Ericsson [1] Expert Performance in Sports: Advances in Research and Sport Expertise. In this book, the authors try to answer two main questions: How much of being the best is related to training, and how much is based on one’s genetic, physical, and emotional makeup? Furthermore, in the quest to be the best, how important are coaches, competition, and access to facilities and resources? If we focus on what the coaching science literature has to say about the different roles that coaches play in athletes’ development, additional information can be found in recent studies where elite athletes and/or coaches have been questioned/interviewed. It is interesting to note that the researchers contributing to this topic are from different countries. In the United-States, the work of Gould and colleagues [2, 3] on Olympic athletes and coaches are instrumental; while in Canada, we must consider the work of Salmela and colleagues [4, 5, 6, 7, 8] and Cote and colleagues [9, 10, 11]. In England we have to mention Lyle [12], Jones and colleagues [13, 14, 15], as well as Jowett and colleagues [16, 17]. Finally, in France, the work of Arripe-Longueville and colleagues [18, 19], and the study of Saury and Durand [20] are often referenced. A search in any sport research databases using the names of these authors will provide reading for hours. For the remainder of this short article, I would like to share with you what the coaching literature has to say about questions that came to my mind while reading John Naber’s story.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".