“ <i>Not everybody has the ability to see talent, but I can</i> ”. A longitudinal case-study of a coach’s process of identifying and selecting talent
Bibliographic record
Abstract
Athlete selection is a necessary component of many sport systems around the world. To inform selection decisions, coaches and recruiters utilize various sources of information to better understand athletes and their strengths, weakness, likelihood of success, and degree of ‘fit’ with the program/system. However, little is known about how the coach experiences identification and selection, the information incorporated in decision-making, or how opinions and perspectives change over time. As such, the present case study documents the in-situ decision-making process of a coach during a real talent identification and team selection period. Over the course of 18 months, regular semi-structured and field interviews were conducted with an elite coach tasked with selecting athletes. Results from the inductive thematic analysis indicate the coach’s perspective shifted (sometimes dramatically) regarding the athlete’s future likelihood of success, sometimes even over relatively short time frames. Further, a six-year follow-up of athlete performance demonstrated that the accuracy of the coach’s predictions about which athletes would and would not succeed, were mixed. This paper advances our knowledge of the coaches’ eye, demonstrating the value of having a multidimensional and holistic approach to athlete assessment, and supports the growing call to delay selections for as long as possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".