10 considerations for athlete selection: A resource and guide for researchers and practitioners
Bibliographic record
Abstract
For good or bad, athlete selection remains a crucial step in the process of high-performance sport participation. Those in selector positions such as coaches, scouts, and senior administrators are responsible for making complex decisions regarding an athlete’s fit to a team, and that athlete’s future performance potential. These decisions ultimately shape the nature of the team or group, and directly influence an individual’s trajectory. Despite its importance, research regarding practices of athlete selection is relatively sparse, which presents a challenge for researchers and practitioners looking to make evidence-informed decisions. Knowing this, the present narrative review synthesises relevant articles focusing on athlete selection practices and theories (where available), and categorises current evidence in the form of “10 considerations for athlete selection”. These considerations, while not exhaustive, serve as a guide for practitioners to reflect upon when making selection decisions. The paper concludes with a list of questions for practitioners to consider when they are performing athlete selections, which doubles as a list for researchers to expand upon from both a theoretical and empirical perspective.
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 | 0.100 | 0.240 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.028 | 0.020 |
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".