Expert youth coaches’ diversification strategies in talent development: A qualitative typology
Bibliographic record
Abstract
The development of expert-level performance in sports is discussed against the background of two different pathways. The early specialization approach emphasizes both early onset and high volumes of sport-specific practice in a desired main sport. On the other hand, the diversification approach promotes diversified involvement in a range of other sports with later specialization. This study examines the way in which youth coaches translate the antagonistic concepts of specialization and diversification into their coaching strategies during the early stages of talent development. Using qualitative research methodology, 44 expert German youth coaches (M age = 45.1 years, SD = 7.5; 39 male and 5 female) in 24 different sports, with an average of 21.7 years accumulated coaching experience (SD = 7.0) were included in the inductive thematic analysis. Analysis showed a differentiated understanding of the process of specialization that considered multiple ways to apply diversification both within sports and across several sports. Although all of the coaches appeared to acknowledge the importance of within-sports diversification, there was considerable variation in the reported purposeful implementation and significance of sporting activities other than the main sport leading to a nuanced typology of strategies. The typology could be divided into the following four categories based on the preferred strategies that the coaches described: (I) changers and late entrants; (II) early engagement in DS + secondary sports; (III) early engagement in DS + supplementary sports; and (IV) early engagement in DS + specialization. The findings strengthened existing suggestions of a gradual and multidimensional understanding of diversification prior to necessary specialization. Furthermore, it can be assumed that the preferred strategies are fundamentally influenced by individual cognitions and contextual aspects that acknowledge the complex and ideographical nature of coaching.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".