Evaluation tools used for talent identification and development in youth soccer: a scoping review
Bibliographic record
Abstract
Research Objectives The primary objective of the present study is to conduct a scoping review aiming to consolidate existing knowledge on the methods employed in talent identification processes in youth soccer. Research Inquiry The research project will address the following query: "Which tools and tests are predominantly utilized for talent identification in association football?" Research Hypothesis The research team posits that physical performance is the primary focal point in researching the talent identification process in soccer. They contend that such research may not furnish coaches with information pertinent to their selection decisions. Anticipated Results The anticipated outcomes of this research encompass: 1) a broader comprehension of the tests and tools utilized in talent identification in association football, 2) a new trajectory for future research and researchers to concentrate on, and 3) heightened awareness of key challenges related to talent identification and development systems. Methodology and Investigation Methods This systematic review was preregistered on OSF Registries (https://osf.io/beqyh/) and adhered to the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines (13). It is noteworthy that the PROSPERO database does not accommodate reviews solely focused on sports performance. The procedures for data identification, selection, and extraction are delineated in Figure 1. Search Strategy A systematic literature search was carried out on PubMed (n = 201), Web of Science (n = 235), and Scopus (n = 338) databases in September 2023 by two authors (VOS and CPF). Gray literature was considered via Google Scholar. The search strategy involved combining the keywords "talent identification" and "soccer" or "football," adhering to the PICOS principles. The researcher conducting the search was not blinded to journal names or manuscript authors. Study Inclusion and Exclusion Criteria Primary data from articles were downloaded to an Excel spreadsheet after removal of duplicates. Authors VOS and CPF independently screened the search results based on inclusion/exclusion criteria established using the Rayyan website (14). Disagreements in inclusion/exclusion status were resolved through discussion between the two authors. Abstracts, conference papers, case studies, and studies with participants over 16 years old were excluded. Data Selection Two authors performed selection and information extraction. The outcomes were summarized based on author, year, sample size, country, study design, and evaluation tools used in the talent identification process (refer to Table 1). Quality Assessment Quality assessment for cross-sectional studies used the Joanna Briggs Institute Critical Appraisal Checklist, while longitudinal studies were assessed using the Newcastle-Ottawa Quality Assessment Scale. Disagreements were resolved by a third reviewer (MAPS).
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 | 0.157 | 0.336 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.066 | 0.047 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".