The football throw-in: A scoping review
Bibliographic record
Abstract
Despite the publication of articles about set pieces in football, research has not provided much focus on throw-in actions. The aim of this review is to offer an overview of the current literature concerning football throw-ins and identify perspectives for future research. Following the PRISMA-ScR recommendations, PubMed, SPORTDiscus and Google Scholar databases were used, and a total of 1676 articles were found. After applying inclusion and exclusion criteria, 33 articles were included in the review. These publications were divided into three main categories: 1) studies on biomechanics, motor learning and training, 2) studies analyzing goals scored from these actions in different competitions and 3) studies analyzing the game and the involvement of throw-ins. This scoping review revealed that throw-ins play an important role in football, as it is the most frequent game interruption in matches, but it seems difficult for teams to retain possession and create goal scoring opportunities after throw-ins. Different variables can influence the outcome of throw-ins, and some key principles could be followed in order to learn and/or improve the throwing motion. Future research should focus on evaluating throw-in performance and finding training methods.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".