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Record W2142666096 · doi:10.1260/1747-9541.9.5.1241

An Evidence-Based Model of Power Development in Youth Soccer

2014· article· en· W2142666096 on OpenAlexaff
César Meylan, John Cronin, Jon L. Oliver, Michael G. Hughes, Sarah A. Manson

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2014
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsCanadian Sport Centre Pacific
Fundersnot available
KeywordsSession (web analytics)Training (meteorology)Power (physics)PsychologyComputer scienceApplied psychologyPhysical medicine and rehabilitationCognitive psychologySimulationMedicine

Abstract

fetched live from OpenAlex

Power is thought as an essential physical characteristic in soccer, but no systematic and evidence-based model exists to develop this attribute in youth. Both the player's pathway and natural power development were discussed and integrated as conditioning factors to the model, where the player's pathway influenced training integration, block duration, and session length and frequency, while the natural power development dictated training emphasis, mode, intensity and volume. Furthermore, a systematic analysis of training studies that investigated the training of power in youth soccer players was conducted to determine current best practice and limitations. An initial phase concentrating on movement competency and velocity was recommended prior to puberty, before an emphasis on force production during mid-puberty (13–15 y). Once the players enter late puberty (>16 y), maximal strength and power training should be implemented. The number of power training sessions in a block, exercise progression and loading parameters should be viewed as key factors of the training design to enhance movement competency and optimise training adaptations. The implementation of a model will ensure optimal integration of power training and its constituent parts (force and velocity) with clear training emphases throughout the developmental stages of a player.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.336
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations44
Published2014
Admission routes1
Has abstractyes

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