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Record W3004825748 · doi:10.26773/mjssm.200305

The Effect of an In-Season 8-Week Plyometric Training Programme Followed By a Detraining Period on Explosive Skills in Competitive Junior Soccer Players

2020· article· en· W3004825748 on OpenAlexfundno aff
Luís Branquinho, Ricardo Ferraz, Pedro Duarte‐Mendes, João Petricia, João Serrano, Mário C. Marques

Bibliographic record

VenueMontenegrin Journal of Sports Science and Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersFederation for the Humanities and Social Sciences
KeywordsSprintPlyometricsCountermovementPhysical therapyJumpJumpingMathematicsPhysical medicine and rehabilitationPsychologyMedicinePhysics

Abstract

fetched live from OpenAlex

The aim of this study was to examine the effect of an eight-week plyometric training and resistance programme, on different motor abilities, kicking speeds, and individual techniques in competitive junior soccer players. A training programme was applied for eight weeks and was composed of countermovement jump, countermovement onto a box, sprints, and sprints with change of direction. The players were evaluated in three moments: before the start of the training programme, after the application of the training programme, and after four weeks of detraining. The applied tests were: countermovement jump, speed dribbling, 30-meter sprint, kicking speed, and a Yo-Yo recovery intermittent recovery test Level 2 (Yo-Yo IE2). The results showed statistically significant differences (p<0.05) in sprint performance, countermovement jump, Yo-yo IE 2, and speed dribbling. The maximum velocity of the ball did not present statistically significant differences (p > 0.05). In addition, these results indicate that general performance may not be sensitive to four weeks of detraining in the trained soccer players. These results demonstrate that the combination of soccer drills and specific power training with no additional training time in-season optimizes some general and soccer-specific performance.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.279
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.290
Teacher spread0.260 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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