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Record W3133998286 · doi:10.1136/bmjsem-2020-000927

Strength development according with age and position: a 10-year study of 570 soccer players

2021· article· en· W3133998286 on OpenAlexaff
Robson Dias Scoz, Bruno Mazziotti Oliveira Alves, Ricardo Lima Burigo, Edgar Ramos Vieira, Luciano Maia Alves Ferreira, Rubens Alexandre da Silva, Rogério Pessoto Hirata, César Ferreira Amorim

Bibliographic record

VenueBMJ Open Sport & Exercise Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsConcentricEccentricElitePhysical therapyPsychologyMedicinePhysical medicine and rehabilitationMathematicsPhysics

Abstract

fetched live from OpenAlex

BACKGROUND/AIM: The purpose of this study was to compare the isokinetic peak torque profiles from the quadriceps and hamstrings muscles during concentric and eccentric contractions in elite Brazilian soccer players across different field positions and age categories. Our hypothesis was that soccer players from different field positions are subjected to different ageing-related effects on their isokinetic peak torque. METHODS: This is a retrospective study based on professional elite-level soccer players between the years 2009 and 2019. It included 570 adult males who played for at least 5 years on first or second Brazilian divisions. Playing positions were divided as: goalkeepers, defenders, sidebacks, midfielders and forwards. Age categories were also divided as: G1 (17-20 years old), G2 (21-24 years old), G3 (25-28 years old), G4 (29-32 years old) and G5 (33 years old or more). RESULTS: =0.021) on torque of concentric extensors; mainly from midfielders and goalkeepers with 29 years or more. CONCLUSIONS: Soccer players from different field positions are subjected to different ageing related effects on their muscular performance during their career special attention should be given to these players to avoid reduction in physical 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.355
Teacher spread0.305 · 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.

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

Citations13
Published2021
Admission routes1
Has abstractyes

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