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Record W2791274159 · doi:10.1519/jsc.0000000000002584

Effect of Match Factors on the Running Performance of Elite Female Soccer Players

2018· article· en· W2791274159 on OpenAlexaff
Joshua Trewin, César Meylan, Matthew C. Varley, John Cronin, Daphne I. Ling

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2018
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsCanadian Sport Centre Pacific
Fundersnot available
KeywordsEliteMatch playMathematicsPsychologyStatisticsPhysical therapyMedicinePolitical science

Abstract

fetched live from OpenAlex

Trewin, J, Meylan, C, Varley, MC, Cronin, J, and Ling, D. Effect of match factors on the running performance of elite female soccer players. J Strength Cond Res 32(7): 2002-2009, 2018-The purpose of this study was to examine the effects of match factors on the match running of elite female soccer players. Players from the same women's national team (n = 45) were monitored during 47 international fixtures (files = 606) across 4 years (2012-2015) using 10-Hz global positioning system devices. A mixed model was used to analyze the effects of altitude, temperature, match outcome, opposition ranking, and congested schedules. At altitude (>500 m), a small increase in the number of accelerations (effect size [ES] = 0.40) and a small decrease in total distance (ES = -0.54) were observed, whereas at higher temperatures, there were decreases in all metrics (ES = -0.83 to -0.16). Playing a lower ranked team in a draw resulted in a moderate increase in high-speed running (ES = 0.89), with small to moderate decreases in total distance and low-speed running noted in a loss or a win. Winning against higher ranked opponents indicated moderately higher total distance and low-speed running (ES = 0.75), compared with a draw. Although the number of accelerations were higher in a draw against lower ranked opponents, compared with a win and a loss (ES = 0.95 and 0.89, respectively). Practitioners should consider the effect of match factors on match running in elite female soccer.

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 score0.261
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.043
GPT teacher head0.364
Teacher spread0.321 · 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

Citations65
Published2018
Admission routes1
Has abstractyes

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