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Record W2330657231 · doi:10.1123/ijspp.2013-0263

Impact of Maximum Speed on Sprint Performance During High-Level Youth Female Field Hockey Matches: Female Athletes in Motion (FAiM) Study

2014· article· en· W2330657231 on OpenAlexaff
Jason D. Vescovi

Bibliographic record

VenueInternational Journal of Sports Physiology and Performance · 2014
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSprintField hockeyMathematicsSpeed skatingStatisticsAthletesSimulationPhysical therapyMedicineComputer scienceFootballGeography

Abstract

fetched live from OpenAlex

The aim of this study was to examine the impact of maximum sprint speed on peak and mean sprint speed during youth female field hockey matches. Two high-level female field hockey teams (U-17, n = 24, and U-21, n = 20) were monitored during a 4-game international test series using global position system technology and tested for maximum sprint speed. Dependent variables were compared using a 3-factor ANOVA (age group, position, and speed classification); effect sizes (Cohen d) and confidence limits were also calculated. Maximum sprint speed was similar between age groups and positions, with faster players having greater speed than slower players (29.3 ± 0.4 vs 27.2 ± 1.1 km/h). Overall, peak match speed in youth female field hockey players reaches approximately 90% of maximum sprint speed. Absolute peak match speed and mean sprint speed during matches were similar among the age groups (except match 1) and positions (except match 2); however, peak match speed was greater for faster players in matches 3 and 4. No differences were observed in the relative proportion for mean sprint speeds for age groups or positions, but slower players consistently displayed similar relative mean sprint speeds by using a greater proportion of their maximum sprint speed.

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)0.000
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 score0.019
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.028
GPT teacher head0.290
Teacher spread0.262 · 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

Citations26
Published2014
Admission routes1
Has abstractyes

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