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Record W1983212422 · doi:10.1519/jsc.0000000000000579

Time-Motion Analysis of Elite Male Kickboxing Competition

2014· article· en· W1983212422 on OpenAlexaff
Ibrahim Ouergui, Nizar Hssin, Monoem Haddad, Émerson Franchini, David G. Behm, Del P. Wong, Nabil Gmada, Ezzedine Bouhlel

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2014
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMathematicsEliteCompetition (biology)Political scienceBiology

Abstract

fetched live from OpenAlex

The objective of the study was to analyze the time structure of high-level kickboxing matches. A total of 45 combats from 2 male World Championships were monitored using a time-motion analysis system. The combat time structure (i.e., high-intensity activity [HIA], low-intensity activity [LIA], and referee breaks or pauses) during competition and weight divisions was determined and compared. Results indicated that the time structures were HIA: 2.2 ± 1.2 seconds; LIA: 2.3 ± 0.8 seconds; pauses: 5.4 ± 4.3 seconds; and 3.4 ± 1.2 seconds between 2 subsequent HIA. The fighting to nonfighting ratio was found to be 1:1. Moreover, the number of HIA and LIA and the time of LIA decreased in latter rounds (e.g., the average number of HIA was 27.1 ± 7.1, 25.1 ± 6.6, and 24.9 ± 6.1, respectively, for rounds 1, 2, and 3), meanwhile the time and number of pauses increased (e.g., the average pause times were 12.8 ± 11.4, 22.3 ± 22.6, and 24.6 ± 23.3 seconds, respectively, for rounds 1, 2, and 3). The activity times did not differ among weight categories. The present results confirm the intermittent nature of kickboxing competition and provide coaches with more information on how to structure training sessions to mimic the physical demands in competition.

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)0.002
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 score0.171
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.339
Teacher spread0.311 · 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

Citations56
Published2014
Admission routes1
Has abstractyes

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