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TIME MOTION ANALYSIS IN SPORTS-A REVIEW

2014· article· en· W2104549720 on OpenAlexaboutno aff
Ravi Singh, Syed Tariq Murtaza, Shamshad Ahmad, Arshad Hussain Bhat, Mohd Sharique

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsnot available
Fundersnot available
KeywordsMotion analysisMotion (physics)BasketballThe InternetMultimediaComputer scienceArtificial intelligenceWorld Wide WebGeography

Abstract

fetched live from OpenAlex

The purpose of this study was to collect the review related to Time Motion Analysis of team game. The review was collected of two decade from 1984 to 2014 using internet. Researchers used Google search engine. Most of the Time Motion Analysis studies were conducted on segmental movement. As the study is delimited to Time Motion Analysis of team game, researchers found many studies on rugby game, very few on basketball and hockey, and a study related to wheelchair sports (wheelchair basketball). Most of the time motion studies of team games were conducted in Canada. Technology is enhancing day by day. In the decade of 80's, video technology was used. In 90's computerised video analysis was used. Now-a-days modern GPS system and tracers are being used for Time Motion Analysis.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.182
Teacher spread0.179 · 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 designSimulation or modeling
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

Citations1
Published2014
Admission routes1
Has abstractyes

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