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Record W3021973020 · doi:10.1080/24748668.2020.1761673

Relationship between number of substitutions, running performance and passing during under-17 and adult official futsal matches

2020· article· en· W3021973020 on OpenAlexaboutno aff
Vinícius Flávio Milanez, Murilo José de Oliveira Bueno, Fabio Giuliano Caetano, Priscila Chierotti, Solange Marta Franzói de Moraes, Felipe Arruda Moura

Bibliographic record

VenueInternational Journal of Performance Analysis in Sport · 2020
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsLeagueAthletesCompetition (biology)Quarter (Canadian coin)MathematicsStatisticsPsychologyComputer scienceSimulationPhysical therapyMedicineBiologyGeography

Abstract

fetched live from OpenAlex

The aim of the study was to analyse the relationship between running performance and passes, as well as the effect of substitutions and the competitive level on these variables, during official futsal matches. Male juvenile athletes were filmed during five official games in the Youth Games competition and adult athletes were monitored during National League. The athletes were tracked to obtain their trajectories for subsequent quantification of physical actions, number of substitutions and passes during the games. Impairment in running performance, number of passes and number of substitutions were recorded using video cameras and analysed on a computer-based tracking system. The number of substitutions contributed singly to increasing total distance covered, very high-intensity running (%VHIR), and certain passes, regardless of possible differences between under 17 and adult categories. Players with a decrease in %VHIR presented lower (p = 0.039) pass efficiency (4% below). The adult players demonstrated that drop in running performance and successful passes in a futsal game may be observed from the second quarter, not only in the second half as shown in previous studies in futsal. Substitution of players is an important strategy to be used by coaches to improve running performance and passes in futsal games.

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 score0.003
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.312
Teacher spread0.280 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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