MétaCan
Menu
Back to cohort
Record W2808336513 · doi:10.1515/hukin-2017-0156

The Effects of Repeated Sprints on the Kinematics of 3-Point Shooting in Basketball

2018· article· en· W2808336513 on OpenAlexaff
Jean Slawinski, Julien Louis, Julien Poli, Eve Tiollier, Charles Khazoom, Daniel Dinu

Bibliographic record

VenueJournal of Human Kinetics · 2018
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBasketballKinematicsPhysical medicine and rehabilitationPlyometricsMedicineMathematicsJumpPhysical therapySimulationComputer sciencePhysics

Abstract

fetched live from OpenAlex

Fatigue modifies the kinematics of various sports-related movements. Basketball induces fatigue, however, the effects of fatigue on the kinematics of shooting have never been studied. This study analysed the effects of fatigue induced by repeated sprints on the kinematics of 3-point shooting (3PS) in young, elite basketball players (U18 level). 3D joint angles were calculated at the maximum and minimum heights of the centre of mass during 3PS, using inertial measurement units (Biomech system, Xsens Technologies BV, Enschede, The Netherlands). Height, velocity and the angle of the ball at the time of release were extrapolated from the wrist joint angles. All players performed four 3PS actions in dynamic conditions before and after a fatigue protocol at 70% of their maximal exercise capacity. The fatigue protocol consisted of a shuttle test with repeated 20-m sprints interspersed with sets of 5 jumps. There was no change in the kinematics of 3PS (p > 0.05), or the ball release variables (p > 0.05) following the fatigue protocol. This suggests that elite basketball players are able to cope with physical fatigue while performing coordinated movements such as 3PS.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.018
GPT teacher head0.293
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Human KineticsSame topicSports Performance and TrainingFrench-language works237,207