MétaCan
Menu
← Back to cohort

Decision making of semi-professional female basketball players in competitive games

2020· article· en· W4210268548 on OpenAlexaboutno aff
Tomáš Vencúrik, Dominik Bokůvka, Jiří Nykodým, Pavel Vacenovský

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsBasketballOffensivePsychologyBall (mathematics)Quarter (Canadian coin)CoachingSocial psychologyApplied psychologyOperations researchMathematics

Abstract

fetched live from OpenAlex

Purpose: Nowadays, not only the research but also coaching is focusing on decision making in basketball. Decision making is critical in basketball, especially in relation to offensive skills (with ball). Generally, the players have to decide what to do with the ball (make an appro-priate decision) and in the shortest time possible. From this point of view, the study aims to identify the factors which can affect the decision making of offensive skills of female basket-ball players. Methods: Eight semi-professional female basketball players participated in this study. Basket-ball players played five competitive games in the second division. During all games, the heart rate was monitored. Decision making was assessed according to Basketball Offensive Game Performance Instrument (BOGPI) and categorized as appropriate and inappropriate. For this purpose, the notational analysis was used. Based on previous research, the four main factors were set as independent variables. Each of these factors was categorized. The first factor was the intensity of load ( 95% of HR ), second factor was ball possession duration (0–8 s, 9–16 s, and 17–24 s), third factor was game period (1st quarter, 2nd quarter, 3rd quarter, and 4th quarter), and the fourth factor was defensive pressure of an opponent (low, moderate, and high). Objectivity was verified by the method of inter-rater agreement, and re-liability was using intra-rater agreement. The influence of factors on decision making was ex-pressed by binary logistic regression. Method of backward stepwise selection was used to find predictors of inappropriate decisions and to find the best model. Results: One regression coeficient in the final model was statistically significant – defensive pressure of the opponent. When the defensive pressure is moderate or high, the chance for inappropriate decisions increased. Conclusion: Based on these findings, the coaches should take into consideration these fac-tors when preparing individual training sessions.

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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.325
Teacher spread0.292 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicSports Performance and Training→French-language works237,207→