MétaCan
Menu
Back to cohort

Effects of Situational Variables and Starting Quarter Score in the outcome of elite women’s water polo game quarters

2014· article· en· W2611721151 on OpenAlexaboutno aff
Miguel‐Ángel Gómez, Ana DelaSerna, Corrado Lupo, Jaime Sampaio

Bibliographic record

VenueInternational Journal of Performance Analysis in Sport · 2014
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsWater poloQuarter (Canadian coin)Situational ethicsOpposition (politics)ElitePsychologyTournamentSocial psychologyMathematicsGeographyPolitical scienceMedicinePoliticsPhysical therapy

Abstract

fetched live from OpenAlex

The present study aimed to identify the interactive effects of Starting Quarter Score and Game Location according to Quality of Opposition in final quarters score in elite women’s water polo. Data comprised 1,828 quarters from 457 close games (goal-difference of less than 4 goals) from the Spanish water polo women’s First division (2010-2011 and 2011-2012 seasons). The interactive effects of situational variables on Starting Quarter Score according to the quality of opposition (three game contexts: HIGH vs. HIGH; HIGH vs. LOW; LOW vs. LOW) during close games were analyzed using linear regression analysis. Results showed that Starting Quarter Score reported significant effects in all the Quality of Opposition game contexts for all game quarters, and second, third, and fourth quarter. Conversely, game location showed that playing away increased by 0.68 goals in the second quarter of HIGH vs. HIGH games, and increased by 0.81 goals in the fourth quarter of LOW vs. LOW games, with respect to counterparts. Therefore, Quality of Opposition, Game Location, and Starting Quarter Score have important effects on women’s water polo game dynamics especially during the first half of close games. Finally, this study provides useful references for coaches’ plans (i.e., effective strategic approach/training proposals) to improve their women players’ performance.

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 score0.005
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.269
Teacher spread0.261 · 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

Citations43
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Performance Analysis in SportSame topicSports Performance and TrainingFrench-language works237,207