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Penalty shots in International water polo: Regular opportunities with robust success despite a greater impact on the game under current rules

2011· article· en· W2707572904 on OpenAlexaboutno aff
KVH SMITH

Bibliographic record

VenueInternational Journal of Performance Analysis in Sport · 2011
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsWater poloSituational ethicsClosenessQuarter (Canadian coin)Outcome (game theory)Ranking (information retrieval)Operations researchComputer sciencePsychologySocial psychologyEconomicsMathematicsMicroeconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the effect of changes in the official water polo regulations, effective since 2005, on the impact, frequency and success of penalty shots relative to the game situation. Analysis of official records from the 192 games of the 2007 and 2009 World Championships compared with 216 games of 1998 and 2001 tournaments revealed a greater impact of penalties on the game under current rules. Penalties more than doubled, were distributed across more games, contributed double to the proportion of all goals scored, and affected the outcome of 20% of games. However, penalty shot success rate remained robust (77%) and was unrelated to aspects of the game situation that prescribe the importance of a goal to winning: the closeness of the score, the quarter or criticality of the game. Analyses of the 438 penalties from all games established that the frequency of occurrence and success of penalty shots did not differ from that expected under any particular situational combination of game criticality, quarter or closeness. The penalty shot thus remains a regular opportunity, unrelated to the game situation describing the importance of a goal, consistently executed with high success by élite water polo players.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.086
GPT teacher head0.318
Teacher spread0.232 · 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.

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

Citations9
Published2011
Admission routes1
Has abstractyes

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