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Record W2195705244 · doi:10.1260/1747-9541.10.5.815

An Analysis of Penalty Kicks in Elite Football Post 1997

2015· article· en· W2195705244 on OpenAlexaff
Kristine Dalton, Michel Guillon, Shehzad A. Naroo

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2015
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTournamentFootballChampionshipEliteMatch playFootball playersWorld championshipPsychologyOperations researchMathematicsPolitical scienceLawPhysical therapyPolitics

Abstract

fetched live from OpenAlex

The penalty kick in football is a seemingly simplistic play; however, it has increased in complexity since 1997 when the rules changed allowing goalkeepers to move laterally along their goal line before the ball was kicked. Prior to 1997 goalkeepers were required to remain still until the ball was struck. The objective of this study was to determine the importance of the penalty kick in the modern game of football. A retrospective study of the 2002, 2006 and 2010 World Cup and the 2000, 2004 and 2008 European Championship tournaments was carried out, assessing the importance of the penalty kick in match play and shootouts and the effect of the time of the game on the shooter's success rate. This study demonstrated the conversion rate of penalties was 73% in shootouts and 68% in match play. Significantly more penalties were awarded late in the game: twice as many penalties in the second half than the first and close to four times as many in the fourth quarter vs. the first. Teams awarded penalty kicks during match play won 52%, drew 30% and lost 18% of the time; chances of winning increased to 61% if the penalty was scored, but decreased to 29% if missed. Teams participating in either the World Cup or European Championship final match had roughly a 50% chance of being involved in a penalty shootout during the tournament. Penalty shots and their outcome significantly impact match results in post 1997 football.

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)0.003
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 score0.011
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.028
GPT teacher head0.354
Teacher spread0.326 · 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

Citations29
Published2015
Admission routes1
Has abstractyes

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