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Record W2037108384 · doi:10.1080/02640414.2010.514281

Laterality differences in elite ice hockey: An investigation of shooting and catching orientations

2010· article· en· W2037108384 on OpenAlexaff
Jared Puterman, Joseph Baker, Jörg Schorer

Bibliographic record

VenueJournal of Sports Sciences · 2010
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsYork University
FundersU.S. National Library of Medicine
KeywordsLateralityIce hockeyPsychologyEliteApplied psychologyPhysical medicine and rehabilitationDevelopmental psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Little is known about the implications of motor asymmetries for skilled performers in dynamic, time-constrained, team-based activities such as ice hockey. Three studies were conducted to examine laterality differences in ice hockey. Study 1 investigated laterality distributions across three leagues of increasing calibre. Among skating players, skill level was related to changes in laterality patterns based on position, while a significant increase in the proportion of left-catching goaltenders was found across the levels of competition. Study 2 examined laterality differences through a 90-year retrospective analysis of player performance measures within an evolving system. Regression analysis indicated right shot preferences were associated with scoring more goals, while left shot preferences were related to assisting more goals. Among goaltenders, right-catching preferences were associated with an increased save percentage compared with left-catching goaltenders. In Study 3, player-goaltender shootout interactions revealed left shooters to be less successful against right-catching goaltenders. Results suggest ice hockey supports models of skilled perception, and provide new information in the area of laterality and strategic frequency-dependent effects in ice hockey.

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)0.001
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 score0.346
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.043
GPT teacher head0.309
Teacher spread0.265 · 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

Citations36
Published2010
Admission routes1
Has abstractyes

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