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Record W4401622363 · doi:10.3389/fspor.2024.1418713

The relationship between off-ice testing and on-ice performance in male youth Ice hockey players

2024· article· en· W4401622363 on OpenAlexafffund
Mark S. Rice, Darren E. R. Warburton, Alejandro Gaytán-González, Veronica Jamnik, Kai Kaufman, Declan R. D. Warburton, Michael Souster, Shannon S. D. Bredin

Bibliographic record

VenueFrontiers in Sports and Active Living · 2024
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsYork UniversityUniversity of the Fraser ValleyUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsIce hockeyPsychologyPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Introduction: Ice hockey demands a unique blend of physical fitness and skill, necessitating a comprehensive understanding of the factors influencing on-ice performance. The present study was designed to examine the relationship between off-ice and on-ice performance measures in male, youth, ice hockey players. Methods: Eleven minor hockey players (Age = 9.8 ± 1.1 years) participated in two testing days: (1) off-ice and (2) on-ice assessments. Off-ice assessments included maximal aerobic power, anaerobic fitness, muscular strength (handgrip and single leg squat), muscular endurance (curl-ups and push-ups), muscular power (standing long jump and vertical jump), and 30 m sprinting speed and acceleration. On-ice testing included a 15.2 m maximum speed test, a 6.1 m acceleration test with a continuation into a 47.9 m top speed test, an agility cornering S turn test, and a shot velocity test. Results: Twenty-four out of 33 off-ice variables were significantly correlated with at least one of the 11 on-ice performance variables. From those 24, 10 were included as predictors for at least one of the on-ice performance variables. Each model was composed of either one or two predictors, where the most common predictors were 30 m Run - Split (6.1 m) Sprint time and 30 m Run - Total (30 m) Sprint Max speed (included in four out of 11 models each). The prediction formulas R2 and coefficient of variation ranged from 0.63% to 0.96% and 1.2% to 15.3%, respectively. Discussion: Diverse off-ice measures of aerobic fitness, anaerobic power, muscular strength, power, and endurance, and sprinting speed, acceleration, and agility are predictive of on-ice performance. The insights gained from this study contribute to the refinement of assessment protocols, fostering a comprehensive approach to optimizing player performance and potential. Understanding the connection between objective off-ice testing and on-ice performance can support tailored training programs and player development in male youth 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 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)0.000
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.263
Teacher spread0.228 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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