Load Management Among Professional Hockey Goalies: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Load management is a sports science concept describing the execution of well-established training principles to measure athletic workloads and enhance performance. The term 'load management' has become common in sports media to refer to a much wider range of scenarios, including the idea that by limiting regular season workload for athletes, their health and playoff performance will improve. Varying links between load and performance have been demonstrated in baseball and soccer. The purpose of this study was to objectively assess the impact of regular season workload on postseason performance among National Hockey League (NHL) goalies. HYPOTHESIS: NHL goalies with lighter regular season workloads will perform better in postseason appearances. STUDY DESIGN: Retrospective cohort. LEVEL OF EVIDENCE: Level 3. METHODS: NHL goalies with a minimum of 20 regular season games played and 3 playoff game appearances in the same season since 2013-2014 were eligible for inclusion. All regular season and postseason workload and performance metrics were collected from publicly available statistical databases. Workload outcomes included games started, minutes played, and shots faced. Performance outcomes included goals against average, save percentage, goals saved above average, and quality start percentage. Multivariable linear regression was used to determine whether regular season workload predicted postseason performance, when controlling for age and injury status. RESULTS: = 0.26). CONCLUSION: Based on data from 6 full seasons, there is no evidence to support a specific regular season game limit among NHL goalies with the aim of improved performance. CLINICAL RELEVANCE: Individualized workload plans may be more appropriate than a single league-wide standard.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".