Association Between Internal Training Load And Match Outcome For Male And Female Varsity Ice Hockey Players
Bibliographic record
Abstract
It has been recognized that establishing a relationship between training load and performance could provide useful insight for athletes during games. Despite growing interest in monitoring internal training loads over the past decade, there has been little investigation into the relationship between training load and match performance measures. Purpose: To investigate the association between internal training load, quantified using Banister’s training impulse (TRIMP) and sessional rating of perceived exertion (sRPE), and match outcome in male and female varsity ice hockey players across a season. Methods: This was a prospective cohort study and included 27 males (22 ± 1 y, 85.9 ± 5.4 kg) and 23 females (20 ± 1 y, 68.0 ± 6.9 kg). Training loads (exposure) were categorized based on a weekly micro-cycle, with training sessions labelled based on match day (MD), starting with five days preceding MD (MD-5) to one day preceding MD (MD-1). Performance (outcome) was defined as match win (success) or loss. Conditional logistic regression analyses between training load on each day and match outcome were performed for TRIMP and sRPE. Training loads were then quartiled, and binomial logistic regression analyses were performed, and odds ratios (OR) with 95% confidence intervals (CI) were presented. Significance was accepted at p < 0.05. Results: For all players, sRPE on MD-5 (OR: 1.006, CI: 1.004-1.009), MD-4 (OR: 1.004, CI: 1.002-1.006), and MD-3 (OR: 1.002, CI: 1.001-1.004) were significantly associated with game outcome. Furthermore, TRIMP on MD-5 (OR: 1.030, CI:1.016-1.044) and MD-3 (OR: 1.012, CI: 1.005-1.019) were significantly associated with game outcome. For males, training on MD-4 and MD-3 with the highest sRPE and TRIMP resulted in significantly greater odds of success, while training on MD-1 with the lowest sRPE and TRIMP significantly predicted a win. For females, MD-5 (sRPE and TRIMP) and MD-4 (sRPE) with the highest load resulted in significantly greater odds of success, and MD-1 with the lowest sRPE and TRIMP was significantly predictive of a win. Conclusion: Micro-cycle periodization of training sessions with higher internal loads early in the week and tapering to lower internal loads immediately preceding game day was associated with a win in varsity female and male ice hockey. Supported by Mitacs and PepsiCo.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".