Effects of Tapering on Performance
Bibliographic record
Abstract
PURPOSE: The purpose of this investigation was to assess the effects of alterations in taper components on performance in competitive athletes, through a meta-analysis METHODS: Six databases were searched using relevant terms and strategies. Criteria for study inclusion were: participants must be competitive athletes; a tapering intervention must be employed providing details about the procedures used to decrease the training load; use of actual competition or field-based criterion performance; inclusion of all necessary data to calculate effect sizes. Data sets reported in more than one published study were only included once in the present analyses. Twenty seven of 182 potential studies met these criteria and were included in the analysis. The dependent variable was performance, and the independent variables were the decrease in training intensity, volume and frequency, as well as the pattern of the taper and its duration. Pre-post taper standardized mean differences in performance were calculated, and weighted according to the within-group heterogeneity to develop an overall effect. RESULTS: The optimal strategy to optimize performance is a tapering intervention of two weeks duration (overall effect = 0.59 ± 0.33, p<0.001), where the training volume is exponentially decreased by 41 - 60% (overall effect = 0.72 ± 0.36, p<0.001), without any modification of either training intensity (overall effect = 0.33 ± 0.14, p<0.001) or frequency (overall effect = 0.35 ± 0.17, p<0.001). CONCLUSIONS: A two-week taper during which training volume is exponentially reduced by 41–60% appears to be the most efficient strategy to maximize performance gains. This meta-analysis provides a framework that can be useful for athletes, coaches and sport scientists to optimize their tapering strategy.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.024 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".