Pre-season block periodisation: Hypertrophy and strength-power effects on youth Australian footballer jump profiles
Bibliographic record
Abstract
The aim of this study was to monitor changes in vertical jump force-velocity-power (FVP) profiles following hypertrophy (HYP) and combined maximal strength and power (CMSP) training in youth Australian Football athletes. This study implemented a single group, within-subject, non-randomised crossover design, prescribing sequential HYP and CMSP training to improve countermovement jump (CMJ) performance measured with FVP profiles. Twenty-one males (age: 17.2 ± 0.7 years; height: 183.8 ± 6.6 cm; body mass: 76.9 ± 6.2 kg) completed pre-testing, four-week HYP-mesocycle, midpoint-testing, four-week CMSP-mesocycle, and post-testing. The 8-weeks training comprised 2-sessions per week (16 sessions total). Pre-mid-post-testing involved unloaded (bodyweight) and loaded (50 kg) CMJ's on dual-force plates to create FVP profiles using Samozino's method to calculate raw (mean concentric force, velocity, and power) and theoretical (maximal force [F0], maximal velocity [V0], force-velocity relationship slope [Sfv], and maximal peak power [Pmax]) variables. One-way ANOVA assessed changes in all variables. Unloaded and loaded CMJ heights significantly increased after HYP and CMSP-mesocycles (post-testing, p ≤ 0.001), with only loaded CMJ height improving after HYP-training (mid-testing, p = 0.034). There was no change in raw variables after HYP training (mid-testing). However, mean concentric force, velocity, and power significantly increased in unloaded and loaded CMJs following HYP and CMSP-mesocycles (post-testing, p ≤ 0.001). Of the theoretical variables, only F0 significantly improved after HYP training (mid-testing, p = 0.023), yet after HYP and CMSP training, F0 and Pmax were significantly improved (post-testing, p < 0.001). Sequential HYP and CMSP block periodised training significantly improved unloaded and loaded CMJ height, all raw FVP variables, and theoretical F0 and Pmax in youth Australian Footballers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".