Peak Locomotor Intensity in Elite Handball Players: A First Insight Into Player Position Differences and Training Practices
Bibliographic record
Abstract
ABSTRACT: Fleureau, A, Rabita, G, Leduc, C, Buchheit, M, and Lacome, M. Peak locomotor intensity in elite handball players: a first insight into player position differences and training practices. J Strength Cond Res 37(2): 432-438, 2023-The aims of the study were to (a) describe the peak locomotor intensity sustained during handball matches and (b) compare them with small-sided games (SSGs) programmed during training in elite handball players. Small-sided game ( n = 342) and match ( n = 121) data were collected among 11 players (25 ± 7 years, 191 ± 8 cm, 89 ± 12 kg) belonging to an elite French Handball team. Players' locomotor activity was recorded using 20-Hz Local Positioning System. Peak total (TD[m]) and high-speed running distance (HS[m]) and mechanical load (Accel'Rate [a.u.]) were calculated during different time periods (1-15 minutes different rolling averages). A plot of log (locomotor variables) against log (time) allowed to obtain a straight line with a slope and an intercept for each variable. Between-position differences during matches and difference between matches and SSGs were assessed with linear mixed model and magnitude-based decisions. Almost certainly higher peak locomotor intensity (intercept) was found in Wingers (TD: 156 ± 13; HS: 96 ± 12; Accel'Rate: 13 ± 3) compared with other playing positions for TD (Back players: 127 ± 10; Pivots: 136 ± 13), HS (Back players: 56 ± 9; Pivots: 57 ± 11), and Accel'Rate (Back players: 11 ± 2; Pivots: 11 ± 2). However, no clear between-position difference was found regarding the slope. Additionally, none of the SSGs format produced an overload in peak locomotor intensity in comparison with matches (TD: 138 ± 16; HS: 66 ± 20; Accel'Rate: 12 ± 2). Because reaching the peak locomotor intensity sustained during match is not possible using SSGs, practitioners should consider using isolated conditioning drills (e.g., short or long intervals, repeated sprints). Moreover, specific attention should be paid for Winger's work supplementation because they present the highest peak locomotor intensity in the team.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".