Movement Patterns of Women's National Team Rugby Players Across a Series of Matches
Bibliographic record
Abstract
ABSTRACT: Betelli, MT, Loturco, I, Ramos, MS, Mercer, VP, Moura, TBMA, Oliveira, LP, Bishop, C, and Pereira, LA. Movement patterns of women's national team rugby players across a series of matches. J Strength Cond Res 40(3): e249-e255, 2026-Given the increasing level of competitiveness of women's rugby union, it is crucial to better understand the physical demands of female players during match play. In this study, we examined the locomotor activities during 6 consecutive matches played by the Brazilian Women's National Team Rugby Union players to identify positional and temporal variations in distance and acceleration-based parameters. Thirty-nine women rugby players (20 backs and 19 forwards) participated in the study. Data were collected using a global positioning system, with a 10 Hz acquisition frequency, to analyze the players' locomotor activities during 6 international matches (i.e., total distance [TD], distance covered at different intensity zones, acceleration load, acceleration density, high metabolic load distance, and player load [PL]). The TD covered during the matches averaged 5,425 ± 629 m. Total distance, distances >20 km·h -1 , acceleration load, and acceleration density were higher for backs in comparison with forwards (11, 11, 12, and 63% difference, respectively; p < 0.001). Higher TD, acceleration load, acceleration density, and PL values were observed in the first quarter compared with subsequent quarters (11, 13, 15, and 14% difference, respectively; p < 0.001). Coaches should consider these findings to design tailored training for backs and forwards. Regardless of their positions, players cover greater distances at varying speeds in the first quarter than in the subsequent quarters. These outcomes reinforce the need for specific training strategies to maintain the level of performance in later quarters and during critical moments of the match.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".