Global Positioning System Analysis Of Positional Locomotive Training Demands In Women’s Varsity Rugby Union
Bibliographic record
Abstract
Rugby union is a full contact, intermittent-intensity sport that requires a combination of power, agility, speed and endurance. In positional gameplay, forwards compete in high force-plays (scrums, rucks, mauls), while backs typically execute sprint and agility focused activities. PURPOSE: To determine the locomotive demands of female varsity rugby union athletes in regular season training, and to assess positional dissimilarities in these demands, using global positioning system (GPS) technology. METHODS: Wearable GPS technology was used to collect spatial and temporal data of female varsity rugby athletes (20.2 ± 2.4 yr) during three regular season training sessions, each ~2 hr in length. Sessions were categorized as endurance training (ET), skill training (ST) or game-based training (GBT). Movements were catalogued into 5 speed zones. Player positions were classified as forward (n=14) or back (n=15). RESULTS: Backs traveled greater total distances on all practice days than forwards, and in ET backs traveled greater distances per minute than forwards (50.07 ± 6.67 m; 47.95 ± 16.64 m, p < 0.01). Positional work-to-rest ratio was higher in forwards vs. backs in ET only (0.244 ± 0.158; 0.230 ± 0.051, p < 0.05). Backs traveled greater total distances in high-intensity zones than forwards (7.23 ± 4.34 %; 4.32 ± 2.50 %, p < 0.05) during GBT. In all practice sessions, significant differences between positions were observed in time spent and distance traveled within the 5 speed zones. CONCLUSION: Locomotive training demands for back positions are of higher intensity in GBT, and greater volume on all practice days, compared to forward positions. ET was the only session that exhibited a significantly higher work-to-rest ratio for forwards. Though GPS technology is effective for quantifying linear movements, it is not capable of quantifying athlete exertion in low-speed, high-power movements, performed by forwards in rugby union. Research funded by a grant from NSERC, Canada.
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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 | 0.000 |
| 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".