Variability in locomotor activity in a female junior international hockey team
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to estimate between and within-athlete variabilities, to form threshold values for interpreting changes in locomotor activity in a female junior international hockey team. DESIGN: Thirty-three female international hockey players (age: 20 ± 0.9 year; height: 166.1 ± 4.4 cm; body mass: 62.5 ± 6.2 kg) competed in thirty-four junior international hockey games.. METHODS: Data were monitored through global positioning system technology. Locomotor activity was quantified as relative distances covered by players for each quarter at three speed zones (<16 km/h, 16-19.9 km/h, >20 km/h). Data were analysed using linear mixed models, accounting for the fixed effects of position (defenders, n = 13; midfielders, n = 8; forwards, n = 12), game result, type, location, and opposition rank. Variabilities are summarised as coefficients of variation (%CV). RESULTS: Variabilities in athletes' game-to-game and quarter-to-quarter locomotor activity differed substantially between lower (<16 km/h) and higher (16-19.9 km/h and >20 km/h) speed zones. Game-to-game variability of low-speed movement (<16 km/h) was 5%; whereas, corresponding variabilities for high- (16-19.9 kmh) and very high-speed (>20 km/h) running were 22% and 34%, respectively. Within-athlete quarter-to-quarter variability increased for each speed zone, and was greatest for midfielders in low-speed movement and for defenders in high and very high-speed running. CONCLUSIONS: The game-to-game variabilities inform thresholds for estimating changes in performance over time. Caution is required when interpreting such data, and coaches should carry out estimates in their specific contexts. Additionally, quarter-to-quarter variabilities in high- and very high-speed running for junior international hockey players outline position specific differences informing training practices to better prepare players for game demands.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".