Effect of Match Factors on the Running Performance of Elite Female Soccer Players
Bibliographic record
Abstract
Trewin, J, Meylan, C, Varley, MC, Cronin, J, and Ling, D. Effect of match factors on the running performance of elite female soccer players. J Strength Cond Res 32(7): 2002-2009, 2018-The purpose of this study was to examine the effects of match factors on the match running of elite female soccer players. Players from the same women's national team (n = 45) were monitored during 47 international fixtures (files = 606) across 4 years (2012-2015) using 10-Hz global positioning system devices. A mixed model was used to analyze the effects of altitude, temperature, match outcome, opposition ranking, and congested schedules. At altitude (>500 m), a small increase in the number of accelerations (effect size [ES] = 0.40) and a small decrease in total distance (ES = -0.54) were observed, whereas at higher temperatures, there were decreases in all metrics (ES = -0.83 to -0.16). Playing a lower ranked team in a draw resulted in a moderate increase in high-speed running (ES = 0.89), with small to moderate decreases in total distance and low-speed running noted in a loss or a win. Winning against higher ranked opponents indicated moderately higher total distance and low-speed running (ES = 0.75), compared with a draw. Although the number of accelerations were higher in a draw against lower ranked opponents, compared with a win and a loss (ES = 0.95 and 0.89, respectively). Practitioners should consider the effect of match factors on match running in elite female soccer.
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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 | 0.001 | 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".