Minimal Number of Events Required for Acceleration–Speed Profiling in Elite Women’s Soccer
Bibliographic record
Abstract
PURPOSE: To determine the minimum number of events (training or matches) for producing valid acceleration-speed (AS) profiles from global navigation satellite system (GNSS) data. METHODS: Nine elite female soccer players participated in a 4-week training camp consisting of 19 events. AS profile metrics calculated from different combinations of athlete events were compared to force-velocity (FV) profile metrics from 2 × 40-m stand-alone sprint effort trials, using the same GNSS 10-Hz technology. Force-velocity profiles were calculated, from which AS profiles were obtained. AS profiles from training and matches were generated by plotting acceleration and speed points and performing a regression through the maximal points to obtain the AS metrics (theoretical maximal speed, x-intercept [in meters per second], theoretical maximal acceleration, y-intercept [in meters per second squared], and the slope per second). A linear mixed model was performed with the AS metrics as the outcome variables, the number of events as a fixed effect, and the participant identifier as a mixed effect. Dunnett post hoc multiple comparisons were used to compare the means of each number of event grouping (1-19 events) to those estimated from the dedicated sprint test. RESULTS: Theoretical maximal speed and theoretical maximal acceleration means were no longer significantly different from the isolated sprint reference with 9 to 19 (small to trivial differences = -0.31 to -0.04 m·s-1, P = .12-.99) and 6 to 19 (small differences = -0.4 to -0.28 m·s-2, P = .06-.79) events, and the slopes were no longer different with 1 to 19 events (trivial differences = 0.06-0.03 s-1, P = .35-.99). CONCLUSIONS: AS profiles can be estimated from a minimum of 9 days of tracking data. Future research should investigate methodology resulting in AS profiles estimated from fewer events.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".