Overestimation of maximal aerobic speed by the Université de Montréal track test and a 1500-m-time trial in soccer
Bibliographic record
Abstract
Introduction: Maximal aerobic speed (MAS), usually measured by cardiopulmonary exercise testing (CPET) on a treadmill, is gaining popularity in soccer to determine aerobic performance. Several field tests are used to estimate MAS, although, gold standard methods are still not clarified. Therefore, this work aims 1) to compare two different CPET based methods to assess MAS and 2) to investigate the convergent validity of two common field tests to estimate MAS in soccer. Methods: Thirteen trained male soccer players completed an CPET on a treadmill to determine two VO2-kinetic based definitions of MAS (MASPlateau = speed at onset of VO2-plateau = gold standard; MAS30s = first speed of 30-s-interval of VO2max), the Université de Montreal Track Test (UMTT; VUMTT = speed of the last stage), and a 1500-m-time trial (1500-m-TT; V1500m = average speed). MASPlateau, MAS30s, VUMTT, and V1500m were compared using ANOVA. Additionally, limits of agreement analysis (LoA), Pearson’s r, and ICC were calculated between tests. Results: MAS30s, VUMTT, and V1500m significantly overestimated MASPlateau by 0.99 km/h (ES = 1.61; p < 0.01), 1.61 km/h (ES = 2.03; p < 0.01) and 1.68 km/h (ES = 1.77; p < 0.01), respectively, with large LoA (-0.21 ≤ LoA≤3.55), however with large-to-very large correlations (0.65 ≤ r ≤ 0.87; p ≤ 0.02; 0.51 ≤ ICC≤ 0.85; p ≤ 0.03). Discussion: The overestimation and large LoA of MASPlateau by all estimates indicate that 1) a uniform definition of MAS is needed and 2) the UMTT and a 1500-m-TT seem questionable for estimating MAS for trained soccer players on an individual basis, while regression equations might be suitable on a team level. The results of the present work contribute to the clarification of acquisition of MAS in 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".