Normalizing Acceleration and Power in Elite Soccer With Acceleration–Speed Profiles: A Case Study of Game Segment, Position, and Goal Differential
Bibliographic record
Abstract
PURPOSE: Acceleration-speed (AS) profiling provides a novel way to quantify soccer players' maximum running ability without requiring dedicated sprint tests. This study explored how normalizing player effort with AS profiles could reveal unique patterns of changes in effort compared with nonnormalized absolute values during distinct goal differential conditions throughout matches and across positions. METHODS: AS profiles were developed from global navigation satellite system sensor data from 3 years of match play from a women's national soccer team. Acceleration and power data were then grouped into low, moderate, high, and very high speed domains using either nonnormalized maximum values or normalized values based on individualized AS profiles. Separate linear mixed model analyses were carried out for normalized and nonnormalized data. RESULTS: The analysis revealed that when examining changes in acceleration effort based on goal differential, both normalized and nonnormalized values showed a general increase in effort when either in a draw, or winning in the first half, and a general trend in sustained effort when losing in the first half or losing/drawn throughout the match. Furthermore, differences were mostly displayed at high to very high running-speed domains for normalized and moderate- to low-speed zones for nonnormalized metrics. CONCLUSIONS: These findings highlight the value of using individual AS and power profiles to normalize effort to facilitate investigation of player- and position-specific differences and reveal important positional behaviors displayed when in draw, losing, or winning states from first to second halves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".