From Novice to Expert: How Expertise Shapes Motor Variability in Sports Biomechanics—a Scoping Review
Bibliographic record
Abstract
With expertise, athletes develop motor strategies that enhance sports performance or reduce functional costs. Motor variability is known as a relevant way to characterize these strategies in athletes with different levels of expertise. The aim of this scoping review is to gather and discuss the latest advances in the impact of expertise on motor variability during sports-related tasks. A search encompassing three databases, Medline, SportDiscus, and Academic Search Complete, was performed. Our research methodology included three core themes: motor variability, laboratory instruments, and sports. Motor variability metrics (e.g., standard deviation and approximate entropy) and laboratory instruments (e.g., motion capture system, EMG, and force plate) were compiled. Athletes' expertise was defined by the time of deliberate practice, the performance results, or the level in which they performed. Overall, 48 of the 59 included studies determined that higher-skilled athletes had lesser motor variability than lower-skilled athletes. This difference in motor variability between skill levels was present within individual athletes (intra-individual) and between athletes (inter-individual). This result was independent of the criteria used to define expertise, the type of instrumentation used, and the metrics used to quantify motor variability.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".