Measuring Lateral Shuffle and Side Cut Performance
Bibliographic record
Abstract
Lateral shuffle and side cut (SSC) movements are defensive basketball movements where movement speed is critical to performance. The purpose of this study was to compare SSC data obtained using timing lights with motion capture system data and to determine the most appropriate method for measuring SSC performance. Shuffle time data were recorded using both timing lights and a motion capture system while 9 male subjects performed 2 different SSC movement sets, with and without controlling for arm movements, which may influence performance times. Shuffle and side cut times and SSC displacements were used to calculate mean shuffle velocity for each trial. The SEs for the motion capture system were estimated for SSC times (± 4.2 milliseconds; ~0.24% of mean shuffle time) and velocities (± 5.5 mm · s; ~0.24% of mean shuffle velocity), respectively, indicating high levels of precision. Timing light movement time variability was significantly higher during the uncontrolled (SD = 42 milliseconds) when compared with the controlled (SD = 9 milliseconds, p < 0.001) condition, indicating a significant reduction in variability by controlling non-performance-related variability such as arm movement. A significant positive correlation was found between SSC time and SSC displacement (r = 0.42) indicating that performance times were dependent on displacement. Furthermore, the variance in motion-captured SSC velocity was significantly smaller than the variance in velocity determined using timing lights (p < 0.05). We concluded that motion-captured SSC velocity data reduced systematic errors and non-performance-related movement variability and, therefore, was better able to reflect true performance. As true performance variability in human movement provides important information, the presented method for calculating SSC velocity in this study is recommended for assessing SSC performance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".