Predicting Performance and Injury Resilience From Movement Quality and Fitness Scores in a Basketball Team Over 2 Years
Bibliographic record
Abstract
The purpose of this study was to see if specific tests of fitness and movement quality could predict injury resilience and performance in a team of basketball players over 2 years (2 playing seasons). It was hypothesized that, in a basketball population, movement and fitness scores would predict performance scores and that movement and fitness scores would predict injury resilience. A basketball team from a major American university (N = 14) served as the test population in this longitudinal trial. Variables linked to fitness, movement ability, speed, strength, and agility were measured together with some National Basketball Association (NBA) combine tests. Dependent variables of performance indicators (such as games and minutes played, points scored, assists, rebounds, steal, and blocks) and injury reports were tracked for the subsequent 2 years. Results showed that better performance was linked with having a stiffer torso, more mobile hips, weaker left grip strength, and a longer standing long jump, to name a few. Of the 3 NBA combine tests administered here, only a faster lane agility time had significant links with performance. Some movement qualities and torso endurance were not linked. No patterns with injury emerged. These observations have implications for preseason testing and subsequent training programs in an attempt to reduce future injury and enhance playing performance.
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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 | 0.003 | 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".