Inertial movement demands comparison between winning and losing quarters in youth basketball players
Bibliographic record
Abstract
The aim of the present study was to compare the relative external load demands of youth male basketball players between winning and losing quarters and across quarters characterized by different score differentials (close, balanced, and unbalanced). Data were collected from 11 male basketball players Under 18 on the same team during 21 official games over two competitive seasons. External load demands of each quarter were recorded using microsensors to derive values for the following variables: PlayerLoad (PL), frequency of total and high-intensity accelerations (ACC), jumps, decelerations (DEC), changes of direction (COD), and total inertial movement analysis (IMA) events combined. K-means clustering was applied to the score-differential values to derive three data-driven categories (close, balanced, and unbalanced). Subsequent comparisons between winning and losing quarters and across these score-differential categories under winning or losing quarters were examined using linear mixed-effects models. Standardized Cohen’s effect sizes were computed to quantify the magnitude of all pairwise contrasts. For all variables, the mixed-effects models showed no statistically significant differences between winning and losing quarters (all p > 0.05). Within losing quarters, small but statistically significant differences were found between the close and balanced quarters for total IMA (p < 0.001, d = 0.36) and COD (p < 0.001, d = 0.35). No significant differences were observed across score-differential categories within the winning quarters (all p > 0.05). Overall, these results indicate that neither quarter outcome nor score margin substantially affects total or high-intensity external load, highlighting the need for future research to examine the influence of other contextual factors—such as opponent quality and game location—on physical demands in youth basketball players.
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".