A video-based time-motion analysis of an elite male basketball team during a season: game demands according to player position, game quarter, and actual time played
Bibliographic record
Abstract
To investigate differences in the game demands of top-level professional basketball players based on their position and the quarter of the game. Thirteen elite male players were assessed on their positions (point guard, guard, forward, and centre) over different quarters (Q1-Q4) during 15 home games. A multivariate analysis of variance was performed using role and quarters as predictors to assess distances, speeds, and accelerations. The guard and point guard covered more distance than the centre and forwards (p > 0.001). The average distance covered was higher (p < 0.01) in Q4 than in the other quarters. Both speed and the percentage of time spent in the jogging to max speed range decreased significantly from Q1 to Q4, while time spent standing and walking tended to increase from Q1 to Q4. The point guard spent the highest percentage of total time performing major accelerations followed by the guard, while the forwards and centre spent less time performing accelerations. Overall, 22.8 ± 0.7% of total playing time was spent performing major accelerations, which decreased from Q1 to Q4. These findings suggest that the positions of elite basketball players vary in terms of the activities demands they place on players, underscoring the need for individualised role-based conditioning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".