Relationship between physical fitness and game-related statistics in elite professional basketball players: Regular season vs. playoffs
Bibliographic record
Abstract
AIMS This study aimed to verify th erelation ship between of anthropometric and physical performance variables with game-related statistics in professional elite basketball players during a competition. METHODS Eleven male basketball players were evaluated during 10 weeks in two distinct moments (regular season and playoffs). Overall, 11 variables of physical fitness and 13 variables of game-related statistics were analysed. RESULTS The following significant Pearson’scorrelations were found in regular season: percentage of fat mass with assists (r = -0.62) and steals (r = -0.63); height (r = 0.68), lean mass (r = 0.64), and maximum strength (r = 0.67) with blocks; squat jump with steals (r = 0.63); and time in the T-test with success ful two-point field-goals (r = -0.65), success ful free-throws (r = -0.61), and steals (r = -0.62). However, in playoffs, only stature and lean mass maintained these correlations (p ≤ 0.05). CONCLUSIONS The anthropometric and physical characteristics of the players showed few correlations with the game-related statistics in regular season, and these correlations are even lower in the playoff games of a professional elite Champion ship, wherefore, not being good predictors of technical 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".