Exploring the effects of substituting basketball players in high-level teams
Bibliographic record
Abstract
Substituting basketball players during competition is a key process to optimise collective performance. Available research on this topic is scarce, probably due to the difficulty in isolating these effects; thus, the aim of this study was to identify the temporal effects of substitutions in basketball (Spanish professional basketball league). The sample was composed of 1118 substitutions gathered from 21 basketball games. The analysed variables were coach-controlled (player and team's personal fouls, player in and player out roles, player's in and out minutes on-court and timeout situation); on-court (foul committed, free throws, 2- and 3-point field-goal effectiveness) and situational variables (scoreline, quality of opposition, game location and game quarter). The results showed positive scoring performances after the substitution for all the analyses. During the first quarter, there were significant effects for fouls committed, scoreline and game location after the substitution. The player's out personal fouls, free-throw effectiveness, player in, minutes on-court player in, timeout situation and 3-point field-goal effectiveness were significant during the second quarter. The team's personal fouls, game location, and scoreline were identified as important in the third quarter. The fourth quarter did not show significant effects on the independent variables. Current findings allow optimising coaches' plans and team management of on-court and bench players throughout the game.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".