Performance analysis of game dynamics during the 4th game quarter of NBA close games
Bibliographic record
Abstract
The purpose of the present study was to identify which situational variables and technical-tactical variables allow to discriminate home and away teams during the fourth quarters of close NBA basketball games according to the scoring trends. The sample comprised 48 men’s NBA close games (below 10 points of difference) during the 2013-14 regular season. The situational variables (starting quarter score, game location, and quality of opposition) and technical-tactical variables (game situation, defense type, outcome, shot type, technical execution, defense on the shooter, play events and mean played clock-time) were studied. The main results showed that the variables that best differentiated home and away teams were: i) starting quarter score, free-throws scored, 3-point fieldgoals from central positions, and defensive fouls during balanced scoring trends between teams; ii) game location, quality of opposition, ball possession success, 2-point field-goals inside and outside the central positions, 3-point field-goals from central and right court positions, and defensive rebounds during home teams’ positive scoring trends; and iii) starting quarter score, game location, quality of opposition, ball possession success, alley-hop, the stop-shot, the 1 defense on the shooter, the 2 or >2 defense on the shooter and block 1 in defense during away teams’ positive scoring trends. The identified trends allow improving the game understanding during last stages of close games and help the coaches to plan practice sessions and deciding better in competition.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".