Evaluating NBA player performance using bounded integer data envelopment analysis
Why this work is in the frame
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Bibliographic record
Abstract
Data envelopment analysis (DEA) assumes that data are continuous. However, there are situations where data are integers and bounded. For example, in basketball games, the total number of points that a player has scored is an integer and cannot exceed three times of the number of point field goals that a player has attempted. Without modelling the correct data type, the DEA results can be biased and erroneous. The current paper applies a bounded integer DEA model to evaluating the performance of National Basketball Association (NBA) players when bounded integer data exist. As a result, we correctly capture the data type. The current study also develops a super-efficiency measure under the bounded integer data. The bounded integer data problem is illustrated with data involving a set of NBA shooting guards in the 2013–2014 season.
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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 | 0.032 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it