Μέτρηση της αποδοτικότητας των ομάδων του ΝΒΑ : η επίδραση στην αλλαγή του salary cap
Bibliographic record
Abstract
The aim of this dissertation is to use a two-stage DEA approach to perform an efficiency analysis of the 30 teams in the NBA. Particularly, our purpose is to estimate efficiency through a two-stage DEA process for NBA teams due to the increase of salary cap, in the first part and in second, the separation of teams based on the Conference (West-East) to which they belong, we estimate metafrontier and finding technology gaps. We decompose the overall team efficiency into two additive efficiencies: the first-stage salary cap efficiency that measures the effectiveness of transforming payrolls to on-court performance and the second-stage on-court efficiency that measures the efficacy of transforming players’ on-court performance to a better winning rate and higher revenue. For this reason, 30 teams in the NBA are being tested for 18 seasons, from 2001-2002 season to 2018-2019. Empirical results show that teams belonging to the Western conference achieve higher overall efficiency than those in the East. Utah Jazz, San Antonio Spurs, Chicago Bulls, Atlanta Hawks, Golden State Warriors and Toronto Raptors are among the top efficient teams, whereas New York Knicks, Dallas Mavericks, Orlando Magic, Minnesota Timberwolves and New Orleans Pelicans rank among the lowest efficient teams. Regarding, the conference efficiency scores the empirical results show that on average the overall team efficiency scores range between 71.2% and 91.03% through the sample period. It is understood that the continuous increase in the salary cap has affected the performance of the teams to the best, and as shown the results, firstly there is an increase in efficiency scores over time as well as a decrease in the variation of efficiency which helps to develop the competitiveness of the teams.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".