Exploring tanking strategies in the NBA: an empirical analysis of resting healthy players
Bibliographic record
Abstract
To date, a number of research studies have examined sport leagues for potential evidence of teams strategically losing games on purpose. Following tournament theory, it is believed sport teams will engage in such practices, often called tanking, in order to gain rewards in the form of better draft picks. Where prior research typically focused on detecting evidence of underperformance by teams, the present research analyzed one possible tanking strategy – the resting of healthy players. Specifically using data from National Basketball Association regular season games from the 2006–07 to 2017–18 seasons, we develop a count model of the number of players who are rested by teams. Furthermore, we utilize a natural experiment to consider whether teams eliminated from playoff contention rest more players. Poisson regression estimates found that eliminated teams will rest more players than others, and that the number of players rested by eliminated teams will increase as the competition for draft picks increases. As such, this study is one of the first to show how teams are able to purposefully lose games, with the strategy being instituted through managerial decisions rather than shirking by workers.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".