Eliminating buyer’s remorse: An examination of the sunk cost fallacy in the National Hockey League draft
Bibliographic record
Abstract
The sunk cost effect describes the tendency to escalate one's commitment toward a certain endeavor, despite diminishing returns, as a consequence of irreversible resource expenditure that has already been made (Organ Behav Hum Decis Process. 1985;35:124). This effect has been observed in a number of professional sports leagues, wherein teams escalate their commitment toward players selected early in the draft, regardless of performance outcomes, due to large financial commitments invested in them (J Sports Econom. 2017;18:282; Adm Sci Q. 1995;40:474). This effect, however, has yet to be explored in the National Hockey League (NHL). The purpose of this study was to test for sunk cost effects in the NHL, by examining the relationship between draft order and playing time, while controlling for a myriad of confounding variables. Findings from our analyses provide support for the existence of this effect in the NHL, as first-round draftees were given significantly more playing time than their peers selected in the second round, regardless of injury, player relocation, penalties, or on-ice performance outcomes. We offer some plausible underlying mechanisms driving this effect. Furthermore, we suggest the observed effects have valuable implications for NHL talent development, given the importance of playing time on various aspects of expertise attainment.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".