Does order matter? An empirical analysis of NHL draft decisions
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the effect of order on the quality of outcomes when making sequential decisions and test the widely‐held belief that choosing earlier is preferable and results in better outcomes than choosing later. Design/methodology/approach Quantitative performance from the sequence of athletic decisions made by the teams of the National Hockey League (NHL) at the annual amateur entry draft is longitudinally analyzed using a participation threshold of 160 games. Findings Analysis indicates that earlier choice does result in outcomes that are significantly and substantially better but that this effect is muted beyond approximately the first 100 decisions, after which there is no discernable advantage. Research limitations/implications The dichotomous performance measure excludes more qualitative or stratified assessments of performance and does not include context of the individual decision choices. The results may not generalize beyond the National Hockey League or other human resource situations. Practical implications The research suggests that sequential decision processes are suboptimal in the presence of large amounts of information and choice. Recommendations include reallocating the amount of confirmatory attention spent on highly‐ranked candidates. Originality/value The paper exposes limitations to the widely‐held belief that choosing earlier is preferable to choosing later.
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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 | 0.025 | 0.191 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".