Leading under pressure: evaluating the decision-making style of NHL coaches
Bibliographic record
Abstract
Purpose The purpose of this paper is to establish the optimal decision-making style in a fast-paced, complex, and dynamic environment. Design/methodology/approach Three decision-making attributes are explored: the use of intuition vs analysis, the proclivity to heuristics, and susceptibility to bias. The intuition/analysis is tested with a questionnaire that has been validated in prior research, while information on the two other dimensions is from an exploratory survey designed for this purpose. Responses to the survey questions provide some insight into the differential decision-making style of elite NHL hockey coaches’vis-à-visamateur coaches and news reporters. Findings The data suggest elite decision makers have no preference for intuitive or analytical settings, but exhibit a significantly higher perception of their ability to perform in both. While current literature shows sports athletes to be more intuitive, it appears coaches excel on the analytical dimension instead. This study finds that while elite hockey coaches have fewer biases overall, they tend in particular to be overly optimistic in comparison to amateur coaches and news reporters. Research limitations/implications The main limitation in this paper is that the survey on heuristics and biases is exploratory, making these results less robust than the findings on intuition and analysis. Originality/value This paper is first to extend the decision-making literature to coaches, and among few papers that obtain insights from NHL coaches directly. The findings are likely to extend to corporate leadership as well, increasing the relevance of the results.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".