Experimental Design Methods in Sport Management Research: The Playoff Safety Bias
Bibliographic record
Abstract
The Playoff Safety Bias occurs when playoff appearances matter more than championships in terms of an individual’s decision-making process when choosing to consume major professional sport from a set of options, referred to as the Sequential Goal Heuristic. This paper (i) demonstrates the potential value of experimental design research in sport management and (ii) provides a consumer-based perspective of playoff structure. Adopting a consumer psychology approach, a 2 (Team performance: good team/bad team) × 3 (Goal: make playoffs every year/ win at least one championship/ maximize number of championships) design was administered via a scenario presented to 152 undergraduate students. The scenario controlled and manipulated the good team/bad team construct by varying the team’s past six season standings. Results revealed that the subjects instructed to maximize the number of playoff appearances had similar estimations of the ideal number of playoff teams, whether fans of a good or bad team. Conversely, of the subjects instructed to either (i) maximize the number of championships won or (ii) maximize the probability of winning at least one championship, fans of good teams over-estimated the optimal number of playoff teams significantly more than fans of bad teams. Implications for future research, practitioner application, and support of similar methods in sport management research are provided.
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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 | 0.109 | 0.229 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".