Playoff beards and unwashed uniforms: a scoping review on athletes’ superstitions and rituals
Bibliographic record
Abstract
Athletes commonly engage in ritualistic behaviors such as performing certain actions, citing verses, and holding objects/items when preparing for competitions. Despite the prevalence of these ‘pre-game/practice routines’ or ‘superstitious behaviours’ in sport, we have relatively little knowledge on what they are or how their use may enhance athletic performance. The present PRISMA-based scoping review aims to identify and analyze literature on athlete rituals, superstitions, and pre-game routines. Articles were screened for the following inclusion criteria: (a) written in English (b) published in peer-reviewed journals, (c) available in full-text form, and (d) examining sport, and specifically athlete populations. A secondary objective involved creating a framework for researchers and practitioners to identify, define, and categorize superstitious behaviors and beliefs. The final dataset included 33 articles with athlete samples from 11 different sports. Key findings indicated that superstitions may arise as a coping method for anxiety resulting from the uncertainty of sport, and praying was identified as the most common superstitious ritual within the articles examined. Findings from this study may inform directions for future research to benefit athletes and related stakeholders across a range of performance and developmental outcomes.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".