Athletes’ perspectives of preparation strategies in open-skill sports
Bibliographic record
Abstract
Preparation strategies (i.e., pre-game rituals, pre-performance routines) have long been a topic of study in sport psychology. However, previous research has focused on closed-skill tasks and employed methodologies that make it difficult to capture athletes’ perspectives of these behaviors. The purpose of this study was to explore the perceptions of open-skill sport athletes regarding their preparation strategies. Nineteen high-performance athletes participated in semi-structured interviews. Participants described their perceptions of the structures and functions of the behaviors they consider to be part of their preparation strategies. These findings contribute to a more nuanced understanding of how athletes perceive what they do to prepare for a performance. Exploring athletes’ perspectives of this phenomenon allowed for existing theoretical representations of strategies to be compared to the lived experiences of the athletes who engage in them having implications for coaches and sport psychology consultants.Lay Summary: Many athletes have strategies they use to help prepare themselves to perform at their best. We asked 19 high-performance open-skill sport athletes to describe what their preparation strategies are and why they engage in them. Coaches and practitioners should consider their athlete’s perspective when working to establish an effective preparation strategy.Implications for practiceSport psychology practitioners should strive to understand what components an athlete perceives to be part of their preparation strategy and why they engage in them.Preparation strategies can help athletes achieve optimal physical, mental, and emotional states, which differ between athletes and contexts.Preparation strategies are tied to athletes’ individual identities, and in team sports, can help to build and maintain a team’s collective identity over the course of a season.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".