The Mental Game of Tennis: A Scoping Review and the Introduction of the Resilience Racket Model
Bibliographic record
Abstract
This review examines the relationship between tennis participation and mental health, highlighting both the psychological benefits and challenges associated with the sport. Using a retrospective, citation-based methodology, peer-reviewed studies published in English, French, and Greek between 2000 and March 2025 were included. The findings indicate that tennis participation is associated with reductions in depressive and anxiety symptoms, improved self-confidence, and enhanced resilience. However, competitive tennis also presents significant psychological demands, including elevated stress levels and susceptibility to maladaptive coping behaviors. To address these complexities, this review introduces the Resilience Racket Model, a conceptual framework representing the integration of physical readiness, psychological resilience, and systemic support. The model uses the metaphor of a tennis racket: the handle reflects foundational physical skills; the strings represent resilience components; the frame denotes environmental and organizational support; and the sweet spot signifies the optimal balance between physical and mental readiness. The review also highlights the effectiveness of psychological interventions, such as cognitive-behavioral therapy and mindfulness, in supporting athlete well-being. These findings advocate for a holistic approach to athlete development, emphasizing parity between mental health and physical training, and call for further research into tailored, sport-specific mental health interventions in tennis.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".