Positional Competition Predicts Athlete Satisfaction in a Large Student-Athlete Sample
Bibliographic record
Abstract
Competition in sports can occur between teams but also within a team. The process of teammates vying for the same playing time is called positional competition and has been linked to several adaptive outcomes. Yet, the relationship between positional competition and performance-related satisfaction has yet to be explored. Hence, the purpose of this research was to examine whether athletes’ perceptions of positional competition could predict satisfaction with individual performance, ability utilization, training and instruction and personal dedication. Using a cross-sectional study design, a sample of 786 University Sport athletes (Mage= 20.31 years, SDage= 1.97) completed the Athlete Satisfaction Questionnaire, Positional Competition in Team Sports Questionnaire, and demographic questions. The seven sub-dimensions of positional competition predicted each of the four chosen sub-dimensions of athlete satisfaction in separate linear regressions. Positional competition significantly predicted the athlete satisfaction in each regression, explaining 20-34% of the variance. Coach communication (β = 0.11- 0.35) and selection (β = 0.12- 0.25) emerged as significant predictors in all regressions. Effort to improve was also a significant predictor in three regressions (β = 0.11- 0.33) while pushing teammates was in two regressions (β = 0.13 - 0.20). The findings indicate that the coach’s behavior play a central role in the relationship between positional competition and athlete satisfaction, particularly in the regressions predicting ability utilization and training and instruction. Future applied research may wish to explore which type of coaching behavior in positional competition affects athletes’ satisfaction.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".