Relationship Between Mental Skills and Penalty Kicks Performance in the Elite Penalty Shooters in Iran’s Professional Football Teams
Bibliographic record
Abstract
The goal of this research was to predict the elite penalty shooters performance in Iran’s professional football teams, depending on the level of their mental skills. Because of this, 20 penalty shooters of professional teams including the Iran’s national team and Persepolis team were chosen by patterning way as available. The mental skills that were studied in the research were evaluated by the Ottawa mental skill assessment tool questionnaire (OMSAT). For determining the operation of penalty shooters in the current research, the whole penalty shoots during the nine leagues from the side of the participating players have been considered in the study. Pearson’s test of correlation index were used to analyze the data. Results showed that among the basic mental skills, psychosomatic and cognitive skills have a meaningful correlation with success in penalty operation and success in the operation of penalty shooters of professional football teams can be predicted by the mental skills. Therefore, football coaches should pay attention to the improvement of players’ and specially penalty shooters’ mental skills and use the practicing programs of mental skill to improve the mental preparation of players and specially penalty shooters.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".