Predicting the action outcome of left- and right-footed penalties in a rep-resentative experimental setting in soccer
Bibliographic record
Abstract
Anticipation of left- compared to right-sided actions of an opponent seems to be more difficult. present study aimed to investigate whether prediction accuracy and gaze behavior of left- vs. right-footed penalties differ from each other in a representative experimental setting. 29 participants (soccer goalkeepers, soccer players, non-soccer players) predicted shot direction (left/right) of left- and right-footed penalties from a goalkeeper's perspective. Stimuli were presented in life-size on a large screen (3.2 x 2.1 m) and occluded at ball contact. Participants had to perform a full-body movement towards the predicted shot direction. Accuracy was defined in terms of the correct re-sponse direction. Percentage of time of gaze was examined for five areas of interest (head, upper body, hip, supporting leg, shooting leg). A 2 (condition) x 3 (group) ANOVA for accuracy revealed a significant main effect for condition (F(1,27) = 5.3, p < .05), with higher accuracy for right- (M = 68%) compared to left-footed (M = 64%) penalties. All other main effects and interactions did not attain significance. A 2 (condition) x 3 (group) x 5 (area) ANOVA for gaze revealed a significant main effect for area (F(4,104) = 4.0, p < .05), showing the longest viewing time toward the shooting leg. All other main effects and interactions did not attain significance. present results indicate higher accuracy for right- compared to left-footed penalties. However, gaze behavior did not differ between left- and right-footed penalties. It can be argued that information processing is different for left- vs. right-sided actions.Acknowledgments: research was funded by the German Research Foundation, International Research Training Group, IRTG 1901, The Brain in Action
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".