The individual training history shapes soccer players’ ability to predict teammates’ and opponents’ moves
Bibliographic record
Abstract
In sports, players constantly engage in understanding others' actions and intentions. Previous studies have highlighted that possessing the observed action in the individual motor repertoire improves the prediction abilities of the observer. Here, we tested the extent to which players' ability to predict soccer actions is influenced by their motor repertoire, which is modulated not only by their generic expertise but also by the specific position played on the field. To these aims, two experiments were conducted by asking players to predict the result of typical soccer actions and comparing accuracies with data concerning their soccer career. Results revealed that both general expertise and position-specific experience significantly impacted prediction performance, with the highest accuracy observed when actions aligned with players' positional expertise. These findings highlight that the motor resonance mechanism is finely attuned to the individual's motor repertoire, which operates as a continuum - from no experience to advanced expertise in a specific position - enabling a dynamic, experience-driven enhancement of action prediction in sports.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".