Exploring key competencies sought to potentialize tactical behavior in soccer players.
Bibliographic record
Abstract
BACKGROUND: Soccer is part of the team sports games category and is characterized by the cooperation and opposition interactions between players in the same space of play and time. Thus, players must adequately decide what action to perform despite the unpredictable, random, and varying nature of the environment of play. AIM: This paper explores tactical competencies that can be appreciated in the way players play and their functioning. METHOD: The argumentation is structured over a review of sixty articles in five languages, selected from the results in an online university library with topic-related keywords. The selected papers were analyzed to identify the most frequently reported concepts related to (i) tactics and action in the play; (ii) decision-making and associated cognitive mechanisms and skills; and (iii) the teaching-learning-training process. RESULTS: The results of this review sum the three following competencies: tactical intelligence, creativity, and co-adaptability. We argue that these competencies can be built through the play's practice and that coaches should seek to use them to the advantage of player’s development. Small-sided and conditioned games reflect a compatible opportunity to nurture the competencies as long as they are configured to solicit the competencies in an environment that promotes them. CONCLUSION: Tactical intelligence, creativity, and co-adaptability can be appreciated in the tactical behavior shown by performing players. For the same reason, those also should constitute more of the player’s development curriculum, therefore leading to players who have a competitive advantage.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".