Identification of football teams styles of play by cluster analysis
Bibliographic record
Abstract
The aim of this study was to characterize performance patterns of attack and defence of football teams, and the inter-team's relation throughout the game. First and second leg of the Brazilian Cup Final 2018 Under-20 category were recorded using two video cameras. Three hundred twelve attacks and defences sequences in the two football matches were analyzed. All players and the ball were tracked throughout the matches, then notational and spatiotemporal variables were measured: attack duration, number of actions per attack, occupied area, team centroid, ratio between number of action and attack duration, total centroid trajectory, and ball displacement. Those variables were grouped using Ward's minimum variance method. The results showed that: (i) teams presented variated styles of play in attack and defence intra and intermatches; (ii) spatial variables such as positions and displacements contributed the most to separate the patterns; (iii) the interteams synchrony found throughout the game revealed different outcomes; and (iv) specific attacking patterns led to shoot to goal. We concluded that football teams vary their style of play within match and intermatches; spatial variables such as positions and displacements contributed the most to separate the patterns; and the interteam relation revealed synchrony throughout the game.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".