Sabotage in dynamic tournaments with heterogeneous contestants: Evidence from European football
Bibliographic record
Abstract
This paper analyses how sabotage, measured as the number of fouls and yellow and red cards, is affected by the relative performance of and asymmetries between teams, determined based on score margin and the odds gap between one team and its opponent. By applying detailed controls for within-match dynamics and differences in team quality, we first observe that badly losing favourites are more willing than losing underdogs to increase their unsporting behaviour in heterogeneous contests. There are more yellow cards and fouls as the game progresses. We further find that most sabotage (yellow cards) can be observed when the (absolute) goal difference is equal to 1. Teams decrease their level of sabotage (fouls) by increasing the number of goals of lead or trail. In addition, weaker teams have an incentive to engage in more unfair and destructive strategies, such as committing more fouls, including those penalized with yellow cards. However, the incidence of red cards is not influenced by the score margin or strength gap.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".