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 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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".