Gambling with leadership succession in Brazilian football: head coach turnovers and team performance
Bibliographic record
Abstract
Purpose Although leadership succession is a popular area of study across different professional sports leagues, existing research has largely ignored South America despite Brazilian football seeming to surpass the limits of coaching turnovers in comparison to any other league worldwide. Design/methodology/approach This study examines the causes and consequences of 594 head coach turnovers in the Brasileirão from 2003 to 2018. A comprehensive longitudinal dataset was compiled (n = 13,012) and a series of regression analysis evaluated the determinants of coaching replacements as well as their effect on team performance. Findings Statistical results revealed that coaching survival is significantly determined by a negative spell of three to four games, parallel competitions and performance expectations with three games in advance. Regarding performance outcomes, it takes seven games for a slight sign of improvement to be identified after a coach turnover, but no clear positive effects are recognized as an aftermath, supporting the ritual scapegoating theory. Practical implications The findings suggest that decision makers should consider the importance of a rational evaluation and the crucial component of time instead of judging coaches based on subjectivity and immediate results. Meanwhile, coaches should avoid voluntary turnovers, exercising priorities ahead of continental cups and sequences with few points accumulated. Originality/value This investigation discloses a valuable reference for coaches, sport managers and academic scholars interested in Brazilian football, as it extends knowledge development and theoretical understanding for a region that still lacks scientific evidence to back up its practical assumptions 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".