Decision-making of English Netball Superleague umpires: Contextual and dispositional influences
Bibliographic record
Abstract
The decisions made by officials have a direct bearing on the outcomes of competitive sport contests. In an exploratory study, we examine the interrelationships between the decisions made by elite netball umpires, the potential contextual and environmental influences (e.g., crowd size), and the umpires' dispositional tendencies – specifically, their propensity to deliberate and ruminate on their decisions. Filmed footage from 60 England Netball Superleague matches was coded using performance analysis software. We measured the number of decisions made overall, and for home and away teams; league position; competition round; match quarter; and crowd size. Additionally, 10 umpires who officiated in the matches completed the Decision-Specific Reinvestment Scale (DSRS). Regression analyses predicted that as home teams' league position improved the number of decisions against away teams increased. A model comprising competition round and average league position of both teams predicted the number of decisions made in matches, but neither variable emerged as a significant predictor. The umpire analyses revealed that greater crowd size was associated with an increase in decisions against away teams. The Decision Rumination factor was strongly negatively related to the number of decisions in Quarters 1 and 3, this relationship was driven by fewer decisions against home teams by umpires who exhibited higher Rumination subscale scores. These findings strengthen our understanding of contextual, environmental, and dispositional influences on umpires' decision-making behaviour. The tendency to ruminate upon decisions may explain the changes in decision behaviour in relation to the home team advantage effect.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".