Referee Decision Making in a Video-Based Infraction Detection Task: Application and Training Considerations
Bibliographic record
Abstract
This study addressed factors that influence referee decision making in basketball. Four different groups of basketball officials were shown video clips testing their ability to detect fouls and violations (infractions). In a knowledge-priming condition, referees were given a rules test before infraction detection. In an infraction-priming condition, referees were instructed to focus on defensive fouls. The results did not show clear effects of knowledge or infraction priming. This implies that neither a pre-game review of the rules or league recommendation, nor the common coach behaviour of asking a referee to focus on a particular infraction influence performance in the calls that are made. Rather, the results indicate that detecting infractions in video clips may be influenced by features of the video tool. Performance is influenced by the specific clips and their format sequencing. These findings illustrate the complexity of referee decision-making, and provide guidance for designing coaching tools for this skill. In particular, this research suggests that referee decision-making tools progress in perceptual difficulty (e.g., on-the-ball to off-the-ball infractions)
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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 | 0.002 | 0.023 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".