Penalty corner routines in elite women's indoor field hockey: Prediction of outcomes based on tactical decisions
Bibliographic record
Abstract
Indoor hockey is a highly competitive international sport, yet no research to date has investigated the key actions within this sport. As with outdoor field hockey, penalty corners represent one of the most likely situations in which goals can be scored. All 36 matches of the round-robin phase of the 2010-2011 England Hockey League Women's Premier Division 'Super Sixes' competition were analysed with the purpose of establishing which factors can predict the scoring of a goal using binary logistic regression analysis. Seventy-two (22.6%) of the 319 observed penalty corners resulted in a goal. The strongest predictor of scoring a goal was taking the penalty corner from the goalkeeper's right. Based on the odds ratio (OR), the odds of the attacking team scoring were 2.27 (confidence interval (CI) = 1.41-3.65) times higher with penalty corners taken from the goalkeeper's right as opposed to the left. Additionally, if the goalkeeper decided to rush to the edge of the circle, the odds of the attacking team failing to score were 2.19 (CI = 1.18-4.08) times higher compared to when the goalkeeper remained near the goal line. These results suggest that strategic decisions from the players and coaches have an important part to play in the success of penalty corners. Future research should investigate the impact of goalkeepers' movement and further examine the technical and tactical intricacies of penalty corners.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".