The creation of goal scoring opportunities at the 2015 women’s world cup
Bibliographic record
Abstract
Women’s international football is growing in terms of popularity, but to date there is limited published research in women’s football and even less that describes effective attacking strategies and the creation of goal scoring opportunities (GSO). The purpose of this research was to investigate and assess the factors related to the creation of GSO that led to a Top 4 position Canada Women’s World Cup 2015. Video footage of each match (n = 52) from the Women’s World Cup (Canada 2015) was analysed using SportsCode Software. The results revealed that the middle third of the pitch was the most effective area for gaining possession and creating GSO. The average time taken to create a GSO was under twelve seconds. The findings of the study can be used by coaches to design training sessions and interventions to successfully create GSO in women’s football. The data from the research may influence the tactical set up of women’s international football teams and help to evolve the game in the same way that research into the men’s game has.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".