Possession tactics in the UEFA women’s EURO 2022 soccer tournament
Bibliographic record
Abstract
The purpose of the current investigation was to compare different types of possession between the 8 quarter-finalists at the Euro 2022 soccer tournament and the 8 teams eliminated at the group stage, as well as compare the percentage of possessions leading to shots between different types of possession. Possessions were classified as having a slow pass rate if there were fewer than 1 pass played per 3s on average, otherwise they had a higher pass rate. Quarter-finalists had a significantly greater number of possessions of 9 passes or more with a slow pass rate than teams eliminated at the group stage (p = 0.028), and they created shots from a significantly greater percentage of these (p = 0.007). Possession type had a significant influence on the percentage of possessions leading to a shot (p < 0.001). Possessions of 9 or more passes with a slow pass rate were the most productive. The percentage of these that led to a shot was significantly greater than for possessions with the same number of passes played at a higher pass rate (p < 0.002). This shows that creating shots not only depends on possession length but also the rate of passes.
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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 | 0.001 | 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) | 0.003 | 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".