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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".