Greater numbers of passes and shorter possession durations result in increased likelihood of goals in 2010 to 2018 World Cup Champions
Bibliographic record
Abstract
Data analysis in football has indicated an increased likelihood of goals with fewer passes within a possession which have resulted in recommendations of fewer passes and more direct play to score goals. These recommendations did not consider where possessions originated and appear to be contradicted by on-field playing tactics by recent championship winning clubs and national teams in elite competition. Therefore, this study examined the influence of number of passes and possession duration on the likelihood of a shot, or a goal scored during possessions originating in the defensive zone. 4465 possessions originating in the defensive zones of the French, German and Spanish Men's National teams at the 2010 to 2018 World Cups were analyzed. The possessions were analyzed for the length in time of possession (TP0.3), the number of passes completed (nPass0.425) and the number of defenders in the offensive zone. Each possession was classified whether or not a shot occurred, a goal occurred or the ball was returned back into the defensive zone. Mixed-effects multivariate logistic regression models were utilized to model the log odds of a shot, goal, or went-back occurrence at the end of each possession. The logs odds of a shot decreased by -0.29 (p = 0.036) with each pass (nPass0.425) and the log odds of a goal decreased with time of possession (TP0.3) by 1.000 (p = 0.014) and increased with number of passes by 0.775 (p = 0.046). The logs odds of the ball being returned to the defensive zone increased with more passes and greater numbers of defensive players while decreasing with a longer possession duration. The results indicate that a greater number of passes had a positive influence on goal scoring while a longer possession duration had a negative effect. The findings suggest that teams with possessions gained in the defensive zone can use a high number of passes in a short period of time can increase their likelihood of scoring goals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".