More direct attacks increase likelihood of goals in 2018- and 2022-Men’s World Cup Soccer Finals
Bibliographic record
Abstract
In soccer, attacking tactics can vary between elaborate, high passing play and play that involves very direct, straight-line action towards the towards the opponent's goal. It is of considerable interest to individuals involved which type of play is more effective in scoring given that goals are a rare event. We propose a geometric measure of directness (DIR) using the ratio between the straight-line distance from the point where possession begins to the centre of goal, and the total distance covered by the ball during that possession. Using 128 matches from the 2018- and 2022-Men's World Cup, we analyzed the influence of directness (DIR), speed of the ball traveling towards the goal (SPG) and the starting position of the possession (XPOS) on the likelihoods of shots and goals. A mixed-effect multivariate logistic regression model was used for both analyses. Following model simplification (AIC = 14579.7, R2 = 0.279), the log odds of a shot resulting from a possession was significantly increased by XPOS (β = 0.019, p < 0.0001), SPG (β = 0.322, p < 0.0001) and a three-way interaction between DIR, XPOS and SPG (β = 0.007, p < 0.0001). The likelihood of a shot was decreased by interactions between DIR and XPOS (β = -0.024, p < 0.0001), DIR and SPG (β = -0.587, p < 0.0001) and XPOS and SPG (β = -0.003, p < 0.0001. The model for the likelihood of a goal (AIC = 1736.9, R2 = 0.020) was simple with DIR being the only significant factor (β = 1.009, p < 0.0001). The results suggest that to increase the likelihood of scoring goals, attacking tactics must be more direct.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".