Causal Analysis of Tactics in Soccer: The Case of Throw-ins
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Accepted by: Phil Scarf This paper investigates optimal target locations for throw-ins in soccer. The investigation is facilitated by the use of tracking data which provide the positioning of players measured at frequent intervals (i.e. 10 times per second). The methods for the investigation are necessarily causal since there are confounding variables that impact both the throw-in location and the result of the throw-in. A simple causal analysis indicates that on average, backwards throw-ins are beneficial and lead to an extra two shots per 100 throw-ins. We also observe that there is a benefit to long throw-ins where on average, they result in roughly four more shots per 100 throw-ins. These results are corroborated by a more complex causal analysis that relies on the spatial structure of throw-ins.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 it