The undervalued set piece: Analysis of soccer throw-ins during the English Premier League 2018–2019 season
Bibliographic record
Abstract
Set pieces in soccer (i.e., free kicks and corners) have been examined in detail and are a common focus for coaches during training and performance preparation. However, limited evidence is available on the impact of throw-ins on soccer performance and if coaches should dedicate time in training towards this specific set piece. Therefore, this research aimed to firstly examine if throw-in performance is linked with soccer performance, and secondly the effect throw-in direction and length has on first contact success rate, possession retention, mean time in possession and shot creation. 16,154 throw-ins from 380 English Premier League matches during the 2018–2019 season were analysed. Higher final league position was correlated to increased throw-in first contact success and possession retention. 83% of throw-in’s resulted in a successful first contact, 54% resulted in possession being retained and 8.8% of throw-ins led to a shot at goal from the possession achieved after a successful first contact. Throw-in’s which went backwards or laterally in direction resulted in increased first contact success, retaining of possession, and shot creation. The least efficient throw-in was forwards and long, which resulted in both reduced first contact success and possession retention. Findings highlight, that throwing the ball laterally or backwards should be a focus for coaches and players during attacking training. In contrast, a team’s defensive strategy should reduce the opportunities to throw backwards or laterally with a higher press and look to force a long forward throw-in, therefore, increasing the likelihood of winning possession and counter attacking.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".