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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".