Key performance indicators at FIFA Women's World Cup in different playing surfaces
Bibliographic record
Abstract
The aim of this study was to examine the key performance indicators of female professional soccer players during the 2011 and 2015 FIFA Women's World Cup, played on different surfaces (natural and artificial turf respectively). A total of 438 women from 24 national teams who participated at Canada 2015 (artificial turf) and 283 players from 16 national teams who played in Germany 2011 (natural grass) were selected for this study. The collected data were provided by OPTA Sports. Twenty-nine key performance indicators were included for analysis. The variables were calculated for the total sample and independently by positions (defense, midfielders and forwards) for matches on natural grass (2011) and artificial turf (2015). A Mann-Whitney U test was used out to identify differences between the sport surfaces. Moreover, a discriminant analysis was performed with the forced entry method to find the variables that better differentiated between the FIFA Women's World Cup 2011 (natural grass) and FIFA Women's World Cup 2015 (artificial turf). Key performance aspects were very similar between the two tournaments, but on natural grass, we observed a significantly higher number of total passes, successful dribbles, total tackles, successful tackles and interceptions. However, on artificial turf there were significantly higher percentages of success in total passes, and a higher number of fouls. This is an important factor for the choice of an elite competition surface because technical actions are crucial to the quality of the game and can influence the future behavior of spectators and fans.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".