‘Setting the Benchmark’ Part 3: Contextualising the MatchDemands of Specialised Positions at the FIFA Women’s WorldCup Australia and New Zealand 2023
Bibliographic record
Abstract
This study aimed to benchmark the match demands of specialised positions at the FIFA Women's World Cup Australia and New Zealand 2023.With FIFA's official approval, all sixty-four games at the tournament were analysed using an optical tracking system alongside FIFA's Enhanced Football Intelligence metrics.During a typical match, defensive and central midfielders covered 5-15% more total distance than centre backs, wide defenders and centre forwards (P < 0.01; Effect Size [ES]: 0.6-1.8).The distances covered at higher intensities (≥19.0 and ≥23.0 km • h -1 ) were 18-89% and 88-163% greater in attacking midfielders, wide midfielders, wide forwards and centre forwards than other positions (P < 0.01; ES: 0.5-2.0 and ES: 1.0-1.3,respectively).Regarding offers made to receive the ball, defensive and central midfielders, attacking midfielders and centre forwards moved more between the lines than centre backs, wide defenders and wide midfielders (P < 0.01; ES: 1.0-1.9).Movements in behind lines were more common for offensive roles such as attacking midfielders, wide midfielders, wide forwards and centre forwards than other positions (P < 0.01; ES: 0.9-2.3).Regarding pressing events, direct pressure was highest for defensive and central midfielders compared to other positions (P < 0.05; ES: 0.5-1.3)and indirect pressure was greater for central midfielders, attacking midfielders, wide midfielders and centre forwards compared to centre backs and wide defenders (P < 0.01; ES: 0.9-2.3).A basic within tournament positional comparison revealed that centre backs and centre forwards demonstrated pronounced changes in their relative sprint distances from Canada 2015, France 2019 through to Australia and New Zealand 2023.These findings could be valuable to benchmark the contemporary positional demands of women's international football, while also providing a framework to design role-specific training drills.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".