‘Setting the Benchmark’ Part 4: Contextualising the MatchDemands of Teams at the FIFA Women’s World Cup Australiaand New Zealand 2023
Bibliographic record
Abstract
The aims of the present study were to: (1) analyse the upper and lower match physical performance benchmarks and variability of teams at the FIFA Women's World Cup Australia and New Zealand 2023, (2) examine the evolving team sprint ranking across three Women's World Cups and (3) investigate noteworthy relationships between collective physical and tactical metrics.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.On average, teams at the FIFA Women's World Cup 2023 covered 103.3 ± 4.4 km in total, with 6.7 ± 0.6 km and 1.9 ± 0.3 km covered at the higher intensities (≥19.0 & ≥23.0 km • h -1 ), respectively.The top five ranked teams from a high-intensity running perspective (Zambia, Spain, Brazil, Canada, Denmark) covered 24-44% more distance than the bottom five ranked teams (Jamaica, Columbia, Costa Rica, Switzerland, Vietnam) at the tournament (P < 0.01; Effect Size [ES]: 2.3-2.5).Match-to-match variation of teams revealed Italy and Panama were particularly consistent for the distances covered at higher intensities (Coefficient of Variation [CV]: 0.3-4.5%),while Costa Rica demonstrated considerable variation (CV: 23.4-40.7%).Teams generally covered more total distance on a per-minute basis in the first versus the second half (P < 0.01; ES: 1.1), but no differences existed at higher intensities (P > 0.05; ES: 0.1-0.2).Correlations were found between the number of high-intensity runs and various phase of play events for defensive transitions and recoveries, in addition to progressions up the pitch and into the final third (r = 0.48-0.88;P < 0.01).A basic comparative analysis revealed Spain demonstrated the most pronounced increase (2015 = 9 th , 2019 = 35 th , 2023 = 90 th percentile; CV: 92.6%) and China PR the most marked decrease (2015 = 22 nd , 2019 = 30 th , 2023 = 0 percentile; CV: 89.6%) in their sprinting percentile rank across the last three FIFA Women's World Cups.The present findings provide a depiction of the current collective demands of international women's football.This information could be useful for practitioners to benchmark team performances and to potentially understand the myriad of contextual factors impacting physical performances.
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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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".