Evidence map›Paper›PMID 41668952›Full record

ArticleBiology of sport2026

Key performance indicators of offensive transitions in elite women's football: a machine learning and explainability approach.

Claudio A Casal, José Luis Losada, Ana M de Benito Trigueros, Rubén Maneiro, Iyán Iván-Baragaño

Abstract read
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Article in Biology of sport, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Claudio A CasalFaculty of Physical Education & Sport Sciences, Catholic University of Valencia, San Vicente Mártir, Valencia, Spain.
José Luis LosadaDepartment of Social Psychology and Quantitative Psychology, University of Barcelona, Barcelona, Spain.
Ana M de Benito TriguerosHeQoL Research Group. Department of Physical and Sport Education, University of León, León, Spain.
Rubén ManeiroFaculty of Education and Sport, University of Vigo, Vigo, Spain.
Iyán Iván-BaragañoDepartment of Sports Sciences, Faculty of Medicine, Health and Sports, European University of Madrid, Madrid, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Women's football has experienced significant growth in sporting, economic, and social interest. However, there remains a shortage of studies examining key technical-tactical performance indicators in elite competitions. This research aimed to identify and quantify the influence of technical-tactical indicators on the effectiveness of offensive dynamic transitions in elite women's football, using a machine learning approach. To this end, 3,610 dynamic offensive transitions recorded across 35 matches from the final stages of the FIFA Women's World Cup 2023, UEFA Women's Euro, and UEFA Women's Champions League 2023/24 were analysed using an observational methodology. An

Indexed as

Dynamic offensive transitionsMatch observerObservational methodologyPerformance analysisWomen’s soccer

Identifiers

PMID41668952
PMCPMC12884906

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.