ArticleJournal of human kinetics2025
Predicting the Match Outcome in the 2023 FIFA Women's World Cup and Analysis of Influential Features.
Article in Journal of human kinetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- An explainable machine learning analysis of technical and tactical indicators associated with CSL match outcomes.Frontiers in psychology · 2026Article
- Efficient talent identification in women's football: A ranking-based approach for goal scoring analysis.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to build an XGBoost model to predict the match outcome and analyze match-related technical, tactical and physical performance features that may influence the predicted outcome of the match. This is an observational study which follows a retrospective design. The FIFA post-match summary reports were downloaded at the end of the 2023 Women's World Cup and used to create a dataset which consisted of match-related technical, tactical and physical performance variables. Then, an XGBoost model was built to predict the match outcome and investigate which performance features might influence the predicted outcome of the match. The overall model achieved accuracy of 0.58 ± 0.05. Losses and wins had similar predictive accuracy (0.67 ± 0.06 and 0.67 ± 0.08, respectively), but the prediction of draws performed was significantly worse with accuracy of 0.32 ± 0.16. The top ten features for predicting wins were: (1) out to in actions by the opponent, (2) attempts at the goal, (3) in-behind actions, (4) interceptions by the opponent, (5) loose ball receptions, (6) sprinting per minute by the opponent, (7) offers received by the opponent, (8) in-front opponent, (9) interceptions, and (10) total distance per minute. The top ten features for predicting losses were: (1) attempts at the goal by the opponent, (2) interceptions, (3) out to in actions, (4) possessions interrupted, (5) loose ball receptions by the opponent, (6) in front movements, (7) distance covered by the opponent, (8) in-behind actions by the opponent, (9) total distance, and (10) sprinting per minute. In conclusion, using an XGBoost model, this is the first study to successfully predict the match outcome for wins and losses from the FIFA Women's World Cup, but also explain which features significantly influence the prediction. This study may serve as a guide for practitioners regarding the use and application of XGBoost models in high performance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.