Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: a machine learning approach
IntroductionResearch in women’s football and the use of new game analysis tools have developed significantly in recent years. The objectives of this study were to create two predictive classification models to forecast the occurrence of a shot or a goal in the FIFA Women’s World Cup 2023 and to iden...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1516417/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832576359536263168 |
|---|---|
| author | Iyán Iván-Baragaño Antonio Ardá José L. Losada Rubén Maneiro |
| author_facet | Iyán Iván-Baragaño Antonio Ardá José L. Losada Rubén Maneiro |
| author_sort | Iyán Iván-Baragaño |
| collection | DOAJ |
| description | IntroductionResearch in women’s football and the use of new game analysis tools have developed significantly in recent years. The objectives of this study were to create two predictive classification models to forecast the occurrence of a shot or a goal in the FIFA Women’s World Cup 2023 and to identify the associated technical-tactical indicators to these outcomes.MethodsA total of 2,346 ball possessions were analyzed using an observational design, mapping two different target variables (Success = Goal and Success2 = Goal or Shot) with a relative frequency of 1.28 and 8.35%, respectively. The predictive capacity was tested using Random Forest and XGBoost and finally and SHAP values were calculated and visualized to understand the influence of the predictors.ResultsRandom Forest technique showed greater efficacy, with recall and sensitivity above 93% in the resampled dataset. However, recall on the original test sample was 13% (Success = Shot or Goal) and 0% (Success = Goal), demonstrating the models’ inability to predict rare events in football, such as goals. The indicators with the greatest influence on the outcome of these possessions were related to the possession zone, attack duration, number of passes, and starting zone, among others.ConclusionThe results highlight the need to incorporate a greater number of predictive variables in the models and underline the difficulty of predicting events such as goals and shots in women’s football. |
| format | Article |
| id | doaj-art-aeacfbb2f7254f909c20b191d9212416 |
| institution | Kabale University |
| issn | 1664-1078 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj-art-aeacfbb2f7254f909c20b191d92124162025-01-31T06:39:48ZengFrontiers Media S.A.Frontiers in Psychology1664-10782025-01-011610.3389/fpsyg.2025.15164171516417Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: a machine learning approachIyán Iván-Baragaño0Antonio Ardá1José L. Losada2Rubén Maneiro3Department of Sport Sciences, Faculty of Medicine, Health and Sports, Universidad Europea de Madrid, Madrid, SpainDepartment of Physical and Sport Education, University of A Coruña, A Coruña, SpainDepartment of Social Psychology and Quantitative Phycology, University of Barcelona, Barcelona, SpainFaculty of Education and Sport, University of Vigo, Vigo, SpainIntroductionResearch in women’s football and the use of new game analysis tools have developed significantly in recent years. The objectives of this study were to create two predictive classification models to forecast the occurrence of a shot or a goal in the FIFA Women’s World Cup 2023 and to identify the associated technical-tactical indicators to these outcomes.MethodsA total of 2,346 ball possessions were analyzed using an observational design, mapping two different target variables (Success = Goal and Success2 = Goal or Shot) with a relative frequency of 1.28 and 8.35%, respectively. The predictive capacity was tested using Random Forest and XGBoost and finally and SHAP values were calculated and visualized to understand the influence of the predictors.ResultsRandom Forest technique showed greater efficacy, with recall and sensitivity above 93% in the resampled dataset. However, recall on the original test sample was 13% (Success = Shot or Goal) and 0% (Success = Goal), demonstrating the models’ inability to predict rare events in football, such as goals. The indicators with the greatest influence on the outcome of these possessions were related to the possession zone, attack duration, number of passes, and starting zone, among others.ConclusionThe results highlight the need to incorporate a greater number of predictive variables in the models and underline the difficulty of predicting events such as goals and shots in women’s football.https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1516417/fullfemale footballwomen’s soccerpredictive modelsmachine learningperformance analysisFIFA Women’s World Cup 2023 |
| spellingShingle | Iyán Iván-Baragaño Antonio Ardá José L. Losada Rubén Maneiro Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: a machine learning approach Frontiers in Psychology female football women’s soccer predictive models machine learning performance analysis FIFA Women’s World Cup 2023 |
| title | Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: a machine learning approach |
| title_full | Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: a machine learning approach |
| title_fullStr | Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: a machine learning approach |
| title_full_unstemmed | Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: a machine learning approach |
| title_short | Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: a machine learning approach |
| title_sort | goal and shot prediction in ball possessions in fifa women s world cup 2023 a machine learning approach |
| topic | female football women’s soccer predictive models machine learning performance analysis FIFA Women’s World Cup 2023 |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1516417/full |
| work_keys_str_mv | AT iyanivanbaragano goalandshotpredictioninballpossessionsinfifawomensworldcup2023amachinelearningapproach AT antonioarda goalandshotpredictioninballpossessionsinfifawomensworldcup2023amachinelearningapproach AT josellosada goalandshotpredictioninballpossessionsinfifawomensworldcup2023amachinelearningapproach AT rubenmaneiro goalandshotpredictioninballpossessionsinfifawomensworldcup2023amachinelearningapproach |