Biol Sport. 2026; 43: 53–64
1. FIFA. FIFA women’s football – member associations survey report 2023. Zurich (Switzerland): FIFA; 2023. https://inside .fifa.com/womens-football/member associations-survey-report-2023.
2.
Real Federación Española de Fútbol. Memoria de actividades. Número de licencias federativas. Madrid (Spain): RFEF; 2024. https://rfef.es/es/federacion /transparencia/licencias.
3.
Football Australia. Legacy’23. Post tournament report. Sydney (Australia): Football Australia; 2024. https://www .footballaustralia.com.au/legacy23.
4.
Valenti M, Scelles N, Morrow S. Women’s football studies: an integrative review. Sport Bus Manag. 2018; 8(5):511–28. https://doi.org/10.1108/SBM-09-2017 -0048.
5.
McCall A, Mountjoy M, Witte M, Serner A, Massey A. Driving the future of health and performance in women’s football. Sci Med Footb. 2022; 6(5):545–6. https://doi.org/10.1080 /24733938.2022.2152543.
6.
Ventaja-Cruz J, Cuevas Rincón JM, Tejada-Medina V, Martín-Moya R. A bibliometric study on the evolution of women’s football and determinants behind its growth over the last 30 years. Sports. 2024; 12:333. https://doi.org /10.3390/sports12120333.
7.
Wang S, Qin Y, Jia Y, Igor KE. A systematic review about the performance indicators related to ball possession. PLoS One. 2022; 17(3):e0265540. https://doi.org /10.1371/journal.pone.0265540.
8.
Bradley PS. ‘Setting the Benchmark’ Part 3: Contextualising the match demands of specialised positions at the FIFA Women’s World Cup Australia and New Zealand 2023. Biol Sport. 2025; 42(1):99–111. https://doi.org/10.5114 /biolsport.2025.139857.
9.
Bradley PS. ‘Setting the Benchmark’ Part 4: Contextualising the match demands of teams at the FIFA Women’s World Cup Australia and New Zealand 2023. Biol Sport. 2025; 42(2):57–69. https://doi.org/10.5114/biolsport .2025.142638.
10.
Shen L, Tan Z, Li Z, Li Q, Jiang G. Tactics analysis and evaluation of women football team based on convolutional neural network. Sci Rep. 2024; 14:50056. https://doi.org/10.1038 /s41598-023-50056-w.
11.
Trower M, Graham N, Cottrell N, Hengster Y. Clustering women’s football players: identifying functional patterns for performance optimisation. Statsbomb Conference; 2023. https://statsbomb. com/wp-content/uploads/2023/10/Cluste ring-Womens-Football-Players-Identify ing-Functional-Patterns for-Performance-Optimisation.pdf.
12.
Narayanan S, Pifer ND. A data-driven framing of player and team performance in U.S. women’s soccer. Front Sports Act Living. 2023; 5:1125528. https://doi .org/10.3389/fspor.2023.1125528.
13.
Iván-Baragaño I, Maneiro R, Losada JL, Ardá A. Multivariate analysis of the offensive phase in high-performance women’s soccer: a mixed methods study. Sustainability. 2021; 13(11):6379. https://doi.org/10.3390/su13116379.
14.
Harkness-Armstrong A, Till K, Datson N, Emmonds S. Influence of match status and possession status on the physical and technical characteristics of elite youth female soccer match-play. J Sports Sci. 2023; 41(15):1437–49. https://doi.org /10.1080/02640414.2023.2273653.
15.
Maneiro R, Losada JL, Casal CA, Ardá A. The influence of match status on ball possession in high performance women’s football. Front Psychol. 2020; 11:487. https://doi.org/10.3389/fpsyg .2020.00487.
16.
Iván-Baragaño I, Maneiro R, Losada JL, Ardá A. Influence of match status in ball possessions in the FIFA Women’s World Cup France 2019. Proc Inst Mech Eng P J Sports Eng Technol. 2022; 239(1):12-19. https://doi.org /10.1177/17543371221133624.
17.
Martínez-Hernández D, Quinn M, Jones P. Most common movements preceding goal scoring situations in female professional soccer. Sci Med Footb. 2024; 8(3):60–8. https://doi.org /10.1080/24733938.2023.2214106.
18.
Hewitt A, Norton K, Lyons K. Movement profiles of elite women soccer players during international matches and the effect of opposition’s team ranking. J Sports Sci. 2014; 32(20):1874–80. https://doi.org/10.1080/02640414 .2014.898854.
19.
Oliva-Lozano JM, Yousefian F, Chmura P, Gabbett TJ, Cost R. Analysis of FIFA 2023 Women’s World Cup match performance according to match outcome and phase of the tournament. Biol Sport. 2025; 42(2):71–84. https://doi.org /10.5114/biolsport.2025.142643.
20.
Ju W, Cost R, Oliva-Lozano JM. Analysis of match performance of elite soccer players across confederations during the Men’s and Women’s World Cup. Sci Med Footb. 2024; 1–13. https://doi.org/10 .1080/24733938.2024.2409679.
21.
Mondal S. She kicks: the state of competitive balance in the top five women’s football leagues in Europe. J Glob Sport Manag. 2021; 8(1):432–54. https://doi.org/10.1080 /2470467.2021.18875629.
22.
Casal C, Stone J, Iván-Baragaño I, Losada J. Effect of goalkeepers’ offensive participation on team performance in the women Spanish La Liga: a multinomial logistic regression analysis. Biol Sport. 2023; 40(1):29–39. https://doi.org /10.5114/biolsport.2024.125592.
23.
Iván-Baragaño I, Ardá A, Losada JL, Maneiro R. Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: a machine learning approach. Front Psychol. 2025; 16:1516417. https://doi.org/10.3389/fpsyg.2025 .1516417.
24.
Markopoulou C, Papageorgiou G, Tjortjis C. Diverse machine learning for forecasting goal-scoring likelihood in elite football leagues. Mach Learn Knowl Extr. 2024; 6(3):1762–81. https://doi.org /10.3390/make6030086.
25.
Freitas DN, Mostafa SS, Caldeira R, Santos F, Fermé E, Gouveia ÉR, Morgado-Dias F. Predicting noncontact injuries of professional football players using machine learning. PLoS One. 2025; 20(1): e0315481. https://doi. org/10.1371/journal.pone.0315481.
26.
Saberisani R, Barati AH, Zarei M, Santos P, Gorouhi A, Ardigò LP, Nobari H. Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approach. Front Sports Act Living. 2025; 7:1425180. https://doi.org/10.3389 /fspor.2025.1425180.
27.
Last F, Douzas G, Bacao F. Oversampling for imbalanced learning based on K-means and SMOTE. Inf Sci. 2017; 465:1. https://doi.org/10.1016 /j.ins.2018.06.056.
28.
Lemaitre G, Nogueira F, Aridas CK. Imbalanced-learn: a Python toolbox to tackle the curse of imbalanced datasets in machine learning. J Mach Learn Res. 2017; 18:1–5. https://doi.org/https://doi .org/10.48550/arXiv.1609.06570.
29.
Anguera-Argilaga MT. Observational typology. Qual Quant. 1979; 13(6):449–484. https://doi.org /10.1007/BF00222999.
30.
Anguera MT, Blanco Villaseñor Á, Hernández Mendo A, Losada López JL. Diseños observacionales: ajuste y aplicación en psicología del deporte. Cuad Psicol Deporte. 2011; 11(2):63–76. https://revistas.um.es/cpd /article/view/133241.
31.
Belmont RT. Ethical principles and guidelines for the protection of human subjects of research. (The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research); 1978.
32.
Anguera MT. Metodología observacional. In: Arnau J, Anguera MT, Gómez J, editors. Metodología de la investigación en Ciencias del Comportamiento. Murcia (Spain): Universidad de Murcia; 1990. p. 125–236.
33.
Fleiss JL, Levin B, Paik MC. Statistical methods for rates and proportions. 3rd ed. Hoboken (NJ): John Wiley & Sons; 2003.
34.
Gravetter FJ, Wallnau LB. Essentials of statistics for the behavioral sciences. Belmont (CA): Wadsworth; 2007.
35.
Breiman L. Random forests. Mach Learn. 2001; 45:5–32. https://link.springer. com/article/10.1023/A:101093340 4324.
36.
Mandorino M, Tessitore A, Leduc C, Persichetti V, Morabito M, Lacome M. A new approach to quantify soccer players’ readiness through machine learning techniques. Appl Sci. 2023; 13(15):8808. https://doi.org/10.3390 /app13158808.
37.
Rico-González M, Pino-Ortega J, Méndez A, Clemente FM, Baca A. Machine learning application in soccer: a systematic review. Biol Sport. 2023; 40(1):249–63. https://doi.org/10.5114 /biolsport.2023.112970.
38.
Ciocan A, Hajjar N al, Graur F, Oprea VC, Ciocan RA, Bolboacă SD. Receiver Operating Characteristic Prediction for Classification: Performances in Cross-Validation by Example. Maths. 2020; 8(10):1741. https://doi.org /10.3390/math8101741.
39.
Lundberg SM, Lee SI. A Unified Approach to Interpreting Model Predictions. Adv Neural Inf Process Syst. 2017. p. 4766–75. https://arxiv.org/abs /1705.07874.
40.
Moustakidis S, Plakias S, Kokkotis C, Tsatalas T, Tsaopoulos D. Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics. Future Internet. 2023; 15(5):174. https://doi.org/10.3390 /fi15050174.
41.
Pappalardo L, Rossi A, Natilli M, Cintia P. Explaining the difference between men’s and women’s football. PLoS One. 2021; 16(8):e0255407. https://doi.org /10.1371/journal.pone.0255407.
42.
Casal-Sanjurjo CA, Andujar MÁ, Ardá A, Maneiro R, Rial A, Losada JL. Multivariate Analysis of Defensive Phase in Football: Identification of Successful Behavior Patterns of 2014 Brazil FIFA World Cup. J Hum Sport Exer. 2021; 16(3):503–16. https://doi.org/10 .14198/jhse.2021.163.03.
43.
Tenga A, Mortensholm A, O’Donoghue P. Opposition interaction in creating penetration during match play in elite soccer: evidence from UEFA champions league matches. Int J Perf Anal Sport. 2017; 17(5):802–12. https://doi.org /10.1080/24748668.2017.1399326.
44.
Zani J, Fernandes T, Santos R, Barreira D. Penetrative passing patterns: Observational analysis of senior UEFA and FIFA tournaments. Apunts, Educ Fis Deport. 2021; (146):42–51. https://doi .org/10.5672/apunts.2014-0983.es. (2021/4).146.05.
45.
Scanlan M, Harms C, Cochrane Wilkie J, Ma’ayah F. The creation of goal scoring opportunities at the 2015 women’s world cup. Int J Sports Sci Coach. 2020; 15(5–6):803–8. https://doi.org /10.1177/1747954120942051.
46.
Sanmiguel-Codina J, Ballester R, Casal CA, Huertas F. Analysis of goal scoring patterns in the UEFA Women’s EURO 2022. Biol Sport. 2024; 42(2):45–56. https://doi.org/10.5114 /biolsport.2025.142646
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