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Technical determinants of success in professional women’s soccer: A wider range of variables reveals new insights

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  • Laura M S de Jong
  • Paul B Gastin
  • Maia Angelova
  • Lyndell Bruce
  • Dan B Dwyer

Abstract

Knowledge of optimal technical performance is used to determine match strategy and the design of training programs. Previous studies in men’s soccer have identified certain technical characteristics that are related to success. These studies however, have relative limited sample sizes or limited ranges of performance indicators, which may have limited the analytical approaches that were used. Research in women’s soccer and our understanding of optimal technical performance, is even more limited (n = 3). Therefore, the aim of this study was to identify technical determinants of match outcome in the women’s game and to compare analytical approaches using a large sample size (n = 1390 team performances) and range of variables (n = 450). Three different analytical approaches (i.e. combinations of technical performance variables) were used, a data-driven approach, a rational approach and an approach based on the literature in men’s soccer. Match outcome was modelled using variables from each analytical approach, using generalised linear modelling and decision trees. It was found that the rational and data-driven approaches outperformed the literature-driven approach in predicting match outcome. The strongest determinants of match outcome were; scoring first, intentional assists relative to the opponent, the percentage of shots on goal saved by the goalkeeper relative to the opponent, shots on goal relative to the opponent and the percentage of duels that are successful. Moreover the rational and data-driven approach achieved higher prediction accuracies than comparable studies about men’s soccer.

Suggested Citation

  • Laura M S de Jong & Paul B Gastin & Maia Angelova & Lyndell Bruce & Dan B Dwyer, 2020. "Technical determinants of success in professional women’s soccer: A wider range of variables reveals new insights," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-12, October.
  • Handle: RePEc:plo:pone00:0240992
    DOI: 10.1371/journal.pone.0240992
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    References listed on IDEAS

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    1. Gunal Bilek & Efehan Ulas, 2019. "Predicting match outcome according to the quality of opponent in the English premier league using situational variables and team performance indicators," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 19(6), pages 930-941, November.
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    3. Marek Konefał & Paweł Chmura & Michał Zacharko & Jan Chmura & Andrzej Rokita & Marcin Andrzejewski, 2018. "Match outcome vs match status and frequency of selected technical activities of soccer players during UEFA Euro 2016," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 18(4), pages 568-581, July.
    4. Luca Pappalardo & Paolo Cintia, 2018. "Quantifying The Relation Between Performance And Success In Soccer," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(03n04), pages 1-30, May.
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    2. Shemuel Y. Lampronti & Elisa Operti & Stoyan V. Sgourev, 2024. "Rivalry as a Contextual Factor of Gender Inequality in Network Returns," Post-Print hal-04894940, HAL.
    3. Patricia Sánchez-Murillo & Antonio Antúnez & Daniel Rojas-Valverde & Sergio J. Ibáñez, 2021. "On-Match Impact and Outcomes of Scoring First in Professional European Female Football," IJERPH, MDPI, vol. 18(22), pages 1-9, November.
    4. Yan Ouyang & Xuewei Li & Wenjia Zhou & Wei Hong & Weitao Zheng & Feng Qi & Liming Peng, 2024. "Integration of machine learning XGBoost and SHAP models for NBA game outcome prediction and quantitative analysis methodology," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-25, July.
    5. Iyán Iván-Baragaño & Rubén Maneiro & José L. Losada & Antonio Ardá, 2021. "Multivariate Analysis of the Offensive Phase in High-Performance Women’s Soccer: A Mixed Methods Study," Sustainability, MDPI, vol. 13(11), pages 1-16, June.

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