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

• Published in: PloS one 2020-10, Vol.15 (10), p.e0240992-e0240992
• Main Authors: de Jong, Laura M S, Gastin, Paul B, Angelova, Maia, Bruce, Lyndell, Dwyer, Dan B
• Format: Article
• Language: English
• Subjects: Accuracy; Achievement; Athletic ability; Athletic Performance - physiology; Australian football; Biology and Life Sciences; Computer and Information Sciences; Datasets; Decision analysis; Decision trees; Engineering and Technology; Evaluation; Feature selection; Female; Humans; Linear Models; Medical ethics; Methods; People and places; Physical Sciences; Physical training; Physiological aspects; Research and Analysis Methods; Research ethics; Sample size; Science Policy; Soccer; Soccer - physiology; Social Sciences; Sports training; Success; Supervision; Tournaments & championships; Training; Variables; Women; Women's soccer
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0240992

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.