High-intensity endurance capacity assessment as a tool for talent identification in elite youth female soccer

ABSTRACT Talent identification and development programmes have received broad attention in the last decades, yet evidence regarding the predictive utility of physical performance in female soccer players is limited. Using a retrospective design, we appraised the predictive value of performance-related measures in a sample of 228 youth female soccer players previously involved in residential Elite Performance Camps (age range: 12.7–15.3 years). With 10-m sprinting, 30-m sprinting, counter-movement jump height, and Yo-Yo Intermittent Recovery Test Level 1 (IR1) distance as primary predictor variables, the Akaike Information Criterion (AIC) assessed the relative quality of four penalised logistic regression models for determining future competitive international squads U17–U20 level selection. The model including Yo-Yo IR1 was the best for predicting career outcome. Predicted probabilities of future selection to the international squad increased with higher Yo-Yo IR1 distances, from 4.5% (95% confidence interval, 0.8 to 8.2%) for a distance lower than 440 m to 64.7% (95% confidence interval, 47.3 to 82.1%) for a score of 2040 m. The present study highlights the predictive utility of high-intensity endurance capacity for informing career progression in elite youth female soccer and provides reference values for staff involved in the talent development of elite youth female soccer players.

[1]  P. Hayes,et al.  Excelling at youth level in competitive track and field athletics is not a prerequisite for later success , 2018, Journal of sports sciences.

[2]  S. Robertson,et al.  Classification of playing position in elite junior Australian football using technical skill indicators , 2018, Journal of sports sciences.

[3]  J. Schorer,et al.  Compromising Talent: Issues in Identifying and Selecting Talent in Sport , 2018 .

[4]  Tim Op De Beéck,et al.  Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning? , 2017, International journal of sports physiology and performance.

[5]  S. Lemeshow,et al.  Assessing the Calibration of Dichotomous Outcome Models with the Calibration Belt , 2017 .

[6]  Barry Drust,et al.  Match Physical Performance of Elite Female Soccer Players During International Competition , 2017, Journal of strength and conditioning research.

[7]  J. Baker,et al.  Searching for the elusive gift: advances in talent identification in sport. , 2017, Current opinion in psychology.

[8]  G. Atkinson,et al.  Changes in Sprint-Related Outcomes During a Period of Systematic Training in a Girls' Soccer Academy , 2017, Journal of strength and conditioning research.

[9]  Silvia Pogliaghi,et al.  Player's success prediction in rugby union: From youth performance to senior level placing. , 2017, Journal of science and medicine in sport.

[10]  Kok-Leong Ong,et al.  Predicting ratings of perceived exertion in Australian football players: methods for live estimation , 2016, Int. J. Comput. Sci. Sport.

[11]  N. Datson,et al.  An analysis of the physical demands of international female soccer match-play and the physical characteristics of elite players , 2016 .

[12]  A. Raynor,et al.  The application of a multi-dimensional assessment approach to talent identification in Australian football , 2016, Journal of sports sciences.

[13]  Kevin Till,et al.  Retrospective analysis of anthropometric and fitness characteristics associated with long-term career progression in Rugby League. , 2015, Journal of science and medicine in sport.

[14]  Heita Goto,et al.  Match Analysis of U9 and U10 English Premier League Academy Soccer Players Using a Global Positioning System: Relevance for Talent Identification and Development , 2015, Journal of strength and conditioning research.

[15]  P. Simon,et al.  Conventional and Genetic Talent Identification in Sports: Will Recent Developments Trace Talent? , 2014, Sports Medicine.

[16]  G. Bertolini,et al.  Comments on ‘Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers’ by Peter C. Austin and Ewout W. Steyerberg , 2014, Statistics in medicine.

[17]  Giovanni Nattino,et al.  A new calibration test and a reappraisal of the calibration belt for the assessment of prediction models based on dichotomous outcomes , 2014, Statistics in medicine.

[18]  B. Drust,et al.  Applied Physiology of Female Soccer: An Update , 2014, Sports Medicine.

[19]  N. Harris,et al.  Physiological Characteristics of International Female Soccer Players , 2014, Journal of strength and conditioning research.

[20]  Robert L Grant,et al.  Converting an odds ratio to a range of plausible relative risks for better communication of research findings , 2014, BMJ : British Medical Journal.

[21]  Ewout W. Steyerberg,et al.  Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers , 2013, Statistics in medicine.

[22]  E. Müller,et al.  Using physiological data to predict future career progression in 14- to 17-year-old Austrian soccer academy players , 2012, Journal of sports sciences.

[23]  Geraldine Naughton,et al.  Draft-camp predictors of subsequent career success in the Australian Football League. , 2012, Journal of science and medicine in sport.

[24]  Richard Williams,et al.  Using the Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects , 2012 .

[25]  David R. Anderson,et al.  AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons , 2011, Behavioral Ecology and Sociobiology.

[26]  N. Obuchowski,et al.  Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures , 2010, Epidemiology.

[27]  R. Philippaerts,et al.  Talent identification and promotion programmes of Olympic athletes , 2009, Journal of sports sciences.

[28]  R. Maughan,et al.  Requirements for ethics approvals , 2009, Journal of sports sciences.

[29]  Franco M Impellizzeri,et al.  Test validation in sport physiology: lessons learned from clinimetrics. , 2009, International journal of sports physiology and performance.

[30]  Carlo Castagna,et al.  Fitness determinants of success in men's and women's football , 2009, Journal of sports sciences.

[31]  Carlo Castagna,et al.  Effects of aerobic training on the exercise-induced decline in short-passing ability in junior soccer players. , 2008, Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme.

[32]  Warren B Young,et al.  Relationship between pre-season anthropometric and fitness measures and indicators of playing performance in elite junior Australian Rules football. , 2007, Journal of science and medicine in sport.

[33]  P. Krustrup,et al.  Physical demands during an elite female soccer game: importance of training status. , 2005, Medicine and science in sports and exercise.

[34]  Peter Krustrup,et al.  The yo-yo intermittent recovery test: physiological response, reliability, and validity. , 2003, Medicine and science in sports and exercise.

[35]  G. Beunen,et al.  An assessment of maturity from anthropometric measurements. , 2002, Medicine and science in sports and exercise.

[36]  A. Williams,et al.  Talent identification and development in soccer , 2000, Journal of sports sciences.

[37]  T. Reilly,et al.  Anthropometric and physiological predispositions for elite soccer , 2000, Journal of sports sciences.

[38]  D. Firth Bias reduction of maximum likelihood estimates , 1993 .

[39]  T. Reilly,et al.  Exercise and the Circadian Variation in Body Temperature Measures , 1986, International journal of sports medicine.

[40]  J. Schorer,et al.  Talent Identification in Sport: A Systematic Review , 2017, Sports Medicine.

[41]  Frank E. Harrell,et al.  Prediction models need appropriate internal, internal-external, and external validation. , 2016, Journal of clinical epidemiology.

[42]  M. Marfell-Jones,et al.  International standards for anthropometric assessment. , 2012 .

[43]  Thomas Reilly,et al.  Anthropometric and fitness characteristics of international, professional and amateur male graduate soccer players from an elite youth academy. , 2010, Journal of science and medicine in sport.

[44]  Joseph Coveney,et al.  FIRTHLOGIT: Stata module to calculate bias reduction in logistic regression , 2008 .