The tremendous popularity of football over the last decades means it is one of the most competitive sports worldwide (Haugaasen and Jordet, 2012). Simultaneously, the development of outstanding football players has become a profitable and prestigious business for clubs and national associations (Relvas et al., 2010). Within this process of talent development, talent identification and talent selection play key roles. Talent selection describes the inclusion of identified talents into a development program (Williams and Reilly, 2000). By subsequently refering only to talent selection, we also imply the process of recognizing participants with the potential to become elite players (talent identification) within that expression. The function of talent selection is to recognize and choose the most promising youth players to receive a superior learning environment (e.g. specialized coaching) within the development systems of football organizations (Williams and Reilly, 2000). In general, it seems to be clear that an optimized and ongoing promotion of any young football participant would be the most promising model of talent development in terms of using the potential of the whole population (Cote and Hancock, 2015). However, resources within football organizations are still limited. Therefore, talent selection and deselections (with the implication of losing potential) have to be taken into account as an inevitable but necessary process of focusing resources on players with the highest potential for future elite performance (Suppiah et al., 2015).
Resulting from the necessity of talent selection, practitioners in the field face the ongoing question of what are the most effective methods for this procedure. Although multidimensional approaches for talent selection have been suggested for some time (Abbott et al., 2005; Vaeyens et al., 2008; Williams and Reilly, 2000), most clubs and associations still rely solely on subjective data from coach assessments (Christensen, 2009; Larkin and Reeves, 2018). Only specific objective data (e.g., from motor performance tests) is common within talent selections in several development programs, in addition to the coaches' eye (Honer et al., 2017). Thus, there is a gap between recommendations of the scientific community and the procedures currently executed in the field (Larkin and Reeves, 2018). In addition to the frequently discussed issues relating to a need for further coach education (Figueiredo et al., 2014), one reason for this gap might be the lack of scientific evidence for the superiority of multidimensional approaches for talent selection over the commonly used coach assessments or motor performance tests. Until now, there has not been a direct comparison between these different methodological approaches to talent selection (coach assessment vs. motor performance tests vs. multidimensional data). The possible differences between the three approaches in their potential to predict future success of young football players remain unclear (Schorer et al., 2017).
Scientific opinion differs on the utility of coach assessments for talent selection in football. On one hand, the holistic nature allows coaches to integrate information from several dimensions and to judge players as a whole (Buekers et al., 2015). Jokuschies et al. (2017) endorse this positive view of the coaches' eye by systematizing talent criteria from five junior national team coaches of the Swiss Football Association. They were able to show that coaches' rating within certain talent criteria were reliable and valid in their appraisal of players overall potential. Furthermore, coaches' ratings of the overall performance of players and overall potential show high interrater reliability (Fenner et al., 2016; Gullich et al., 2017; Zuber and Conzelmann, 2014). However, it could be argued that coaches' decisions within talent selection seem to be guided by subjective feelings (Johansson and Fahlen, 2017; Lund and Soderstrom, 2017), and practitioners in the field do not have a generally accepted talent model (Jokuschies et al., 2017). Additionally, ratings of overall in-game performance are, for example, influenced by the number of actions players have in a game (Tromp et al., 2013). Biological maturation also influences the subjective ratings of in-game performance (Cripps et al., 2016), although an experienced coaches' eye has the potential to be a valid estimator of maturation (Romann et al., 2017).
The value of motor performance tests for talent selection in football has been demonstrated in several cases through the measurement of physiological data and general motor performance (Dodd and Newans, 2018; Gonaus and Muller, 2012; Le Gall et al., 2010; Murr et al., 2018), as well as technical skills (Forsman et al., 2016; Honer and Votteler, 2016; Sarmento et al., 2018). However, the prognostic validity of physiological data (e.g., aerobic capacity) and general motor performance tests (e.g., sprint performance) in the long-term talent prediction of youth players is vigorously questioned due to development-related influences such as biological maturation and relative age (Johnson et al., 2017; Malina et al., 2017; Muller et al., 2017; Romann et al., 2018). For that reason, domain specific test items (e.g., technical skills) are thought to provide higher prognostic validity than general motor performance tests, although the reliability of the former is generally lower (Lidor et al., 2009). Therefore, the overall value of motor performance tests for talent selection in football is still under discussion (Leyhr et al., 2018).
In addition to coach assessments and motor performance tests, common scientific recommendations for multidimensional modelling in talent selection refer to psychological characteristics, familial support, and training history as potential predictors of future success (Figueiredo et al., 2009; Huijgen et al., 2014; Williams and Reilly, 2000). In particular, psychological characteristics are increasingly receiving attention in the field. For example, motivational, volitional, and self-regulation skills are particularly relevant (Gledhill et al., 2017; Zuber et al., 2015). Notably, coaches' perceptions of talent in elite youth football players are predominantly influenced by psychological characteristics (Jokuschies et al., 2017). However, confounding influences (such as limited knowledge about personality changes over time, difficulties with the operationalization of psychological items, the wide variety of designs used in research) inhibit a clear view on the value of psychological characteristics in talent selection in youth football (Gledhill et al., 2017; Sarmento et al., 2018). The influence of familial support, which can be expressed through emotional, financial, or organizational means, is traditionally discussed in the context of talent development (Cote, 1999; Knight et al., 2017), while its predictive power for talent selection has hardly been investigated (Zibung and Conzelmann, 2014). Therefore, a greater understanding of the possible impact of familial support in talent selection is still needed (Sarmento et al., 2018). Finally, the predictive value of training history, especially up to 12 years of age, is vigorously debated. Although there is evidence that some kind of early engagement in a specialized-sampling model, with extensive volume and a broad range of activities within football (Ford and Williams, 2017; Sieghartsleitner et al., 2018), seems to be fruitful for later success, data remain contradictory (Hornig et al., 2016).
Overall, the commonly used and recommended methodological approaches to talent selection each have pros and cons. Coach assessments are inherently subjective, which is always a bone of contention when considering psychometric properties (Johansson and Fahlen, 2017; Jokuschies et al., 2017; Lund and Soderstrom, 2017). However, the holistic character of coach assessments reflects the potential for coaches to integrate information from several different dimensions and to judge players more as a whole. This provides a clear benefit over other assessment methods and leads to easier decision making in terms of overall assessments in selecting or de-selecting a player (Buekers et al., 2015). In contrast, for motor performance tests and multidimensional measurements, psychometric properties in terms of objectivity and reliability are generally accepted by the scientific community and practitioners in the field (Honer et al., 2017). However, there is only limited evidence in support of the prognostic validity of each dimension in predicting later success in football (Sarmento et al., 2018). Furthermore, motor performance tests and multidimensional test batteries provide results consisting of several variables from various items and dimensions, and the issue of integrating these variables into an overall assessment and determining the load of specific variables is critical (Bergman and Trost, 2006; Till et al., 2016; Till et al., 2018). Given the importance of overall decision making on a single player, this is particularly problematic.
Given the current uncertainty on the use of different talent selection instruments, there is an increasing interest and requirement for a direct comparison of their relative values in terms of prognostic validity (Buekers et al., 2015; Schorer et al., 2017). If all methodological approaches have separate strengths and weaknesses, which is most useful in predicting late success: coach assessments, motor performance tests, or multidimensional data? Schorer et al. (2017) considered part of this question in a sample of female team handball players. They found that a logistic regression model of motor performance tests predicted a higher percentage (85.2%) of correctly selected female handball talents over ten years than national team coach assessment (79.3%). However, because of the exploratory nature of their...