## 1 Introduction  
Talent identification programs aim to select individuals most likely to become successful professional adult players. The opinion of expert scouts who observe and evaluate potential recruits during matches and training sessions is still the dominant method of identifying talented youth players [1, 2, 3, 4]. Because scouts must often assess players from just single matches or sessions, even the most experienced professional scouts can be vulnerable to biases and errors in judgement with such limited information [5, 6]. Further complicating the task of scouts is that youth players compete within annual age cohorts where there is substantial variation in age and physical maturity among the players. For example, in an age cohort of under 13 s, those players born in the month of January will be approximately 10% older than those born in December. Older players are more likely to be judged as better simply because they are mature, and physically stronger--and, consequently, youth football academies across the world are dominated by individuals born in the first half of the year [7]. This selection bias--referred to as the relative age-effect--is one of the most insipid forms of discrimination in team sports. Selection biases in youth football are not just a problem of discrimination and poor practice but they are likely to result in many talented young players missing out on being selected to representative teams or elite academies. Any method that increases the probability that promising players are selected, even when they are younger and less physically developed than their peers, could provide an advantage for clubs adopting this approach. Bio-banding is one attempt to mediate the effects of age-biases in team sports where players are placed into \"bands\" or groups based on stages of physical development rather than age for specific competitions and training [8; 9; 10; 11; 12; 13]. Despite the positive attitude towards these measures, \"bio-banding\" is rarely, if ever, used when players are being selected for academies [8; 9; 10; 11; 12; 13]. An alternative approach to addressing biases in youth selection is to develop and use age- and size-corrected assessments of individual players [14]. By comparing players using metrics of performance that are corrected for age (in days) and size, one could then provide fairer, more robust and accurate assessments of relative performances. But which metrics of relative performance should be utilised when accounting for age and size during talent identification? Assessments of individual performance in soccer-specific skill tests could be a profitable pathway for developing such a quantitative assessment tool. Individual performance in tests of closed skill are more reliable predictors of success in match-realistic games than athletic traits like speed or strength [15]. For example, individuals with higher performances in tests of closed skill are more likely to be better in 1v1 games [16], small-sided possession games [17] and 11v11 match success [15]. By quantifying how age and size affect individual performances in tests of skill associated with success in matches, or match-realistic scenarios, one could provide a tool for comparing individuals across the variation in ages and sizes observed within any age cohort. Rather than replacing the expertise of scouts in talent identification, these data could then be used to increase the information available when selecting players and reducing the probability of costly errors in selection. Quantifying how age- and size- affect a player's ability to control and pass the ball is one such potential tool for improving talent identification. The ability to quickly control the ball and then pass it accurately to a teammate is one of the most frequent and important technical skills performed during a football game [18]. Every professional player must be able to perform this skilled action with speed, accuracy, and consistency.