The database search was conducted in March 2026. This study was designed as a systematic review with an umbrella component and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (Page et al., 2021) guidelines.

Records were eligible if they addressed talent identification, selection, or development among youth or emerging athletes; were published in English between January 2020 and March 2026; and provided evidence directly relevant to the pre-defined review questions. Primary empirical evidence included quantitative and qualitative studies, cross-sectional and longitudinal studies, observational research, mixed-methods studies, and computational studies or predictive models.

Systematic reviews directly addressing the review questions were eligible for the umbrella component. Publications of conceptual relevance were retained if they directly addressed methodological and structural challenges in contemporary sports talent identification and development and contributed to the interpretation of empirical evidence.

Records focusing exclusively on adult performance enhancement, non-sporting talent domains, conference abstracts, or editorials and commentaries not substantively linked to the pre-defined review questions as well as publications that did not address talent identification, selection, or development were excluded. The final synthesis included 32 eligible records comprising primary empirical evidence, supplementary conceptual evidence, and two systematic reviews (Han et al., 2023; Tang et al., 2026) that constituted the core component. Evidence types were distinguished throughout the synthesis process, and primary studies already included within the systematic reviews were not counted as independent, additional support at the study level.

The study selection process followed the PRISMA framework. The initial search yielded 703 records. After removal of 186 duplicates, 517 records remained for title and abstract screening. During screening, 438 records were excluded. A total of 79 full-text reports were assessed for eligibility, of which 47 were excluded because they did not satisfy the predefined eligibility criteria. Ultimately, consequently, 32 eligible records were retained in the final synthesis. Title and abstract screening and full-text eligibility assessment were conducted by the principal investigator using the prespecified eligibility criteria. Because screening was undertaken by a single reviewer, independent duplicate screening was not performed and no reviewer-disagreement resolution procedure was applicable.

A systematic literature search was conducted across PubMed, Scopus, Web of Science Core Collection, SPORTDiscus, and IEEE Xplore. The strategy was adapted to the syntax and searchable fields of each source while retaining four principal concept groups: talent identification/development, sport/athletes, youth/developmental populations, and assessment/prediction-related factors. Boolean operators, phrase searching, truncation where supported, and source-specific field searching were used.

The searches covered publications from 1 January 2020 through March 2026. The electronic searches yielded 674 records: PubMed (n = 142), Scopus (n = 198), Web of Science Core Collection (n = 164), SPORTDiscus (n = 96), and IEEE Xplore (n = 74). An additional 29 records were identified through reference-list searching, producing 703 records before duplicate removal. Complete source-specific strategies are reported in Table 1.

Table 1.Electronic information sources and complete search strategy.

Data extraction was conducted by the principal investigator using a standardized extraction framework. The following information was recorded:

Author(s) and year of publication;Sport and sample characteristics;Study design;Predictor domains examined (e.g., biological, technical, psychological);Analytical or modeling methods;Outcome definitions;Key findings;Reported limitations.

Due to variation across sports, predictor variables, and research designs, a meta-analysis was not considered appropriate.

Instead, findings were organized into five thematic evidence domains:

1. Conceptual and structural foundations of talent identification;

2. Biological maturation and relative age effects;

3. Multidimensional performance profiling;

4. Reliability of subjective versus objective assessments;

5. Machine learning and computational modeling.

This thematic approach allowed for the identification of consistent patterns and methodological trends across the included studies.

Methodological limitations were assessed using a design-specific framework based on established critical appraisal tools. Quantitative observational studies were evaluated against the relevant Joanna Briggs Institute (JBI) domains; qualitative and mixed-methods studies were appraised using qualitative design principles and the appropriate MMAT criteria, respectively; and prediction or machine learning studies were assessed against PROBAST domains covering participants, predictors, outcomes, analysis, validation, and application. The two systematic reviews included in the overarching component were appraised separately against AMSTAR 2 domains. Given the methodological diversity of the included evidence, appraisal results were not converted into a standardized numerical quality score. Conceptual evidence was not subjected to an empirical risk-of-bias assessment. Table S1 summarizes study-level methodological strengths and issues. The assessment was conducted by the lead researcher and was not independently replicated.

Consequently, 32 studies were included in the final synthesis. The study selection process is summarized in Figure 1.

The 32 included records, published between 2020 and 2026, represented a wide range of sporting contexts and methodological approaches. Soccer accounted for the largest proportion, with 18 records. The remaining records represented volleyball (3), rugby union (2), multi-sport analyses (3), basketball (1), swimming (1), tennis (1), triathlon (1), track and field (1), and Australian football (1). Together, these categories account for all 32 included records.

The evidence base included cross-sectional and observational investigations, prospective and retrospective longitudinal studies, qualitative and mixed-methods research, prediction and machine-learning studies, complementary conceptual evidence, and two systematic reviews. Because prediction modelling and machine learning may be embedded within longitudinal or observational designs, these methodological categories were not treated as mutually exclusive study-design classifications.

Sample sizes varied considerably, ranging from small qualitative studies involving expert coaches or scouts (8 - 25 participants) to large-scale national studies with more than 10,000 young athletes.

In the included studies, a wide range of predictive domains were examined, including anthropometric characteristics, measures of physiological performance, technical and tactical skills, psychological traits, and environmental influences.

Methodological quality varied across the included records, reflecting heterogeneity in study design, population, outcome definition, and analytical approach (Table S1). Frequent concerns among observational and cross-sectional studies included context-specific sampling, potential residual confounding, and reliance on contemporaneous selection or squad membership as a proxy for longer-term talent outcomes.

Prospective and longitudinal investigations generally provided stronger temporal evidence. Large-scale studies such as Höner et al. (2021), Leyhr et al. (2025), and Saward et al. (2020) linked earlier athlete characteristics with subsequent developmental or selection outcomes and were therefore given greater interpretive weight for prognostic questions. Prediction and machine-learning studies introduced additional concerns relating to model complexity, potential optimism, external validation, interpretability, and transportability. The two systematic reviews (Han et al., 2023; Tang et al., 2026) were interpreted in relation to heterogeneity and methodological limitations in their underlying evidence.

Biological maturation and relative age have emerged as recurring factors associated with selection and representation processes within youth athlete development pathways. Studies across sports such as soccer, rugby union, and volleyball have highlighted disparities linked to relative age and/or maturation status in the selection and development of young athletes (Kelly et al., 2021; King et al., 2024; Morganti et al., 2025; Rubajczyk & Rokita, 2020; Saward et al., 2020).

Athletes born earlier in the year and/or those who are more biologically mature may exhibit temporary advantages in anthropometric measurements and physical performance during adolescence. Such differences can influence selection decisions when the primary focus is on current physical performance levels (Kelly et al., 2021; King et al., 2024; Rubajczyk & Rokita, 2020). Evidence from longitudinal studies indicates that growth and development trajectories can shift during adolescence, and that early physical advantages should not be viewed as definitive indicators of long-term elite potential (Saward et al., 2020; Höner et al., 2021).

A significant methodological trend is the notable increase in the use of multidimensional assessment methods; studies have integrated a range of indicators including anthropometric, physiological, skill-based, tactical, and cognitive factors, alongside other performance metrics rather than relying exclusively on isolated physical measures (Dimundo et al., 2021; Höner et al., 2021; King et al., 2024; Leyhr et al., 2025; Sgrò et al., 2024; Barraclough, 2025b).

Evidence indicates that multidimensional assessment yields information that extends beyond discrete performance indicators, although the contribution of each domain varies depending on the sport, the developmental stage, and the outcomes of the selection process (Höner et al., 2021; King et al., 2024; Leyhr et al., 2025).

Many studies included in this research have examined objective performance tests, expert subjective assessments, or the relationship between these two approaches (Bergkamp et al., 2022; Dugdale et al., 2020; Höner et al., 2021; Höner et al., 2025). Objective assessments provide standardized measurements of selected physical and motor performance characteristics, whereas coaches and talent scouts can incorporate contextual information related to performance within sport-specific environments.

Evidence comparing these approaches suggests that objective and subjective assessments capture at least partially different aspects of an athlete’s potential (Höner et al., 2021; Höner et al., 2025). Consequently, the available evidence supports exploring their complementary use within structured, hybrid assessment frameworks, rather than assuming the inherent superiority of one approach over the other.

There is growing interest in exploring computational methods and machine learning techniques to analyze multidimensional data related to talent identification (Siener et al., 2021; Liu et al., 2024; Tang et al., 2026). In the realm of junior tennis, the predictive validity of statistical methods based on motor abilities has been examined (Siener et al., 2021), while “random forest” models have been applied to anthropometric and physiological characteristics to predict talent in swimming (Liu et al., 2024).

However, methodological evidence indicates persistent challenges particularly regarding external validation, inconsistent outcome definitions, model interpretability, and the generalizability of findings across different sports and populations (Tang et al., 2026). Therefore, machine learning should be viewed as an advanced approach to support decision-making, rather than an established substitute for traditional assessment methods or expert judgment.

Available evidence indicates that talent identification is typically studied as a multidimensional process encompassing a combination of physical, technical, tactical, cognitive, psychological, and contextual characteristics (Dimundo et al., 2021; Höner et al., 2021; Leyhr et al., 2025; Sgrò et al., 2024). Furthermore, both biological maturation and relative age have been linked to patterns of youth selection and representation (Kelly et al., 2021; King et al., 2024; Morganti et al., 2025; Rubajczyk & Rokita, 2020). Objective tests and expert assessments appear to provide complementary information, rather than demonstrating the consistent superiority of one method over the other (Bergkamp et al., 2022; Dugdale et al., 2020; Höner et al., 2021; Höner et al., 2025). While computational methods have shown promising potential, they require further robust external validation (Siener et al., 2021; Liu et al., 2024; Tang et al., 2026).

A key finding in the reviewed literature was the growing shift from reductionist talent identification models to multidimensional assessment frameworks. Historically, talent identification systems have relied heavily on isolated anthropometric and physiological indicators, such as height, speed, and strength. Although these variables are important in assessing short-term athletic performance, the evidence compiled in this review demonstrates their limited ability to predict long-term elite success.

This finding supports contemporary theoretical perspectives that portray talent as a dynamic developmental construct arising from complex interactions between biological, psychological, technical, and environmental factors (Baker & Wattie, 2018; Johnston et al., 2018). Multidimensional profiling approaches, which combine physical testing with assessments of technical skills, tactical perception, and psychological traits, have consistently been associated with improved predictive power across various athletic contexts.

The adoption of multidimensional assessment frameworks reflects a broader shift in athlete development research toward systems-based performance development models, which highlight an athlete’s potential through the interplay between individual abilities and environmental opportunities. Consequently, effective talent identification systems must move beyond isolated performance indicators and instead incorporate integrated assessment models capable of accommodating the complexity of athletes’ developmental pathways.

Another key finding of this review is the continued influence of biological maturation and relative age on youth athletic talent identification systems. In multiple sporting contexts, athletes born earlier in the selection year were disproportionately represented in elite development programs. These findings are consistent with previous research showing that early-born athletes often possess temporary developmental advantages during adolescence due to differences in the timing of biological maturation (Cobley et al., 2009; Kelly et al., 2021).

These advantages are typically manifested through increased body size, strength, and neuromuscular development, which may improve short-term performance outcomes in youth competitions. However, longitudinal evidence suggests that these early physical advantages do not reliably predict outstanding long-term performance. As maturation differences gradually diminish during late adolescence, athletes who matured later and were previously overlooked may achieve similar or even better performance levels.

These findings suggest that relative age effects represent structural biases inherent in youth sports development systems, rather than accurate indicators of athletic potential. Consequently, many researchers have advocated for the implementation of maturity-adjusted assessment strategies, such as biological age estimation, growth trajectory monitoring, and age grouping policies designed to minimize selection bias.

Addressing these structural biases is crucial for improving the accuracy and fairness of talent identification systems, particularly in sports where early selection decisions can significantly impact access to coaching, training resources, and competitive opportunities.

The reviewed studies also highlight the complementary roles of objective diagnostic tests and expert assessment in talent identification processes. Objective assessments, including standardized motor skills tests and physiological performance measurements, provide reliable and quantifiable indicators of athletes’ abilities. However, these measures are often limited in their ability to capture complex performance characteristics, such as tactical awareness, cognitive decision-making, and the ability to adapt within dynamic competitive environments.

In contrast, expert assessments, conducted by experienced coaches and scouts, can provide valuable contextual insights into athletes’ performance within specific game contexts. However, subjective assessments can be influenced by cognitive biases, inferential methods, and inconsistencies in assessment criteria. The evidence synthesized in this review supports the complementary integration of objective assessment and structured expert judgment through hybrid assessment models. This hybrid approach reflects the understanding that talent identification is not simply a measurement problem, but a decision-making process that requires integrating quantitative data with structured expert judgment.

Recent advances in artificial intelligence and machine learning have opened new possibilities for analyzing complex talent identification datasets. Machine learning algorithms are particularly well-suited to modeling nonlinear relationships between multiple, interacting performance variables, making them valuable tools for analyzing multidimensional athlete development data.

Several studies included in this review have applied machine learning models, such as neural networks and random forest algorithms, to athlete datasets containing a large number of performance indicators. These models have demonstrated promising capabilities in identifying patterns associated with athlete development and performance outcomes.

However, despite their analytical advantages, the application of machine learning in talent identification is still in its early stages. Many studies have focused on predicting short-term performance outcomes rather than long-term elite success, and few models have undergone external validation across diverse athlete populations.

Furthermore, machine learning approaches raise significant ethical and methodological challenges, including potential algorithmic bias, limitations in interpreting complex models, and the risk of reinforcing existing structural inequalities in athlete selection systems.

For these reasons, current evidence suggests that machine learning should be viewed as a decision support tool, not a replacement for human expertise in talent identification processes.

Taken together, the findings of this review support further development and evaluation of integrated hybrid systems that combine multidimensional athlete profiling, expert assessment, and advanced computational analysis.

These systems will incorporate several key principles:

Multidimensional assessment of athletes’ abilities;Maturity-adapted assessment strategies;Longitudinal monitoring of athletes’ developmental trajectories;Integration of expert opinions and objective diagnoses;Transparent and ethically sound computer modeling.

Implementing these principles will enable talent identification programs to better account for the complexity and uncertainty inherent in athlete development, while minimizing the structural biases that currently influence youth selection processes.

The findings of this review point to several important implications for practitioners, sports organizations, and policymakers responsible for athlete development systems. First, early selection processes should be approached with caution, particularly in sports where biological maturation differences significantly impact youth performance. Talent development pathways may benefit from delaying specialization and providing broader opportunities for athletes to participate, allowing more time for developmental differences to stabilize.

Second, talent identification systems should prioritize longitudinal monitoring of athletes rather than evaluation at a single point in time, enabling coaches and practitioners to track developmental trajectories over time.

Finally, as machine learning and data analytics become increasingly integrated into sports science practices, governing bodies should establish clear ethical guidelines and transparent criteria for algorithmic decision-making within athlete selection systems.

This review has several important strengths. First, it focuses on integrating recent empirical evidence from various sports and methodologies, providing a comprehensive overview of contemporary talent identification research. Second, the study integrates biological, psychological, and computational perspectives, enabling a multidimensional understanding of athlete development. Finally, the application of the PRISMA framework enhances the transparency and methodological rigor of the review process.