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Record W4416976947 · doi:10.3389/fspor.2025.1745607

Editorial: 40 years of relative age effects in sport: lessons from the past and directions for the future

2025· article· en· W4416976947 on OpenAlexaffabout
Adam L. Kelly, Kathryn Johnston, A. McAuley, Joseph Baker

Bibliographic record

VenueFrontiers in Sports and Active Living · 2025
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
Fundersnot available
KeywordsIce hockeyAthletesCoining (mint)Age groupsPopulation

Abstract

fetched live from OpenAlex

2024 marked 40 years since Grondin, Deshaies, and Nault's (1984) seminal work on relative age effects in sport. In their article, Trimestres de Naissance et Participation au Hockey et au Volleyball [Birth Trimesters and Participation in Hockey and Volleyball], they analysed the birthdate distribution of competitive youth and professional ice hockey and volleyball cohorts from North America. They revealed that players born in the early months of the selection year were overrepresented, whereas those born later in the year were underrepresented in ice hockey, although this pattern was not observed in their volleyball sample. Unaware of this article, Barnsley, Thompson, and Barnsley (1985) shortly after reaffirmed these results in North American ice hockey, coining the term "relative age effects" (RAEs) to describe the phenomenon. They argued that the age group structures used to band athletes contributed to the loss of potentially talented individuals, as the abilities of relatively younger athletes were not accurately reflected in their performance due to age-related disadvantages. These foundational studies went on to inspire hundreds of relative age investigations worldwide.Fast forward to today, and despite these early warnings, RAEs have proven remarkably persistent and difficult to mitigate in high-performance sport systems (see Cobley et al., 2009, Musch & Grodin, 2001, Smith et al., 2018, and Webdale et al., 2019 for reviews; see Dixon et FRONTIERS EDITORIAL 2 al., 2020, Kelly, Côté, Turnnidge, et al., 2021, and Kelly Côté, Jeffreys, et al., 2021 for editorials). Notably, RAEs have been shown to be widespread across both boys' and girls' youth sport, significantly influencing the processes of identification, selection, and development of young athletes (McAuley et al., 2024). These effects have, in turn, shaped long-term outcomes related to performance, participation, and personal development in sport (Kelly et al., 2022;Kelly, Wilson, et al., 2020). Over the years, the relative age literature has reinforced several other key insights. For example, RAEs have tended to be more pronounced in popular sports such as basketball, soccer, ice hockey, and rugby (Romann et al., 2020), as well as in more competitive contexts as athletes progress into talent development systems (Ulbricht et al., 2015).The 40 th anniversary of the original relative age studies offered a timely opportunity to reflect on developments in both research and practice. It also served as a prompt to look ahead and consider how athlete development systems can be improved to better serve all individuals, regardless of birth month. To help achieve this, we created this research topic as a platform for researchers and practitioners to reflect on RAEs and consider how we can move this area forward. As part of the call for articles, we felt it was critical for the field to adopt methodologies that not only reviewed the extensive body of literature across different sport settings, but also applied theoretical frameworks to deepen our understanding of how RAEs occur. Importantly, to influence practice beyond academic discussion, we believed it was essential to also capture studies that designed, implemented, and evaluated a range of relative age solutions across In total, 85 authors from 14 countries contributed to the 23 articles included in this research topic. Studies were a mixture of quantitative (n=16), qualitative (n=2), and mixed method (n=1) approaches. The majority of submissions were empirical studies (n=20), with perspectives (n=2; Grondin, 2024;Sweeney et al., 2024) and a reflection (n=1; Barnsley, 2025) also included. Of the empirical studies, 14 used analyses of large datasets to examine the prevalence of RAEs over at least one season, and of those articles, eight tracked RAEs over two seasons or more to examine historical trends. The remaining empirical studies used a mixture of measurement data (n=2), questionnaire data (n=1), interview data (n=1), and some combinations of these approaches (n=2). These empirical studies included a total of 213,789 participants (or individuals within the sample under investigation if secondary analysis was used), with 91,802 of those being women and girls, and 118,288 being men and boys. The individuals at the focus of the research came primarily from Europe and North America, with the two most represented countries being Germany (n=4) and Spain (n=3).Within the articles, 16 focused on athletes and RAEs, two focused on the perceptions of interest holders on RAEs and potential solutions, one focused on the relative age of coaches, and one focused on the perspective of coaches. The most popular sport researched was, unsurprisingly, soccer (n=11), followed by ice-hockey (n=3), and handball (n=2), while under researched sports such as fencing (Bonito et al., 2025), orienteering (Ferriz-Valero et al., 2025), squash (Kelly Shanmugaratnam, et al., 2025), and swimming (Difernand et al., 2025) were also examined. Women and girls were the sole focus of four studies, men and boys were the sole focus in eight studies, while another five studies included mixed genders, with the remaining two empirical studies not reporting the gender of participants.With regard to procedures and analyses, some new and novel approaches were used. For example, Schorer et al. ( 2025) employed a retrospective and a longitudinal method. This approach helps to illuminate changes over time in birth quartile distribution, reiterating the need for relative age research to embrace longitudinal designs. It was also encouraging to see two studies (Kelly, Shanmugaratnam, et al., 2025;Smith et al., 2025) employ interviews to gain the subjective experiences of those who influence and interact with RAEs. As well, one of the few studies that examined RAEs in the coaches themselves was included in this special issue (see Grondin et al., 2025). This variety of samples and approaches presents a more nuanced and dynamic portrait of the impact of RAEs across multiple sport systems (see Table 1 for a summary of the articles included). As expected, there were several studies that examined RAEs across athlete development pathways, particularly in soccer. While some of these used typical approaches to identifying and considering RAEs (e.g., Heilmann et al., 2024;Peña-González et al., 2025), the samples and research questions explored extended our knowledge to new populations. For instance, Pérez- Several papers analysed the role of relative age and biological age. This work emphasised that, while they are both vital to consider during the identification, selection, and development processes in sport (Kelly & Williams, 2020), they are two separate constructs that operate independently (Radnor et al., 2023). Out of three studies, two focused on soccer, with one comprised of participants in a Spanish professional academy (Peñín-Grandes et al., 2025), and the other the first examination of relative and biological age constructs in an Austrian soccer context (Wenger & Csapo, 2025). The third study investigated the association of maturation and relative age with talent selection in German youth handball players, whereby the authors presented evidence that a unique inter-play may exist between the constructs, as it could be crucial for relatively younger players to mature earlier to increase their selection odds (Thieschäfer et al., 2025).Research on a range of sports including cricket (Walter et al., 2021), handball (de la Rubia et al., 2023), rugby (McAuley et al., 2025), and soccer (Bradley et al., 2019) has showed 'bio-banding' (i.e., a format that groups athletes based on maturity status rather than chronological age; Cumming et al., 2017) to be an effective way at mitigating biological age effects. It is important to emphasise, though, that bio-banding is a potential solution for maturation-related biases, and not for RAEs. Thus, integrating bio-banding alongside relative age solutions, or combining solutions together (e.g., Abbott et al., 2020;Helsen et al., 2021;Hogan et al., 2022;Sedeaud et al., 2025), may be fruitful areas for future research.A persistent call from relative age researchers, including in this research topic, is identifying potential solutions. Although many authors reiterated these sentiments in their articles, only three empirically explored this area, with only one evaluating the implementation of the solution. Building on previous quantitative work on 'birthday-banding' (i.e., each individual athlete competes with those of the same chronological age, moving up to the next birthday-band on their birthday), which corresponded with no RAEs exist in the England Squash Talent Pathway (Kelly, Jackson, et al., 2020), Kelly, Shanmugaratnam, et al. (2025) explored coaches' perspectives of the approach. They noted how birthday-banding produced fairness for athletes who might have been removed due to their birthday, offering a possible relative age solution for other racket sports.The Royal Netherlands Football Association (KNVB) relative age solutions project also contributed two studies. First, following a call to action for interest holders to propose relative age solutions to KNVB, almost 200 proposals were categorised into 13 independent approaches (Kelly, Zwenk, et al., 2025a). Interestingly, whilst no new suggestions outside the existing literature were proposed, only two have been empirically tested in soccer (Helsen et al., 2021;Mann & van Ginneken, 2017). Second, Kelly, Zwenk, et al. (2025b) evaluated these 13 proposed solutions, using a two-round adapted e-Delphi study with 15 international experts (i.e., researchers and practitioners). Generally, highly rated solutions perceived to effectively moderate RAEs were expected to be more challenging to implement, whereas those more feasible to implement were considered less effective. Results also showed regular disagreement amongst the international experts, highlighting that achieving consensus on possible relative age solutions may be challenging.As part of this research topic, we were fortunate to include four decades worth of reflections from the inaugural researchers of RAEs-Professor Simon Grondin and Professor Roger Barnsley. In his opinion article, Grondin (2024) closed by repeating a key takeaway message from his original 1984 paper, emphasising how even after 40 years, many sport systems have still not comprehended this simple message: "It must be remembered that two children born in the same year do not necessarily have the same age" (Grondin et al., 1984, p. 3). Barnsley (2025) repeated these sentiments in his perspective article, showcasing that the data they published from the 1983 Western (WHL) and Ontario (OHL) Major Junior A Hockey Leagues (Barnsley et al., 1985) and from the 1989 FIFA U17 and U20 World Cups (Barnsley et al., 1992) remains almost identical, whereby those born in the first three months are overrepresented (cumulative ~40%) and those born in the last three months of the selection year are underrepresented (cumulative ~12.5%).Overall, it is fair to say that whilst our understanding of RAEs has grown considerably over the last four decades, not a lot has changed in practice. Indeed, both Grondin and Barnsley expressed their disappointment with how the applied field has not utilised the comprehensive research knowledge. We call for researchers and practitioners to work closer together to be solution-focused, helping create more developmentally appropriate learning environments for all young athletes to achieve their potential in sport, irrelevant of their birthdate.Wi a large body of research has documented the presence of RAEs across various sporting contexts, and despite the ongoing recommendations from researchers and practitioners, efforts to design, implement, and evaluate effective and feasible solutions have been limited. Moreover, while several theoretical explanations have been proposed and greater clarity provided on the differences between biological and relative age, there remains a lack of empirical studies that clearly identify the underlying causes of RAEs in sport. Such studies would be invaluable in guiding the development of context-sensitive interventions (Kelly, Côté, Turnnidge, et al., 2021). In addition to the continuously growing number of relative age studies across a variety of sport environments, an important next step will be to systematically review the literature, using meta-analyses, in widely researched sports. This will aid the development of sport-specific consensus on RAEs and outline next steps.Finally, we call for researchers and practitioners to focus their efforts not only on replicating relative age research in different contexts, but also ensuring each study meaningfully adds new knowledge to the field. Researchers should use relative age studies as an opportunity to examine multi-, inter-, and trans-disciplinary features that contribute towards RAEs, as well as explore the direct and indirect effects of relative age to help explain how they occur. Doing so will inform strategies that are effective and feasible for sport-specific contexts, helping to create youth sport environments that foster long-term performance, participation, and personal development outcomes for every young person, regardless of when they are born.It was a pleasure to assemble this research topic. We hope the reader finds the articles useful in helping to advance their own research and practice. Thanks to the authors, reviewers, and participants for their support-without their efforts, advancing our knowledge of RAEs would not have been possible.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.019
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.029
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0050.002
Science and technology studies0.0030.004
Scholarly communication0.0070.007
Open science0.0060.002
Research integrity0.0190.022
Insufficient payload (model declined to judge)0.0170.014

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.281
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2025
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Same venueFrontiers in Sports and Active LivingSame topicSport Psychology and PerformanceFrench-language works237,207