Low prevalence of relative age effects in Luxembourg’s male and female youth football

Abstract

The relative age effect (RAE) is a well-established phenomenon in football. However, while the majority of previous studies focussed on established football nations, it remains unclear if the constraint of a limited population of soccer players in smaller countries associated with less strict selection procedures may reduce the risk of RAE. This study aims to investigate the RAE in Luxembourg that follows an ‘open-door’ selection policy in youth football due to the limited pool of players. Birthdates from all licensed and actively playing Luxembourgish youth footballers including all players of the youth national teams (396 girls and 10981 boys) competing in the season 2018/2019 were analysed and categorised into birth quarters and semesters. To further investigate a performance dependence of the RAE in amateur leagues, success was determined based on the teams’ rankings at the end of the season. Differences between observed and expected birthdate distributions were calculated across all licensed players and age groups, within the national teams, and for the top- and bottom-tier football teams using chi-square statistics. While a RAE was absent across all age groups (except U7), significant RAEs with high effect sizes were observed in the top-level and national teams. These findings contrast the substantial RAE effects in large football nations and suggest that open selection systems might reflect an environmental constraint that limit the prevalence of RAE in football. Further, this study indicates that a performance dependence of the RAE is not limited to high level football but already occurs on an amateur level.

Relative Age Effect Analysis in the History of the Ballon d’Or (1956–2023)

“…While our study did not reveal a relative age effect among top-ranked players in the Ballon d’Or, it is noteworthy that in badminton, lower categories demonstrated a significant RAE between 2014 and 2018, particularly in players winning medals at European championships [ 36 ]. This finding, despite the different contexts and age groups analyzed in our study (senior category), aligns with the established trend that RAE tends to be more consistent in lower categories [ 37 ] both in male sports [ 3 , 6 , 11 , 14 , 38 ] and in female sport [ 6 , 8 ], although in the female category, this trend is less consistent.…”

“…In sports, the cutoff date is commonly set as the 1st of January in most competitions, giving players born in the first months of the year a sporting advantage (explained by the maturational profile associated with a late maturation of athletes born in the last months of the year) [ 3 ]. In most countries, the cutoff date for youth sports is the 1st of January [ 4 ], and in football, the Fédération Internationale de Football Association (FIFA) youth tournaments have also used this cutoff date to establish age groups since 1997 [ 5 , 6 ]. In professional football the cutoff date can vary between countries; for example, England uses August 1st [ 7 ].…”

The relative age effect and its influence on athletic performance in Chinese junior female’ tennis players

“…Secondly, when organizing junior tournaments, the CTA should mitigate the negative impact of the RAE by reforming the grouping designations of junior tournaments and the cut-off dates for rotating competition groupings. Finally, as Simon et al [ 68 ] suggested, coaches need to retain all players to some extent and avoid selecting only the most athletically accomplished so that late-born athletes can have the same opportunities to train and compete as early-born athletes. We suggest that Chinese tennis coaches, when training and organizing their teams, should not limit themselves to the performance of junior athletes in terms of physical morphology indicators and so on, but should give full consideration to athletes born in different years and times of the year.…”

The Relative-Age Effect in Male Japanese Football: A CrossSectional Analysis from Youth to Senior Competitive Level

“…The statistical analysis was completed using the Jamovi Project. Chi-square goodness of fit test was used to compare the birth distribution of academy players to the national population distribution for Japan (observed vs expected), with Cramer's V: small (0.06 ≥), medium (0.17 ≥), and large (0.29 ≥) to analyse effect sizes [5,21]. Correspondingly, the odd ratio test (OR) was used to compare the odds of players in Q1, Q2, and Q3 with the odds of a player in Q4 to interpret the differences across quarters [13,22].…”