Prevalence of the Relative Age Effect in Spanish Orienteering: An Analysis by Sex and Competitive Level

Abstract

This study investigates the relative age effect (RAE) in Spanish orienteering, comparing birth distributions between the general population and federated athletes, as well as across different age groups. A cross-sectional and retrospective observational analysis was conducted on data from 34,718 athletes federated (female: n = 12,338; male: n = 22,380) with the Spanish Orienteering Federation (FEDO) between 2005 and 2023. Birth distributions by quartiles and semesters were compared with birth data from the Spanish National Institute of Statistics. Chi-square tests, Z-tests for proportions, and odds ratio (OR) analysis were used to assess differences and the magnitude of the RAE. A significant RAE was found in the total population of federated athletes and the youth and male elite subgroups, with an over-representation of athletes born in the first semester of the year. The effect was small in magnitude but persistent in youth categories and intensified in the male elite category. In contrast, no significant RAE was observed in the female elite category. The RAE exists in Spanish orienteering, although its magnitude is smaller compared to other sports. The persistence of the RAE in the male elite category suggests that advantages accumulated in formative stages influence access to higher levels. Strategies to mitigate the impact of the RAE in talent identification and development are recommended.

1. Introduction

In the sports domain, birth dates are frequently used to classify young athletes into different categories. Previous studies have indicated that young athletes born near the selection cut-off date (i.e., the oldest in their group) and those born far from this date (i.e., the youngest) may exhibit significant differences in their physical and psychological development [1]. This distribution bias results from the age groups established by sports organizations, which typically group young athletes into categories spanning two consecutive birth years. Although this approach aims to balance competition among athletes, it causes notable differences in relative age: two children competing in the same category may differ by up to 23 months if they are born in different years, while those born in the same year can show a variation of up to 11 months in age [2]. This generates an advantage for athletes born close to the cut-off date in terms of sports performance compared to younger athletes [1]. As a result, during assessments conducted by sports organizations, the former are more easily identified as talented or promising compared to their later-born peers [3]. This increases their chances of being selected by coaches and scouts [4,5,6]. Consequently, there is a disproportionate representation of athletes born near the selection date. This phenomenon is known as the relative age effect (RAE) [7,8].

Various mechanisms triggering the RAE have been explored in numerous studies, with the “maturation-selection” hypothesis being the most widely accepted explanation for its existence [9]. This is due to the delay in the physical, cognitive, and emotional development of children born later within the same competitive year [1]. However, according to Hancock et al. [10], physical maturity should not automatically be equated with talent, as social agents often falsely associate physical maturity with actual skill differences, thereby accelerating the RAE through this misconception. These authors propose that parents influenced by the Matthew effect, coaches through the Pygmalion effect, and athletes through the Galatea effect. Therefore, the interaction of these three effects helps to understand how the RAE can have a lasting and significant impact on sports performance and success [10]. A child born in the early months of the year may be perceived as more talented due to their greater physical maturity at the time of comparison. Coaches’ expectations may be higher for this athlete (Pygmalion effect), leading the athlete to develop greater confidence and belief in their abilities (Galatea effect). As the athlete receives more training and opportunities, their initial advantage is amplified (Matthew effect), perpetuating their success and reinforcing the perception of their talent. According to the heuristic model of development systems based on constraints proposed by Wattie et al. [11], the RAE is multifactorial and cannot be understood as an isolated phenomenon but rather as the result of a complex system of interactions among three types of constraints (individual, environmental, and sport-specific), highlighting the need for interventions adapted to the multiple levels affecting athlete development. Thus, the RAE can impact the developmental capacity of later-born athletes, potentially leading them to abandon their sport altogether [8,12,13,14], resulting in a significant loss of talent. Given the pressure to achieve short-term results, players with earlier birth dates often have a higher likelihood of being selected, leading to more incentives and real opportunities [9], while the opposite is true for later-born athletes.

In addition to the RAE, recent research has also emphasized the influence of birth order on an athlete’s likelihood of success. A study by Yamamoto et al. [15] specifically examined how both birth month and birth order affect the probability of becoming a professional football player. Their findings revealed that, alongside the RAE, the probability of reaching the professional level significantly decreased with increasing birth order. In fact, players born later in the birth order were less likely to make it to the top level of competition. The study also highlighted an interesting nuance: second-born players with an older brother tended to have higher salaries than those without siblings, suggesting that birth order may play a role in the development of certain skills or characteristics that contribute to success in sports. Similarly, Gil-Madrona et al. [16] examined the role of family characteristics, including birth order, in psychomotor development and found that children with siblings exhibited greater motor and affective development than only children. This research supports the notion that family dynamics, including birth order, also may influence the developmental trajectories of athletes.

The RAE has been the subject of numerous studies across a wide range of sports disciplines, being observed in both youth and adult sports [17,18,19]. However, soccer and ice hockey stand out among team sports, with the latter serving as the starting point for research on this phenomenon in the sports field [20]. The RAE in ice hockey has been widely studied, particularly in the United States and Canada, with the study by Barnsley et al. [7] being one of the first to address this issue. The RAE has been identified both in professional leagues [7,21] and in youth competitions [22,23]. Although the literature consistently supports the existence of the RAE in this sport, several factors can influence its magnitude, including age, competition level, season, and playing position. Specifically, data from Barnsley et al. [7] show a significant decrease in the likelihood of reaching higher competitive levels for athletes born in the last months of the year, a trend observed from youth categories to professional leagues. This trend is also evident in soccer, one of the most widely studied sports in relation to the RAE due to its global reach. Research across different countries and competition levels consistently shows a significant overrepresentation of players born in the first months of the selection year. For instance, Verhulst [24] found that 55–60% of professional players in Belgium, France, and the Netherlands were born in the first semester, while Musch and Hay [25] reported similar patterns in Japan (66%) and Germany (56%). In youth international tournaments, Barnsley et al. [26] observed that up to 79% of players in U-17 and U-20 World Cups were born in this period. The RAE is particularly pronounced in youth categories, where selection processes favor early-born players [3]. Studies indicate that this advantage is more pronounced at younger ages and is directly influenced by the cut-off date used for age group classification. Beyond talent selection, the RAE has also been linked to competitive success [27] and long-term player retention, with dropout rates increasing for those born later in the year [14]. Overall, soccer exemplifies how the RAE shapes player development pathways, influencing both short-term opportunities and long-term career progression.

Similarly, the RAE has also been analyzed in individual sports, such as swimming [28,29,30,31], tennis [32,33,34], shooting sports [35,36,37], fencing [38,39], badminton [40,41], chess [42], wrestling [43], e-sports [18], canoeing [44] or weightlifting [45], although the results show considerable variability. In general terms, there is a tendency to favor athletes born in the early months of the year, although with less intensity compared to team sports [20]. Moreover, differences in the manifestation of this effect appear to be linked to the characteristics of each sport [46]. On the other hand, the influence of the RAE varies by sex, showing a greater impact on male athletes [9] and a more heterogeneous response in female athletes [1]. In some professional sports, an inversion of the RAE has even been identified, as in alpine skiing [47] or gymnastics [46], where athletes born in the later months of the year tend to excel at the highest levels of competition. One potential reason for this reversed RAE is that the relatively younger athletes develop enhanced skills to adapt and succeed within a system that initially disadvantages them, a phenomenon known as the “Underdog Effect” [48]. Another possible explanation for this pattern is that relatively younger athletes, after being overlooked for selection in certain team sports, explore alternative disciplines. This leads to a higher representation of athletes born in the later months of the selection year in less popular sports [10,37,39], similar to what is observed in activities where physical attributes play a lesser role, such as rhythmic gymnastics, aerobics, or dance. In contrast, in other disciplines, this effect has not been observed or is not expected, such as in golf [49,50] or taekwondo [51]. These researchers suggested that the lack of an RAE in golf could be attributed to several factors, including the later starting age of organized golf compared to sports like hockey or baseball, the more flexible age group classifications in youth golf, and the reduced impact of physical attributes such as height and weight on performance in this sport. Furthermore, the absence of the RAE in certain contexts may be influenced by external factors, such as the size of the athlete population and the level of competition. For instance, studies in Israel have suggested that the small population size and lower number of children engaging in sports may contribute to the lack of a discernible RAE in some disciplines [52]. These findings reinforce the idea that the RAE is not a universal phenomenon but rather that its impact depends on multiple factors associated with each sport and specific context [1].

Studies have observed the prevalence of the RAE in endurance sports such as athletics [18,19,53,54], cross-country skiing [18,55,56,57], and triathlon [58,59,60], with the impact being more significant in men than in women. In the case of orienteering, although it is considered an endurance sport, it differs from traditional races due to the constant changes in running pace and the need to navigate irregular and unfamiliar terrains [61]. Furthermore, it requires prior mastery of map and compass skills, as well as the ability to design effective navigation strategies to minimize both the distance covered and the time spent [62]. Since orienteering combines high physical demands with strong technical and strategic requirements, the impact of the RAE in this sport may differ from that observed in disciplines where the physical component predominates. This particularity underscores the importance of specific studies analyzing its influence on the trajectory of orienteers and explaining possible differences with other sports. However, scientific literature on this topic is scarce, with only four studies addressing the RAE in orienteering [18,36,63]. Of these, only the works of de Agricola et al. [63] and Ferriz-Valero et al. [64] focus exclusively on this discipline, while the other two [18,36] include orienteering as part of a broader analysis encompassing multiple sports.

Despite advancements in understanding the RAE, there is a knowledge gap regarding its manifestation in orienteering within the Spanish context. While previous studies have identified the RAE in other orienteering populations [18,36], its variation by competitive level and sex in Spain remains unexplored. Moreover, it is unclear whether this effect diminishes with age or persists at the elite level, which could have implications for talent identification and athlete development. Understanding the influence of the RAE on orienteering is crucial for designing strategies that ensure equity in athlete participation and selection. The persistence of the RAE could indicate that current systems inadvertently favor those born in the early months of the year, while its absence at higher levels could suggest that younger orienteers find compensatory mechanisms to reach the elite. The age at which the relative age effect diminishes or ceases to be significant is not universally established and can vary depending on multiple factors, including the sport, competitive level, and sex of the athlete [1].

This study aims to address these issues through a detailed analysis of the distribution of birth quartiles across different age groups and competitive levels. Based on the birth dates of all male and female athletes affiliated with the Spanish Orienteering Federation (FEDO) during the 2005–2023 seasons, the objectives of this study were two-fold. First, to analyze the birth distribution in the total population of federated orienteering athletes in Spain, as well as in specific subgroups of youth and elite athletes, differentiated by sex. Second, to determine how this distribution varies across different age groups, from the mixed U-10 category (under 10 years old) to the elite category. Given the known effects of relative age in sports, we hypothesize that older orienteers with the same birth year will be overrepresented compared to their younger counterparts in the total population of federated athletes. Similarly, we expect this overrepresentation to persist within age groups, with older athletes being more prevalent than younger ones born in the same calendar year.

2. Materials and Methods

2.1. Procedure

This study employs a cross-sectional design and a retrospective observational model to compare the distribution of birth quartiles between the general Spanish population and the age groups of federated athletes. This approach allowed us to analyze data collected within a single time frame without conducting longitudinal tracking and to study associations without intervening in the natural context of the analyzed groups.

Anonymous information on sex, date of birth, and age groups was collected through a collaboration agreement signed with the FEDO, which was duly informed about the study’s objective and gave its consent for the publication of the data. The study protocol received approval from the Ethics Committee of the University of Alicante (UA-2024-06-17).

In this sports context, the cut-off date for grouping athletes is January 1. Athletes were classified into four relative age quartiles (Q) and two semesters (S) based on their month of birth, regardless of the year of birth. The quartiles were defined as follows: Q1 (January–March), Q2 (April–June), Q3 (July–September), and Q4 (October–December). The semesters were divided into S1 (January–June) and S2 (July–December). Thus, in addition to providing a detailed segmentation through quartiles, the use of semester classification allows for the identification of broader trends in birth distribution. While quartiles detect subtle differences in distribution throughout the year, in situations where most athletes are concentrated in one or two quartiles, valuable information about the overall trend may be lost. Therefore, semester classification complements the analysis, facilitating comparisons with the birth distribution in the general population and allowing for the detection of significant differences between the first and second halves of the year.

The observed birth date distributions for each quartile were calculated and compared with expected distributions obtained from live birth data recorded between 2009 and 2023 by the Spanish National Institute of Statistics [65]. This period was chosen due to the availability of data disaggregated by month and sex. The distribution proportions by relative age quartile in the total population were as follows: Q1 = 24.34%, Q2 = 24.37%, Q3 = 25.98%, and Q4 = 25.31%.

2.2. Participants

The present study included all Spanish athletes with an orienteering federation license (N = 34,718; female: n = 12,338; male: n = 22,380) who participated in the Spanish Orienteering League (SOL) between 2005 and 2023. Additionally, a specific subgroup of 10,635 orienteers, comprising youth under 20 years old and elite athletes, was analyzed. This analysis was conducted separately for both sexes and subgroups, as detailed in

Table 1. The total number of participants (n) by birth quartile (Q) and studied population group, along with their abbreviation and sex.

Population Group	Abbreviation	Q1	Q2	Q3	Q4	Total (n)
Total Federated	TF	8985	9033	8545	8155	34,718
Female	TF-F	3162	3250	2926	3000	12,338
Male	TF-M	5823	5783	5619	5155	22,380
Youth	Y	2416	2469	2048	2029	8962
Female	F-Y	1011	996	789	884	3680
Male	M-Y	1405	1473	1259	1145	5282
Elite	E	391	375	281	356	1403
Female	F-E	118	149	123	130	520
Male	M-E	273	226	158	226	883

Note. Q1 = born in the first quarter of the year (January–March); Q2 = born in the second quarter of the year (April–June); Q3 = born in the third quarter of the year (July–September); Q4 = born in the fourth quarter of the year (October–December).

. Likewise, the populations of youth and elite athletes were divided and analyzed according to their age group, providing an additional level of detail to the analysis, as specified in

Table 2. The total number of participants (n) by birth quartile (Q), age groups, and sex.

Sex	Age Groups	Q1	Q2	Q3	Q4	Total (n)
Mixed	U-10	114	81	91	72	358
Female	F-12	189	195	153	153	690
	F-14	260	286	205	259	1010
	F-16	221	250	187	221	879
	F-18	176	137	124	132	569
	F-20	115	101	88	83	387
	F-Elite	118	149	123	130	520
Male	M-12	254	289	235	196	974
	M-14	358	378	312	295	1343
	M-16	322	324	300	292	1238
	M-18	228	239	191	182	840
	M-20	179	189	162	144	674
	M-Elite	273	226	158	226	883

Note. U-10 (<10 years); M/F-12 (11–12 years); M/F-14 (13–14 years); M/F-16 (15–16 years); M/F-18 (17–18 years); and M/F-20 (19–20 years), where M means “male” and F means “female”. Q1 = born in the first quarter of the year (January–March); Q2 = born in the second quarter of the year (April–June); Q3 = born in the third quarter of the year (July–September); Q4 = born in the fourth quarter of the year (October–December).

. It is important to note that although some athletes may have held a license or competed in multiple seasons, each individual was counted only once within each analysis group (i.e., Total, Youth, and Elite). This approach ensures consistency in subgroup comparisons, even if an athlete appears in more than one group depending on their participation across years. Specifically, the age groups are organized into three main groups: the Total Federated group, which includes all categories of the SOL for both female (TF-F) and male (TF-M) athletes; the Youth subgroup (Y), which encompasses the Youth category, divided further into the U-10 (mixed), F-Y (F-12, F-14, F-16, F-18, and F-20), and M-Y (M-12, M-14, M-16, M-18, and M-20) categories; and finally, the elite subgroup (E), consisting of the Elite female categories (F-E), and male categories (M-E).

2.3. Statistical Analysis

Statistical analysis of the data were conducted using IBM^® SPSS^® software, version 28, and Microsoft Excel^® for Mac, version 16.83. Differences between the relative age quartiles in the studied populations and age groups were evaluated using the Chi-square goodness-of-fit test, comparing them against the actual birth distribution in Spain.

For the Chi-square tests, the effect size was calculated following the recommendations of Wattie et al. [11]. Specifically, Cramér’s V was computed, a commonly used statistic in a similar study [18]. Cramér’s V is one of the most frequently used measures to evaluate the strength of association when the Chi-square statistic is significant [66]. Its calculation follows the following formula (Equation (1)):

$$Crame{r}^{\prime }s V=\sqrt{{\displaystyle \frac{{\chi }^2}{N (k-1)}}}$$

(1)

where χ2 is the Chi-square value, N is the sample size, and k is the smaller number of rows or columns in the contingency table.

Cramér’s V is also known as the effect size (ES).

Table 3. Effect size for the Chi-square test, Cramér’s V, and its interpretation [38].

Degrees of Freedom	Small	Medium	Large
1	0.10	0.30	0.50
2	0.07	0.21	0.35
3	0.06	0.17	0.29
4	0.05	0.15	0.25
5	0.04	0.13	0.22

shows the reference values for classifying the strength of the association according to degrees of freedom [67].

Additionally, to determine statistically significant differences between quartiles, a Z-test for proportions was applied. The alpha level was set at 0.05.

Finally, odds ratios (OR) and their corresponding 95% confidence intervals (95% CI) were calculated for the distribution of relative age quartiles, differentiating between the various samples and considering the sex factor, following the method described by Cobley et al. [1].

The OR is interpreted as a comparison between the probabilities of exposure (e.g., in a sports context) and the probabilities of non-exposure (e.g., in the general population). Although Cramér’s V was used as the primary measure to assess the strength of association, odds ratios were calculated as a complementary measure to provide additional information on the relative likelihood of belonging to specific birth quartiles. To calculate the OR, we compared the frequency of athletes born in each quartile with the combined frequency of athletes born in the remaining three quartiles within each competitive group. Specifically, the OR for each quartile was computed using the following formula (Equation (2)):

$$OR=(A / B) ÷ (C / D)$$

(2)

where A represents the number of athletes in a given quartile, B represents the number of athletes in the other three quartiles combined, and C and D are the corresponding values from the general population distribution.

This method allows for the assessment of the overrepresentation or underrepresentation of athletes born in each quartile relative to the expected distribution. The magnitude of the OR indicates the strength of the association between variables and can be interpreted according to Olivier and Bell [68], where an OR ≈ 1.22 represents a small effect, ≈1.86 is a medium effect, and ≈3.00 is a large effect. Additionally, the confidence intervals (95% CI) reflect the uncertainty of the estimate: if they include 1, there is insufficient evidence to consider a significant association, whereas if they do not include 1, they suggest a positive association (OR > 1) or a negative association (OR < 1) [69]. The OR and 95% CI calculations were conducted using the method proposed by Szumilas [70].

3. Results

Table 4. Results of the Chi-square test (χ2), significance values (p), and effect size (Cramér’s V) for the different population groups analyzed in relation to the quarterly distribution of births (Q): χ2 (critical) = 7.8147 (p = 0.05).

Population Group	Q1 (%)	Q2 (%)	Q3 (%)	Q4 (%)	χ2	p	V
Total Federated	25.88	26.02	24.61	23.49	143.187	<0.001	0.06
TF-F	25.63	26.34	23.72	24.32	57.351	<0.001	0.07
TF-M	26.02	25.84	25.11	23.03	98.244	<0.001	0.07
Youths	26.96	27.55	22.85	22.64	121.514	<0.001	0.12
F-Y	27.47	27.07	21.44	24.02	57.468	<0.001	0.12
M-Y	26.60	27.89	23.84	21.68	74.826	<0.001	0.12
Elite	27.87	26.73	20.03	25.37	29.535	<0.001	0.15
F-E	22.69	28.65	23.65	25.00	5.599	0.133	0.10
M-E	30.92	25.59	17.89	25.59	38.516	<0.001	0.21

Note: Total Federated includes all categories of the SOL, for both female (TF-F) and male (TF-M) athletes. The youth subgroup includes the U-10 category (mixed), F-Y categories (F-12, F-14, F-16, F-18, F-20), and M-Y categories (M-12, M-14, M-16, M-18, M-20). Additionally, the elite subgroup consist of the elite female (F-E) and male (M-E) categories. Q1 = born in the first quarter of the year (January–March); Q2 = born in the second quarter of the year (April–June); Q3 = born in the third quarter of the year (July–September); Q4 = born in the fourth quarter of the year (October–December).

shows that all the analyzed population groups present significant differences (p < 0.05) between the observed and expected proportions for each group, except for the female elite group [F-E] (p = 0.133). The most significant effect is observed for the male elite group [M-E] (V = 0.21), while the smallest effect is found in the Total Federated group [TF] (V = 0.06), regardless of sex (V_TF-F = V_TF-M = 0.07).

The bar charts for the distribution (%) by birth semester of the different populations and subgroups studied, shown in Figure 1, indicate an overrepresentation of athletes born in the first semester (S1) compared to those born in the second semester (S2) for all populations and subgroups, regardless of sex, except for the female sample of the elite subgroup. Statistically significant differences between those born in S1 and those born in S2 are observed in all cases except for the previously mentioned female elite subgroup. The Cramer’s V values obtained (0.07–0.16) indicate a small effect, which is practically identical to those obtained for the quartile distribution, also showing a small effect size.

Table 5. Results of the Chi-square test (χ2), significance values (p), and effect size (Cramér’s V) for the different age groups analyzed in relation to the quarterly distribution of births (Q): χ2 (critical) = 7.8147 (p = 0.05).

Age Groups	Q1 (%)	Q2 (%)	Q3 (%)	Q4 (%)	χ2	p	V
U-10	31.84	22.63	25.42	20.11	12.601	0.006	0.19
F-12	27.39	28.26	22.17	22.17	13.463	0.004	0.14
F-14	25.74	28.32	20.30	25.64	19.885	<0.001	0.14
F-16	25.14	28.44	21.27	25.14	13.722	0.003	0.12
F-18	30.93	24.08	21.79	23.20	15.032	0.002	0.16
F-20	29.72	26.10	22.74	21.45	8.923	0.030	0.15
F-Elite	22.69	28.65	23.65	25.00	5.599	0.133	0.10
M-12	26.08	29.67	24.13	20.12	24.088	<0.001	0.16
M-14	26.66	28.15	23.23	21.97	20.671	<0.001	0.12
M-16	26.01	26.17	24.23	23.59	5.983	0.112	0.07
M-18	27.14	28.45	22.74	21.67	16.269	0.001	0.14
M-20	26.56	28.04	24.04	21.36	10.221	0.017	0.12
M-Elite	30.92	25.59	17.89	25.59	38.516	<0.001	0.21

Note. U-10 (<10 years); M/F-12 (11–12 years); M/F-14 (13–14 years); M/F-16 (15–16 years); M/F-18 (17–18 years); and M/F-20 (19–20 years), where M means “male” and F means “female”. Q1 = born in the first quarter of the year (January–March); Q2 = born in the second quarter of the year (April–June); Q3 = born in the third quarter of the year (July–September); Q4 = born in the fourth quarter of the year (October–December).

shows that most of the analyzed age groups present significant differences (p < 0.05) in the birth distribution across quartiles. Only two age groups, M-16 (p = 0.112) and F-Elite (p = 0.133), do not show significant differences. Regarding effect size (Cramer’s V), the age groups with the most significant effects are the M-Elite (V = 0.21) and the U-10 category (V = 0.19), although the effect is moderate. In the remaining age groups, the effect magnitude is small-to-medium (V values between 0.12 and 0.16).

The bar charts for the birth semester distribution in Figure 2 (U-10 category and female categories) and Figure 3 (male categories) reflect a classic relative age effect with an overrepresentation of athletes born in Q1 and Q2 (first semester, S1) and an underrepresentation of athletes born in Q3 and Q4 (second semester, S2). As shown in the data of both figures, the only age group that does not present significant differences between birth semesters is the female elite category. The Cramer’s V values obtained (0.07–0.16) indicate a small effect.

Table 6. Odds ratios (OR) and confidence intervals (95%) for comparisons between birth quartiles in the different studied population groups.

Population Group	OR (Lower CI–Upper CI)	Q1/Q2	Q1/Q3	Q1/Q4	Q2/Q3	Q2/Q4
Total Federated	0.993 (0.96–1.03)	1.069 * (1.03–1.11)	1.137 * (1.10–1.18)	1.077 * (1.04–1.11)	1.146 * (1.11–1.19)	1.063 * (1.03–1.10)
TF-F	0.964 (0.91–1.02)	1.108 * (1.05–1.17)	1.073 * (1.01–1.14)	1.150 * (1.09–1.22)	1.113 * (1.05–1.18)	0.968 (0.91–1.03)
TF-M	1.009 (0.97–1.05)	1.049 * (1.01–1.09)	1.175 * (1.13–1.23)	1.039 (1.00–1.08)	1.164 * (1.12–1.22)	1.120 * (1.07–1.17)
Youths	0.971 (0.91–1.04)	1.246 * (1.16–1.33)	1.261 * (1.18–1.35)	1.284 * (1.20–1.37)	1.299 * (1.21–1.39)	1.012 (0.94–1.09)
F-Y	1.021 (0.92–1.13)	1.388 * (1.25–1.54)	1.198 * (1.08–1.33)	1.360 * (1.22–1.51)	1.174 * (1.06–1.30)	0.863 ** (0.77–0.96)
M-Y	0.937 (0.86–1.02)	1.158 * (1.06–1.26)	1.309 * (1.20–1.43)	1.236 * (1.13–1.35)	1.397 * (1.28–1.53)	1.131 * (1.03–1.24)
Elite	1.059 (0.90–1.25)	1.543 * (1.30–1.84)	1.136 (0.96–1.34)	1.457 * (1.22–1.74)	1.073 (0.91–1.27)	0.737 ** (0.62–0.88)
F-E	0.731 ** (0.55–0.97)	0.947 (0.71–1.26)	0.881 (0.66–1.17)	1.296 (0.98–1.71)	1.205 (0.92–1.58)	0.929 (0.70–1.23)
M-E	1.301 * (1.06–1.60)	2.054 * (1.65–2.56)	1.301 * (1.06–1.60)	1.578 * (1.26–1.98)	1.000 (0.81–1.24)	0.634 ** (0.50–0.80)

Note. (*) Positive statistical significance (95% CI > 1); (**) Negative statistical significance (95% CI < 1). Total Federated includes all categories of the SOL, for both female (TF-F) and male (TF-M) athletes. The youths subgroup encompasses the F-Y categories (F-12, F-14, F-16, F-18, F-20), and M-Y categories (M-12, M-14, M-16, M-18, M-20). Additionally, it includes the elite subgroup, consisting of the elite female (F-E) and male (M-E) categories. Q1 = born in the first quarter of the year (January–March); Q2 = born in the second quarter of the year (April–June); Q3 = born in the third quarter of the year (July–September); Q4 = born in the fourth quarter of the year (October–December).

presents the results of the odds ratio (OR) analysis and their corresponding 95% confidence intervals (CI 95%) for the comparisons between all birth quartiles in the different population groups studied. Pairwise quartile comparisons were performed using the Z-test, and the results align with the interpretations of the 95% confidence intervals calculated for the ORs. Therefore, in Table 6, the ORs whose 95% confidence intervals do not include the value 1 are marked with an asterisk (*), indicating significant differences between the compared quartile pairs, while the OR value indicates the effect magnitude. For instance, in the male elite population group, Q1 shows a particularly strong overrepresentation compared to Q3 (OR = 2.054; CI = 1.65–2.56; Z = 6.371; p < 0.001).

In general, men (TF-M, M-E) show stronger associations between quartiles, especially between the Q1/Q4 and Q1/Q3 pairs, compared to women. In the Total Federated population (TF), significant associations are observed in all comparisons except Q1/Q2. Similarly, in the subgroup of young athletes (Youths), significant differences are observed between all compared pairs except Q1/Q2 and Q3/Q4. Notably, differences between Q1/Q2 only exist in the F-E group (inverse relationship) and M-E group.

Meanwhile,

Table 7. Odds ratios (OR) and confidence intervals (95%) for comparisons between birth quartiles in the different age groups studied.

Age Groups	OR (Lower CI–Upper CI)	Q1/Q2	Q1/Q3	Q1/Q4	Q2/Q3	Q2/Q4
U-10	1.598 * (1.15–2.22)	1.371 (0.99–1.89)	1.856 * (1.32–2.60)	0.858 (0.61–1.21)	1.162 (0.81–1.66)	1.354 (0.95–1.92)
F-12	0.958 (0.76–1.21)	1.324 * (1.04–1.69)	1.324 * (1.04–1.69)	1.383 * (1.08–1.76)	1.383 * (1.08–1.76)	1.000 (0.78–1.29)
F-14	0.878 (0.72–1.07)	1.361 * (1.11–1.68)	1.005 (0.82–1.23)	1.551 * (1.26–1.90)	1.145 (0.94–1.39)	0.738 ** (0.60–0.91)
F-16	0.845 (0.68–1.04)	1.243 (1.00–1.55)	1.000 (0.81–1.24)	1.471 * (1.18–1.83)	1.183 (0.96–1.46)	0.805 (0.64–1.01)
F-18	1.412 * (1.09–1.83)	1.607 * (1.23–2.09)	1.483 * (1.14–1.93)	1.138 (0.86–1.50)	1.050 (0.80–1.38)	0.923 (0.70–1.22)
F-20	1.197 (0.87–1.64)	1.437 * (1.04–1.98)	1.549 * (1.12–2.14)	1.200 (0.86–1.67)	1.293 (0.93–1.80)	1.078 (0.77–1.51)
F-Elite	0.731 ** (0.55–0.97)	0.947 (0.71–1.26)	0.881 (0.66–1.17)	1.296 (0.98–1.71)	1.205 (0.92–1.58)	0.929 (0.70–1.23)
M-12	0.836 (0.69–1.02)	1.109 (0.90–1.36)	1.400 * (1.13–1.73)	1.327 * (1.09–1.62)	1.675 * (1.36–2.06)	1.262 * (1.02–1.56)
M-14	0.928 (0.78–1.10)	1.201 * (1.01–1.43)	1.291 * (1.08–1.54)	1.294 * (1.09–1.54)	1.392 * (1.17–1.66)	1.075 (0.90–1.29)
M-16	0.992 (0.83–1.19)	1.099 (0.92–1.32)	1.139 (0.95–1.37)	1.108 (0.92–1.33)	1.148 (0.96–1.38)	1.036 (0.86–1.25)
M-18	0.937 (0.76–1.16)	1.266 * (1.01–1.58)	1.347 * (1.08–1.68)	1.351 * (1.09–1.68)	1.438 * (1.15–1.79)	1.064 (0.85–1.34)
M-20	0.928 (0.73–1.18)	1.143 (0.89–1.46)	1.331 * (1.04–1.71)	1.232 (0.97–1.57)	1.434 * (1.12–1.84)	1.165 (0.90–1.50)
M-Elite	1.301 * (1.06–1.60)	2.054 * (1.65–2.56)	1.301 * (1.06–1.60)	1.578 * (1.26–1.98)	1.000 (0.81–1.24)	0.634 ** (0.50–0.80)

Note. (*) Positive statistical significance (95% CI > 1); (**) Negative statistical significance (95% CI < 1). Q1 = born in the first quarter of the year (January–March); Q2 = born in the second quarter of the year (April–June); Q3 = born in the third quarter of the year (July–September); Q4 = born in the fourth quarter of the year (October–December).

presents the ORs and their 95% CIs for the comparisons between birth quartiles in the different age groups evaluated. As with the population groups, associations between birth quartiles and participation in various competitive categories were examined using the Z-test, and the results are consistent with the interpretations of the ORs’ 95% confidence intervals. Therefore, comparisons where the OR values reflect significant positive (95% CI > 1) or negative (95% CI < 1) associations are marked with an asterisk (*) for positive statistical significance (95% CI > 1) and a double asterisk (**) for those with a significant but less than one OR. The results indicate a consistent trend toward the overrepresentation of those born in the first quarter of the year (Q1) compared to those born in later quarters, particularly against the third (Q3) and fourth (Q4) quarters. This pattern is prominently observed in several categories, especially in the Q1/Q4 comparisons in the U-10 category (OR = 1.856; 95% CI = 1.32–2.60; Z = 3.579; p < 0.001), F-18 (OR = 1.483; 95% CI = 1.14–1.93; Z = 2.936; p = 0.003), F-20 (OR = 1.549; 95% CI = 1.12–2.14; Z = 2.636; p = 0.008), and all male categories except M-16, where no statistically significant differences are observed (OR = 1.139; 95% CI = 0.95–1.37; Z = 1.396; p = 0.163). On the other hand, in some specific categories, such as F-E, negative associations (95% CI < 1) were identified in those born in Q2 compared to Q1, suggesting a lower probability of participation at this competitive level for those born in the first quarter.

When performing OR analyses to compare quartiles, the fourth quartile is typically used as the reference group—i.e., the relatively youngest individuals [1]. In this regard, Figure 4 represents the ORs and 95% CIs for birth quartile Q1 compared to Q4 for all populations (and subgroups) and age groups studied. OR and CI values to the left of one, as observed in the elite subgroups and female elite subgroup (F-E), indicate no significant differences between birth quartiles Q1 and Q4, as the confidence interval includes the value one. Conversely, in the other evaluated population groups, significant differences are identified, increasing proportionally with the rightward displacement in the graph. For example, in the male elite subgroup (M-E), the ORs and their 95% CIs reflect a significant effect favoring those born in the first quarter of the year (Q1) compared to those born in the fourth quarter (Q4).

Regarding age groups (Figure 5), following the same reasoning as above, no significant differences between birth quartiles Q1 and Q4 are observed in the F-E, F-14, F-16, and M-16 categories, as the value one is included in their confidence intervals. However, the U-10, F-20, and F-18 categories present the highest OR values, indicating a more pronounced birth quartile effect in these categories. Regarding the effect trend as athletes age, the effects are more pronounced at younger ages (U-10, F-12, M-12), weaken (M-14), or even disappear (F-14 and F-16) between ages 13 and 17, reappearing in junior ages (F/M-18 and F/M-20) and persisting in adulthood for men (M-E) while disappearing for women (F-E).

4. Discussion

This study had a dual purpose. On the one hand, it compared the birth distribution of the general population in Spain with that of licensed FEDO athletes, as well as the birth distribution in the subgroups of orienteers (i.e., youth and elite), both in the female and male samples. On the other hand, it compared the birth distribution in different age groups, from the U-10 mixed category (under 10 years old) to the adult stage (elite category). In relation to the initial hypotheses, the data fully supported the premise that older orienteers with the same birth year are overrepresented among the total population of licensed FEDO athletes. However, the overrepresentation of relatively older athletes within age groups exhibited some inconsistencies, with the strength of this effect varying depending on the specific age group and sex. These variations could be attributed to several factors, including the specific dynamics of orienteering as a sport, potential variations in the developmental trajectories of athletes in different age groups, and variations in the sample size of the different categories.

4.1. Comparison Between the General Population and the Population of Federated Athletes

The most notable findings revealed a significant difference in the distribution of births by semester in favor of older athletes (S1) within the same year. This trend was observed in both the total population of federated athletes (TF) and the other subgroups analyzed, regardless of sex, except for the female sample of the elite athlete subgroup. The effect size in the TF was small (V = 0.06), both in the female (V = 0.07) and male (V = 0.07) samples. The values obtained in this study are similar to those reported by Jakobsson et al. [18] in orienteering, where the effect size was 0.13 for women and 0.11 for men. However, the effect size observed by Jakobsson et al. [18] could be slightly higher than in the present study due to the great popularity of orienteering in Sweden, where the sport has deep-rooted traditions and a large participation base, which could intensify the relative age effect (RAE) in competitive categories. In fact, according to Musch and Grondin [8], the popularity of a sport, the number of active participants, and the competitive level can influence the magnitude of the RAE. Romann et al. [36], in their study on the RAE in 70 sports in Switzerland, concluded that less relevant sports, including orienteering, exhibited insignificant or low-magnitude RAEs. Moreover, this same study revealed that non-Olympic sports have a lower RAE risk compared to Olympic sports. The authors speculate that this could be due to the higher appeal of Olympic sports, resulting from their greater media presence and funding, while non-Olympic sports are less popular and thus attract fewer young participants [71]. It is possible that athletes with talent but born at the end of the year leave high-participation sports and opt for less popular sports, such as orienteering in Spain, to increase their chances of competing at a high level [72]. In all the pairwise comparisons between quartiles that showed statistically significant differences, the OR values ranged between 1.06 and 1.18, values that are below the threshold of 1.22 is considered indicative of a small effect. This confirms that, in this study, the impact of the RAE is small in magnitude.

When analyzing the youth subgroup, a pattern emerged that aligns with the literature, which suggests that the RAE is more pronounced in the early stages of athletic development. This is due to the cognitive, social, physical, and maturational advantages of athletes born in the first half of the year, as well as higher accumulated play practices [8]. In this sense, in the youths subgroup, a small RAE was observed (V = 0.12), with an overrepresentation of athletes born in S1 (~55%) compared to those born in S2 (~45%), with these differences being statistically significant. It is worth noting that, although the OR values still show a small effect (between 1.13 and 1.39), they are slightly higher than those obtained in the Total Federated population, suggesting that, overall, the RAE is greater in the youths subgroup. Taking Jakobsson et al. [18] as a reference, the results obtained in the female and male samples match those observed in the total population of youths. While the maturation hypothesis can be applied to both team and individual sports, physical differences may be offset when performance largely depends on technical and cognitive skills, as in orienteering. This assumption could explain why the effect size observed in this study for young orienteers is small. In fact, studies on technical sports such as figure skating, gymnastics, or golf have not identified the presence of the RAE [46,49]. In contrast, Romann et al. [34] did not find significant differences in the distribution of quartiles in a sample of orienteers aged 7 to 20 years, both in females and males. While it is true that girls enter puberty about two years earlier than boys and that physical differences between girls are less pronounced compared to boys [73], it would be expected that results for women and men would differ. It should be noted that in these studies [18,36], the young population has been analyzed as a single group or by age groups up to 20 years, without distinctions between age groups, which has been addressed in this study.

Additionally, the OR values for the Q1 vs. Q4 pair (Table 6 and Figure 5) complement and reinforce the trends observed in the distribution of births by semester, providing additional evidence of the RAE in orienteering. These values indicate statistically significant differences in the TF and youth populations, regardless of sex, with a greater effect size in male samples. These results differ from those reported by Romann et al. [36], who found a homogeneous quartile distribution in female athletes (OR < 1), while in males, the RAE was not significant (OR = 0.79; 95% CI: 0.51–1.23). In the elite subgroup, the OR also revealed significant differences in the male sample, but not in the female sample nor in the overall elite subgroup. Although an apparent inversion is observed in the female sample, this was not statistically significant.

4.2. Analysis by Age Groups and Gender Differences

In this regard, the findings of this study indicate the presence of a small but persistent RAE in almost all age groups established by the FEDO, except for the female elite category. In the M-16 category, a detailed analysis of the distribution by birth quartiles (Table 5) shows no significant differences between athletes. This result suggests that, in this specific category, the RAE is not clearly detected when athletes are segmented by quarters (Q1 to Q4). However, when the analysis is performed considering the semesters of birth (Figure 3), significant differences are identified, indicating that this broader segmentation approach (S1 vs. S2) captures distribution patterns that are not as evident when segmented into quartiles. This finding could be explained by the less detailed nature of quartiles compared to the more generalized division of semesters, which allows for a clearer view of how athletes born in the first half of the year (S1) generally have a relative advantage compared to those born in the second half of the year (S2), regardless of the quartiles in which they are grouped. On the other hand, there was an overrepresentation of athletes born in the first semester (S1) in the mixed U-10 category, as well as in both female (Figure 2) and male categories (Figure 3). Typically, the presence or absence of RAE is examined by comparing the birth date distribution of athletes with that of the general population, provided that the premise holds that the population of licensed athletes reflects that distribution [2]. However, the results of this study suggest that, in Spanish orienteering, there is a moderate disparity in the birth date distribution between licensed athletes, from the U-10 category to the adult category, except for the female elite category. Regarding the effect size, the values obtained are generally comparable between female and male categories—except for the elite category—without observing a systematic trend (neither increasing nor decreasing) as athletes move from younger to older categories, whether analyzing the data in quartiles or semesters. While it is commonly assumed that RAE is less pronounced in female sports due to less competition [2] and that maturational advantages have less of an impact on women [73], the results of this study indicate that RAE manifests consistently in both sexes, in contrast to previous studies [9,74,75]. Smith et al. [9] conducted a meta-analysis of 57 studies spanning 25 sports, revealing a significant but small RAE in female athletes, with a higher magnitude observed in pre-adolescents (≤11 years), adolescents (12–14 years), and at higher competition levels. The effect was more pronounced in sports with high physiological demands. Similarly, Saavedra-García et al. [74] found that RAE was present in Spanish female athletics, though its influence was weaker than in male athletics and was only detected in youth and absolute categories. Schörer et al. [75] examined RAE in the German handball talent development system, finding notable differences between males and females. In men, RAE was strongest at the initial selection stage and gradually weakened as the competition level increased, becoming negligible at the senior national team level. In contrast, while RAE was initially weaker in female athletes, its effect intensified in later stages, particularly in youth national teams, before balancing out at the senior level. This pattern suggests that selection pressures may differ by sex, with early-stage competition being more pronounced in males, whereas increased competitiveness in later stages might exacerbate RAE in females. Taken together, these studies suggest that while RAE is generally less pronounced in female athletes, its presence is modulated by sport-specific and contextual factors. In contrast, our findings indicate a more consistent manifestation of RAE across both sexes in orienteering, challenging the notion that it is inherently weaker in female sports.

In this context, in the mixed U-10 category (under 10 years old), it is observed that those born in Q1 have nearly twice the likelihood of participation compared to those born in Q4 (OR = 1.856; 95% CI = 1.32–2.60). In the childhood stage (under 10 years old), various factors can condition children’s participation in sports activities, including the influence of parental decisions. Some studies have noted that families with higher socioeconomic status tend to enroll children born in the later months of the year or those with delayed maturation less frequently [76], amplifying the inequalities associated with RAE [11]. The lack of autonomy at this stage means that sports participation does not only depend on the child’s will but also on multiple external factors, such as the parents’ knowledge of child development and their expectations regarding sports performance [11]. Some studies suggest that parents of relatively younger children are less likely to enroll them in sports initiation categories [77,78]. In this early participation phase, there are no selection processes, and all children can join the activity, suggesting that parents’ perceptions of their children compared to others might condition the manifestation of RAE [11]. In contrast, during adolescence, the selection process for athletes depends on coaches, who play a key role in identifying and developing athletic talent [1,79].

When analyzing the male categories (Table 7 and Figure 5), there is an overrepresentation of athletes born in Q1 compared to those born in Q4, as indicated by OR values greater than one. This pattern is also observed in the M-16 category; however, although the OR is 1.139, these differences are not statistically significant, as the confidence interval (95% CI = 0.95–1.37) includes the value of one. This trend could be related to apparent differences in dropout rates in the M-14 category, as the dropout rate for athletes born in Q1 is around 10%, while for those born in Q4, it is only about 1%. This low dropout rate for those born in Q4 in the M-14 category could be explained by the fact that these athletes, initially perceived as disadvantaged due to their relative age, benefit from training and competing with relatively older peers [48]. This allows them to develop technical and psychological attributes, such as resilience, constant effort, and greater adaptability, which compensate for their initial disadvantage in physical development or maturity, allowing them to catch up with or even surpass their older peers in later sporting stages [80].

Regarding the female categories (Table 7 and Figure 5), a small effect is observed (OR = 1.324; 95% CI = 1.04–1.69) in the F-12 category (11 and 12 years old), which disappears in the F-14 and F-16 categories (13 to 16 years old). However, the effect reappears between the ages of 17 and 20 (F-18 and F-20), with a larger magnitude than in F-12 (ORF-18 = 1.483, 95% CI = 1.14–1.93; ORF-20 = 1.549, 95% CI = 1.12–2.14), and subsequently disappears in the adult stage (F-E). The results obtained in F-12 could be explained by the maturation selection hypothesis, which suggests that older athletes are associated with anthropometric advantages, such as taller height, and physical advantages, such as greater muscular strength [8]. Although it is recognized that maturation processes can vary considerably between individuals, it is likely that athletes with greater relative age experience transformations associated with puberty (approximately 12–14 years in girls) earlier than their younger counterparts [1,73,81]. Regarding the RAE increase in the F-18 category, it has been observed that the dropout rate among athletes born in Q4 (~40%) is almost double that of those born in Q1 (~20%). This fact could be related to what is known as the “maturation selection hypothesis” [1,14,82]. As a result of these selection and maturation processes, RAE contributes to the premature elimination of athletic potential, preventing younger athletes from having the necessary time to develop their sports experience [1,81]. In fact, it has been proposed that athletes with lower relative age are more likely to experience negative sports experiences and quit the sport at an earlier age [12]. This is especially the case when sports development systems introduce selection processes and representative competition levels [83].

While the magnitude of RAE remains relatively constant with age in the younger categories, in the male elite category, the effect intensifies. This finding contradicts reports in other team and individual sports, where RAE tends to attenuate at higher performance levels [1]. This suggests that, in male orienteering, access to the elite category may depend more on the accumulation of advantages gained during early formative stages—such as experience, maturation, and quality training—rather than on current performance evaluations.

In the elite category, when analyzing RAE independently in each subgroup, it is observed that in the male group, statistically significant differences are identified between birth quartiles, while in the female group, no significant differences are detected when analyzing either quartiles or semesters. Specifically, in the male elite category, the data indicate that the probability of an athlete born in Q1 being part of this group is almost double that of one born in Q3 (OR = 2.054). This finding suggests that, within the male group, RAE is more pronounced, while in the female group, the birth date distribution is more homogeneous. Several studies [1,18,75] have shown that RAE depends on the competitive level. In particular, they have observed a greater RAE in the early stages of sports development and a more pronounced impact on pre-elite athletes than on elite athletes, which does not align with the findings of this study. According to Delorme et al. [2], if there is already a skewed distribution throughout the licensed athlete population in a given sport, it would be expected that this asymmetry would also be reflected at the elite level. However, this pattern is not evident in this study. Despite the distribution of the TF population being significantly different from that of those born in Spain, its effect is notably smaller than that observed in the male elite subgroup or category. This suggests that the asymmetric birth date distribution among elite athletes is not merely a reflection of the distribution of licensed athletes (TF), but could be attributed to RAE. In contrast, in the female elite category, no statistically significant differences have been identified. This could be because athletes born in the later months of the year return to sports practice at later stages, after having had fewer development opportunities than their peers born in the earlier months of their cohort during childhood and adolescence [18]. Other explanations suggest that athletes born in the later months of the year have developed adaptive strategies that favor their continued participation in sports [75] or that, conversely, older athletes quit the sport at earlier ages [75,84]. Finally, in the elite category of our study, female participation (n = 520) is approximately 40% lower than male participation (n = 883). This lower internal competition means that selection processes are less strict [75], which reduces the presence of RAE [39]. Among these explanations, the most plausible in the context of orienteering could be the late return hypothesis, given that female participation in the elite category is significantly lower than in the male category. This suggests that many women who reach this level may have followed less linear trajectories, in contrast to men, whose progression seems more aligned with the relative age advantage accumulated in earlier stages. Therefore, the findings found in the female elite category raise questions about the structure of opportunities for women in this sport.

The results observed in the elite category in orienteering are in line with those obtained in other individual sports, such as alpine skiing, where an RAE was identified exclusively in adult men [18,85], while no such effect was observed in women. In contrast, Jakobsson et al. [18] observed RAE in both men and women in orienteering in the age range of 21 to 39 years. In their study on various winter sports, Baker et al. [46] observed that RAE was present in most of the disciplines analyzed, with differences in the magnitude of the effect depending on the sport and gender. In general, RAE was more pronounced in men than in women, although significant effects were observed in both groups in several disciplines. For example, in cross-country skiing, RAE was present in both men and women. In ski jumping, it was observed in men but not in women. In snowboarding, RAE was seen in men, while an inverse RAE was detected in women, with an overrepresentation of athletes born in the third quartile of the year (Q3).

4.3. Implications of the Findings and Underlying Mechanisms

The results of this study have important implications for understanding the RAE in the sport of orienteering, particularly in the Spanish context. The evidence of a higher representation of athletes born in the first half of the year in most competitive categories reinforces the idea that relative age influences athlete recruitment and retention in this sport, even though its magnitude is small compared to other sports with more stringent selection systems. This observation aligns with Wattie et al.’s [11] developmental systems model, which explains how RAE operates through complex interactions between individual, task, and environmental constraints. Additionally, the stratification of analyses by age groups provides a more nuanced view of the phenomenon, allowing for a more precise identification of the stages and categories where RAE has the greatest impact. These findings can serve as the basis for future talent detection and development policies in orienteering, helping to mitigate potential inequalities arising from this structural bias.

From a practical perspective, these results highlight the need for strategies that minimize the impact of RAE on the identification and development of talent in orienteering. Beyond its practical applications, this study also contributes to the theoretical understanding of the RAE by providing empirical evidence of its manifestation in a unique sports context. The observed variations in the RAE across different age groups and between sexes offer valuable insights into the complex interplay of biological, psychological, and social factors that influence athlete development. Furthermore, the findings underscore the importance of considering sport-specific characteristics when investigating the RAE, as the relative influence of physical attributes, technical skills, and cognitive abilities can modulate its expression. These theoretical advancements can inform future research aimed at refining models of talent identification and development in sports, ultimately promoting a more equitable and effective approach to fostering athletic potential.

Furthermore, as the first study on this phenomenon in the Spanish context, its results contribute to the international literature on RAE in individual endurance and navigation sports. This contribution is particularly significant as Cobley et al. [1] demonstrated through meta-analysis that RAE patterns vary significantly across different sports contexts. Moreover, it opens new lines of research that could explore the interaction between relative age and other performance factors in orienteering.

There are three main implications of these findings. First, in terms of practical applications, the results offer valuable insights for sports administrators, coaches, and talent development experts in orienteering. They can inform the design and implementation of strategies aimed at reducing the impact of relative age, such as adjusted selection criteria [8,22], diversified training programs, and awareness campaigns [1,8,86]. Andronikos et al. [86] conducted research with talent development experts, highlighting that these strategies should extend beyond physical considerations to include psychological support mechanisms, as RAE significantly impacts athletes’ self-confidence and motivation. These approaches can help ensure more equitable opportunities for athletes born later in the year, promoting greater inclusivity and maximizing talent development within the sport.

From a theoretical perspective, this study expands the understanding of the RAE by providing empirical evidence of its presence and specific manifestations in orienteering. By highlighting the influence of sport-specific characteristics on the RAE, it contributes to the development of more detailed models of athlete development. This aligns with research by Romann et al. [36], who found that RAE mechanisms interact with biological maturation and sport-specific demands. It also underscores the importance of considering contextual factors when studying relative age across different sports.

Finally, from a scientific standpoint, the results open new research opportunities in sports science. They encourage further investigation into the underlying mechanisms of the RAE in orienteering, as well as its interaction with other factors, such as training methodologies, psychological variables, and long-term athlete development. Of particular interest is the relationship between RAE and physiological development in navigation-based sports, as suggested by recent nationwide analyses across multiple sports [36]. This research could lead to the refinement of talent identification models and the development of evidence-based practices to optimize athlete performance and well-being.

However, while this study makes significant contributions, it is also important to acknowledge its strengths and methodological limitations.

4.4. Strengths, Limitations, and Future Research Directions

One of the main strengths of this study is the use of the actual distribution of births in the Spanish population as a reference for distribution analyses, which enhances the external validity of the findings and reduces the risk of selection bias. Additionally, the sample size is considerably large (N = 34,718), allowing for robust inferences about the distribution of birth quartiles in different competitive and population categories. The inclusion of stratification by sex and competitive level represents a methodological advance over previous studies, aligning with recent research highlighting the importance of gender-specific analyses in RAE studies [9], and allowing for the identification of differences in the manifestation of RAE throughout the sporting development process.

However, certain limitations should be noted. Although the study includes longitudinal data on participation in each age group from age 10 to adulthood, a detailed analysis of the rate of sports dropout in relation to relative age was not conducted. This represents a methodological limitation, as identifying dropout patterns by birth quartiles would allow for the evaluation of whether relatively younger athletes face a higher dropout rate at certain stages of competitive development, a phenomenon documented in other sports such as basketball [12] and ice hockey [87]. Furthermore, the present study compares birth distributions in youth and elite groups with the birth distribution in the general Spanish population, rather than with the entire federated population, which could introduce biases in the interpretation of the results. This consideration is relevant, as previous studies have suggested that differences in the participation base may influence the magnitude of RAE at different competition levels [2].

Given the above, future research could address these limitations with complementary methodological approaches. Based on the gaps identified in this study and current research trends, future research could benefit from a mixed-method approach to better understand RAE in orienteering. While quantitative analyses of birth distributions provide valuable insights, qualitative research could help explain why athletes born in later quartiles may face higher dropout rates. Interviews with coaches, athletes, and parents could reveal specific challenges and barriers that relatively younger athletes encounter. Additionally, longitudinal tracking should include not only participation data but also performance metrics and psychological factors such as motivation and perceived competence.

Another possible direction for research would be to explore the impact of RAE on sports performance within orienteering, considering different performance indicators or measures of sporting success. The suggestion to compare RAE patterns with Scandinavian countries where orienteering is more established could be expanded to include the analysis of different competition structures and development systems. This cross-cultural comparison should examine how various sporting cultures and organizational approaches might mitigate or exacerbate the RAE. Particular attention should be paid to countries that have implemented specific policies or interventions to address RAE, as these could provide valuable insights for policy recommendations.

A recommendation for future studies would be to focus more on orienteering as a sport, as this is truly one of the few studies that specifically analyze the RAE within this discipline. To facilitate the analysis of long-term developmental patterns in local sports systems, it is crucial, as suggested by Baker et al. [88], to continuously collect data on various environmental factors, including the RAE, and compare them across different periods of time. Future research should, in particular, explore the relationship between RAE and athlete dropout rates in orienteering through intervention studies. Longitudinal studies that track athletes over several years could help determine whether those born in later quartiles are more likely to drop out of the sport at particular developmental stages. This would provide valuable insight into whether the RAE not only influences initial participation and selection but also affects long-term retention in orienteering.

Moreover, further investigations are needed to understand the reasons behind athlete dropout, especially among younger athletes. Factors such as perceived lack of success, fewer opportunities for advancement, or differences in coaching attention could contribute to higher dropout rates among those born later in the year, as documented in various team sports [11]. It is also recommended that sports authorities and federations consider measures such as relaxing selection criteria in formative categories or raising awareness about the potential biases that relative age can introduce into sports decision-making. Additionally, future research should examine how the RAE interacts with other factors, such as gender, particularly in female categories, where its impact might be moderated by other variables, as suggested by comprehensive meta-analyses of RAE in female sports [9].

5. Conclusions

This study confirms the existence of RAE in the sport of orienteering in Spain, although its magnitude is generally small. An unequal distribution has been identified in the Total Federated population, with an overrepresentation of athletes born in the first half of the year. This phenomenon persists across all age groups up to 20 years and remains in the elite male category, where it slightly intensifies. In contrast, in the elite female category, RAE is not significant, suggesting possible differences in the selection and retention of high-level female athletes.

The detailed analysis by age groups has shown that, although RAE is small compared to other sports, its presence is constant and does not completely disappear with age. This reinforces the idea that relative age can influence the sporting trajectory of orienteers, shaping their access and progression through different competition stages. Furthermore, the persistence of RAE in the elite male category suggests that relatively older athletes may have higher chances of reaching higher levels in this sport.

The unequal distribution among all licensed athletes suggests a structural bias that favors those born in the first months of the competitive year [2]. Since participation in sports offers significant benefits in social integration, self-esteem, and health, it is crucial to continue investigating the factors contributing to the persistence of RAE and its potential long-term implications [8]. If this phenomenon is not addressed, there is a risk that many boys and girls will distance themselves from sports participation, which could have negative long-term health repercussions for the population.

In conclusion, although RAE in orienteering does not reach the magnitude seen in other sports with more restrictive selection systems, its sustained presence throughout competitive development indicates that it remains a factor to consider in athlete access and progression. A better understanding of the dynamics of RAE in this sport will contribute not only to improving equity in access to sports opportunities but also to optimizing long-term talent development in orienteering.