Relative Age Effect: Beyond the Youth Phenomenon

Abstract

Introduction. The relative age effect (RAE) refers to performance advantage of youth born in the first quarter of the birth year when auditioning for select, age-restricted sports. This advantage conferred to the older athlete is a result of being more physically and emotionally mature, therefore, assumed to be a more advanced player. We hypothesize an RAE exists in Olympic athletes, and this extends across selected categories of athletes (by gender), such as team versus individual sports, winter versus summer athletes, and sports using a ball versus those not using a ball. Methods. We extended the exploration of an RAE beyond specific sports by examining the birth quarter of more than 44 000 Olympic athlete’s birthdates, born between 1964-1996. The data were summarized by birth quarter (January 1 to March 31, etc) and presented as percentages and 95% confidence intervals. Results. The fractions of births in the first versus the fourth quarter were significantly different ( P < .001) from each other for the summer and winter Olympians, ball and nonball sports, and team as well as individual sports. Conclusions. The general presence of an RAE in Olympic athletes exists regardless of global classification.

‘It has been postulated that individuals born closer to the beginning of the year, . . ., have an indirect advantage . . .’

The development of an elite athlete is more than a combination of work ethic, genetic predisposition, and the possession of the “heart of a champion.” Literature has provided evidence that there are secondary factors, which have an indirect effect on the success of the athlete.¹ For example, Côté et al² demonstrated that place of birth and the size of the town are predictors of improved NHL draft stock in elite hockey players. Furthermore, there has been a considerable amount of literature demonstrating relative age effect in sports such as soccer and hockey.^1,3,4 Helsen et al⁵ in 2000, demonstrated a positive linear relationship between practice and excellence on the playing the field, thus demonstrating that there is more to athletic excellence than genetics.

Prior to maturity, this may not be much of an issue, but as children become adolescents and go through puberty, this is likely to become more of a factor. An older child, relative for his or her age cohort, is more apt to go through puberty sooner; therefore, developing physically earlier, and in turn, this would provide a physical advantage on the athletic field. Cutoff dates for sports and enrollment appear to be arbitrary and there is no standard internationally.⁴ If a child is more mature physically, then they are more apt to be selected to a more elite team, and therefore, receive better coaching, competition, and training facilities.

It has been postulated that individuals born closer to the beginning of the year, compared with those born just before the end of the year, have an indirect advantage that could predict advancing to elite status in that sport. The assumption is that the individual born “early” in the defined birth year is older (ie, more mature) compared with the individual born just before the cutoff date.

When the selection process favors the more mature athlete, a substantial number of players are denied access to more advance play simply because they are less mature, not because they are less skilled. The participation and performance advantage of players born early in the birth year is collectively known as the “relative age effect” (RAE).

Initial descriptions include, Grondin in 1984, and Barnsley in 1985 discussing a relationship between relative age and scholastic achievement.⁶ This was also identified in Canadian hockey in 1988.⁷ The most accepted hypothesis for the “RAE” is that older children will be more physically and emotionally mature than their relatively younger counterparts early in their athletic career. This would lend itself to the older child being more successful earlier; therefore, the older athlete would garner a better environment (coaching, competition, etc).^1,2

The massive number of participants over the years at both summer and winter Olympics present a valuable opportunity to determine just how widespread this RAE may be. We hypothesized that all Olympic sports, regardless of age of the athlete when competing in the Olympics, will demonstrate this RAE. Thus, we propose, that more Olympians will be born in the first quarter of the year (January to March) compared with those athletes born in the last quarter of the year (October to December). Furthermore, we hypothesized that this effect will be present according to specific Olympics (summer, winter), gender, and sport classification (team, individual; ball sports, nonball sports).

Materials and Methods

This study was approved by the institutional review board. The data were collected from the International Olympic Committee’s database on Olympic athletes. The data ranges from 1896 to 1996, and compiles a total of 112 152 athletes and their corresponding birthdates.

Each athlete was entered into the database according to their first Olympic participation. Their respective birthdate, gender, and sport where assembled in the database. Olympians who competed in more than one Olympiad, or who competed in more than one event (ie, multiple events in track and field, swimming, etc) were listed as only competing once. Therefore, the database registers every registered Olympian dating back to the first Olympics in the modern era, 1896, and ending in 1996.

The birth year was started in January, as that is the start for a majority of the sports already documented in the literature.^8-12 Additionally, some of the literature compares the birthdates from the first half of the year to the last half; however, a majority of the literature separates and compares the birthdates by quartiles.^8,10 Thus, the birthdates in the database were distributed by quartile.

The quartiles where divided as follows: first, January-March; second, April-June; third, July-September; and fourth, October-December. Although, quartiles are distributed equally by month, they are not distributed equally by day, when using a standard 365.25 day calendar (0.25 day allows for leap year athletes).

This is evident by the fact that not every month has the same number of days; therefore, the quartiles would not necessarily have the same number of days. We calculated the number of days in each quartile, and by dividing them against the number of days in the year; we had the percentage of the year each quartile of months represented: first quartile, 24.2%; second quartile, 24.4%; and third and fourth quartile, 25.7% each. These percentages represented the control, assuming all the birthdates where equally distributed throughout the year.

However, the distribution according to the day of the month showed a greater than expected number of births for the first day of the month. Therefore, we dropped all born on the first of the month due to arbitrary assignment of the first as the birth day in many lesser developed countries.

The exact date of when children started to be grouped by age cohort for sports competition is not well documented. There is no guarantee that age cutoff dates for sports participation were used in 1896. As a result, only the last 30 years of the sample data were employed. A total of 44 087 birthdates comprise the data, which provide sufficient numbers to power the study.

The data were summarized by birth quarter (January 1 to March 31, etc) and presented as percentages and 95% confidence intervals. Observed distribution tested versus theoretical distribution (number days in each quarter/365.25) and tested using chi-square goodness of fit with a significance level of P ≤ .05 considered to be significant. (JMP, SAS Institute, Cary, NC). Comparisons between birth quarters of significant distributions were by overlapping confidence intervals. The main comparison performed throughout the literature and in this data set is to compare the first quarter of the year with the last.

Results

The total number of athletes and birthdates is 112 152; compiled from 1896 to 1996. Our statistical measurements were derived from the period between 1964 and 1996, therefore, all prior birthdates were not used (n = 67 419). The first date in each month was subtracted, as the distribution according to the day of the month showed a greater than expected number of births for the first day of the month. Therefore, we dropped all born on the first of the month due to arbitrary assignment of the first as the birth day in many lesser developed countries (n = 646). This difference yielded total of 44 087 Olympic athletes and their respective birth dates in our study sample.

There were a total of 27 372 male Olympians and a total of 16 761 female Olympians. A total of 36 030 Olympians competed in the summer games, whereas 8057 competed in the winter games. The ball sports had a total of 11 411 and the nonball sports totaled 32 676. Team sports totaled 10 169 and individual sports had a total of 33 918. Each one of these categories was further compared by gender, with male and female distributions being compared.

Olympics

For the entire Olympic dataset, every paired comparison of birth quarters was significantly different from each other (P < .001) (Table 1). When the data are categorized by gender, the visual trend of decreasing fractions of athletes born in each quarter remains (Figures 1-3).

Table 1.

Fractional Distribution (%, Upper/Lower 95% Confidence Interval) of Birth Month by Category.^a

Category	Q1			Q2			Q3			Q4
	Lower	%	Upper	Lower	%	Upper	Lower	%	Upper	Lower	%	Upper
Expected		24.2			24.4			25.7			25.7
Olympic	27.2	27.6	28	25.3	25.7	26.1	24.3	24.3	24.8	22.3	22.3	22.7
Summer	27.1	27.6	28.1	25.3	25.7	26.2	24.3	24.3	24.8	22.3	22.3	22.8
Winter	26.4	27.4	28.3	24.9	25.8	26.8	23.7	24.6	25.6	21.3	22.2	23.1
Ball	27.4	28.2	29.1	25	25.7	26.6	23.7	24.5	25.3	20.8	21.6	22.3
Nonball	26.9	27.4	27.9	25.3	25.7	26.2	23.9	24.3	24.8	22.1	22.6	23
Team	27.5	28.3	29.2	25	25.8	26.7	23.6	24.4	25.3	20.6	21.4	22.2
Individual	26.9	27.4	27.9	25.2	25.7	26.2	23.9	24.3	24.8	22.1	22.6	23

^aAll distributions (Q1%, Q2%, Q3%, Q4%) were significantly different from expected (all P < .001).

Figure 1.

General Olympic population.

Figure 2.

Male Olympic population.

Figure 3.

Female Olympic population.

The general male (n = 27 372) and female (n = 16 761) Olympic athlete population demonstrated a distribution that was significantly different than expected (P < .001). The fractions of births in each quarter were significantly different from each other and from what was expected (Table 2).

Table 2.

Fractional Distribution (%, upper/lower 95% Confidence Interval) of Birth Month by Gender and Category.

Category	Gender	Q1			Q2			Q3			Q4
		Lower	%	Upper	Lower	%	Upper	Lower	%	Upper	Lower	%	Upper
	Female	26.6	27.4	28.2	24.7	25.4	26.2	23.6	24.3	25.1	22.2	22.9	23.6
	Male	27.2	27.7	28.4	25.3	25.9	26.5	23.8	24.4	25	21.4	22	22.5
Summer	Female	26.9	27.8	28.7	24.5	25.4	26.2	23.5	24.4	25.3	21.7	22.5	23.3
Winter	Female	23.7	25.5	27.3	23.9	25.8	27.6	22.4	24.1	25.9	22.9	24.6	26.4
Summer	Male	26.9	27.6	28.3	25.3	25.9	26.6	23.7	24.3	25	21.6	22.2	22.9
Winter	Male	27.1	28.5	30	24.5	25.9	27.3	23.5	24.9	26.4	19.5	20.7	22
Ball	Female	26.6	28.1	29.6	24.6	26.1	27.5	22.2	23.5	25	21	22.3	23.7
Nonball	Female	26.2	27.1	28	24.3	25.2	26.1	23.7	24.6	25.5	22.2	23.1	24
Ball	Male	27.1	28.3	29.6	24.3	25.5	26.7	23.9	25.1	26.4	19.9	21	22.1
Nonball	Male	26.8	27.6	28.3	25.3	26	26.7	23.5	24.2	24.9	21.6	22.3	22.9
Individual	Female	26.3	27.2	28.1	24.3	25.1	26.1	23.8	24.7	25.6	22.2	23	23.8
Team	Female	26.3	27.9	29.5	24.7	26.2	27.8	21.9	23.3	24.8	21.1	22.6	24
Individual	Male	26.8	27.5	28.2	25.3	26	26.7	23.5	24.2	24.8	21.7	22.3	23
Team	Male	27.3	28.7	30	24.2	25.5	26.8	24	25.2	26.6	19.5	20.6	21.8

^aAll distributions (Q1%, Q2%, Q3%, Q4%) were significantly different from expected (all P < .001) except for Female Winter (given in boldface; P = .3414).

There were significantly more female athletes born in the first quarter versus the second, third, and fourth quarter. There were also significantly more female athletes born in the second quarter versus the fourth quarter (Table 2).

Among the summer (n = 36 030) and winter Olympians (n = 8057), male and female, there was a distribution that was significantly different from that expected (P < .001). The fractions of births in each quarter were significantly different from each other for the summer Olympians. Among the winter Olympians, the birth quartiles were only statistically significant between the first versus the third and fourth quarters, as well as the second versus the fourth quarter (Table 1).

Summer Olympians

Male summer Olympians (n = 22 294), demonstrated a distribution that was significantly different than expected (P < .001); with the fractions of births in each quarter being significantly different from each other (Table 2).

Female summer Olympians (n = 13 782), demonstrated a distribution that was significantly different than expected (P < .001). There were significantly more female summer Olympians born in the first quarter versus the second, third, and fourth; as well as the second and third quarter versus the fourth quarter (Table 2).

Winter Olympians

Male winter Olympians (n = 5078), demonstrated a distribution that was significantly different than expected (P < .001). Quarter 1 versus quarters 3 and 4, as well as, quarter 2 and 3 versus 4, demonstrated significantly more winter male Olympians were born (Table 2).

Female winter Olympians (n = 2979) did not demonstrate a distribution that was statically significant from that expected (P = .3414) (Table 2).

Ball Versus Nonball Sports

When comparing all ball (n = 11 411) and nonball (n = 32 676) sports, male and female, the distribution of birth was significantly different than that expected (P < .001). There was a significant difference between the fractions of births in each quarter, except when comparing the second versus third quarter of ball sport athletes (Table 1).

For male ball sports, there were significantly more athletes born in the first quarter when compared with the second, third, and fourth quarter; in addition, to the second and third quarter when compared to the fourth quarter. When comparing male nonball sports, the fractions of births in each quarter were significantly different from each other (Table 2).

There were significantly more female ball athletes born in the first quarter of the year versus the third and fourth quarter, and in the second quarter when compared with the fourth quarter. When comparing female nonball athletes, there were significantly more born in the first quarter of the year compared to the remaining three quarters, as well as, the second quarter compared to the fourth quarter (Table 2).

Team Versus Individual Sports

Team sports (n = 10 169) and individual sports (n = 33 918) demonstrated a significant difference between the fractions of births in each quarter, except when comparing the second versus third quarter of team sport athletes (P < .001) (Table 1). This significant difference was further demonstrated in male (n = 5935) and female (n = 4234) team sports (P < .001), as well as male (n = 21 437) and female (n = 12 527) individual sports (P < .001) (Table 2).

Male team athletes demonstrated a significant birth quartile distribution from the first quarter versus the latter three quarters, and the second and third quarter versus the last quarter. There were significantly more female team athletes born in the first quarter compared with the final half of the year, and the second quarter compared with the fourth quarter (Table 2).

Male individual Olympians demonstrated fractions of birth in each quarter that were significantly different from each other. Female individual Olympians demonstrated significant difference in birth quarters comparing the first with the remainder of the year, as well as, the second with the fourth (Table 2).

Discussion

Relative age effect (RAE) in sports is a phenomenon with initial descriptions by Barnesly in 1985 and 1988, after studying Canadian minor league hockey players. Barnesly postulated that an athlete born after the cutoff date for sport participation is relatively older, therefore, relatively more physically and emotionally mature, compared with other athletes in the same age cohort. Accordingly, this “relatively” older athlete may get advanced to the more elite level of competition early in their career. This will facilitate better coaching, competition, and training.

In team and individual sports, the more success an athlete achieves, the more likely that athlete is to continue with their sport, and the more likely they are to become elite. We demonstrate further that RAE exists at the most elite level; it transcends sex and type of sport. Most notably, our data show an RAE in nonball sports and in individual sports.

Barnesly and Thompson⁸ found in 1988 that older players, born in the first half of the year, played minor league hockey to a later age than the younger players, born in the second half of the year. This was further demonstrated in his article by the fact that almost 70% of the top tier minor league hockey players in their study were born in the first half of the year (January-June). This has been demonstrated in other sports as well. RAE has been demonstrated in professional baseball, American football, first round of the NHL draft, professional soccer, and professional basketball.^{1,3,4,8,11,13,14}

In a 2009 meta-analysis, it was demonstrated that RAE exists in numerous team and ball incorporating sports.¹⁰ We propose that the RAE is not only present in a few select sports, but can be seen affecting all sports, and at the elite level.

Our data are in agreement with the current literature when evaluating team sports in the summer and winter Olympic Games, as well as for male and female team sports. An RAE is not only present in our entire Olympic population, but is present for both female and male Olympians.

Additionally, the data are in agreement with the literature when we evaluated team sports. Our data revealed that overall 28.3% of Olympic team sport athletes where born in the first quartile versus 21.4% where born in the fourth quartile. Further evaluation demonstrates an RAE in male and female team sports. Male and female team sports demonstrate significant increase in births in the first quarter of the year compared with the final quarter of the year; 28.7% versus 20.6%, and 27.9% versus 22.6% respectively. This suggests that even a 6-month difference in age can impart increased physical and emotional maturity, which, in turn, can lead to more success, better training, better coaching, and enhanced competition for the competitor.

Vaeyens et al⁴ demonstrated an RAE in ball sports, specifically in Belgium soccer. A total of 32% of the Belgium national soccer team is born in the first quartile of the year. This literature represents team sports and sports that incorporate a ball.

Our data for ball sports are in agreement with the data already provided in the literature,^4,7,10 with 28.2% of ball athletes born in the first 3 months of the year versus 21.6% born in the last 3 months. Additionally, 28.3% of the entire Olympic male ball and 28.1% of all Olympic female ball sport athletes were born between January and March, and only 21% of male and 22.3% of female ball sport athletes were born between October and December.

In nonball sports, our data show that 27.4% of all the athletes, 27.6% male and 27.1% female, were born between January and March; whereas, 22.6% of all nonball athletes, 22.3% male and 23.1% female, were born in the final 3 months of the year. These data, when used in conjunction with the team sports data, concludes, that regardless of type of sport, an RAE exists, in general, for team sports.

Analyzing the individual sports data, a similar RAE can be appreciated. A total of 27.4% of the athletes born during the first quartile, 27.5% male and 27.2% female; and 22.6% born in the final quartile, 22.3% male and 23% female. Our data demonstrate an RAE for individual sports (ie, wrestling, gymnastics, etc). Thus, it can be inferred, that a child that is more physically and emotionally mature for their age, may be conferred an advantage in individual sports.

A majority of the literature is focused on male competitors. A study, examining RAE in French basketball players, ages 7 to 17 years, demonstrated a slight RAE in their athletes comparing the births in the first half of the year, compared with those in the latter half.¹¹ Furthermore, a study examining Brazilian female youth volleyball players, younger than 14 years,¹⁵ demonstrated that 74% of the athletes in their study were born in the first 6 months of year. The latter study focused on prepubescent females, and many other studies, which do not demonstrate a RAE in female sports, especially soccer, focus on postpubescent females.¹³ Their hypothesis is that a postpubescent women’s body habitus changes to a more endomorphic form, which is disadvantageous for sports.

We also demonstrated an RAE was present in all female sports; ball and nonball, team and individual (Table 2). The only exception was when we isolated only female winter Olympians; there was no statistically significant RAE present.

An explanation for this finding may be the result of more summer Olympic sports (ie, gymnastics, diving, swimming, etc) lend themselves to involve prepubescent females, than winter Olympic sports (ie, figure skating). Also, we included all team, individual, ball, and nonball sports together; and did not differentiate between specific summer and winter Olympic sports. Therefore, the RAE presents in the summer and winter Olympic sports (Table 1) may be masking the lack of RAE present in female winter Olympic sports. This can be elucidated with further research of each summer and winter Olympic sport.

Our data do not differentiate between pre- and postpubescent females. However, our data does demonstrate amongst all Olympic females, ball and nonball females, as well as team sport and individual sport females, there is a significant RAE between the first 3 months of the year compared to the last 3 months; 27.3% versus 22.9%.

Although our study does not breakdown each sport and examine if an RAE is present, it does answer some more global questions that has not been well reported in the literature with such a large study population. Another limitation is the size of our study. The higher population of athletes, and therefore birthdates in the study, the more likely it will have a statistically significant outcome; the greater number the subjects, the more likely a significant difference will be seen. Hence, the significant results are in some part, driven by the number of records in our analysis. However, estimates that are more “population-based” are going to be more stable than estimates based on a subsample of a population (eg, any specific sport).

Pierson et al¹⁶ discuss 3 policies to help minimize the RAE: alternating initial birthday requirements for youth sporting leagues, rotating age cutoffs so that players equally at some point gain aging advantage, and dedicated support to least skilled athletes in the youngest sporting leagues.

We do demonstrate an RAE in individual and non-ball sports, as well as in an overall large population of female sports. All male sports, summer and winter, ball and nonball, team and individual demonstrate a statistically significant RAE. Further research is needed to elucidate which Olympic sports demonstrate an RAE, both male and female, and in some cases, if there are any sports that demonstrate a reverse RAE.

Conclusion

An RAE likely exists in Olympic individual and nonball sports, as well as in an overall large population of female sports. All male sports, summer and winter, ball and nonball, team and individual, demonstrate a statistically significant RAE. Further research is needed to elucidate which Olympic sports demonstrate the RAE and why.