Abstract
Background
The relative age effects (RAE) have been a hot topic in the field of sport research, and although the phenomenon has been found in most sports in China, there is currently no literature on the RAE phenomenon in Chinese basketball, which has implications for explaining some of the phenomena in that sport. A large body of research suggests that RAE contributes significantly to the phenomena of brain drain and inequality in sport participation. The purpose of this study was to analyze whether RAE exists in Chinese youth men’s basketball and whether RAE has an impact on the performance of athletes and teams, thus supporting the subsequent proposal of measures to balance the age effect.
Methods
This study analyzed the date of birth and performance rankings of all male athletes (n = 3926) who participated in the Chinese Youth Basketball Championships (U14, U15, and U17) from 2021 to 2023. The study divided the month of birth into quarters and semesters. Differences between actual observed and theoretical birth date distributions were statistically analyzed using the chi-square test, and subsequent calculations were made using the dominance ratio test.
Results
It was found that RAE was present in all three groups of Chinese youth male basketball players (p < 001). The percentages of athletes born in the first half of the year were 61.2% (U14), 59.9% (U15), and 59.0% (U17), and the percentages of athletes born in the first quarter were 38.6% (U14), 35.8% (U15), and 34.8% (U17).
Conclusions
As a result of RAE, U15 athletes born closer to the beginning of the year are likely to achieve better results, whereas athletes born closer to the end of the year are less likely to achieve athletic excellence.
Keywords: Youth basketball players, Relative age effects, Talent identification, Youth training
Introduction
To underscore the equity and competitiveness of athletic competitions, sports commonly categorize young individuals by age, employing specific cut-off dates. Nonetheless, this policy inadvertently engenders subtle time-age differentials among athletes born in different years, recognized as “relative age effects” (RAE) [1, 2] Initially identified in other domains, researchers have discerned that RAE manifests more prominently in the realm of sports [3]. Representing a developmental concern for athletes, coaches, and policymakers involved in sports, RAEs denote deviations in the distribution of an athlete’s date of birth from the overarching normal distribution [3]. Research has explored the impact of RAE in areas as diverse as sport, education, medical diagnosis, and psycho-cognition. The origins of RAE investigations can be traced back to the field of education, with scholars initially identifying the phenomenon among Canadian amateur, competitive, and professional volleyball and ice hockey players [2]. Subsequently, a substantial body of literature has emerged delving into the impact of RAE in various sports, including football [4–6] basketball [7, 8], volleyball [9]、ice hockey [10]、and rugby [11] In fact, this is an observed phenomenon. In youth sports, where special provisions are made for particular age groups or cohorts, there is a clear majority of relatively older people.
Studies have demonstrated that the RAE endows athletes born in selection or competition years with superior physical, psychological, skillful, and performance-related maturity compared to their counterparts born in the same year [5, 12]. This advantage increases their likelihood of selection for high-level teams and talent development programs [13]. Furthermore, an uneven distribution of training resources can exacerbate the impact of RAE [14]. This disparity may result in a preference for athletes who are more physically and psychologically mature, thereby squandering opportunities for others [15]. Consequently, it is more probable that some athletes may withdraw from the sport [16]. Therefore, the above study confirms that the emergence of RAE, which has a greater impact on the performance of youth sports, is an issue that needs to be addressed as soon as possible.
To solve RAE, we must first understand the mechanism of his formation. Notably, diverse factors contribute to the manifestation of RAE, encompassing age distribution and position in the game [17], socio-cultural background [18], family education, competitive level [19], and among others. These factors collectively position RAE as a prominent and influential factor in the realm of sports, potentially introducing biases in talent identification, selection, and development [20, 21]. While some researchers have explored strategies to mitigate the impact of RAE from various perspectives [22], such studies are notably lacking in China. Despite the intensifying competition associated with team-based projects, relevant research and initiatives addressing the mitigation of RAE remain scarce in the Chinese context, as elucidated by the existing literature.
Currently, most of the RAE studies on basketball have chosen professional and amateur basketball leagues as their subjects. Research on professional leagues suggests that RAE does not exert a discernible impact at the elite level. However, an analysis of players’ birthdate distributions reveals that RAE does influence performance during adolescence, affording these athletes a substantial advantage in their trajectory towards professionalism [23]. RAE is also conspicuous in youth basketball, where studies indicate a correlational link with athletic performance [24, 25]. Notably, in the Spanish National Basketball Youth Teams, a subset of gifted players has ascended to significant levels, transitioning from amateur status to the junior national team and eventually securing professional athlete status [26]. In other European youth national basketball teams, the talent selection process has been influenced by factors such as initial selection age, RAE, and long-term national team performance [27].
In Chinese sports, RAE has been identified as a factor influencing athletes’ performance during the early adolescent phase across various sports, including table tennis [28], tennis [29], and football [30]. Although there is no evidence to confirm the presence of RAE in Chinese youth basketball, studies have been conducted in other countries to demonstrate its occurrence in youth basketball. For instance, researchers examining the birth months of adolescent basketball players in the French ACB and Spanish basketball leagues (LEB1 and LEB2) observed that male athletes born in the first quarter exhibited a higher prevalence of superior performance compared to those born in the fourth quarter [8]. Additionally, a study on RAE in the German professional basketball league revealed its persistent existence as an inequality that could potentially impact athletes later in their careers [31].
In summary, RAE is considered to seriously affect and interfere with adolescents’ participation in sports, negatively influencing their motivation to participate and their future development. In China, few studies have discussed the presence of RAE in youth basketball; therefore, this study will explore RAE in Chinese youth men’s basketball and reveal its impact on the youth men’s basketball. In this study, we analyzed whether athletes participating in the 2021–2023 Chinese Youth Basketball Championships (U14, U15, and U17) were affected by RAE by focusing on three aspects: sample characteristics, subject-specificity, and study variables. We proposed the hypothesis that RAE exists among athletes participating in the Chinese Youth Basketball Championships. Based on this hypothesis, we explored the Chinese youth basketball selection mechanism of Chinese youth basketball athletes and assessed the talent selection of Chinese basketball with the aim of mitigating or preventing the deterioration of RAE. In addition, this study will further explore the effects of other factors, such as social status, economy, and family income, on the motivation of adolescent male athletes to participate in basketball, providing Chinese evidence for future research on RAE in youth sports.
Methods
Participants
This study details the dates of birth and final game results of 3926 male athletes who participated in the National Youth Basketball League (U14, U15, and U17), spanning the years 20,221 to 2023. The rankings and birth dates of the study participants were obtained from a publicly accessible online database on the official website of the Chinese Basketball Association (www.cba.net.cn). Data retrieval included basic information such as each athlete’s name, date of birth, height, weight, position, and last game’s score. This primary dataset was used in the data analysis in Sect. 3. The list of athletes participating in the National Youth Basketball League (U14, U15, and U17) in 2022 and 2023 was available for review and extraction. All results were from official national competitions, and due to the representativeness that this tournament has, the above dataset was chosen for analysis in this study. It is important to note that informed consent and ethics committee approval were not required for this study as it used exclusively publicly available data.
Statistical analysis
Based on the date of birth, each athlete was assigned to four relative age quarters (Q1-Q4) to calculate RAEs for different age categories. Based on the cut-off dates used by FIBA and the Chinese Basketball Association (CBA) and determining relative ages, the year was divided into four quarters: Q1 = January, February, and March; Q2 = April, May, and, June; Q3 = July, August, and September; Q4 = October, November, and December [32].
To test the RAE, the distribution of participants’ months of birth from Q1 to Q4 was examined using a chi-square t (X²) goodness-of-fit test [33]. The theoretical (expected) frequency distributions were Q1 = 90.25/365, Q2 = 91/365, Q3 = 92/365, and Q4 = 92/365. The relative values were Q1 = 24.71%, Q2 = 24.91%, Q3 = 25.19%, and Q4 = 25.19% [15]. The effect sizes were set at the alpha level, with p < 0.05 being statistically significant and p less than 0.01 being a highly significant difference, Comparisons between the first and fourth quartile distributions (i.e., Q1 vs. Q4) and the first half vs. second half distributions (i.e., Q1 and Q2 vs. Q3 and Q4) were calculated using odds ratios (ORs) and 95% confidence intervals (CI). When the CI was 1 (e.g., 95% CI ranging from 0.90 to 1.10), the findings were interpreted as indicating no significant association [34].
All statistical analyses were performed using SPSS 26.0 software, and finally, in order to compare more clearly the effect of the date of birth on the performance of young basketball players, we evaluated the total number of players in each group and compared the athletes from the top 5 and bottom 5 teams involved in the tournament.
Results
Relative age effects in the boys’ U14 group, 2022–2023
As illustrated in Table 1, the distribution of athletes in the U14 group across quarters 1–4 revealed proportions of 38.6%, 22.6%, 21.2%, and 17.5%, respectively. Additionally, the proportions of athletes born in the first and second half of the year were 61.2% and 38.8%, respectively. Statistical analysis, specifically the chi-square test, yielded a significant result with a chi-square value (X²) of 67.279 and a p-value less than 0.001. This indicates a highly significant difference in the skewness of the athletes’ dates of birth.
Table 1.
Distribution and analysis of U14 male athletes by year of birth, 2022–2023
| Year | Classify | Birthdate distribution | Semesters | Date analysis | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Total | S1 | S2 | X² | P | OR Q1vsQ4 |
OR Q1&Q2vsQ3&Q4 |
||
| 2022 | n | 77 | 45 | 49 | 40 | 211 | 122 | 89 | 15.635 | 0.001** | 2.3 [1.7,3.0] | 1.4[0.8,2.5] |
| % | 36.5 | 21.3 | 23.2 | 19.0 | 100.0 | 57.8 | 42.2 | |||||
| 2023 | n | 172 | 101 | 88 | 73 | 434 | 273 | 161 | 53.171 | 0.000** | 2.5 [2.1,3.1] | 2.2[0.9.3.4] |
| % | 39.6 | 23.3 | 20.3 | 16.8 | 100.0 | 62.9 | 37.1 | |||||
| Total | n | 249 | 146 | 137 | 113 | 645 | 395 | 250 | 67.279 | 0.000** | 1.8 [1.4,2.3] | 1.6 [1.3,2.0] |
| % | 38.6 | 22.6 | 21.2 | 17.5 | 100.0 | 61.2 | 38.8 | |||||
Q1–Q4: The first to the fourth quarter, X²: chi-square value p > 0.05: no significant difference, p < 0.05: significant difference*, p < 0.01: very significant difference**, OR: ratio of Q1 and Q4
The findings revealed a notable RAE among Chinese U14 men’s basketball players. The distribution of athletes’ dates of birth by year exhibited skewness, and statistically significant differences (p < 0.05) were observed for athletes competing between 2022 and 2023. The odds ratios (ORs) for each quarter (Q1 vs. Q4 and Q1&Q2 vs. Q3&Q4) indicated varying degrees of effect for both 2022 and 2023. Overall, the difference in the skewness of U14 athletes’ dates of birth was statistically significant (p < 0.05), suggesting a discernible tendency for the RAE to be further intensified among male basketball players in the U14 category who participated between 2022 and 2023.
Relative age effects in the boys’ U15 group, 2022–2023
As depicted in Table 2, the distribution of athletes in the U15 group across quarters 1–4 showed proportions of 35.8%, 24.1%, 21.0%, and 19.1%, respectively. The proportions of athletes born in the first and second halves of the year were 59.9% and 40.1%. respectively. The chi-square value (X²) was 83.987, indicating a highly significant difference in the skewness of the athletes’ dates of birth.
Table 2.
Distribution and analysis of U15 male athletes by year of birth, 2022–2023
| Year | Classify | Birthdate distribution | Semesters | Date analysis | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Total | S1 | S2 | X² | P | OR Q1vsQ4 |
OR Q1&Q2vsQ3&Q4 |
||
| 2022 | n | 200 | 133 | 108 | 94 | 535 | 333 | 202 | 49.119 | 0.000** | 2.2 [1.3,3.2] | 1.9 [1.2,2.8] |
| % | 37.4 | 24.9 | 20.2 | 17.6 | 100.0 | 62.3 | 37.8 | |||||
| 2023 | n | 236 | 160 | 147 | 139 | 682 | 396 | 286 | 34.868 | 0.000** | 2.1 [0.9,3.9] | 2.1 [1.3,3.4] |
| % | 34.6 | 23.5 | 21.6 | 20.4 | 100.0 | 58.1 | 42.0 | |||||
| Total | n | 436 | 293 | 255 | 233 | 1217 | 729 | 488 | 83.987 | 0.000** | 2.4 [1.5,3.8] | 1.9 [1.0,3.5] |
| % | 35.8 | 24.1 | 21.0 | 19.1 | 100.0 | 59.9 | 40.1 | |||||
Q1–Q4: The first to the fourth quarter, X²: chi-square value, p > 0.05: no significant difference, p < 0.05: significant difference*, p < 0.01: very significant difference**, OR: ratio of Q1 and Q4
For Chinese U15 male basketball players, an overall significant difference in proportions was observed. Specifically, the proportion of athletes born in the first half of the year in 2022 exceeded that of 2023, and despite a declining trend, it remained significantly higher than those born in the second half of the year. The odds ratios (OR) values from (Q1 vs. Q4 and Q1&Q2 vs. Q3&Q4) exhibited varying degrees of impact. In the total number of U15 players, the deviation in date of birth was more pronounced, with a highly significant difference (P < 0.01). Consequently, RAE is evidently present among China’s U15 men’s basketball players. Overall, there is an indication of a deceleration in the RAE effect within the U15 group from 2022 to 2023, although a substantial effect persists.
Relative age effects in the boys’ U17 group, 2022–2023
As indicated in Table 3, the distribution of athletes in the U17 group across quarters 1–4 showed proportions of 34.8%, 24.2%, 21.8%, and 19.2%, respectively. The proportions of athletes born in the first and second halves of the year were 59.0% and 41.0%, respectively. The chi-square value (X²) was 66.904, signifying an extremely significant difference in the skewness of the athletes’ dates of birth.
Table 3.
Distribution and analysis of U17 male athletes by year of birth, 2022–2023
| Year | Classify | Birthdate distribution | Semesters | Date analysis | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Total | S1 | S2 | X² | P | OR Q1vsQ4 |
OR Q1&Q2vsQ3&Q4 |
||
| 2022 | n | 85 | 53 | 39 | 57 | 234 | 138 | 96 | 19.655 | 0.000** | 3.1 [2.0,4.8] | 2.2 [1.0,3.5] |
| % | 36.3 | 22.6 | 16.7 | 24.4 | 100.0 | 58.9 | 41.1 | |||||
| 2023 | n | 328 | 234 | 219 | 171 | 952 | 562 | 390 | 53.479 | 0.000** | 3.5 [2.4,5.2] | 2.7 [2.1,3.7] |
| % | 34.5 | 24.5 | 23.0 | 18.0 | 100.0 | 59.0 | 41.0 | |||||
| Total | n | 413 | 287 | 258 | 228 | 1186 | 700 | 486 | 66.904 | 0.000** | 2.8 [1.8,4.6] | 1.9 [1.1,3.6] |
| % | 34.8 | 24.2 | 21.8 | 19.2 | 100.0 | 59.0 | 41.0 | |||||
Q1–Q4: The first to the fourth quarter, X²: chi-square value, p > 0.05: no significant difference, p < 0.05: significant difference*, p < 0.01: very significant difference**, OR: ratio of Q1 and Q4
For the slightly older Chinese youth basketball players (U17), the discernible skewness in the athletes’ date of birth persists, with similar ages observed for 2022 and 2023. The odds ratios (OR) values for (Q1 vs. Q4 and Q1&Q2 vs. Q3&Q4) indicate varying degrees of effect, and the significant difference in the skewness of the total distribution of athletes’ dates of birth is highly noteworthy (P < 0.01). The RAE among Chinese U17 male basketball players is significant, suggesting a notable impact. Overall, there is no significant regular change in the RAE observed among U17 male basketball players from 2022 to 2023.
Relative age effects and athletic performance in young male basketball players
To gain a clearer understanding of the impact of the RAE on the performance of young basketball players, we conducted an assessment and comparison of the overall number of athletes in each group, specifically evaluating the number of athletes in the top 5 teams at the end of the year and the number of athletes in the bottom 5 teams. This analysis aimed to provide insights into how RAE influences the distribution of players across performance tiers, shedding light on potential disparities in achievement and team success among young basketball players.
As evident from Table 4; Figs. 1, 2 and 3, across all age groups, athletes born in the first quarter notably surpassed those born in the subsequent quarters, followed by those born in the second quarter, and a further decrease in numbers for those born in the third and fourth quarters. In other words, athletes born in the first half of the year were consistently outnumbered by those born in the second half of the year in all groups. Athletes born in the first quarter even constituted as much as 42.9% of the total.
Table 4.
Distribution of date of birth, chi-square and OR ratio analyses for different achievement rankings, 2021–2023
| Group | Classify | Birthdate distribution | Semesters | Date analysis | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Total | S1 | S2 | X² | P | OR Q1vsQ4 |
OR Q1&Q2vsQ3&Q4 |
||
| U14 | n | 249 | 146 | 137 | 113 | 645 | 395 | 250 | 67.279 | 0.000** | 1.9 [1.4,2.6] | 1.6 [1.2,2.7] |
| % | 38.6 | 22.6 | 21.2 | 17.5 | 100.0 | 61.2 | 38.8 | |||||
| U14-TOP5 | n | 55 | 31 | 30 | 20 | 136 | 86 | 50 | 19.471 | 0.000** | 2.4 [2.0,2.9] | 1.8 [1.6,3.9] |
| % | 40.4 | 22.8 | 22.1 | 14.7 | 100.0 | 63.2 | 36.8 | |||||
| U14-Bottom 5 | n | 60 | 35 | 25 | 23 | 143 | 95 | 48 | 24.245 | 0.000** | 2.1 [1.1,3.9] | 1.6 [0.9,2.3] |
| % | 42.0 | 24.5 | 17.5 | 16.1 | 100.0 | 66.5 | 33.6 | |||||
| U15 | n | 436 | 293 | 255 | 233 | 1217 | 729 | 488 | 82.126 | 0.000** | 2.1 [1.1,3.0] | 1.9 [1.4,2.6] |
| % | 35.8 | 24.1 | 21.0 | 19.1 | 100.0 | 59.9 | 40.1 | |||||
| U15-TOP5 | n | 67 | 32 | 30 | 27 | 156 | 99 | 57 | 27.128 | 0.000** | 3.7 [1.9.4.2] | 2.6 [1.9,4.1] |
| % | 42.9 | 20.5 | 19.2 | 17.3 | 100.0 | 63.4 | 36.5 | |||||
| T15-Bottom 5 | n | 53 | 35 | 44 | 24 | 156 | 88 | 68 | 11.846 | 0.008** | 1.6 [0.8,3.1] | 1.2 [0.8,2.3] |
| % | 34.0 | 22.4 | 28.2 | 15.4 | 100.0 | 56.4 | 43.6 | |||||
| U17 | n | 413 | 287 | 258 | 228 | 1186 | 700 | 486 | 66.904 | 0,000** | 2.7 [2.4,3.2] | 1.9 [1.1,3.4] |
| % | 34.8 | 24.2 | 21.8 | 19.2 | 100.0 | 59.0 | 41.0 | |||||
| U17-TOP5 | n | 51 | 40 | 33 | 28 | 152 | 91 | 61 | 7.842 | 0.049* | 2.0 [1.6,2.5] | 1.9 [0.9,2.2] |
| % | 33.6 | 26.3 | 21.7 | 18.4 | 100.0 | 59.9 | 40.1 | |||||
| U17-Bottom 5 | n | 48 | 33 | 23 | 31 | 135 | 81 | 54 | 9.681 | 0.021** | 1.8 [1.1,2.7] | 1.3 [1.1,2.5] |
| % | 35.6 | 24.4 | 17.0 | 23.0 | 100.0 | 60.0 | 40.0 | |||||
Q1–Q4: The first to the fourth quarter, X²: chi-square value, p > 0.05: no significant difference, p < 0.05: significant difference*, p < 0.01: very significant difference**, OR: ratio of Q1 and Q4
Fig. 1.
Distribution of birth quarters of athletes with different performance rankings in U14
Fig. 2.
Distribution of birth quarters of U15 athletes with different performance rankings
Fig. 3.
Distribution of birth quarters of U17 athletes with different performance rankings
However, within all three groups, only the first five first-quarter-born players in U15 exhibited an 8.9% higher proportion compared to the last five first-quarter-born players. This difference was not observed in the remaining groups. Specifically, the birthdates of players from the top 5 teams carried a higher weight in the first quarter compared to the overall number only in the U15 group (8.9%). Moreover, a significantly higher proportion of players born in the first half of the year was noted in the top 5 teams compared to the bottom 5 teams in the U15 group, while the other groups did not exhibit a significant difference in this regard.
The data presented in Table 4 underscores a significant skewed distribution of athletes’ dates of birth across all groups. The Q1&Q2 vs. Q3&Q4 odds ratio (OR) analysis reveals that only the U15 group among the top 5 teams exhibited a substantial effect, with an OR exceeding 2 and reaching 2.6. Conversely, the other groups in both the top 5 and bottom 5 displayed a comparatively smaller effect, with the following OR values: U14-top5 (1.8), U14-Bottom5 (1.6); U17-Top5 (1.9), U17-Bottom5 (1.3). This suggests that the RAE is more pronounced in the U15 group among the top-performing teams, while the impact is less substantial in the other groups.
Simultaneously, the proportion of individuals born in the first quarter of the year to the total number of people in each group was 38.6% (U14), 35.8% (U15), and 34.8% (U17). Likewise, the proportion of individuals born in the first half of the year was 61.2% (U14), 59.9% (U15), and 59.0% (U17), respectively. The odds ratios (ORs) for these proportions were 1.6, 1.9, and 1.9, respectively, in descending order.
Across all groups, only the U15 category exhibited a notable difference in the deviation degree between the birth dates of the first five and last five teams’ athletes. Specifically, the athletes in the leading teams tended to have birth dates closer to the beginning of the year, while those in the trailing teams exhibited a smaller distribution towards the beginning of the year. This phenomenon was not observed in the other age groups (U14 and U17). In other words, for U14 and U17 level basketball competitions, the proximity of birth dates to the beginning of the year did not significantly affect competition results. Nevertheless, the overall proportion of athletes born in the first quarter and first half of the year remained greater. The results of the study suggest that U15 teams with a higher proportion of players’ birthdates closer to the beginning of the year may achieve better sporting results, while Chinese youth basketball teams with birthdates more evenly distributed throughout the year may be at a competitive disadvantage because of RAE.
Discussion
Based on the reality of the results, this study verified whether there was a RAE among Chinese youth male basketball players. It was found that there was an uneven distribution of birthdates among the age groups of athletes, especially in the U14 and U17 groups. This indicates that the RAE phenomenon exists in Chinese youth men’s basketball, validating the previous hypothesis. This further supports previous studies on the relative age effect in youth basketball and tennis players [8, 29]. It also increases the global recognition of the existence of relative age effects in youth basketball. It is noteworthy that this study identified the RAE phenomenon for the first time in Chinese youth basketball players, which provides a basic reference for future research work related to basketball and youth sports in China.
In our study, we observed that the proportion of players in each age group born in the first quarter (Q1) of the year was U14 (38.6%), U15 (35.8%) and U17 (34.8%), respectively. In addition, the proportions of players born in the first half of the year are U14 (61.2%), U15 (59.9%) and U17 (59.0%). It is worth noting that the RAE on youth basketball players diminishes with age, as the proportion of players born in the first quarter and the first half of the year decreases from U14, U15, and U17, which further supports the idea that the RAE diminishes with age [35].
This finding validates and supports several theoretical models for the existence of RAE, namely, the Pygmalion effect, which suggests that the greater the expectations that are read into a person, the greater the results that person achieves [36]. As this theory suggests, this pattern may lead to athletes born later in life abandoning the sport prematurely, while athletes born more recently develop physical, mental, and skill strengths in response to these influences, thus building a foundation of strengths in their early years of development. Cumming et al. found that physically precocious players were able to receive more involuntary support from their coaches, allowing the creation of a favorable psychosocial environment that would be more conducive to the athlete’s continued participation in sport compared to those born at the end of the year [37]. This creates a halo effect, meaning that relatively older players receive more involuntary support and thus more expectation and attention, which, along with the Pygmalion effect, continues to perpetuate the relative age advantage of this group of athletes [38]. Although the physiological differences begin to diminish after growing into the U17 group or even higher age groups, the high quality of competition experience and training experience allows these athletes to develop a stacked and magnified advantage [35]. Thus, the RAE theory holds that athletes with birth dates closer to the beginning of the year will achieve better athletic performance, and conversely, athletes with birth dates closer to the end of the year are less likely to have better athletic performance. The results of our study showed that the relationship between RAE and athletic performance did not occur in the U14 and U17 groups. However, we found that teams with age distributions closer to the beginning of the year in the U15 group achieved good results, which is consistent with previous findings, such as those of Gutierrez et al., who found that RAE was more prevalent in high-level sports and more performance-oriented teams [39].
As a result of the more competitive atmosphere in basketball (which was also the case for the youth basketball players in this study, as well as for the youth basketball players in this study), many studies have found similar things. For example, Diaz, in a study examining the RAE in a Spanish U12 basketball tournament, found that 83.4%of the 2,268 athletes were born in the first half of the year [8]. The present study further validated this result by finding the same RAE in the U14 and U17 age groups. We found that the presence of RAE may be correlated with a team being successful. Therefore, we did a correlation analysis between highly ranked and lowly ranked teams in the three groups. Interestingly, there were no significant differences in the U14 and U17 teams, but there was performance-related RAE in the U15. This seems to indicate that there is a correlation between grades and RAE, but not in all groups. This validates previous research that there are significant and consistently large differences in exercise performance between relatively older and relatively younger individuals. For example, in track and field, older male long jumpers showed a 5 to 47% difference in performance compared to younger athletes [40]. And this can lead to the emergence of some competitive imbalances, that is to say, older people will have more experience in terms of physical fitness, athletic experience, mental fitness, etc., giving them more advantages when faced with the process of selection and talent identification [32]. Ensuring equal opportunities for all young individuals to participate in sports is crucial. However, existing approaches to athlete selection and talent identification systems often fall short of guaranteeing such equitable opportunities. The prevalent reliance on the performance of young athletes as a primary criterion for selecting youth basketball players in China exacerbates the likelihood of this inequity. In the current talent selection process, children born in the second half of the year may face implicit discrimination.
As a result of these potential factors, RAE was found to be significantly present in the U14 and U17 groups of Chinese youth basketball players. In the distribution of birth dates in the elite Chinese youth group, the proportion of athletes born in the first half of the year was as high as U14 (61.2%), U15 (59.9%), and U17 (59.0%), with a significant bias (P < 0.01), especially in the U14 group (P < 0.01, OR 2.8). Therefore, it has been suggested that RAE leads to the exclusion of late-born athletes at the adolescent stage, where coaching perceptions, behavioral variables, the training environment, and the social environment become influential factors in their development [41]. Not only that, but in the subsequent analyses of Q1 vs. Q4, we both found significant representative results, such as 2.5 [2.1,3.1] for U14 and 3.5 [2.4,5.2] for U17, suggesting that relatively older athletes were 2.5 times more likely to gain an advantage in the U14 group and even more so in the U17 group, where the likelihood was 3.5 times higher.
Taking the above findings together, it is crucial to address and mitigate the presence of RAE. Based on the perspective of sustainable development, sports organizations, associations, coaches, athletes, and parents should be fully aware of the negative aspects of relative age effects that affect the growth and success of athletes, so that there will be less wastage of talent and brain drain. Firstly, from the perspective of selection, to reduce the short-lived disadvantages of young athletes, the development potential and future development space of young athletes should be fully considered in the talent selection and elimination process, and coaches and sports organizations should reverse their own perceptions and look at athletes’ potentials with a developmental perspective. It has been demonstrated that a policy of equal access requires a degree of retention of all athletes, avoiding the selection of only the best performers, so that late-career athletes can remain in their clubs and be given the same opportunities as other athletes to participate in training and competitions [42, 43]. Secondly, policy development should change. Youth sport organizers should have a clear understanding of the impact of RAE on youth participation in sport. Everything from establishing flexible selection deadlines to modifying age requirements for participation in different seasons and changes to the selection process can lead to relief from RAE. Not only that, but administrators could also consider grouping competitions according to biological age so that they are played according to actual age to enhance the fairness of youth events and ensure that every athlete is given an equal opportunity. Therefore, in order to mitigate the impact of the RAE in Chinese basketball, we suggest that the Chinese Basketball Association (CBA), as well as youth sports organizations, schools, coaches, athletes, and parents, should be fully aware of the negative aspects of the RAE, and by reforming the selection process of youth athletes, the selection of talent, the identification of talent, and the criteria for participating in the grouping of athletes, take into account, in a more scientific and reasonable manner, the comprehensive consideration of the athletes’ physical morphology and physical quality while considering the effects of RAE on training and selection. When forming teams, it is recommended that coaches consider the proportion of athletes born in different years and seasons. As much as possible, these measures described above are used to protect and remedy athletes who mature later in life, to balance out the negative effects of the age effect.
Although this study is the first to identify the effect of RAE on Chinese youth basketball players and attempts to reveal the relationship between athletic performance and RAE, there are still some limitations in this study. First, we believe that the distribution of birth dates should be equal, but we did not consider the distribution of birth dates of the country in which this study belongs, and whether there are different effects among different regions, etc. Secondly, due to the limitations of the database, only male athletes were considered in the sample of this study; RAE among adolescent female basketball players is an important topic that will be further explored and studied in the future. Finally, the conduct of this study can provide coaches, athletes, and sport administrators with useful support regarding RAE, but based on the policy environment in China, some of the recommendations might be compromised. Therefore, in the future, it is hoped that colleagues will conduct further research on female basketball players, the distribution of births in different countries, and how to eliminate the negative effects of age.
Conclusion
This study identified a heterogeneous distribution of birth dates among Chinese youth male basketball players in the U14, U15, and U17 age groups. There was a significant overrepresentation of athletes born in the first quarter (Q1) compared to those born in the remaining three quarters (Q2, Q3, and Q4). Additionally, athletes born in the first half of the year outnumbered significantly those born in the second half of the year. Consequently, the RAE phenomenon was observed in all three groups of Chinese adolescent male basketball players. Moreover, as the age of Chinese adolescent male basketball players increased, the impact of RAE tended to intensify gradually.
Upon further investigation into the relationship between the RAE and athletic performance, it was observed that in the U15 age group, the RAE exerted an impact on the competitive performance of adolescent basketball players. Specifically, athletes born closer to the beginning of the year were more likely to achieve better results, whereas athletes born closer to the end of the year faced greater challenges in achieving favorable outcomes. Finally, the development of this study can provide useful support to coaches, athletes, and sport administrators on RAE, thus providing theoretical references and research ideas for further follow-up in research practice.
Author contributions
D. JX. wrote the main text of the manuscript and plotted figures. L. YF reviewed and revised the paper, and W. T. and L. WC collected and analysed the data. All authors reviewed the manuscript.
Funding
This work was supported by the Chinese National Social Science Foundation [grant number 22XTY013]. And this work was supported by the Chengdu Institute of Physical Education “14th Five-Year Plan” Scientific Research and Innovation Team Project. [grant number 23CXTD05]
Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
No experiments or data requiring ethical approval were used in this study.
Consent for publication
No, We declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.