Trends in Relative Age Effects of Top-Level Female Soccer Players: A Japanese Study

Abstract

We investigated recent trends in relative age effect (RAE) findings of top-level female soccer players in Japan, using data from the 2016 to 2020 seasons. We conducted two main analyses: (a) An examination of RAE for all registered players in the Japan Women’s Soccer League (Nadeshiko League) from 2016 to 2020; and (b) an examination of RAE of newly registered players in the league from 2017 to 2020. In the first analysis, we found a significant difference between the number of players born in Q1 (April–June) versus Q4 (January–March), with the number of players born in Q1 greater and with the ratio between these groups ranging from 1.5 to 1.7. In the second analysis, we found a significant relationship between Q1 and Q4 for the 2017 season alone. However, the Q1/Q4 ratio ranged from 1.4 to 1.9, and the semester ratio of S1 (Q1 + Q2))/S2 (Q3 + Q4) ranged from 1.2 to 1.3, suggesting a birth month bias. Thus, there was a RAE in female soccer players playing recently in Japan’s top-level leagues; and the size of the effect did not change significantly across recent seasons.

Keywords

RAE elite athletes sport popularity and RAE RAE trends

Introduction

The relative age effect (RAE) refers to differences in athlete selection that are related to subtle age differences between athletes, such that differences in time of year of the athletes’ births differ within a given age year group (Helsen et al., 2012; Musch & Grondin, 2001; Pedersen, et al., 2022). For example, in Japan, April is the cutoff month for division play because schools begin in April. Although children born before and after April belong to the same grade, there is usually a difference in their physical and psychological development, with those born earlier in the year showing greater maturation. This developmental difference is particularly pronounced in younger children and results in sport selection disadvantages for children born in later months farther from the cutoff date (Cobley et al., 2009; Smith et al., 2018). The RAE has been seen in various sports (Brazo-Sayavera et al., 2018; Cobley et al., 2018, 2019; Delorme et al., 2011; Katsumata et al., 2018; Müller et al., 2015, 2017; Nakata, 2017; Nakata & Sakamoto, 2011; Schorer et al., 2009; Till et al., 2010), including soccer (Brustio et al., 2018; Delorme et al., 2009; Figueiredo et al., 2021; Götze & Hoppe, 2020; Helsen et al., 2012; Hirose, 2009; Pedersen et al., 2022; Yagüe et al., 2018). Worldwide RAE studies involving professional soccer players trace to around 1990 (Dudink, 1994).

Dudink (1994) found more soccer players born in months closer to the cutoff date than in months farther from it in both Dutch and English soccer leagues. Since then, the RAE has been reported for soccer players in various countries, including those participating in the World Cup (Delorme et al., 2009; Figueiredo et al., 2021; Götze & Hoppe, 2020; Pedersen et al., 2022; Sasano et al., 2020; Vincent & Glamser, 2006). To our best knowledge, Uchiyama and Maruyama (1996) published the first RAE study of professional soccer players in Japan, and several similar studies followed (Nakata & Sakamoto, 2011; Sasano et al., 2020). There have been more studies of RAEs on male than on female soccer players, probably because of gender differences in the number of players and gender differences in the sport’s popularity. However, research targeting the RAE among female soccer players has gradually increased. Vincent and Glamser (2006) found no RAE among U-17 American female soccer players, and Delorme et al. (2009) reported no RAE among French female professional soccer players. Likewise, no RAE was reported for Portuguese U-7–U-19 female soccer players except at the U-9 level (Figueiredo et al., 2021). On the other hand, a recent study of female professional soccer players in Germany found an overall RAE (Götze & Hoppe, 2020), and a study of Spanish female soccer players found a RAE in these players on national and regional teams and in the first and second divisions (Sedano et al., 2015). Thus, RAE among female soccer players has been variably evident. A study of RAE among female soccer players who participated in the World Cup showed no RAE in the U-17 and U-20 age groups in earlier years (2008, 2010, 2012, and 2014) but the RAE appeared in more recent years (2016 and 2018) (Pedersen et al., 2022). In the U-18 age group, there was no RAE in 2002–2016, but there was a RAE in 2018 among female players in the World Cup (Pedersen et al., 2022). Thus, there may be an emerging trend in recent years toward more evidence for RAE in female soccer players, perhaps reflecting growing popularity and competitiveness in this women’s sport. Considering the variability of these results, further data from around the world would contribute to a more definitive understanding of possible trends.

To the best of our knowledge, only one study (Nakata & Sakamoto, 2012) investigated the RAE of top-level Japanese female soccer players, and this was during 2010 league play and resulted in no reported RAE. A Japanese female soccer team won the FIFA Women’s World Cup in 2011, the 2012 Olympic Games, and was the 2015 FIFA World Cup runner-up, indicating increased competitiveness among Japanese female soccer teams in recent years, seemingly echoing trends reported by Pedersen et al. (2022) in worldwide soccer. Following Nakata and Sakamoto’s (2012) study on top-level 2010 Japanese female soccer players, there have been no other RAE studies of Japanese female soccer players, making an updated, detailed analyses of RAEs for Japanese female soccer players relevant and informative.

In this study, we investigated recent RAE trends (from the 2016 to 2020 soccer seasons) of top-level female soccer players in Japan. First, we examined the RAE of all registered players in the Japan Women’s Soccer League (Nadeshiko League) in each season between 2016 and 2020 and then we narrowed our RAE analysis to study only newly registered players in the league for each season from 2017 to 2020. We hypothesized that we would find the RAE in recent, but not in earlier, top-level female Japanese soccer players.

Method

Participants

In our first analysis, prospective participants were 4409 players registered in the Japan Women’s Soccer League in each season from 2016 to 2020. From this total, we drew 1599 players from 1263 players the first division, 1440 players in the second division, and 1706 players in the third division (challenge league). We excluded players who were officially described as foreign players, though some Japanese players who grew up overseas and were naturalized in Japan may have been among the participants, because it was not possible to identify them on the official website. The players’ average age was 22.4 (SD = 4.3) years, and we relied on detailed data about the players’ birth dates that have been published publicly since the 2016 season (http://www.nadeshikoleague.jp/).

In our second analysis, our participants were 712 female professional soccer players who were newly registered in the Japan Women’s Soccer League in each season from 2017 to 2020. Specifically, we drew 143 newly registered players in the first division, 196 players in the second division, and 373 players in the third division (challenge league). Again, we excluded officially described foreign players from these analyses. These newer players’ average age was 19.7 (SD = 3.6) years.

Data Collection

We obtained data on the subject’s birth dates from the official website of the Japan Women’s Soccer League (http://www.nadeshikoleague.jp/). We excluded players with missing data for birth date. The data collection period was extended from August 2021 to September 2021. As these data were a matter of public record, there was no need for informed consent from participants.

Statistical Analysis

We conducted the same statistical analyses for both phases of this study and participant groups. First, we calculated the number of players born in each month of the year. Following a previous study (Musch & Hay, 1999), we calculated the Spearman-rank correlation coefficient and determined the significance to establish the relationship between the frequency with each month represented a players’ birth month and the number of participants born in each month. Because the school year begins in April in Japan, April was considered month 1 in this analysis.

Following procedures in previous studies (Nakata & Sakamoto, 2011; Sasano et al., 2020), we classified participants’ birth months into four groups: Q1 (April–June), Q2 (July–September), Q3 (October–December), and Q4 (January–March), and we then calculated the proportion of players born in each quarter. We conducted a goodness-of-fit test to examine the difference in ratios from Q1 to Q4. The comparative expected values for participants’ birth months were calculated from the frequency distribution of birth months of all Japanese people in a demographic survey conducted by the Ministry of Health, Labor, and Welfare of Japan. We used the period of 1994–1998 to index these expected values, because the average age of the participants in our first analysis was approximately 22.4 years old. The expected values so deduced from statistics for the general population were 25.0%, 26.2%, 24.7%, and 24.0% in Q1, Q2, Q3, and Q4, respectively. We also indexed the birth months of Japanese people born from 1997 to 2000, because the average age of the participants in our second analysis was approximately 19.7 years old. For this younger cohort, the expected values were 24.9%, 26.1%, 24.7%, and 24.2% in Q1, Q2, Q3, and Q4, respectively. We conducted a multiple comparison test (Ryan’s procedure) to determine significant differences observed in a goodness-of-fit test with ω calculated as the effect size. Effect sizes ω are considered to be small, moderate, and large, when ω is 0.10, 0.30, and 0.50, respectively (Cohen, 1988). Following procedures in a previous study (Sasano et al., 2020), we compared the proportion of births in Q1 and Q4. In addition, we calculated births in 6-month semesters (S1 and S2) to determine the difference between the sum of births in Q1 and Q2 (S1) versus Q3 and Q4 (S2). We used the Statistical Package for the Social Sciences (SPSS, Version 27, IBM Corp.) and js-STAR for statistical analysis, and we set statistical significance at 5%.

Results

Registrants of Japan Women’s Soccer League in Each Season From 2016 to 2020

For our first analysis, Table 1 shows the frequencies of the players’ births for each month of each season, the percentages of all Japanese top-level soccer players with births that month, and the Spearman’s rank correlation coefficient. Spearman’s rank correlation coefficient was –0.72 or higher, indicating significance, for all seasons (p < .01). Figure 1 shows the ratio of each month in relation to the birth month of the participants in each season, and this is reflected within Table 1.

Table 1.

Distribution of Participant Birth Months for Each Season of 2016–20.

		April	May	June	July	August	September	October	November	December	January	February	March	Sum	r	p
2016	f	110	87	76	67	76	76	60	77	76	67	56	59	887	−0.72*	<.01
2016	%	12.4	9.8	8.6	7.6	8.6	8.6	6.8	8.7	8.6	7.6	6.3	6.7	100.0	−0.72*	<.01
2017	f	104	83	79	70	73	77	74	73	82	54	51	52	872	−0.75*	<.01
2017	%	11.9	9.5	9.1	8.0	8.4	8.8	8.5	8.4	9.4	6.2	5.8	6.0	100.0	−0.75*	<.01
2018	f	95	91	80	80	75	80	79	70	73	53	62	50	888	−0.95*	<.01
2018	%	10.7	10.2	9.0	9.0	8.4	9.0	8.9	7.9	8.2	6.0	7.0	5.6	100.0	−0.95*	<.01
2019	f	108	98	76	77	79	78	73	72	76	62	62	47	908	−0.90*	<.01
2019	%	11.9	10.8	8.4	8.5	8.7	8.6	8.0	7.9	8.4	6.8	6.8	5.2	100.0	−0.90*	<.01
2020	f	90	88	79	70	75	77	67	69	69	63	56	51	854	−0.95*	<.01
2020	%	10.5	10.3	9.3	8.2	8.8	9.0	7.8	8.1	8.1	7.4	6.6	6.0	100.0	−0.95*	<.01

Note. f: frequency, *p < .05.

Figure 1.

Ratio of Players’ Birth Months in Each Month of Seasons 2016–20.

Table 2 shows the frequencies and ratios for births in Q1–Q4 for each season of 2016–2020, test results for the goodness-of-fit test, effect sizes, and results of multiple comparison tests. The goodness-of-fit test showed a significant difference in the ratios of Q1 to Q4 in all seasons. A multiple comparison test showed a significant difference between Q1 and Q4 in all seasons. ω ranged from 0.13 to 0.17. The ratio Q1/Q4 was 1.5–1.7, and the ratio S1/S2 was 1.2–1.3.

Table 2.

Distribution of Players in Q1–Q4 for Each Season of 2016–20.

		Q1	Q2	Q3	Q4	Sum	χ²	p	ω	Multiple Comparison Test	Q1 vs Q4	S1 vs S2
2016	f	273	219	213	182	887	17.2*	<.01	0.14	Q1 > Q2, Q3, Q4	1.5	1.2
2016	%	30.8	24.7	24.0	20.5	100.0	17.2*	<.01	0.14	Q1 > Q2, Q3, Q4	1.5	1.2
2017	f	266	220	229	157	872	24.8*	<.01	0.17	Q1, Q2, Q3 > Q4	1.7	1.3
2017	%	30.5	25.2	26.3	18.0	100.0	24.8*	<.01	0.17	Q1, Q2, Q3 > Q4	1.7	1.3
2018	f	266	235	222	165	888	19.6*	<.01	0.15	Q1, Q2, Q3 > Q4	1.6	1.3
2018	%	30.0	26.5	25.0	18.6	100.0	19.6*	<.01	0.15	Q1, Q2, Q3 > Q4	1.6	1.3
2019	f	282	234	221	171	908	23.5*	<.01	0.16	Q1 > Q3	1.6	1.3
2019	%	31.1	25.8	24.3	18.8	100.0	23.5*	<.01	0.16	Q1, Q2, Q3 > Q4	1.6	1.3
2020	f	257	222	205	170	854	15.0*	<.01	0.13	Q1, Q2 > Q4	1.5	1.3
2020	%	30.1	26.0	24.0	19.9	100.0	15.0*	<.01	0.13	Q1, Q2 > Q4	1.5	1.3

Note. f: frequency, *p < .05.

New Registrants of Japan Women’s Soccer League in Each Season From 2017 to 2020

Table 3 shows the frequencies and ratios for the birth months of new players in each season of 2017–2020, and the Spearman’s rank correlation coefficients for these values. The Spearman’s rank correlation coefficient was significant only for the 2019 season (r = −0.58, p = .05). Figure 2 shows the ratios of new players’ birth months of seasons 2016–20 and reflects part of Table 3.

Table 3.

Distribution of Newly Registered Players’ Birth Months for Each Season.

		April	May	June	July	August	September	October	November	December	January	February	March	Sum	r	p
2017	f	15	18	14	18	13	26	22	15	17	8	9	8	183	−0.49	.11
2017	%	8.2	9.8	7.7	9.8	7.1	14.2	12.0	8.2	9.3	4.4	4.9	4.4	100.0	−0.49	.11
2018	f	17	24	16	18	16	17	15	13	8	12	19	10	185	−0.56	.06
2018	%	9.2	13.0	8.6	9.7	8.6	9.2	8.1	7.0	4.3	6.5	10.3	5.4	100.0	−0.56	.06
2019	f	24	25	17	11	14	15	17	12	15	16	11	12	189	−0.58	*.05
2019	%	12.7	13.2	9.0	5.8	7.4	7.9	9.0	6.3	7.9	8.5	5.8	6.3	100.0	−0.58	*.05
2020	f	12	15	16	13	15	20	14	14	9	8	6	13	155	−0.51	.09
2020	%	7.7	9.7	10.3	8.4	9.7	12.9	9.0	9.0	5.8	5.2	3.9	8.4	100.0	−0.51	.09

Note. f: frequency, *p < .05.

Figure 2.

Ratio for Newly Registered Players’ Births in Each Month.

Table 4 shows the frequencies and ratios of births in Q1–Q4 for each season, the test results for goodness-of-fit test, effect sizes, and the results of multiple comparison tests. The goodness-of-fit test showed significant differences between numbers for Q1 to Q4 in 2017 and 2019. A multiple comparison test showed a significant difference between Q1 and Q4 births for 2017, with no significant difference found for 2019. ω ranged from 0.17 to 0.26. The ratio for Q1/Q4 varied between 1.4 and 1.9, and that for S1/S2 varied between 1.3 and 1.4.

Table 4.

Distribution of Newly Registered Players’ Births in Q1–Q4 for Each Season.

		Q1	Q2	Q3	Q4	Sum	χ²	p	ω	Multiple Comparison Test	Q1 vs Q4	S1 vs S2
2017	f	47	57	54	25	183	11.9*	<.01	0.26	Q1, Q2, Q3 > Q4	1.9	1.3
2017	%	25.7	31.1	29.5	13.7	100.0	11.9*	<.01	0.26	Q1, Q2, Q3 > Q4	1.9	1.3
2018	f	57	51	36	41	185	5.10	.16	0.17		1.4	1.4
2018	%	30.8	27.6	19.5	22.2	100.0	5.10	.16	0.17		1.4	1.4
2019	f	66	40	44	39	189	10.5*	.01	0.24	Not significant	1.7	1.3
2019	%	34.9	21.2	23.3	20.6	100.0	10.5*	.01	0.24	Not significant	1.7	1.3
2020	f	43	48	37	27	155	4.90	.18	0.18		1.6	1.4
2020	%	27.7	31.0	23.9	17.4	100.0	4.90	.18	0.18		1.6	1.4

Note. f: frequency, *p < .05.

Discussion

Among all registered players in the Japan Women’s Soccer League for each season, the number of players with birth dates in each month decreased for months from April to March, and Spearman’s rank correlation coefficient was negative and statistically significant (Table 1). Figure 1 shows a downward slope in these values, and Table 2 shows a statistically significant difference in the numbers of births in Q1 versus Q4 across all seasons, with the highest numbers of births in Q1 and the lowest number of births in Q4. The ratio of players’ birthdates in Q1/Q4 ranged from 1.5 to 1.7, denoting a RAE among players from 2016 to 2020 in Japanese top-level female soccer. In the only other RAE study of top-level female soccer players in Japan (Nakata & Sakamoto, 2012), investigators found that, in the 2010 season, 27.3% of players were born in Q1, 25.6% in Q2, 26.1% in Q3, and 21.0% in Q4, with no RAE evident, since the difference between number of players’ birthdates in Q4 and Q1 was not significant. Nakata and Sakamoto (2012) examined first division and challenge league players in a single season, while we studied three divisions (first division, second division, and challenge league) in multiple seasons and used more recent seasons. Nakata and Sakamoto (2012) studied 238 players, a number significantly smaller than our sample size. While a simple comparison between the two studies is not possible, the different findings between the two studies may indicate a trend difference in RAE over time, with a significant RAE detected recently, but not dating back to the period of the earlier study (the 2010 season). However, as will be described below, the RAE results of all registered players differed from that for newly registered players, requiring close attention in the interpretation of our results. Simply put, it is necessary to consider that in our data, a single player plays over multiple seasons, while in Nakata and Sakamoto’s data (2012), all registered players of a single season were analyzed.

Japanese women’s soccer has achieved high competitive success in recent years, including winning the FIFA Women’s World Cup in 2011. Additionally, the popularity of women’s soccer in Japan has increased along with this increased competitiveness. According to the website of the Japan Football Association (https://www.jfa.jp/about_jfa/organization/databox/player.html), the number of registered female soccer players increased from 19,147 in 2000 to 25,278 in 2010 and 27,249 in 2020. This increased popularity and competitiveness has likely contributed to the RAE in the selection of athletes from a young age. Because body size, muscle strength, power, and speed are important factors in soccer play, these elements must be considered when selecting young players, giving young players who are physically superior at a young age (often players whose birth months are earlier in the eligibility year and closer to the cutoff date) a competitive selection advantage. Notably, intense competition from a young age is thought to make RAE more pronounced. Thus, the increased popularity and competitiveness in Japanese women’s soccer may have produced a RAE that was not evident in earlier years.

In another recent study of RAE among players who participated in the U-17, U-20, and senior World Cups (Pedersen et al., 2022), the RAE was recently observed in women’s U-17 and U-20 age groups, perhaps reflecting a similar influence on RAE in women’s soccer related to a worldwide increase in popularity and competitiveness that has increased the incentive for coaches to select young players who are more biologically mature as is thought to be the basis for the RAE. In a long-term analysis, changes in the RAE of female soccer players have been observed worldwide (Pedersen et al., 2022), giving us reason to expected this trend for female soccer players in Japan. However, because our effect sizes did not reflect a constant change over 2016-2020 seasons when we compared “all registered” and “newly registered” players (Tables 2 and 4), it is difficult to be certain of a change in the strength of the RAE within this relatively short term (4 year) perspective.

The RAE in women’s soccer has varied with the group(s) being investigated. For example, in a study of U-7 to U-19 female soccer players registered with the Portuguese Football Federation for the 2019/2020 season, a RAE was only found in the U-9 group (Figueiredo et al., 2021). Yet, a RAE was found for Spanish women’s soccer players on national teams, regional teams, and in the first and second but not in the third divisions (Sedano et al., 2015). The RAE was also found among German female soccer players (Götze & Hoppe, 2020). The presence of the RAE may differ from country to country because of differences in the popularity and competitiveness of women’s soccer and, perhaps, as a function of the recency or time span between comparison dates. In general, there have been fewer RAE studies for female than male soccer players. Our results and those of previous studies indicate that the RAE of female soccer players has been less stable than among males players, but it may be changing with increasing competitiveness and popularity of women’s soccer. Further research is still necessary to affirm that possibility.

In Japan, the RAE of male professional soccer players has been established with data from 1993, 2001, 2010, and 2018 seasons (Nakata & Sakamoto, 2011; Sasano et al., 2020). The Q1/Q4 ratio of player birthdates was 2.19 in 1993, 2.54 in 2001, 2.38 in 2010, and 2.06 in 2018, and the S1/S2 ration of player birthdates was 2.04 in 1993, 1.95 in 2001, 1.72 in 2010, and 1.71 (Sasano et al., 2020). The Q1/Q4 ratios in our comparisons of all registered and newly registered soccer players’ birthdates in this study were small by comparison, indicating that the extent of the RAE differed significantly for women versus men soccer players. This gender difference may be related not only to gender differences in the popularity and competitiveness of the sport, but perhaps also to gender differences in the speed and variance of body growth at young ages that seem to precipitate differences in selection of athletes at young ages.

For our results regarding newly registered female soccer players in the 2017–2020 seasons, the Spearman’s rank correlation coefficient ranged from −0.49 to −0.58 (moderate correlations) and was significant only in 2019. Figure 2 shows a slight downward trend in this relationship. There were significant differences in the distribution of player birthdates in Q1 compared to Q4 in the 2017 and 2019 seasons (Table 4). The results of the multiple comparison test showed the frequency of player birthdates in Q1, Q2, and Q3 to be greater than Q4 only in 2017, and there was no significant difference in the frequency of these birthdates observed in 2019. However, the ratio of Q1/Q4 players birthdates ranged from 1.4 to 1.9, and the ratio of S1/S2 players’ birthdates ranged from 1.3 to 1.4. The small number of observed statistically significant differences in the timing of newly registered female soccer players’ birthdates may have been due to the small number of participants in this analysis. Yet, there was an observed bias in birth month frequency among these players, and we conclude that a RAE exists in recent young female soccer players in Japan. Our study clarified recent overall trends in the RAE among Japan’s top-level female soccer players. Previous studies have also examined the differences in the RAE of female soccer players by position and by league (Götze & Hoppe, 2020; Sedano et al., 2015), and it will be important to examine the RAE of Japanese female soccer players by position and by league.

Limitations and Directions for Further Research

In this study we were able to identify what may be an emerging trend toward a RAE among top level female Japan soccer players that is relatively small compared to the RAE among top male soccer players in Japan; but it will be necessary to continue to examine RAE among female soccer players in Japan to affirm this possible trend. In this study, we did not address the basis for this apparent trend except to identify its association with increased popularity and competitiveness of this sport which suggests the possibility of changes in coaches’ selection strategies. These possibilities might be directly investigated in separate research paradigms. A competing theory to explain weaker evidence of RAE among female versus male athletes has been the notion that female athletes mature earlier than male athletes generally (Sanborn & Jankowski, 1994), meaning that there is a smaller biological difference between female players at younger ages, creating less coaching incentive to select slightly older females. However, the trend we identified toward an increased RAE as women’s soccer becomes more popular and competitive suggests that these factors may be beginning to outweigh RAE-negating gender-based maturation rate differences. More RAE research is needed, particularly research addressing ways to ameliorate this subtle age bias for selecting young athletes, including the effects of rotating the cutoff date (Helsen et al., 2012) or segmenting young soccer players into leagues of players comprised of those with earlier and later maturation.

Conclusion

Our analysis of the birth months of all registered players in Japanese female soccer from 2016 to 2020 revealed a significant negative correlation between the birth month and the ratio of the players in each month, and there was a significant difference in the proportions of births between Q1 and Q4, with the Q1/Q4 ratio ranging from 1.5 to 1.7. These results suggest a RAE in Japan’s top-level female soccer players. Because the effect size did not change significantly over the course of these seasons, there may have been no change in the strength of the RAE within the short period from 2016 to 2020 seasons. Among players who were newly registered between 2017 and 2020, there was a significant difference in the ratio of Q1 and Q4 player birthdates only in 2017. However, the Q1/Q4 ratio ranged from 1.4 to 1.9, and the S1/S2 ratio ranged from 1.2 to 1.3, suggesting a bias in birth months. It will be necessary to continue detailed research on the RAEs of female soccer players.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Shigeki Matsuda

Author Biographies

Shigeki Matsuda is a professor in Faculty of Education, Shiga University, Japan. His work focuses on soccer, such as RAE, balance ability, and training.

Hiroaki Ishigaki is a research student in Department of Education, The University of Tokyo, Japan. His work focuses on RAE of Japanese professional soccer players.

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