Examining the longitudinal variation of the relative age effect in youth football players

Examining the Longitudinal Variation of the Relative Age Effect in

Youth Football Players

Dissertação de Mestrado Internacional em Análise da Performance Desportiva

João Miguel Dias da Silva

Orientador: Professor Doutor Nuno Miguel Correia Leite

Examining the Longitudinal Variation of the Relative Age Effect in

Youth Football Players

Dissertação de Mestrado Internacional em Análise da Performance Desportiva

João Miguel Dias da Silva

Orientador: Professor Doutor Nuno Miguel Correia Leite

Composição do Júri:

Professora Doutora Catarina Isabel Neto Gavião Abrantes

Professor Doutor Luís Miguel Teixeira Vaz

Professor Doutor Nuno Miguel Correia Leite

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Declaração

Nome: João Miguel Dias da Silva C.C: 14860696

Telemóvel: (+351) 924313612

Correio Eletrónico: joaomiguel.silva@live.com.pt

Mestrado: Mestrado Internacional em Análise da Performance Desportiva

Título da dissertação: “Examining the Longitudinal Variation of the Relative Age Effect in

Youth Football Players”

Orientador: Professor Doutor Nuno Miguel Correia Leite

Ano de Conclusão: 2019

Declaro que esta dissertação de mestrado é o resultado de uma pesquisa e trabalho pessoal efetuada por mim e orientada pelo meu orientador. O seu conteúdo é original e todas as fontes consultadas estão devidamente citadas no texto e mencionadas na bibliografia final. Declaro ainda que este trabalho não foi apresentado em nenhuma outra instituição para a obtenção de qualquer grau académico.

Vila Real, outubro de 2019 João Miguel Dias da Silva

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Dedicatória

Quero dedicar esta dissertação ao meu pai.

A pessoa que hoje estaria mais orgulhosa não vai estar presente. A dedicatória desta dissertação é o mínimo que posso oferecer em sua homenagem.

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Agradecimentos

Ao Professor Doutor Nuno Leite, pela sua orientação com experiência e profundo conhecimento sobre o tema. Foi para mim um orgulho ter trabalhado com o Professor.

Ao treinador Carlos Celso e ao treinador Gerardo Rodrigues, por todo o conhecimento e partilha, e por me fazerem “Sentir o Gil”.

À Flávia, minha companheira de viagem nesta magnifica aventura. Com ela visitei metade da Europa, e com um apoio mútuo conseguimos sempre ultrapassar da melhor forma os obstáculos que se impunham.

To all my IMPAS classmates that shared with me this amazing journey.

A quem eu considero ser a minha família de Vila Real. Azevedo, Bino, Catarina, Eduardo, João Silva, Miguel e Rita, nos bons e nos maus momentos vocês estiveram sempre lá.

A todos os meus amigos e familiares, em especial ao meu primo Licínio e à minha avó Mecias. Se nós somos moldados por aquilo que vivemos, este trabalho acaba por ter um bocadinho de cada um deles.

À minha namorada, por todo o amor, apoio e paciência, mas acima de tudo por nunca me deixar desistir, dando-me a mão e caminhando comigo. Sempre!

Ao meu irmão, por tudo aquilo que partilhamos, esta conquista também é dele. Ele sabe que sim.

À minha mãe, por me proporcionar todas as condições que me permitiram chegar aqui, e principalmente por todo o amor, compreensão e apoio incondicional.

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Resumo

A identificação de jogadores com potencial para chegar a um nível de elite continua a ser um dos principais desafios no futebol. O fenómeno do efeito da idade relativa (RAE) explica a predominância de atletas nascidos no início do ano comparativamente com atletas nascidos perto do final do ano. Este efeito tem sido observado no futebol e relacionado com o abandono de atletas relativamente mais novos. Nesse sentido, o presente estudo tem dois objetivos principais: O primeiro visa estudar este efeito no Campeonato do Mundo FIFA Sub-17 (India, 2017) (n = 439) e no Campeonato do Mundo FIFA Sub-20 (Polónia, 2019) (n = 448); O segundo visa estudar a variação longitudinal do efeito da idade relativa nos jogadores que participaram simultaneamente no Campeonato Europeu UEFA Sub-17, no Campeonato Europeu UEFA Sub-19 e no Campeonato do Mundo FIFA Sub-20 (de 2010 a 2019) (n = 81). Os jogadores foram divididos em quatro quartis (Q) de nascimento de acordo com as suas datas de nascimento. Testes de qui-quadrado (χ²) foram usados para comparar as diferenças entre as datas de nascimento observadas e o que seria esperado, e o teste One-Way ANOVA Medidas Repetidas foi usado para obter a significância estatística entre os minutos jogados por jogo por jogadores nascidos em cada quartil. O nível de significância foi mantido em 5%. Relativamente aos Campeonatos do Mundo FIFA Sub-17 e Sub-20, os resultados mostraram que 41% dos jogadores Sub-17 e 38% dos jogadores Sub-20 nasceram no Q1 (p < 0.001). O efeito da idade relativa foi identificado em todos os continentes exceto para os jogadores nascidos em África. Apesar de não haver diferença estatística significativa entre a média de minutos jogados por jogo, os jogadores nascidos no Q4 jogaram mais minutos por jogo (57 min) relativamente aos colegas. Estas evidências confirmam a existência do efeito da idade relativa e sugerem que o mesmo desvanece com o passar dos anos. Depois de estudar a variação longitudinal do efeito da idade relativa, os resultados mostraram que os jogadores nascidos no Q1 tiveram similar tempo de jogo ao longo dos anos, no entanto, os jogadores nascidos no Q4 jogaram menos minutos nos Sub-17 mas foram tendo mais tempo de jogo ao longo dos anos, e nos Sub-20 jogaram mais minutos que os jogadores nascidos no Q1. Tal sugere que os jogadores nascidos perto do final do ano vão conquistando um papel mais importante na equipa ao longo do tempo, eventualmente superando os colegas relativamente mais velhos.

Palavras chave: Identificação e desenvolvimento de talento; Desportos coletivos;

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Abstract

Talent identification of players with potential to reach elite level remain one of the biggest challenges in football. The phenomenon of the relative age effect (RAE) explains the overrepresentation of athletes born near to the beginning of the year over athletes born near to the end of the year. This effect has been noticed in football and linked with dropout of athletes relatively younger. In this sense, the present study has two main aims: The first is to study this effect in the FIFA World Cup 17 (India, 2017) (n = 439) and FIFA World Cup U-20 (Poland, U-2019) (n = 448); And the second is to study the longitudinal variation of the effect of relative age in the athletes who have participated simultaneously in UEFA Euro U-17, UEFA Euro U-19 and FIFA World Cup U-20 (from 2010 to 2019) (n = 81). Athletes were divided into four quartiles (Q) of birth according to their birth dates. Chi-squared tests (χ²) were used to compare differences between the observed and expected birth rate distribution and One-Way ANOVA Repeated Measures test was used to access the statistical significance between minutes played per game by athletes born in each quartile. The level of significance was kept at 5%, and all data was analysed with SPSS software for Windows (version 25.0). Concerning FIFA World Cup U-17 and U-20, results showed that 41% of U-17 athletes and 38% of U-20 athletes were born in Q1 (p < 0.001). The RAE was identified in every continent, with exception for athletes born in Africa. Although no statistically significant difference was found, athletes born in Q4 played more minutes per game (57 min) over the colleagues in both competitions. These evidences confirm the existence of RAE and suggests that the effect is fading over the years. After studying the longitudinal variation of the effect, results showed that athletes born in Q1 had a similar playing time over the years, however, athletes born in Q4 played fewer minutes in U-17 but got more minutes over the time and in U-20 played more minutes than athletes born in Q1. This suggests that athletes born near to the end of the year are having a more important role on the team over the years, and eventually overtake the colleagues relatively older.

Key words: Talent identification and development; Team sports; Biological age; Chronological

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Table of Contents

Table of Contents ... viii

List of Tables ... ix

List of Figures ... ix

List of Symbols and Abbreviations ... x

Introduction ... 1 Methods ... 3 Sample ... 3 Statistical Analysis ... 4 Results ... 5 Discussion ... 8 Conclusion ...13 References ...14

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List of Tables

Table 1 – Birth distribution by quartiles [n (%)] in FIFA World Cup U-17 (2017) and FIFA World Cup U-20 (2019). ... 5

Table 2 – Relative Age Effect among FIFA designated regions, in FIFA World Cup U-17 (2017) and FIFA World Cup U-20 (2019). ... 6

Table 3 – Distribution of the mean of minutes played per game (min) by the players belonging to each quartile of birth, throughout the competitions of UEFA Euro U-17, UEFA Euro U-19 and FIFA World Cup U-20. ... 8

List of Figures

Figure 1 - Mean of minutes played per game (min) in FIFA World Cup U-17 and FIFA World Cup U-20 distributed by quartiles of birth of the players. ... 7

Figure 2 - Variation of the mean of minutes played per game (min) by the players belonging to each quartile of birth, throughout the competitions of UEFA Euro U-17, UEFA Euro U-19 and FIFA World Cup U-20. ...11

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List of Symbols and Abbreviations

AFC Asian Football Confederation

ATT Attackers

CAF Confédération Africaine de Football

CONCAF The Confederation of North, Central America and Caribbean Association

CONMEBOL Confederación Sudamericana de Fútbol

DEF Defenders

ES Effect Size

FIFA Fédération Internationale de Football Association

GK Goalkeepers

M Mean

MID Midfielders

OFC Oceania Football Confederation

Q Quartile

RAE Relative Age Effect

SD Standard Deviation

TalentID Talent Identification and Development Models

UEFA Union des Associations Européennes de Football

UNICEF United Nations Children's Fund

U-nº Under nº Years Old

χ² Chi-squared

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Introduction

The pursuit and identification of players with potential to perform at an elite level remains one of the biggest challenges for scouting departments, coaches and football agents. Accurate identification is a crucial factor regarding talent development, due to its high costs and limited resources available (Helsen et al., 2012).

In Fédération Internationale de Football Association (FIFA) youth tournaments, players are grouped according to their chronological age (also younger players are eligible), aiming to provide a fair competition and an equal chance of success for all.

The phenomenon of the relative age effect (RAE) explains the overrepresentation of athletes born near to the beginning of the year over athletes born near to the end of the year (Saavedra-García, Matabuena, Montero-Seoane, & Fernández-Romero, 2019). This effect has been observed in some areas such as school (Roberts & Fairclough, 2016) and sports, like basketball (Arrieta, Torres-Unda, Gil, & Irazusta, 2016; Leite, Borges, Santos, & Sampaio, 2013) , ice hockey (Fumarco, Gibbs, Jarvis, & Rossi, 2017), rugby (McCarthy & Collins, 2014), athletics (Saavedra-García, Gutiérrez-Aguilar, Sa-Marques, & Fernández-Romero, 2016), volleyball (Campos, Stanganelli, Rabelo, Campos, & Pellegrinotti, 2016), tennis (Edgar & O’Donoghue, 2005) or football (Sierra-Díaz, González-Víllora, Pastor-Vicedo, & Serra-Olivares, 2017). According to literature, these individuals relatively older are more likely to achieve better performances over those who are born later, especially in younger stages.

Talent identification and development models (TalentID) have contributed to a better understanding of the long term athlete development, however, there will always be gaps due to considering a limited range of measures, possibly a narrow view focusing on current performance and anthropometric factors (Gil et al., 2014). The most accepted explanation of RAE is that, regarding TalentID, children with a better anthropometric profile and cognitive status appear to be more likely to be identified as “talented” (Fragoso, Massuca, & Ferreira, 2015; Gil et al., 2014). Subsequently, these children are selected to specialised training programs, and, allied with increasing motivation, they will have better chances to improve their skills. This process leads to a cycle where early-born children have constantly an advantage over late-born children (Helsen et al., 2012; McCarthy, Collins, & Court, 2016; Sæther, 2015; Towlson et al., 2017). This selection of players may result in potentially talented late-maturing children dropping out of the sport at an early age (Ostojic et al., 2014).

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Elite sport is not characterised by an equal opportunities policy, as its main focus is winning in the short term and promote home-made players into the first team (Augste & Lames, 2011; Jimenez & Pain, 2008). However, clubs should be aware that some evidence show that, at a certain point, there seems to be a reversal on the question, suggesting that late-maturing players catch up on physical attributes (Meylan, Cronin, Oliver, & Hughes, 2010) and then have a higher chance to reach a professional career over early-maturing and on time maturing players, in elite youth level (Ostojic et al., 2014).

Regarding the international panorama, this thematic is still scarcely explored. Among the few studies published, Barnsley and colleagues (1992) analysed 1990 World Cup and 1989 under 17 years old (U-17) and U-20 World Cups, and observed that players born early in the selection year were over-represented. Furthermore, this effect was stronger in U-17 and U-20 World Cups. Another study, conducted by Helsen and colleagues (2005) also showed an overrepresentation of players born in the first quarter of selection year in the national teams at U-15, U-16, U-18 and U-21, although the effect was more evident the younger the category got. Jimenez & Pain (2008) found a more evident RAE in clubs’ youth teams and Spanish youth national squads (U-17 to U-21) when comparing with professional and national team players, also from Spain, suggesting that this phenomenon weakens with aging. In another study, six U-17 FIFA World Cups (from 1997 to 2007) were examined by Williams (2010) where it was found that 40% of the players were born in the first quarter of the selection year, whereas only 16% were born in the last quarter. This effect was observed in all FIFA designated regions, except for Africa, where there seems to be a reverse of the RAE. González-Víllora and colleagues (2015) found a RAE in senior level, U-21, U-19 and U-17 (Europeans championships), however, RAE was decreasing the older the category got. Sierra-Díaz and colleagues (2017) examined 28 manuscripts published from 2010 to 2016 and concluded that RAE is increasing during the last years in youth elite competitions, however no significant RAE was found in elite football teams (adults) due to the no existence of physical advantages in older players. The same research indicates that RAE depends on diverse factors such as physical advantages, the player position on the pitch, the size of the country and the talent detection process. Rubajczyk and Rokita (2018) verified that over 72% of the players called up for Polish national teams from U-17 to U-21 at the year 2015, were born in the first semester of the year.

Although available literature presents some important evidences that should be considered, this is still a field that should be more explored. In the will of trying to reduce this discrepancy, studies should be brought expanding the examinations to unexplored areas, by using different approaches. In this sense, the purpose of the present study is divided into two aims. The first

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one is to study the dates of birth of the international players that have participated in the FIFA World Cup U-17 hosted by India (2017) and FIFA World Cup U-20 hosted by Poland (2019), trying to understand the effects of the relative age and its implications when it comes to individual performances. The second objective is to study the longitudinal variation of the RAE in the players who have participated simultaneously in the UEFA Euro U-17, UEFA Euro U-19, and FIFA World Cup U-20 in its respective categories, from 2010 to 2019. This second purpose arises from the absence of evidence in the literature.

Regarding the first aim, according with literature it is expected to find overrepresentation of players born in the first part of the year both in U-17 and U-20 World Cup categories, and the effect it is expected to be stronger in U-17. Concerning the second aim, it is expected that the major of the players were born in the first part of the year, however, it is expected that players born near the end of the year are getting more minutes played per game the older they are.

Methods

Sample

For the first part of the study, all players who have played at least one minute on the final stages of male FIFA World Cup U-17 (India, 2017) and male FIFA World Cup U-20 (Poland, 2019) were selected. According to this criterion, 121 players present in their national squad for the respective competition were excluded from the sample since they didn’t play. In the U-17 category, 31 goalkeepers (GK), 141 defenders (DEF), 155 midfielders (MID) and 112 attackers (ATT) were included (n = 439), while in the U-20 category, 32 GK, 146 DEF, 138 MID and 132 ATT were included (n = 448), making a total sample of 887 players.

Date of birth and minutes played were obtained. The FIFA World Cup U-17 counted with 5 teams from Asian Football Confederation (AFC) (Asia), 4 teams from Confédération Africaine de Football (CAF) (Africa), 4 teams from The Confederation of North, Central America and Caribbean Association (CONCACAF) (Central and North America), 4 teams from Confederación Sudamericana de Fútbol (CONMEBOL) (South America), 2 teams from Oceania Football Confederation (OFC) (Oceania) and 5 teams from Union des Associations Européennes de Football (UEFA) (Europe). In its turn, 4 teams from AFC (Asia), 4 teams from CAF (Africa), 4 teams from CONCACAF (Central and North America), 4 teams from CONMEBOL (South America), 2 teams from OFC (Oceania) and 6 teams from UEFA (Europe) have participated in FIFA World Cup U-20.

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In the second part of the study, a total of twelve competitions were analysed. The players of four different generations (1993, 1995, 1997 and 1999) were tracked and those who have been in their respective national squads simultaneously for the final stages of UEFA Euro U-17, UEFA Euro U-19 and FIFA World Cup U-20 (from 2010 until 2019) were included. Spain (n = 6), France (n = 4), Portugal (n = 7), Greece (n = 8) and England (n = 5) have participated simultaneously in UEFA Euro U-17 (Lichtenstein, 2010), UEFA Euro U-19 (Estonia, 2012) and FIFA World Cup U-20 (Turkey, 2013). Germany (n = 7) was present in UEFA Euro U-17 (Slovenia, 2012), UEFA Euro U-19 (Hungary, 2014) and FIFA World Cup U-20 (New Zealand, 2015). England (n = 5), Germany (n = 2) and Portugal (n = 9) played the UEFA Euro U-17 (Malta, 2014), UEFA EuroU-19 (Germany, 2016) and FIFA World Cup U-20 (South Korea, 2017). By last, France (n = 3), Italy (n = 7), Portugal (n = 8) and Ukraine (n = 10) competed in the UEFA Euro U-17 (Azerbaijan, 2016), UEFA Euro U-19 (Finland, 2018) and FIFA World Cup U-20 (Poland, 2019). A total of 81 players were included in the sample. Date of birth and minutes played. All data is free and available on www.fifa.com, www.uefa.com and www.playmakerstats.com.

Statistical Analysis

The birth month was compiled to define the birth quarter, and four birth quartiles (Q) were stablished: Q1 = January to March; Q2 = April to June; Q3 = July to September; Q4 = October to December. For both hypothesis under study, data was displayed quantitatively with frequencies (N and respective percentage). Shapiro-Wilk test was used to reject the normal distribution of the variables studied. Chi-squared tests (χ²) were used to evaluate the significance of the inter-quartile differences. Phi effect size (ES) (ɸ) was calculated and can be interpreted as small (0.1 - 0.3), moderated (0.3 - 0.5) and large (> 0.5) (Cohen J., 1988). When there was a statistically significant difference between the theoretical expected number of players born per quartile (25%) and the number observed, the effects of relative age was identified. Minutes played per game was calculated for each individual (minimum 1 minute; maximum 90 minutes), and data was displayed as mean ± standard deviation (M ± SD). One-Way ANOVA Repeated Measures test was used to access the statistical significance of this variable between quartiles. The level of significance was kept at p ≤ 0.05. All data was analysed with SPSS software for Windows (version 25.0) and Microsoft Excel 2019 was used to build the graphs.

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Results

Regarding the first goal of the study, table 1 shows the players who participated in the categories of the FIFA World Cup U-17 (2016) and U-20 (2019), distributed by the quartile of birth. In both categories, the distribution of players was statistically significantly different (p < 0.001). To identify the magnitude of the phenomenon, ES (ɸ) was calculated, revealing moderated effect in both categories.

Table 1 – Birth distribution by quartiles [n (%)] in FIFA World Cup 17 (2017) and FIFA World Cup U-20 (U-2019). Q1 Q2 Q3 Q4 Total χ² p-value ES (ɸ) U-17 181 (41%) 108 (25%) 96 (22%) 54 (12%) 439 76.326 < 0.001** 0.417 (moderated) U-20 168 (38%) 123 (27%) 96 (21%) 61 (14%) 448 54.589 < 0.001** 0.349 (moderated) ** p < 0.001

In order to further deepen the study, it was considered appropriate to evaluate the existence of this effect in each of the six FIFA designated regions, by grouping the players by their respective region. Following this, table 2 shows the differences in the birth quartiles of the players at the FIFA World Cup U-17 and U-20. Only for African teams no statistical significant differences were found between quartiles of birth, with a small ES in both competitions (at U-17, 32% of the players were born in Q1, 16% in Q2, 28% in Q3 and 25% in Q4; at U-20, 31% of the players were born in Q1, 24% in Q2, 24% in Q3 and 21% in Q4). The distribution of players was statistically significantly different in all the other continents for U-17 and U-20 category. In U-17, 53% of European players were born in Q1, 18% in Q2, 21% in Q3 and 8% in Q4, meanwhile in U-20 46% of them were born in Q1, 26% in Q2, 14% in Q3 and 14% in Q4. Regarding Asian players, in U-17 38% were born in Q1, 25% in Q2, 24% in Q3 and 13% in Q4, whereas in U-20 31% of the players were born in Q1, 34% in Q2, 27% in Q3 and 8% in Q4. It is important to note that, against the trend, the most represented quartile in U-20 in Asian players was Q2 (34%). Concerning teams from Oceania, in U-17 23% of their players were born in Q1, 40% in Q2, 23% in Q3 and 13% in Q4, and in U-20 21% were born in Q1, 46% in Q2, 21% in Q3 and 13% in Q4. To emphasize that, once more against the trend, Q2 is the most represented both in U-17 and U-20 teams from Oceania. About Central and North American players, in U-17 46% of them were born in Q1, 24% in Q2, 24% in Q3 and 6% in Q4,

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while in U-20 42% were born in Q1, 19% in Q2, 22% in Q3 and 18% in Q4. Lastly, in U-17 47% of the South American athletes were born in Q1, 29% in Q2, 13% in Q3 and 11% in Q4, whereas in U-20 42% of the players were born in Q1, 26% in Q2, 25% in Q3 and 7% in Q4.

Table 2 – Relative Age Effect among FIFA designated regions, in FIFA World Cup U-17 (2017) and FIFA World Cup U-20 (2019).

U-17 U-20

Region _{χ² p-value n ES (ɸ) χ² p-value n ES (ɸ)}

Africa 3.754 0.289 69 0.233 (small) 1.427 0.699 75 0.138 (small) Europe 41.968 < 0.001** 95 0.665 (large) 30.611 < 0.001** 113 0.525 (large) Asia 8.588 0.035* 68 0.355 (moderated) 11.085 0.011* 71 0.395 (moderated) Oceania 8.800 0.032* 60 0.383 (moderated) 9.923 0.019* 39 0.504 (large) Central and North America 23.817 < 0.001** 71 0.579 (large) 11.514 0.009* 74 0.394 (moderated) South America 26.316 < 0.001** 76 0.588 (large) 19.263 < 0.001** 76 0.503 (large) * p < 0.05 ** p < 0.001

Besides the representative view of the players, we found relevant to investigate their own performance. In this sense, the mean of minutes played per game was calculated for each individual, and then distributed by birth quartiles. Therefore, figure 1 shows the mean of minutes played per game, distributed by the quartile of birth of the respective players present in U-17 and U-20 FIFA World cup categories. In U-17, the mean of minutes played per game (M ± SD) by athletes born in Q1 was 54 ± 30, in Q2 was 54 ± 29, in Q3 was 54 ± 29 and in Q4 was 57 ± 29, while in U-20 the players born in Q1 played, in mean, 50 ± 30, in Q2 played 55 ± 28, in Q3 played 53 ± 29 and in Q4 played 57 ± 28. It is possible to see that, parallel in both competitions, players born in the last three months of the year played, in mean, more minutes per game over the colleagues. In U-20, players born in Q1 were the ones who played less time per game. However, the difference between the means for each birth quartile was statistically not significant (p = 0.865 for U-17 and p = 0.285 for U-20).

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Figure 1 - Mean of minutes played per game (min) in FIFA World Cup U-17 and FIFA World Cup U-20 distributed by quartiles of birth of the players.

Moving on to the second objective, where players who have participated simultaneously in UEFA Euro U-17, UEFA Euro U-19 and FIFA World Cup U-20, in the respective years, were selected (n = 81), 40 players (49%) were born in the first quartile of the year, 16 (20%) in the second, 12 (15%) in the third and 13 (16%) in the fourth quartile. It should be noted that players born in the first three months of the year represent almost half of the total sample. Statistical tests showed a significant difference between quartiles of birth (p < 0.001) with respective large ES (ɸ = 0.568). In order to deepen the subject, it seemed pertinent to study the influence of this effect in individual performance, and the performance criteria chosen was, once more, the mean of minutes played per game. This mean was calculated for each individual and in table 3 it is possible to observe the mean for each quartile of birth and its respective standard deviation, in UEFA Euro U-17 (2010; 2012; 2014; 2016), UEFA Euro U-19 (2012; 2014; 2016 and 2018) and FIFA World Cup U-20 (2013; 2015; 2017 and 2019).

54 54 54 57 50 55 53 57 46 50 54 58 Q1 Q2 Q3 Q4 M in u te s Pl ay e d /Gam e ( m in ) Quartiles U-17 U-20

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Table 3 – Distribution of the mean of minutes played per game (min) by the players belonging to each quartile of birth, throughout the competitions of UEFA Euro U-17, UEFA Euro U-19 and FIFA World Cup U-20. Category Q1 Q2 Q3 Q4 M ±SD M ±SD M ±SD M ±SD U-17 61 28 58 33 77 19 47 35 U-19 62 32 79 15 58 26 65 21 U-20 59 32 64 18 55 29 61 33 n 40 16 12 13

Discussion

The effect of relative age has been studied and confirmed in football across the world by several researchers (Sierra-Díaz, González-Víllora, Pastor-Vicedo, & Serra-Olivares, 2017). The present study confirms the existence of this effect in the two major international youth competitions organised by FIFA. It seems clear that the players born in the first quartile are more likely to be selected to their national teams. This relative age effect was stronger in U-17 (ES = 0.417) than in U-20 (ES = 0.349). Additionally, numerical representation of the players born in Q1 has dropped 3% and born Q4 increased 2% from U-17 to U-20, however these differences are small. These findings converge with the actual scientific paradigm that suggests that the effect of relative age weakens with aging (González-Víllora et al., 2015; Helsen et al., 2005; Jimenez & Pain, 2008; Ostojic et al., 2014), and confirm our hypothesis. In that sense, probably the RAE would be even stronger in younger categories, however, the lack of official competitions encompassing teams all over the world arise as a limitation.

One variant studied aimed to understand the variation of this effect among continents. It was decided to use the six FIFA designated regions as guideline. It is important to note that the continent of Antarctica is not listed as a FIFA confederation, and in some cases, some countries are stated diplomatically in a certain continent, but are affiliated with a different FIFA confederation. The relative age effect was present in all confederations with exception for the African (CAF). These findings are in accordance with the study conducted by Williams (2010), that, after analysing six U-17 male FIFA World Cups (from 1997 to 2007), verified the effect of relative age in all the FIFA designated regions with exception for Africa. In the same study,

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Williams reported a reversal of the effect in Africa, suggesting that U-17 African players were more likely to have been born in the latter part of the year. Although there was no significantly higher number of players born in any specific quartile, in U-17, 53% of African athletes were born in the second part of the year. These findings of African players can be delicate mainly because of two factors. The first is the potential error in reporting actual birth dates. According to United Nations Children's Fund (UNICEF) (2018), in some African countries, only half of the children under five years of age are properly registered. According to the same source, African continent has the lowest civil registration coverage and weak vital statistics systems. Another factor is the large quantity of athletes from lower categories playing for U-17 and U-20 national teams. Africa is the region with more underaged players, and in the present study players were not separated by year of birth, and this emerges as a limitation of the study. If the findings mentioned above were somehow expected due to previous literature, the same does not happen regarding birth distribution of Asian and Oceanian players. It is possible to assume that the effect of relative age is present for both groups in both competitions, however, unlike the tendency, Q2 was the quartile where more births were registered for Asian athletes in U-20 and Oceanian athletes in U-17 and U-U-20. This result was unexpected since studies in the area (Australia) reported that Q1 is the most represented in U-17 and U-20 players (van den Honert, 2012). Further, no evidence was found that these countries have a different way to select players for the respective categories of internal competitions, rather than the system used by FIFA (civil year). Some literature points that in small countries and some countries where football is not the main sport, due to flexible policies (“open doors for everybody”), RAE has lower influence (Sierra-Díaz, González-Víllora, Pastor-Vicedo, & Serra-Olivares, 2017). Note that, the number of Asian and Oceanian players is the lowest comparing with other confederations, what can reduce the power of the results. On the other hand, as expected, the RAE is clearly present in European, Central and North American, and South American teams, where players born in Q1 were the most represented in the three confederations. This difference, and once more agreeing with literature, faded slightly in U-20 comparing with U-17. As these three regions are the ones with higher number of participants, these latest findings are similar to the overall findings (table 1).

In literature RAE has been studied and associated with anthropometric factors and actual performance. This performance has been linked mainly with the teams’ output (wins/losses; rank in the competition). Teams’ performance by itself places all players from a certain team on the same level. In this sense, it seemed proper to study the relation of RAE and individual performance and, restricted by the available data, the individual performance indicator chosen was the mean of minutes played per game. Although no statistically significant differences

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were found between the mean of minutes played from athletes born in each quartile, some results should be brought into discussion. Players born in Q4 played more time per game over the colleagues in both U-17 and U-20, and in U-20 players born in Q1 played less time per game than the others (in U-17, players born in Q1, Q2 and Q3 played, in mean, the same time per game). Players born in Q4 played, in mean, more 3 minutes per game in U-17 and more 7 minutes in U-20 over players born in Q1. These findings may support the theory of a possible reversal of the RAE, as literature suggests (González-Víllora et al., 2015; Helsen et al., 2005; Jimenez & Pain, 2008; Ostojic et al., 2014). However, as it was mentioned before, players were not separated by year of birth, and it is possible that some players born in Q1 may be underage (also in other quartiles, but mainly in Q1), what may influence the interpretation of the results. Besides that, stats available limit the examination and establishment of performance profiles and its analysis.

The present research brings an innovative approach to study the effects of the relative age. This new perspective aims to investigate the longitudinal variation of the relative age effect. In this context, it was done a “follow up” of the players in three competitions (UEFA Euro U-17; UEFA Euro U-19; FIFA World Cup U-20), being that 49% of the players were born in Q1, 20% in Q2, 15% in Q3 and 16% in Q4. As observed before in FIFA World Cup U-17 and U-20, also here there’s an overrepresentation of players born in Q1. This disparity in the number of players born in the first three months of the year (almost half of the sample), can be explained by the fact that RAE is more evident in younger categories, reducing its effects at older categories (González-Víllora et al., 2015; Helsen et al., 2005; Jimenez & Pain, 2008; Ostojic et al., 2014). According to the methods, players present in U-19 and U-20 competitions, but not U-17, are excluded from the sample. In this sense, the presence of players born in Q3 and Q4 in U-17 might be lower comparing with U-19 an U-20 because some of them might be late maturing players. This limitation regarding the small sample reveals the importance of considering the so called “ghosts”. This term refers to the athletes with some athletic ability that never have been identified before as talented. It is possible that this happened because of their biological age, or because of the environment that was given was not good enough (Ross Tucker, September 19, 2017, Montreux, Switzerland).

With the intention to supplement the information showed in table 3, and better understand the longitudinal variation, figure 2 was drawn. It illustrates the variation of the minutes played per game (in mean) by the individuals of each quartile of birth throughout the years in each competition, by trend lines for each of the quartiles.

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Figure 2 - Variation of the mean of minutes played per game (min) by the players belonging to each quartile of birth, throughout the competitions of UEFA Euro U-17, UEFA Euro U-19 and FIFA World Cup U-20.

Players born in Q1 seem to have an identical importance in the team over the years (61 minutes in U-17; 62 minutes in U-19; 59 minutes in U-20). Players born in Q3 were playing progressively less time over the years. In the opposite, players born in Q2 and Q4 were getting more playing time over the years. The anthropometric profile and advanced biological maturation status are linked with the selection process in football (Fragoso et al., 2015; Gil et al., 2014), and it can explain the reduced use of the players relatively younger in U-17. Possibly, these players with inferior physical profile at younger ages, developed better tactical and technical aspects to compete with others with superior physical profile. Because of that, throughout the years, when players relatively younger (Q4) “catch up” their colleagues in physical attributes, and getting more playing time, are gaining more important roles on the teams overcoming their teammates. This observation is in accordance with previous literature (González-Víllora et al., 2015; Helsen et al., 2005; Jimenez & Pain, 2008; Ostojic et al., 2014). On the other hand, no explanation was found to clarify the loss of minutes played by players born in Q3 over the years, however, it is logical that if players born in Q4 are playing more time over the years, some players born in other quartiles have to play less. Thus, the results lead to a paradox. Nevertheless, these findings confirm the hypothesis previously presented. These findings are also delicate due to a reduced sample number, especially players born from April to December. Youth national teams are characterised by a high player turnover, what makes difficult to track the players and get trustable and standardised data to compare to each other, resulting in reduced samples. Further, just because the players participated, in this case, in the three competitions, doesn’t mean by itself that they are the best from their generation. There can be cases of players who were not in the U-17 and/or U-19 squad but in the U-20

45 50 55 60 65 70 75 80

U-17 U-19 U-20

M in u te s Pl ay e d /Gam e ( m in ) Category Q1 Q2 Q3 Q4

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possibly turned out to be key players, and by this methodology (that we still think to be the most suitable) were excluded from the sample. Moreover, the UEFA Euro U-19 takes place at the end of July, and every year many of the potentially best players are not allowed to participate, being required to stay at the clubs, that are in pre-season at the time. This last three issues are understood as the major limitations of the second part of the study.

In the future, this thematic needs to be more explored in African countries, where RAE was observed to be lower. Further investigations should also focus on tactical and technical aspects and not only physical aspects, in order to understand the reason why the players born near the cut-off date are performing better than their pairs in older categories. Although this study brings a new approach to discussion, it should be more explored covering a larger number of competitions in order to increase sample number, and also analysing other competitions around the world. Moreover, it could be pertinent to include players that participated in U-19 and U-20 but not in U-17 competitions, to cover the emergence of new players.

In order to prevent the potential drop out of talented players and make the TalentID process more efficient and equal, coaches and technical staff should be instructed and conscious to pay less attention to the momentary performance status and adopt a longitudinal perspective, giving more importance to developing individual potential for the future rather than winning games. Regarding the traditional process of selecting athletes by chronological age, the federations of each country should create more competitions with less importance of winning, especially in younger categories, in order to give more playing time for those who are not playing at the “main competition”, allowing and promoting more players turnover from “A” and “B” team.

As have been said, children from the same chronological age may vary in biological maturity. One strategy adopted by some football associations, with the English Premier League as the “front-runner”, was the introduction of bio-banding competitions. Bio-banding consists in grouping athletes according to their biological age. The still scarce available literature suggests that this alternative reduces the variance in physical attributes, resulting in a more competitive and fairer environment (Abbott, Williams, Brickley, & Smeeton, 2019; Cumming, Lloyd, Oliver, Eisenmann, & Malina, 2017). This field needs to be more explored in order to understand how this new approach can oppose the argumentation that a greater challenge associated with being least mature could work as a stimulus toward superior long-term development. Nevertheless, this pathway should be considered by football entities among the world.

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Conclusion

In elite youth football, the will of winning in the short term makes coaches select players according to their own need, not considering the process of development of the athlete. In this sense, physically advantaged players are favoured over the others, that could be at risk of leaving their clubs due to its poor participation. There is an overrepresentation of players born in the first part of the year, especially in the first three months, in FIFA World Cup U-17 (2017) and FIFA World Cup U-20 (2019), with this difference fading over the years. Furthermore, although RAE was found in every continent except in Africa, it seems to be bigger in continents with higher football culture. Despite being less represented, players born near the end of the year played more minutes per game over the colleagues relatively older, however the difference was not significant. After studying the variation of the effect of relative age it is possible to conclude that the players born in the first months of the year seem to have a linear importance on the team over the years, however, players born in the last three months of the year play less while youngers, but got a more important role over the years, until they “catch up” with their colleagues relatively older in physical attributes, and then have a similar or even a more important role on the team.

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