Population grouping by its chronological age is common in certain areas of our society (school, sports, clinical, etc.). The different categories that emerge from these groupings reveal differences in development among the subjects that comprise it. These differences of up to twelve months of age is what is known as relative age, and its consequences make up the so-called Relative Age Effect (RAE) (Campbell, 2013; Dixon et al., 2011; Gutierrez Diaz del Campo, 2013). In sports, January 1, is globally accepted as the beginning of the year of selection, although in certain sports and countries, such as August 1 or September 1, have even been used. In soccer, the FIFA (International Federation of Football Association) established in 1997 the cut-off date on January 1, and is based on our twelve-month calendar. This circumstance that aims to comply with the principle of equal opportunities, which in principle seems appropriate and correct, has the weakness of avoiding the idea that the maturation of each individual occurs at a different chronological age (Torres-Unda et al., 2012). This agreement, in the end, can negatively affect those born in the last months of the year (sports abandonment, demotivation due to sports, low self-esteem, etc.). By contrast, would benefit those born in the first months of the year, granting them greater possibilities of success (Sykes et al., 2009).
RAE studies in Sports context have their beginnings in similar research in educational field (Hurley et al., 2001), where different studies analyze RAE in education. Gutierrez Diaz del Campo (2013) organizes them around the following contents: academic achievements; specific learning or academic problems; leadership; physical education and school sports, and self-esteem in schoolchildren.
As in Educative field, since the first studies aimed at sports of Grondin et al. (1984), with Canadian ice-hockey and volleyball players, and Barnsley et al. (1985) with ice-hockey players in Canada, search in RAE as the main variable, has grown in different fields, including team sports (Delorme et al., 2013; Hancock et al., 2013; Romann and Fuchslocher, 2013; Schorer et al., 2013) and individuals (Albuquerque et al., 2012; Delorme et al., 2009; Edgar and O'Donoghue, 2005; Loffing at al., 2010). On the other hand, considering not only the verification of the RAE, but the relationship with other variables, some data can be obtained in relation to any of these issues: individual performance of the player or athlete (minutes played), nationality, demarcation of the players, collective performance of the sports group (classification of teams, level of competition of the teams, sports results), etc.; yet considering, there are already some investigations that address several of these variables (Arrieta et al., 2016; Auguste and Lames, 2011; Delorme et al., 2009; Saavedra et al., 2016; Sedano et al., 2015; Vaeyens et al., 2005).
The broad spectrum of studies relating RAE and soccer, can be organized around three research contexts: professional elite male football, women's soccer, and minor categories. Although it must be borne in mind that some studies contrast and compare some of these contexts (Barnsley et al., 1992; Edgar and O'Donoghue, 2005; Helsen et al., 2005; Salinero et al., 2013; Vincent and Glamser, 2006).
Research in professional football revolve around two areas: the professional leagues of each nation and the international competitions of national teams (World Cups, European Cups, etc.). Examples of professional football studies are those carried out in Belgium (Helsen et al., 2005; Vaeyens et al., 2005), in Germany (Auguste and Lames, 2011; Cobley et al., 2008), in Australia (Van den Honert, 2012), in Turkey (Mulazimoglu, 2014), in Norway (Wiium et al., 2010) and in Spain (Gutierrez Diaz del Campo et al., 2010; Prieto et al., 2015; Salinero et al., 2014).
In this way, several studies are centered on different countries, thus Salinero et al. (2013), with a sample of 2763 players from the leagues of the United Kingdom, Germany, Italy, France and Spain, corroborate the presence of the RAE. In the same line, the study by Padron-Cabo et al. (2016), aims to analyze the incidence and magnitude of the RAE on professional soccer players, and how it affects depending on their position, the leagues where they play, the level of the competition, the level of the team and its nationality. The sample consisted of 12,144 professional players who participated in 15 FIFA professional leagues (1st and 2nd division) during the 2014-2015 season. The RAE influence was found in all the leagues analyzed, except in the Premier League (England) and the K-League Classic (South Korea). Finally, another example is the study of Musch and Hay (1999) that deals with the study of professional players from Germany, Australia, Brazil and Japan, this latter, being the one that shows the highest decompensation by semesters (66 in semester 1/34 in semester 2) and Germany the lowest, (56 in semester 1/44 in semester 2).
Although it is evident that most RAE studies are referred to men's football, there is also evidence of this effect in women's football (Romann and Fuchslocher, 2013; Sedano et al., 2015; Van Den Honert, 2012). The results seem to show, regardless of the sport, a lower presence than in their male counterparts or even absence of the RAE. Vincent and Glamser (2006) compare the RAE of 1,344 17-year-old female and male soccer players included in the United States Olympic Development Program (ODP) in 2001. Results revealed a strong effect in the case of male's sport, compared to a weak effect in the case of women's. However, Sedano et al., (2015) conducted a study with 4035 female players from five levels of competition in Spain and the results revealed that the date of birth distributions of all the soccer players' groups, except for the lowest level, showed an overrepresentation of players born in the first quartile.
Despite the relative age effect is a widely studied topic, there are not too many studies with such a large sample, and to the best or our knowledge, there is no research aimed at the professional elite male football of the UEFA Confederation best leagues, correlating the variables players positions, and final classification. Therefore, the aim of this study is to verify the RAE in the professional male soccer of the ten best national leagues of the UEFA Confederation during the 2016/2017 season, as well as to verify the possible differences and correlations between the RAE and the players' position, and the final classification.
The universe of this study is composed of all the players of the top 10 leagues of the UEFA Confederation, corresponding to the 2016/2017 season (Table 1). The leagues analyzed were : Premier League (England), Ligue 1 (France), Santander League (Spain), Bundesliga (Germany), Serie A (Italy), Primeira Liga (Portugal), Eerste Klasse A (Belgium), SuperLig (Turkey), Bundesliga (Austria) and Eredivisie (Holland). The population under study is made up of 5201 male professional soccer players distributed in the 178 teams belonging to those leagues.
In the present study, different variables have been handled, being categorized and conceptualized in Table 2.
To select the 10 best leagues of the UEFA Confederation, the International Federation of Football History & Statistics (IFHHS) website was consulted, in order to verify the list "The strongest league in the world 2015" (published in January 2016). The information concerning this research was obtained from the websites www.livefutbol.com and www.transfermarkt.es, verifying the information with the official website of the corresponding soccer leagues: England (www.premierleague.com), France (www.ligue1.com), Spain (www.lfp.es), Austria (http://www.oefb.at). Germany (www.bundesliga.de), Italy (www.legaseriea.it), Portugal (www.ligaportugal.pt), Belgium (www.jupilerleague.be), Turkey (http://www.tff.org) and the Netherlands (www.eredivisie.nl). The year was divided into four quarters. Players born between January 1 and March 31, belong to the first quartile (Q1). Born between April 1 and June 30 were included in the second quartile (Q2). Those between July 1 and September 30 in the third quartile (Q3). Finally, players born between October 1 and December 31, belonged to the fourth quartile (Q4).
In the present study, a frequency analysis was carried out through the elaboration of contingency tables, showing both the frequency (fr) and the percentage (%). To check the homogeneity of the distribution throughout the four quartiles, an analysis of the frequencies observed and expected from the birth months was performed, using the chi-squared test (x2) and the degrees of freedom (gl), according to the different leagues under study. Most of the investigations assume the theory that the distribution is similar in all the quartiles of the year, that is, 25% of cases per quartile...