Relative Age Effect in Italian Elite Football League - Seria A
Author: Hrayr KarapetyanUvA Id: 11105534 Supervisor: Erik Plug
Abstract:
Relative age effect refers to differences in birth date distribution among players favoring the ones born right after the selection period and disadvantaging footballers born in at the end of the selection period. This phenomenon has its effect on the decision-making process for the coaches. I attempted to examine the REA in Italian elite soccer league. Moreover, I compared the REA effect after the selection period regulation change by biggest football association, FIFA. Data on players was divided into three groups, Italians, Europeans and South Americans representatives. Histograms and
regression analysis were carried out in to illustrate the effect of REA. Results obtained represent the presence of significant REA in Italian Seria A for all the groups, in addition, results point up higher REA after 1997 FIFA regulation change.
Bachelor Thesis Labor Economics
University of Amsterdam
Faculty of Economics & Business
Statement of Originality
This document is written by Hrayr Karapetyan who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
1.1 Introduction
Football academies all around the world group children by chronological age in order to ensure equal opportunities for all the youngsters. Football institutions have a selection period upon which the chronological grouping is done. In 1997 Fédération Internationale de Football Association (FIFA) changed the cutoff date from first of September to first of January (fifa.com, 2018; Helsen, Hodges, Van Winckel, & Starkes, 2000). Even though the division of children in yearly groups is done to ensure equal chances to succeed, there still can be significant differences between the growth and/or
maturity of youngsters born in the same year but in different months (Simmons & Paull, 2001). Aforementioned implies that a child born on 1st of January of the year 1995 may have a one year advantage (in growing and developing) over a child born on 31st of December same year. This has an effect on the higher probability of early-born on being selected for further training.
The concept that introduces the differences among birth of month in the same year is referred as "Relative Age". Furthermore, the consequences because of the above-mentioned concept are known as Relative Age Effect (Barnsley & Thompson, 1988). Many scientists and researchers have reported the presence of the Relative Age Effect in Sports (Musch & Grondin, 2001). This is supported by the findings of researchers who focused on anthropometric, physical condition, cognitive skills and psychological/emotional maturity differences between children who are born in the same year. They argue that significant variation of above-mentioned factors is noticeable among children with a year difference (Helsen, Hodges, Van Winckel, & Starkes, 2000; Simmons & Paull, 2001). On the other hand, some economists claim low or insignificant REA effect on elite football players (Mulazimoglu, 2014). There is an ongoing discussion about the relative age effect in football leagues around the world. This brings me to the central question of the research paper –Is the Relative Age Effect present in Italian Seria A among Italian and Foreign players?".
This paper will focus on finding whether or not the concept of relative age effect is present among the Italian and foreign soccer players in Seria A. Besides Italians, the study is conducted on Brazilian, Uruguayan, Argentinian, Spanish, Dutch, Belgian and Portuguese legionnaires. These countries have had the most represented players in Seria A among all internationals. This article will make use of 2152 players from above-mentioned countries. Likewise finding the presence of REA for Italian and foreign players, this paper will also illustrate the difference in REA before and after FIFA regulation change. In the next section, I will briefly present previous studies and the findings of the researchers on the RAE on male football players, after I will discuss the theoretical framework applied to this paper. After the results and limitations of the paper will discussed. In the final part the conclusion of this study wil be presented, which would be followed by reference and appendix section.
1.2 Literature Review
Barnsley and Thompson (1985) conducted the first study on relative age effect in 1985. Authors analyzed the birth dates of US hockey players. The study showed that players who were born during the first quarter of the year had advantages over those born during the last quarter of the year, due to their participation in the competition. The first study on RAE and soccer was conducted by
Barnsley, Thompson and Legault (1992). Under-20 and under-17 players who participated in the 1990 World Football Cup were investigated. Results indicated that there was over-representation of the players born in the first quarter while players born in the last quarter of the year were under-represented.
Findings of number of researchers presence of REA in European football leagues were aligned with the positive results of Barlnsey and Thopmson. Mainly, Delorme, Boiché, and Raspaud (2010) using the national population to calculate the expected distribution for elite players, conducted a study on the birthdate distribution for the soccer players affiliated to the French Soccer Federation (FSF). The results showed that there was an over-representation of players born in Q1 and Q2 and an under-representation of players born in Q3 and Q4. Furthermore based on their study on German elite under-17 year old football players August and Lames (2011) claim that the number of the early born players is significantly higher in the teams. Likewise high Relative age effect was found in Spanish elite football league BBVA1 was found. The results represent ratio of 61.12/38.88 of percentage of players born in the first quartile compared to percentage players born in other three quartiles (Lesma, Pérez-González, and Salinero, 2011). In addition, authors found ration of [RAE of 63.53/36.47] among international players playing in Spanish first division. Again, significant effect of REA also was noted in the findings of Deprez, Coutts, Fransen, Deconinck, Lenoir, Vaeyens, and Philippaerts (2013), who claim that 42.3% of players were born in the first quarter of month, where only 13,2% in the fourth quartile. The study was conducted on Belgian elite youth football players.
Relative Age Effect is also present in South America, a continent that is famous for its football superstars like Pele and Maradona. Teoldo, Albuquerque, and Garganta (2012) in their study on Brazilian football players have a strong claim on the existence of REA in Brazilian football league. However, they note that the effect is not significant until 1960, a year when Brazilian coaches accepted physical preparation. In addition, Argentinean researcher states “Other things equal, a young player born in January is almost three times as likely to advance to the “A” league as one born in December. A player born during the first quarter is more than twice as likely to make it as one born in the last quarter”-(Gonzalez & Bertomeu, 2016). This further indicates existence of high RAE in South American elite football leagues.
On the other hand, some previous study results do not align with the findings of above listed research papers. In Norwegian elite soccer league, the REA is applicable to only a certain degree. Findings by Wiium, Atle-Lie, Ommundsen, and Eksen (2010) show that only 60 percent of players were born in the 1st and 2nd quarter, in addition July was the month when majority of the players were born.
Furthermore, a research from Turkeys Super League (highest soccer division) and lower divisions, found that even though RAE is among top teams in the highest league in significantly lower ,than the effect among youth competitions (Mulazimoglu, 2014). Similar results were noted in Slovakian national teams, starting with under 16-age category (U-16) to the A-senior national football team. All the age categories were influenced by REA, but the senior ones, which included Under 19, Under 21 and the Senior A team (Mikulic, Gregora, Benkovský, and Peráˇcek, 2015).
1.3 Theoretical framework
The argument for the existence of Relative Age effect in sports is supported by the physical and psychological differences among children born in the same year. This is everything else equal a child born in January has an advantage over one born in December because they have more time to grow and develop. It all comes to fact that early-born "mature" faster, and most probably they are the ones who get selected for further participation in the sport. Where late born, due to the time constraint in the time of selection are not physically, emotional and/or physiologically grown enough, and since couches consider all same year born children equal, the late-born get disadvantaged in the selection. In addition because of the reasons listed above, there is the concept of "initial performance
advantage" (Helsen, Van Winckel & Williams, 2005). "Initial performance advantage" consists of two factors, positive external and positive self-feedback because of increased competence due to RAE. This has a positive effect on child's motivation, hence the performance. However, the complete opposite can be observed for late-born who may be demotivated by the perception of failure, again due to present relative age effect.
For this paper Italia Seria A league was chosen. I will consider players of Seria A to be successful footballers. There a many reasons for this. The main one being that Seria A is considered one of the top five most competitive leagues in the world (uefa.com, 2018). Secondly, Italian teams are very successfully with International club competitions. Italian clubs have in total 12 Champions League Trophies, being behind only from Spanish club, who have in total 16 cups (fifa.com, 2018). Moreover, Italian national team has won the World Cup four-time, being the second country in the world by World Cup Titles.
As mentioned before, in the year of 1997 FIFA applied new regulation for the selection dates in football competitions. Before 1997 football calendar would start on 1st of September and would continue until the end of August (fifa.com, 2018). For this purposes, the collected data will be divided into three parts. First part will consist of only South American players because the cutoff date for them was not changed. For the European players who were born before 1987 the cut-off date first of September will be used, and for the rest first of January will be used. The reason for me to use 1987 cutoff year for the players is that on average children join football academies starting from under-10 teams. In addition authors in a recent research paper on REA, authors take into consideration youth teams from U-10 age group and above (Massa, Caldas-Costa, Moreira, Rogérin-Thiengo, Rodrigues-de-Lima, Quispe-Marquez, & Saldanha-Aoki, 2014; Lovell, Towlson, Parkin, Portas, Vaeyens, & Cobley, 2015). So, for Italian, Spanish, Belgian, Dutch, Portuguese, French players two different cut of dates for selection period will be considered, and only one for Brazilian, Argentinian and Uruguayans players. Data was provided by "gracenote" company, which is a part of the huge music, video and sports metadata supplier "Niealson Corporation" headquartered in the US. Head of sports Analysis section Mr. Simon Gleave delivered the data to me. Hence, it is assumed that data source is reliable and date of birth of the players are valid.
2.1 Method
Data on date of births and the nationality of the players is collected. Data represents all the foreign and Italian players who participated in Seria A league in the 21st century. Each player is assigned to a specific quarter in the year according to his month of birth. Histograms will be constructed to observe the distribution of the count of players among the quarters.
This paper is going to test the Hypothesis of Q1>Q2>Q3>Q4 for the footballers born before the year 1987. The hypothesis is explained as such, there are more players born in the first quarter of the year than in second, more players born is the second quarter than in third, and more players born in the third courter than on fourth. The alternative hypothesis would be that number of players has a uniform distribution between the quarters. Findings from Munford (2012) illustrate that if no external effect such as REA is present then it may be assumed that birthdates of the population are uniformly distributed within a year. Similar Hypothesis is used for the players born before the year 1987. However, players born in the third quarter will be expected to have more representatives, since as it mentioned in the theoretical framework, the cut of the month that applied to them was September, hence Q3>Q4>Q1>Q2.
To complement histograms and to show whether the results are significant or not, regression analysis will be carried out. The expected results are that a positive relationship between the number of players born in the specific quarter and the cut off quarter excites. Regression analysis will be constructed as such, for each player i born in a quarter j a dummy variable is assigned. So four possible outcomes - if a player is born in the first quarter - (1,0,0,0), second quarter - (0,1,0,0), et cetera – I will call this outcome Y; Further two dummies will be constructed for the cutoff dates. For recent cut off day, which is considered to be the first of January the dummy will be as such - (1,0,0,0). And for the cut off day that was used before 1997 FIFA regulation, the dummy will be as such - (0,0,1,0). This outcome I will call X. Cut off dummies were assigned to players based on their year of birth and the nationality.
Three-regression analysis will be carried out. First, one on all the Italian players playing in Seria A, the second one on all the foreign players from above mentioned eight countries. Third regression analysis will be carried out on full data, to be able to illustrate the impact of the change of the cut off date by FIFA. For the latest one, interaction variable will be constructed. This variable will contain the
interaction between the cutoff date and a dummy, which will take a value of 1 if the player is born after 1987 and value of 0 if the footballer is born before 1987. This will enable to observe the stronger association of Y with the X2, which in this case is cut off dummy multiplied by 1 or 0. Outcome Y will be regressed on X to find whether a positive association exists. Hence the hypothesis for this part is: there is a positive association between, meaning positive betas. So H0: β1>0 and H1: β1≤0. For the third regression, the β2 will be expected to have higher value then β1, hence the null hypothesis is β1> β2.
The significance of the outcomes will be assessed on the results of t-statistics, which shows the ratio of the departure of the estimated value of a parameter from its hypothesized value to its standard error. T-statistics absolute value higher than 1.96, will be considered as significant.
In addition, I will group the players as such, the first group will represent all the Italians, Second all the European players, and third South American footballers. For the regression analysis, the South
Americas will be grouped with other European players.
2.2 Results and Discussion
As mentioned before Italian players and Internationals representing eight countries will be analyzed. The Histograms for European players are divided into two columns. The first column shows the representation of all players who were born before 1987. As mentioned in the theoretical framework these players are assumed to fall under the cut off date of September. The expected number of players in the first column histograms would be Q3>Q4>Q1>Q2. The second column shows the histograms constructed for the players born after the year 1987. For this time the expected distribution of the number of the players born in the quarter is Q1>Q2>Q3>Q4.
Starting from Italians, for seasons 2000-2018 there were 977 players registered in the Seria A teams, who were born before 1987. The results on histogram contradict to the generated hypothesis. Since there is over-representation of the players born in the first counter. Count of the players born in the second quarter is higher than in the third only by two but the fact that only 173 players were born in the last courter gives me enough evidence to reject the null hypothesis. The picture is different when analyzing the second histogram for Italians players. There one may clearly observe patter of
decreasing the number of players born from the first quarter to last one. Moreover, 67% of the players are born in the first half of the year, which is the first and second quarter. Hence it can be argued the there is enough evidence to not reject the null hypothesis.
Even though the null hypothesis is rejected for Italian players born before 1987, in the appendix 4 the regression outcome illustrates that there is a positive association between the quarter of birth and the cut-off quarter. Moreover, the results are significant as t-statistics is equal to 2.39. Hence, it can be concluded that a significant Relative Age Effect is present in Seria A for Italians players, which means that children born right after the cutoff date, which nowadays is January, have significantly higher chances of becoming successful football players than those born in the second half.
The null hypothesis is again rejected for the Europeans players born before 1987. As it can be observed in the histogram, the second quarter has the most representatives, where I excepted third quarter to be the highest. In addition, more players were born in the first half of the year than in second, which again contradicts to my formulated expectation. However third quarter indeed has more representatives than the fourth and the first one. For the players born after 1987 a clear pattern of decreasing count of players from first to fourth quarter can be observed, hence the null hypotheses are not rejected. Moreover, 62.5 percent of the European players were born in the first half of the year.
For South American representatives the clear decreasing patter may not be observed, the only exception is that there are six more players born in the first quarter than in the second. However, there are fewer representatives born in the third quarter and even less in fourth. Again arguably the null hypotheses are rejected. On the other hand, it may also be observed that 65 percent of the players were born in the first half of the year. South American players are not analyzed before and after 1987, because as mentioned in the theoretical framework the FIFA regulation was only applied in European countries.
The regression analysis for foreigners playing in Seria A show higher positive association than for Italians. In addition, the results are significant with t-statistics being 2.00. Here again, it may be deducted that relative age effect is also present for the foreigners playing in Seria A. So again, children born right after the cutoff date, have significantly more chances of becoming successful football players.
Country Players born before 1987 Players born after 1987 Italy
European
South America
Source: Gracenote, A Nielsen Company
Lastly, from appendix 5 it is observed that actually, the FIFA regulations influenced negatively to the RAE, meaning relative age effect for footballers born after 1987 became stronger. This fact is
illustrated by a higher coefficient of the interaction variable between the cutoff quarters and dummy which takes 1 for the players born after 1987. In addition, the significant t-statistics gives me enough evidence to conclude that 1997 FIFA regulation change increased the effect of REA in Seria A. Limitations
Some limitations are present for this research paper. Firstly, the joining age to a football academy was assumed to be 10 years. Although for a majority of the children this is the case, however, this might vary from an individual to individual. The practice shows that some top football players like actually joined their teams a bit older. This would mean that for the data analysis some cut off dates do not
296 255 253 173 0 50 100 150 200 250 300 350 1 2 3 4 157 126 105 63 0 50 100 150 200 1 2 3 4 94 115 101 66 0 20 40 60 80 100 120 140 1 2 3 4 111 100 70 56 0 20 40 60 80 100 120 140 1 2 3 4 147 153 90 72 0 50 100 150 200 1 2 3 4
actually correspond to the cut off date that actually applied to the given player. Secondly, it was assumed that South American players went to the academy in their countries; this might not be the case for everyone as well since some might have moved to Europe, which would mean a different cut off date might have applied to them. Lastly, since a sample was taken from Italian Seria A, which is considered one of the most competitive championships, some players were exceptionally good, and talented from the day born. This would mean that no matter the cutoff date, those kinds of players would become successful.
3.1 Conclusion
The study aimed to determine the presence of relative age effect in Italian elite soccer league. Data on nationality and birth dates of all the players participated in Seria A from season 2000-2018 was analyzed. Footballers from six European countries including Italy and from three South American countries were considered. Results and regression analysis of this paper provide enough evidence to conclude that relative age effect is present is Seria A. Meaning there is significant over-representation of players in the following quarter of the cutoff date. Findings also illustrate that relative age effect is higher among foreign representatives of Seria A. In addition, the regression analysis depicts higher RAE after in 1997 FIFA changed the regulation for the cut-off dates. The null hypothesis is rejected for South African representatives, and the fact that cutoff date was not altered, portray comparable lower RAE for them. Findings on the existence of relative age effect may assist coaches and trainers on decision-making process among young talents. The question of, how to eliminate or minimize RAE effect may be considered by scientist and football institutions like FIFA and UEFA for future research.
4.1 References:
1. Barnsley, R., Thompson, A. and Legault, P. (1992). Family Planning: Football Style. The Relative Age Effect in Football. International Review for the Sociology of Sport, 27(1), pp.77-87.
2. Del Campo, D. G. D., Vicedo, J. C. P., Villora, S. G., & Jordan, O. R. C. (2010). The relative age effect in youth soccer players from Spain. Journal of sports science & medicine, 9(2), 190.
3. Delorme, N., Boiché, J. and Raspaud, M. (2010). Relative age effect in elite sports:
Methodological bias or real discrimination?. European Journal of Sport Science, 10(2), pp.91-96.
4. Deprez, D., Coutts, A. J., Fransen, J., Lenoir, M., Vaeyens, R., & Philippaerts, R. M. (2013). Relative age, biological maturation and anaerobic characteristics in elite youth soccer players. International journal of sports medicine.
5. F. Helsen, W., Hodges, N. J., Winckel, J. V., & Starkes, J. L. (2000). The roles of talent, physical precocity and practice in the development of soccer expertise. Journal of sports sciences, 18(9), 727-736.
6. Helsen, W., Baker, J., Michiels, S., Schorer, J., Van winckel, J. and Williams, A. (2012). The relative age effect in European professional soccer: Did ten years of research make any difference?. Journal of Sports Sciences, 30(15), pp.1665-1671.
7. Helsen, W., van Winckel, J. and Williams, A. (2005). The relative age effect in youth soccer across Europe. Journal of Sports Sciences, 23(6), pp.629-636.
8. Lesma, M. L., Pérez-González, B., & Salinero, J. J. (2011). Relative age effect (RAE) in Spanish football league. Journal of Sport and Health Research, 3(1), 35-46.
9. Lovell, R., Towlson, C., Parkin, G., Portas, M., Vaeyens, R. and Cobley, S. (2015). Soccer Player Characteristics in English Lower-League Development Programmes: The Relationships between Relative Age, Maturation, Anthropometry and Physical Fitness. PLOS ONE, 10(9), p.e0137238.
10. Massa, M., Costa, E. C., Moreira, A., Thiengo, C. R., Lima, M. R. D., Marquez, W. Q., & Aoki, M. S. (2014). The relative age effect in soccer: a case study of the São Paulo Football Club. Revista Brasileira de Cineantropometria & Desempenho Humano, 16(4), 399-405.
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13. Munford, A. (1977). A Note on the Uniformity Assumption in the Birthday Problem. The American Statistician, 31(3), pp.119-119.
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15. Salinero, J. J., Pérez, B., Burillo, P., & Lesma, M. L. (2013). Relative age effect in european professional football. Analysis by position. Journal of Human Sport and Exercise, 8(4).
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17. Simmons, C., & Paull, G. C. (2001). Season-of-birth bias in association football. Journal of Sports Sciences, 19(9), 677-686.
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4.2 Appendices Appendix 1
Country Born before 1987 – n1 Born after 1987 – n2 Italy (n1=977 ,n2=451 )1428 Belgium (n1= 7,n2=15 ) Netherlands (n1=13 ,n2=16 ) France (n1=50 ,n2=40 ) 296 255 253 173 0 50 100 150 200 250 300 350 1 2 3 4 157 126 105 63 0 50 100 150 200 1 2 3 4 2 1 2 2 0 0.5 1 1.5 2 2.5 1 2 3 4 4 6 2 3 0 1 2 3 4 5 6 7 8 1 2 3 4 2 5 4 2 0 1 2 3 4 5 6 7 1 2 3 4 9 3 2 2 0 2 4 6 8 10 12 1 2 3 4 12 10 16 12 0 5 10 15 20 1 2 3 4 16 11 6 7 0 5 10 15 20 1 2 3 4
Spain (n1=31 ,n2=25 ) Portugal (n1=36 ,n2=19 )
Source: Gracenote, A Nielsen Company 13 5 8 5 0 2 4 6 8 10 12 14 16 1 2 3 4 10 6 5 4 0 2 4 6 8 10 12 1 2 3 4 6 8 11 10 0 2 4 6 8 10 12 14 1 2 3 4 3 7 6 3 0 2 4 6 8 10 1 2 3 4
Regression analysis Appendix 3
All Foreign Players
Quarter of Birth - Y Coefficient Std.error t p>|t| 95% Confidence Interval 1. cutoff
0.158998 0.079452 2.00 0.046 0.003008 0.314988
2. _cons
2.051684 0.127670 16.07 0.000 1.801028 2.302341
Source: Gracenore, A Nielsen Company Appendix 4
Italians
Quarter of Birth - Y Coefficient Std.error t p>|t| 95% Confidence Interval 1. cutoff
0.146053 0.0612159 2.39 0.017 0.025970 0.2661361
2. _cons
2.011802 0.1069532 18.87 0.000 1.808224 2.227829
Source: Gracenore, A Nielsen Company
Appendix 5
With interaction variable
Quarter of Birth - Y Coefficient Std.error t p>|t| 95% Confidence Interval 1. cutoff 0.1383473 0.1905261 2.54 0.026 0.124734 0.663064 2. interaction 0.2105367 0.0284475 2.04 0.042 0.124179 0.482050 3. _cons 1.6017564 0.63866 15.68 0.000 0.510830 2.866343 Source: Gracenore, A Nielsen Company
