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Calculating Home Advantage in the First Decade of the 21th Century UEFA Soccer Leagues

Calculating Home Advantage in the First Decade of the 21th Century UEFA Soccer Leagues , 141-150 Section III ­ Sports Training DOI: 10.2478/hukin-2013-0054 by Miguel Saavedra García1, Óscar Gutiérrez Aguilar2, Paulo Sa Marques3, Gabriel Torres Tobío1, Juan J. Fernández Romero1 Home advantage has been studied in different sports, establishing its existence and its possible causes. This article analyzes the home advantage in soccer leagues of UEFA countries in the first part of the 21st century. The sample of 52 countries monitored during a period of 10 years allows us to study 520 leagues and 111,030 matches of the highest level in each country associated with UEFA. Home advantage exists and is significant in 32 of the 52 UEFA countries, where it equals 55.6%. A decrease can be observed in the tendency towards home advantage between the years 2000 and 2010. Values between 55 and 56 were observed for home advantage in the top ten leagues in Europe. It has also been observed that home advantage depends on the level of the league evaluated using UEFA's 2010/11 Country coefficients. The home advantage is calculated taking into account the teams' position and the points obtained in each of the leagues. A direct relationship was observed with the number of points gained and an inverse relationship was observed with the team position. Key words: Home advantage, football, UEFA, performance analysis. Introduction The analysis of playing at home started with the studies of Schwartz and Barsky (1977) in American sports such as basketball, ice hockey, American football and baseball. These studies, followed by Nevill and Holder (1999), determined that the advantage is real. This kind of study has been used in three types of sport. On the one hand, we have sports that use a points system with a high level of subjective discrimination on the part of the judges, such as gymnastics. Another group of sports are the ones based on objective values, like time or distance, sports such as athletics. The third group is formed by sports where judges have a certain subjective intervention, such as team sports, where referees apply the rules with different possibilities of interpretation. The approach in this type of study has also been varied. Some have analyzed the case of playing at home in competitions with only one headquarters, with just one home side, such as the Olympic Games, taking into account both Summer and Winter Games. Balmer et al. (2001; 2003) analyzed these competitions and determined that in sports that belong to the first group, those judged mainly in a subjective manner, there was a clear existence of an advantage for home teams, thus, they achieved a very important point deviation in their favour. The home advantage analysis in team sports has been calculated in many different sports, for example baseball (Adams and Kupper, 1994; Bray - Sports Science and Physical Activity Faculty, University of A Coruña (Spain). - University Miguel Hernández of Elche (Spain). 3 ­ Maia Higer Institute (Portugal). . Authors submitted their contribution of the article to the editorial board. Accepted for printing in Journal of Human Kinetics vol. 38/2013 on September 2013. et al., 2005; Dosseville, 2007; Levernier and Barrilla, 2007), football (Carmichael and Thomas, 2005; Dosseville, 2007; Nevill et al., 1996; Pollard, 1986; 2000; 2006b; Sánchez et al., 2009; Seckin and Pollard, 2007; Thomas et al., 2004; Thomas et al., 2006), ice hockey (Agnew and Carron, 1994), basketball (Greer, 1983; Jones, 2007; Moore and Brylinsky, 1993; Varca, 1980), volleyball (Marcelino et al., 2009), rugby (Thomas et al., 2008) and handball (Gutierrez et al., 2012). Various studies have tried to identify the factors that influence home advantage. Courneya and Carron (1992) found significant such factors as rules of the competition, public, trips made, referees and familiarity with the stadium or grounds. Other home advantage factors analyzed were the influence of the public with regard to the number of fans, density of the crowd, proximity of them to the playing area or intensity of their support (Pollard, 2006a; Pollard and Pollard, 2005; Wolfson et al., 2005). Further factors were possible alterations caused by the journeys made by the players (Brown et al., 2002; Clarke and Norman, 1995; Pollard, 2006a), better knowledge of the playing ground, such as specific conditions or non-standard dimensions (Barnett and Hilditch, 1993; Clarke and Norman, 1995; Pollard, 1986), unfamiliar climatic conditions (Pollard et al., 2008; Seckin and Pollard, 2007) or the behaviour of the referees, in the frequency of their use of disciplinary sanctions (Glamser, 1990; Nevill et al., 1996). The study of home advantage evaluated the existence of alterations at the physiological level (Neave and Wolfson, 2003), verifying a rise in hormonal activity in home teams. The use of different game tactics in home and away teams has also been studied, in order to determine mainly if there are any variations in the performance of the teams (Carmichael and Thomas, 2005; Seckin and Pollard, 2007; Tucker et al., 2005). The objectives of this study were as follows: to determine and verify the home advantage in soccer in the UEFA zone in the first decade of the 21st century and its evolution during this time. We evaluated the behaviour of home advantage taking into account the UEFA ranking and described home advantage in the most powerful leagues of Europe. Material and Methods Participants The sample of this study consisted of 111,030 male soccer matches played in 52 of the highest league categories in UEFA countries between 2000 and 2010. Each game was analyzed from both the home team and away team point of view, meaning a total of 222,060 records. Different league formats exist in the UEFA territory. Some leagues combine the round-robin system with eliminatory systems; in other leagues, an initial league is formed which is followed by other smaller leagues of teams that descend or win the tournament. Eliminatory phases of different competitions are not used, only regular leagues and play-offs with league format. Although the intention of this study was to use a sample from each country, there are some exceptions. In Liechtenstein league competitions are not held, meaning that in the 53 countries of the UEFA there are only 52 leagues. In the Azerbaijan league the 2002/03 season was cancelled, so only 9 seasons were studied. Serbia and Montenegro started their independent leagues in 2006/07 (4 leagues). In the end, the sample was made up of all the league tournaments which took place in UEFA territory in the 21st century (520 examples over 10 seasons). Some countries (13) organise their competitions in such a way that they start and end the league in the same year, whereas most countries (36) start the season in the fall and finish it in the spring of the following year. This means that the first group of countries, those that count a season from January to December, have a total of 11 leagues whilst the second group have only 10 leagues during the same period. Measures The variables registered were the final results of matches played as a home team and as an away team, the points obtained in the league, the country, the season, the type of competition and the actual UEFA ranking. The data used was obtained from the UEFA web page (http://en.uefa.com), and two independent web pages Soccer way (http://www.soccerway.com) and futbol 24 (http://www.futbol24.com). The data collected from these 3 sources was compared in an by Miguel Saavedra García et al. independent way to minimize the number of errors. Procedures The European soccer leagues are based on a "round-robin tournament" system, which means that the same number of games is played away as at home. In this type of competition, the home advantage is quantified by taking into account the number of games won at home expressed as a percentage of the total number of points obtained (Pollard, 1986). When an independent analysis of the home advantage is made for each team, the home advantage is established by comparing the performance obtained when playing at home with the performance obtained when playing away. This analysis uses the percentage of games won at home in relation to the total of games won both away and at home, understanding draws as half victories. Analysis The degree of significance of the home advantage is calculated with the variable of points obtained, taking for granted the truth of the hypothesis that playing at home poses no advantage (Pollard, 1985; 1986), meaning that one out of every two games played at home is won (50%). To make the contrast, the sign test with Wilcoxon range was used. The groups used were formed of 10 countries in a function of decreasing values of the UEFA ranking, with the exception of the last group, formed of 12 countries (from 41 to 52), called Group A, B , C, D and E, respectively. In order to compare home advantage between the countries of one group or between the different groups the Kruskal-Wallis test was used. The bivariate correlations of Pearson were used to establish the level of association between the numbers of points obtained with the advantage of playing at home. Also the Spearmen bivariate correlations were used to assess the association between the classification of a team in a league and the UEFA ranking with the advantage of playing at home. results are shown in Table 1. Home advantage evolution in UEFA territory football A decrease in the tendency of home advantage can be observed between the years of 2000 and 2010 in the UEFA territory soccer leagues. The magnitude of the decrease exceeds two percentage points. Home advantage in different countries in the UEFA territory A significant home advantage was found in 32 (61.5%) of the 52 UEFA territory countries (p<0.05). The most powerful leagues in Europe have statistical significance in home advantage with values between 55 and 56. After this first group, there is a series of countries where home advantage is more heterogeneous: in some nations the existence of home advantage exists and the variability is higher than in the first group. The home advantage reaches a maximum of 76.10 points (Bosnia-Herzegovina) and a minimum of 50.03 (Republic of Ireland). Some cases of disadvantage of playing at home were found (Lithuania, Latvia, Estonia, Wales, Malta, Northern Ireland, Andorra and San Marino). The minimum value observed in San Marino is 45.52 (Table 2). Home Advantage and 2010/11UEFA Country coefficients The UEFA countries were classified by the 2010/11 UEFA Country coefficients in groups of 10 countries. This way, Group A is formed by the ten countries with the highest coefficients. Group B is formed by countries with rankings between 11 and 20, and so on down to Group E, formed by countries between 41 and 52. The home advantage of each country in each group was compared in order to assess the intragroup homogeneity, obtaining a large homogeneity in Group A (0.974). The rest of the groups show a significant heterogeneity (p<0.001). Significant differences were also found between the home advantage of the groups (p<0.001) (Table 3). In all countries of Group A, the home advantage is significant with minimum oscillations in its values. In the last ten years, the home advantage decreases by 1.8 points. Between the ten countries of the group, a variation of 1.26 points in home advantage was found. In Group B, 60% of the countries showed the existence of home advantage with more oscillations. Results The analysis of the sample used reveals a home advantage of 55.6±0.19 in the highest categories of European soccer, and it is significant in all cases analyzed, except for the season 11. The Table 1 Home advantage (HA) in the highest categories of UEFA football Years 2000-2010 Leagues Played Home Record Won Drawn HA Lost % SD Signification1 <0.001 Season 1 2 3 4 5 6 7 8 9 10 11* Leagues 50 50 49 50 50 50 52 52 52 52 13 Played 10466 10523 10223 10475 10855 10920 10929 11390 11450 11498 2301 Won 5145 5142 4970 5036 5120 5136 5096 5443 5375 5378 1012 Drawn 2424 2481 2361 2485 2626 2595 2802 2742 2779 2762 552 Lost 2897 2900 2892 2954 3109 3189 3031 3205 3296 3358 737 % 56.9 56.7 56.3 56 55.2 55 55.2 55.8 55 54.8 52* SD 0.20 0.20 0.20 0.19 0.19 0.20 0.19 0.20 0.19 0.20 0.19 Signification1 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.146 Significance of the Wilcoxon range sign test. *Season 11 is completed by only 13 of 52 countries. The HA is not representative of the territory countries of UEFA, because of the 13 countries, only one of them is in the top ten (Russia in 7th position) and the rest of the countries are ranked between 23 and 50. Figure 1 Home advantage evolution in the highest categories of UEFA football by Miguel Saavedra García et al. Table 2 Home Record, home advantage and Ranking in the highest categories of UEFA football Home Record Country Leagues Drawn Lost Played Won Home Advantage Ranking2 85.785 82.043 69.436 60.552 53.678 51.196 44.707 43.883 40.129 35.050 34.166 30.550 27.000 25.824 25.141 24.900 22.000 20.850 20.700 18.124 17.875 16.124 16.083 15.916 14.499 14.375 14.250 14.125 9.124 8.966 8.708 8.500 7.749 7.708 7.415 6.957 6.165 6.124 5.207 4.957 4.374 3.875 3.874 3.791 2.790 2.583 2.416 2.249 1.416 1.374 1.000 0.916 1st Season Last Season Sig.1 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.136 <0.001 <0.001 <0.776 <0.001 <0.140 <0.001 <0.001 <0.056 <0.001 <0.001 <0.099 <0.001 <0.001 <0.001 <0.027 <0.025 <0.001 <0.301 <0.914 <0.001 <0.302 <0.789 <0.898 <0.002 <0.227 <0.001 <0.001 <0.350 <0.001 <0.004 <0.001 <0.827 <0.842 <0.469 <0.523 <0.305 <0.230 <0.887 <0.310 <0.036 SD 0.16 0.14 0.15 0.15 0.13 0.16 0.15 0.18 0.18 0.16 0.20 0.18 0.17 0.16 0.26 0.17 0.19 0.14 0.15 0.23 0.20 0.18 0.19 0.17 0.16 0.15 0.16 0.16 0.15 0.16 0.16 0.18 0.22 0.23 0.25 0.21 0.23 0.16 0.20 0.16 0.22 0.17 0.16 0.25 0.19 0.25 0.24 0.19 0.20 0.20 0.28 0.21 England Spain Germany Italy France Portugal Russia Ukraine Netherlands Turkey Greece Denmark Belgium Romania Scotland Switzerland Israel Czech Republic Austria Cyprus Bulgary Croatia Belarus Poland Slovakia Norway Serbia Sweden BosniaHerzegovina Finland Republic of Ireland Hungary Moldova Lithuania Latvia Georgia Azerbaijan Slovenia FYROM Iceland Kazakhstan Montenegro Albania Estonia Wales Armenia Malta Northern Ireland Faroe Islands Luxembourg Andorra San Marino 57.28 61.05 59.80 56.32 58.17 58.93 56.25 55.31 55.23 57.49 61.94 49.66 57.52 64.86 51.32 60.11 48.15 62.08 59.63 54.03 63.74 57.81 54.17 58.06 60.00 53.30 51.77** 48.90 70.27 54.04 50.00 58.08 51.49 46.30 42.56 54.04 56.06 63.80 61.17 54.07 61.27 61.11** 72.16 45.83 51.80 55.65 51.01 49.07 53.33 51.77 48.33 45.02 59.21 59.39 50.22 57.89 55.61 52.78 55.42* 56.67 56.75 53.49 58.99 50.17 54.80 54.14 50.44 60.37 51.21 54.58 59.26 53.82 54.58 60.00 51.01* 54.03 59.43 59.03* 58.75 57.08* 71.94 45.97* 48.52* 55.97 56.39 46.42* 47.16* 54.44 49.65 48.15 57.48 48.74* 54.51* 56.90 64.10 48.52* 49.13 50.60* 50.25 49.27 54.07* 49.63 42.92 47.62 *Last season is #11.** First season is 2006/07 (#7). Significance of the Wilcoxon range sign test.2Country coefficients 2010/11. The association or country rankings take into account the results of all clubs from each association and are used to determine the number of entries an association is granted for forthcoming seasons (www.uefa.com). Table 3 Home Record and home advantage according to the level of the group by the UEFA Ranking Home Record Leagues Drawn Played Lost Level Won Home Advantage Ranking Sig.1 SD % Comparison Intragroup3 % SD 55.95 55.90 58.35 53.70 52.12 0.35 2.68 6.76 4.02 5.11 Intergroup3 Group A Group B Group C Group D Group E 14776 8291 8282 55.95 0.16 <0.001 1-10 0.974 10789 5638 6215 55.90 0.20 <0.001 11-20 0.000 10504 4716 5227 58.35 0.18 <0.001 21-30 0.000 0.000 7711 3812 5128 53.70 0.20 <0.001 31-40 0.000 9073 4152 6716 52.12 0.23 <0.001 41-52 0.000 1 Significance of the Wilcoxon range sign test. 2 Values grouped by Country coefficients 2010/11 in tens, forming five levels of categories of UEFA countries. 3 HA of countries in a group (intra) or between groups (inter) using the Kruskall-Wallis test. Table 4 Home advantage variations according to the level of the group by the UEFA Ranking Level % sig.HA1 100 60 80 40 33 HA 1st Season 57.58 56.93 57.81 53.76 53.20 HA Last Season 55.78 54.78 59.79 53.68 51.23 HA Max. HA 56.80 59.40 76.10 63.23 63.18 Min. HA 55.54 52.44 51.38 49.09 45.52 HA Group A Group B Group C Group D Group E 1.80 2.15 -1.98 0.08 1.97 Percentage of countries in a group with a significant home advantage (HA). Figure 2 Home advantage according to the group levels established by the UEFA Ranking by Miguel Saavedra García et al. During the decade of the study, home advantage has decreased by 2.15 points. The variation between countries is 6.96 points. In Group C, the percentage rose to 80% and the variation of home advantage is maximum between countries (24.72 points). Home advantage decreased by 1.98 points during the ten years of the study. In Groups D and E, the percentage fell to 40% and 33%, respectively. The variations of home advantage between these countries were high (14.14 and 17.66, respectively). Between 2000 and 2010, home advantage decreased by 0.08 points in Group D and 1.97 points in Group E (Table 4). The top 20 ranked teams in the UEFA Ranking (Groups A and B) have a very similar home advantage, though the dispersion in Group B is greater than in Group A. After the first 20 leagues, the variability in home advantage increases, reaching 58.35 points in Group C and decreases to 52.12 in Group E (Tables 3 and 4, Figure 2) An association exists (p<0.001) between home advantage and the UEFA Ranking, therefore it is very weak (0.056). Home Advantage, Season Rank and Total points Both the classification of a team and the number of points won has a significant association (p<0.001) with home advantage. The points obtained by a team are positively associated with home advantage (0.721). The classification of a team in a league has an inverse association with home advantage and a value of 0.674. North American Soccer, 63.3% for basketball and 63.9% for European soccer. Later on, Pollard (2006b) proved the existence of variations in home advantage in the same sport between different countries, which explains factors such as size of the country, producing longer journeys or factors such as territoriality, defined by Neave and Wolfson (2003). More recent studies, like the one by Pollard and Pollard (2005) establish for football and basketball values of 60% and for ice hockey 55%, values very similar to the ones found in this study (55.6%). Clarke (2005) found a value of 59.9% in Australian soccer, while García et al. (2009) found home advantage of 55.2% in Spanish basketball and finally, Gutiérrez et al. (2012) found a value of 61.3% in Spanish handball. The evolution of home advantage during the first decade of the 21st century decreases by two percentage points. This drop seems to indicate that professional teams are progressively overcoming home advantage, decreasing the disadvantage of playing away. But the tendency of this decrease has been different. In the first fiveyear period of the century, the decrease of home advantage was 1.88 points, while in the second part of the decade, the drop was only 0.2 points, which seems to indicate that home advantage stabilizes at around the value of 54.8. The degree of evolution of home advantage agrees with the conclusions of Pollard (1986), because as the seasons advance, home advantage declines. In the Pollard study, home advantage changes from 67.9% in the first half of the period to 63.9% almost 100 years later, while in the present study, the drop is more or less 2 points, but it is also true that the Pollard's study is of a whole century while this study only considers a decade. For the analysis of home advantage with regard to the quality of teams, 5 groups of 10 teams were established, except for group E, which had 12 teams. To establish this classification, the Country coefficients of UEFA for the 2010/11 season were used: in leagues of group A, the percentage of nations with a home advantage is 100%. In group B, the percentage is 60%, in group C, the percentage increases considerably, reaching 80% of the countries. In groups D and E, which are formed of the 22 countries with the lowest UEFA ranking, there is a low percentage of Discussion The objective of the present study was to analyse the behaviour of home advantage in UEFA-zone soccer in a complete sample: all the leagues in the first decade of the 21st century. According to the results obtained, home advantage of 55.6 has been found in the highest soccer categories of Europe, being significant in every season, except for the season 11, as this last season was made up of only 13 of the 52 countries analyzed, those nations whose seasons coincide with the natural year as opposed to the majority, who play from late summer to the following spring. Pollard (1986) showed values that oscillated between 53.6% in baseball and 65.5% in Calculating home advantage in the first decade of the 21th century UEFA soccer leagues finished in a draw, while in the present study the percentage is 23.9% of the games analyzed. However, other studies have obtained results similar to those of this research. The analysis of the category variable coincides with the conclusions of Pollard (1986), as in both studies, the lower the team's category, the higher the home advantage. This finding could be explained by the fact that teams in lower categories suffer difficulties such as uncomfortable journeys, players having to work or study, lower level of the players in these leagues, or other factors like local pressures. The same conclusion was obtained by Sánchez et al. (2009), who compared home advantage in the two highest categories of Spanish soccer and concluded that home advantage was higher in the first category competition. Finally, similar associations were found by Gutiérrez et al. (2012) in Spanish handball. countries with a significant home advantage (40% and 33%, respectively). Except for group C, there is a tendency towards a decline in the percentage of nations with a significant home advantage in line with the Country coefficients, which is an indicator of the level of competition. If we focus on the analysis of the top five, we can see that the first five countries (England, Spain, Germany, Italy and France) have a very similar home advantage, as their scores hardly oscillate more than 1.3 points. In other countries, the rest of the groups prove to have an important increase in their heterogeneity values, oscillating between 76.10 (Bosnia-Herzegovina) and 50.03 (Republic of Ireland), even reaching negative values in a few countries, which means that for them there is a disadvantage of playing at home. When taking into account the influence of the level of the team, the home advantage shows a significant association as there is a positive relation between the points won by a team and home advantage (0.721). The classification of a team in its league has an inverse association with home advantage (-0.674). These results contradict the study of Morton (2006) in rugby and Jacklin (2005) as both concluded that there were no differences in home advantage and the level of the participating teams. Differences also exist between the results of this study and those of Bray (1999) in ice hockey, as he finds that home advantage is similar for all teams independent of the quality of the team. It is necessary to highlight the fact that in ice hockey, the possibility of obtaining a draw is lower than in football. In the matches analyzed by Bray over 20 years, only 13% Conclusions Fifty-two of the fifty-three countries that make up the UEFA territory have league competitions. Only in 32 of them there was a significant home advantage in league competitions at the highest level. In the first decade of the 21st century it was 55.59±0.20. The countries that made up group A through their UEFA Ranking have a greater homogeneity when taking into account home advantage. In the rest of the groups, home advantage is much more heterogeneous. The teams with a better classification and more points have better home advantage values. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Human Kinetics de Gruyter

Calculating Home Advantage in the First Decade of the 21th Century UEFA Soccer Leagues

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de Gruyter
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Abstract

, 141-150 Section III ­ Sports Training DOI: 10.2478/hukin-2013-0054 by Miguel Saavedra García1, Óscar Gutiérrez Aguilar2, Paulo Sa Marques3, Gabriel Torres Tobío1, Juan J. Fernández Romero1 Home advantage has been studied in different sports, establishing its existence and its possible causes. This article analyzes the home advantage in soccer leagues of UEFA countries in the first part of the 21st century. The sample of 52 countries monitored during a period of 10 years allows us to study 520 leagues and 111,030 matches of the highest level in each country associated with UEFA. Home advantage exists and is significant in 32 of the 52 UEFA countries, where it equals 55.6%. A decrease can be observed in the tendency towards home advantage between the years 2000 and 2010. Values between 55 and 56 were observed for home advantage in the top ten leagues in Europe. It has also been observed that home advantage depends on the level of the league evaluated using UEFA's 2010/11 Country coefficients. The home advantage is calculated taking into account the teams' position and the points obtained in each of the leagues. A direct relationship was observed with the number of points gained and an inverse relationship was observed with the team position. Key words: Home advantage, football, UEFA, performance analysis. Introduction The analysis of playing at home started with the studies of Schwartz and Barsky (1977) in American sports such as basketball, ice hockey, American football and baseball. These studies, followed by Nevill and Holder (1999), determined that the advantage is real. This kind of study has been used in three types of sport. On the one hand, we have sports that use a points system with a high level of subjective discrimination on the part of the judges, such as gymnastics. Another group of sports are the ones based on objective values, like time or distance, sports such as athletics. The third group is formed by sports where judges have a certain subjective intervention, such as team sports, where referees apply the rules with different possibilities of interpretation. The approach in this type of study has also been varied. Some have analyzed the case of playing at home in competitions with only one headquarters, with just one home side, such as the Olympic Games, taking into account both Summer and Winter Games. Balmer et al. (2001; 2003) analyzed these competitions and determined that in sports that belong to the first group, those judged mainly in a subjective manner, there was a clear existence of an advantage for home teams, thus, they achieved a very important point deviation in their favour. The home advantage analysis in team sports has been calculated in many different sports, for example baseball (Adams and Kupper, 1994; Bray - Sports Science and Physical Activity Faculty, University of A Coruña (Spain). - University Miguel Hernández of Elche (Spain). 3 ­ Maia Higer Institute (Portugal). . Authors submitted their contribution of the article to the editorial board. Accepted for printing in Journal of Human Kinetics vol. 38/2013 on September 2013. et al., 2005; Dosseville, 2007; Levernier and Barrilla, 2007), football (Carmichael and Thomas, 2005; Dosseville, 2007; Nevill et al., 1996; Pollard, 1986; 2000; 2006b; Sánchez et al., 2009; Seckin and Pollard, 2007; Thomas et al., 2004; Thomas et al., 2006), ice hockey (Agnew and Carron, 1994), basketball (Greer, 1983; Jones, 2007; Moore and Brylinsky, 1993; Varca, 1980), volleyball (Marcelino et al., 2009), rugby (Thomas et al., 2008) and handball (Gutierrez et al., 2012). Various studies have tried to identify the factors that influence home advantage. Courneya and Carron (1992) found significant such factors as rules of the competition, public, trips made, referees and familiarity with the stadium or grounds. Other home advantage factors analyzed were the influence of the public with regard to the number of fans, density of the crowd, proximity of them to the playing area or intensity of their support (Pollard, 2006a; Pollard and Pollard, 2005; Wolfson et al., 2005). Further factors were possible alterations caused by the journeys made by the players (Brown et al., 2002; Clarke and Norman, 1995; Pollard, 2006a), better knowledge of the playing ground, such as specific conditions or non-standard dimensions (Barnett and Hilditch, 1993; Clarke and Norman, 1995; Pollard, 1986), unfamiliar climatic conditions (Pollard et al., 2008; Seckin and Pollard, 2007) or the behaviour of the referees, in the frequency of their use of disciplinary sanctions (Glamser, 1990; Nevill et al., 1996). The study of home advantage evaluated the existence of alterations at the physiological level (Neave and Wolfson, 2003), verifying a rise in hormonal activity in home teams. The use of different game tactics in home and away teams has also been studied, in order to determine mainly if there are any variations in the performance of the teams (Carmichael and Thomas, 2005; Seckin and Pollard, 2007; Tucker et al., 2005). The objectives of this study were as follows: to determine and verify the home advantage in soccer in the UEFA zone in the first decade of the 21st century and its evolution during this time. We evaluated the behaviour of home advantage taking into account the UEFA ranking and described home advantage in the most powerful leagues of Europe. Material and Methods Participants The sample of this study consisted of 111,030 male soccer matches played in 52 of the highest league categories in UEFA countries between 2000 and 2010. Each game was analyzed from both the home team and away team point of view, meaning a total of 222,060 records. Different league formats exist in the UEFA territory. Some leagues combine the round-robin system with eliminatory systems; in other leagues, an initial league is formed which is followed by other smaller leagues of teams that descend or win the tournament. Eliminatory phases of different competitions are not used, only regular leagues and play-offs with league format. Although the intention of this study was to use a sample from each country, there are some exceptions. In Liechtenstein league competitions are not held, meaning that in the 53 countries of the UEFA there are only 52 leagues. In the Azerbaijan league the 2002/03 season was cancelled, so only 9 seasons were studied. Serbia and Montenegro started their independent leagues in 2006/07 (4 leagues). In the end, the sample was made up of all the league tournaments which took place in UEFA territory in the 21st century (520 examples over 10 seasons). Some countries (13) organise their competitions in such a way that they start and end the league in the same year, whereas most countries (36) start the season in the fall and finish it in the spring of the following year. This means that the first group of countries, those that count a season from January to December, have a total of 11 leagues whilst the second group have only 10 leagues during the same period. Measures The variables registered were the final results of matches played as a home team and as an away team, the points obtained in the league, the country, the season, the type of competition and the actual UEFA ranking. The data used was obtained from the UEFA web page (http://en.uefa.com), and two independent web pages Soccer way (http://www.soccerway.com) and futbol 24 (http://www.futbol24.com). The data collected from these 3 sources was compared in an by Miguel Saavedra García et al. independent way to minimize the number of errors. Procedures The European soccer leagues are based on a "round-robin tournament" system, which means that the same number of games is played away as at home. In this type of competition, the home advantage is quantified by taking into account the number of games won at home expressed as a percentage of the total number of points obtained (Pollard, 1986). When an independent analysis of the home advantage is made for each team, the home advantage is established by comparing the performance obtained when playing at home with the performance obtained when playing away. This analysis uses the percentage of games won at home in relation to the total of games won both away and at home, understanding draws as half victories. Analysis The degree of significance of the home advantage is calculated with the variable of points obtained, taking for granted the truth of the hypothesis that playing at home poses no advantage (Pollard, 1985; 1986), meaning that one out of every two games played at home is won (50%). To make the contrast, the sign test with Wilcoxon range was used. The groups used were formed of 10 countries in a function of decreasing values of the UEFA ranking, with the exception of the last group, formed of 12 countries (from 41 to 52), called Group A, B , C, D and E, respectively. In order to compare home advantage between the countries of one group or between the different groups the Kruskal-Wallis test was used. The bivariate correlations of Pearson were used to establish the level of association between the numbers of points obtained with the advantage of playing at home. Also the Spearmen bivariate correlations were used to assess the association between the classification of a team in a league and the UEFA ranking with the advantage of playing at home. results are shown in Table 1. Home advantage evolution in UEFA territory football A decrease in the tendency of home advantage can be observed between the years of 2000 and 2010 in the UEFA territory soccer leagues. The magnitude of the decrease exceeds two percentage points. Home advantage in different countries in the UEFA territory A significant home advantage was found in 32 (61.5%) of the 52 UEFA territory countries (p<0.05). The most powerful leagues in Europe have statistical significance in home advantage with values between 55 and 56. After this first group, there is a series of countries where home advantage is more heterogeneous: in some nations the existence of home advantage exists and the variability is higher than in the first group. The home advantage reaches a maximum of 76.10 points (Bosnia-Herzegovina) and a minimum of 50.03 (Republic of Ireland). Some cases of disadvantage of playing at home were found (Lithuania, Latvia, Estonia, Wales, Malta, Northern Ireland, Andorra and San Marino). The minimum value observed in San Marino is 45.52 (Table 2). Home Advantage and 2010/11UEFA Country coefficients The UEFA countries were classified by the 2010/11 UEFA Country coefficients in groups of 10 countries. This way, Group A is formed by the ten countries with the highest coefficients. Group B is formed by countries with rankings between 11 and 20, and so on down to Group E, formed by countries between 41 and 52. The home advantage of each country in each group was compared in order to assess the intragroup homogeneity, obtaining a large homogeneity in Group A (0.974). The rest of the groups show a significant heterogeneity (p<0.001). Significant differences were also found between the home advantage of the groups (p<0.001) (Table 3). In all countries of Group A, the home advantage is significant with minimum oscillations in its values. In the last ten years, the home advantage decreases by 1.8 points. Between the ten countries of the group, a variation of 1.26 points in home advantage was found. In Group B, 60% of the countries showed the existence of home advantage with more oscillations. Results The analysis of the sample used reveals a home advantage of 55.6±0.19 in the highest categories of European soccer, and it is significant in all cases analyzed, except for the season 11. The Table 1 Home advantage (HA) in the highest categories of UEFA football Years 2000-2010 Leagues Played Home Record Won Drawn HA Lost % SD Signification1 <0.001 Season 1 2 3 4 5 6 7 8 9 10 11* Leagues 50 50 49 50 50 50 52 52 52 52 13 Played 10466 10523 10223 10475 10855 10920 10929 11390 11450 11498 2301 Won 5145 5142 4970 5036 5120 5136 5096 5443 5375 5378 1012 Drawn 2424 2481 2361 2485 2626 2595 2802 2742 2779 2762 552 Lost 2897 2900 2892 2954 3109 3189 3031 3205 3296 3358 737 % 56.9 56.7 56.3 56 55.2 55 55.2 55.8 55 54.8 52* SD 0.20 0.20 0.20 0.19 0.19 0.20 0.19 0.20 0.19 0.20 0.19 Signification1 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.146 Significance of the Wilcoxon range sign test. *Season 11 is completed by only 13 of 52 countries. The HA is not representative of the territory countries of UEFA, because of the 13 countries, only one of them is in the top ten (Russia in 7th position) and the rest of the countries are ranked between 23 and 50. Figure 1 Home advantage evolution in the highest categories of UEFA football by Miguel Saavedra García et al. Table 2 Home Record, home advantage and Ranking in the highest categories of UEFA football Home Record Country Leagues Drawn Lost Played Won Home Advantage Ranking2 85.785 82.043 69.436 60.552 53.678 51.196 44.707 43.883 40.129 35.050 34.166 30.550 27.000 25.824 25.141 24.900 22.000 20.850 20.700 18.124 17.875 16.124 16.083 15.916 14.499 14.375 14.250 14.125 9.124 8.966 8.708 8.500 7.749 7.708 7.415 6.957 6.165 6.124 5.207 4.957 4.374 3.875 3.874 3.791 2.790 2.583 2.416 2.249 1.416 1.374 1.000 0.916 1st Season Last Season Sig.1 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.136 <0.001 <0.001 <0.776 <0.001 <0.140 <0.001 <0.001 <0.056 <0.001 <0.001 <0.099 <0.001 <0.001 <0.001 <0.027 <0.025 <0.001 <0.301 <0.914 <0.001 <0.302 <0.789 <0.898 <0.002 <0.227 <0.001 <0.001 <0.350 <0.001 <0.004 <0.001 <0.827 <0.842 <0.469 <0.523 <0.305 <0.230 <0.887 <0.310 <0.036 SD 0.16 0.14 0.15 0.15 0.13 0.16 0.15 0.18 0.18 0.16 0.20 0.18 0.17 0.16 0.26 0.17 0.19 0.14 0.15 0.23 0.20 0.18 0.19 0.17 0.16 0.15 0.16 0.16 0.15 0.16 0.16 0.18 0.22 0.23 0.25 0.21 0.23 0.16 0.20 0.16 0.22 0.17 0.16 0.25 0.19 0.25 0.24 0.19 0.20 0.20 0.28 0.21 England Spain Germany Italy France Portugal Russia Ukraine Netherlands Turkey Greece Denmark Belgium Romania Scotland Switzerland Israel Czech Republic Austria Cyprus Bulgary Croatia Belarus Poland Slovakia Norway Serbia Sweden BosniaHerzegovina Finland Republic of Ireland Hungary Moldova Lithuania Latvia Georgia Azerbaijan Slovenia FYROM Iceland Kazakhstan Montenegro Albania Estonia Wales Armenia Malta Northern Ireland Faroe Islands Luxembourg Andorra San Marino 57.28 61.05 59.80 56.32 58.17 58.93 56.25 55.31 55.23 57.49 61.94 49.66 57.52 64.86 51.32 60.11 48.15 62.08 59.63 54.03 63.74 57.81 54.17 58.06 60.00 53.30 51.77** 48.90 70.27 54.04 50.00 58.08 51.49 46.30 42.56 54.04 56.06 63.80 61.17 54.07 61.27 61.11** 72.16 45.83 51.80 55.65 51.01 49.07 53.33 51.77 48.33 45.02 59.21 59.39 50.22 57.89 55.61 52.78 55.42* 56.67 56.75 53.49 58.99 50.17 54.80 54.14 50.44 60.37 51.21 54.58 59.26 53.82 54.58 60.00 51.01* 54.03 59.43 59.03* 58.75 57.08* 71.94 45.97* 48.52* 55.97 56.39 46.42* 47.16* 54.44 49.65 48.15 57.48 48.74* 54.51* 56.90 64.10 48.52* 49.13 50.60* 50.25 49.27 54.07* 49.63 42.92 47.62 *Last season is #11.** First season is 2006/07 (#7). Significance of the Wilcoxon range sign test.2Country coefficients 2010/11. The association or country rankings take into account the results of all clubs from each association and are used to determine the number of entries an association is granted for forthcoming seasons (www.uefa.com). Table 3 Home Record and home advantage according to the level of the group by the UEFA Ranking Home Record Leagues Drawn Played Lost Level Won Home Advantage Ranking Sig.1 SD % Comparison Intragroup3 % SD 55.95 55.90 58.35 53.70 52.12 0.35 2.68 6.76 4.02 5.11 Intergroup3 Group A Group B Group C Group D Group E 14776 8291 8282 55.95 0.16 <0.001 1-10 0.974 10789 5638 6215 55.90 0.20 <0.001 11-20 0.000 10504 4716 5227 58.35 0.18 <0.001 21-30 0.000 0.000 7711 3812 5128 53.70 0.20 <0.001 31-40 0.000 9073 4152 6716 52.12 0.23 <0.001 41-52 0.000 1 Significance of the Wilcoxon range sign test. 2 Values grouped by Country coefficients 2010/11 in tens, forming five levels of categories of UEFA countries. 3 HA of countries in a group (intra) or between groups (inter) using the Kruskall-Wallis test. Table 4 Home advantage variations according to the level of the group by the UEFA Ranking Level % sig.HA1 100 60 80 40 33 HA 1st Season 57.58 56.93 57.81 53.76 53.20 HA Last Season 55.78 54.78 59.79 53.68 51.23 HA Max. HA 56.80 59.40 76.10 63.23 63.18 Min. HA 55.54 52.44 51.38 49.09 45.52 HA Group A Group B Group C Group D Group E 1.80 2.15 -1.98 0.08 1.97 Percentage of countries in a group with a significant home advantage (HA). Figure 2 Home advantage according to the group levels established by the UEFA Ranking by Miguel Saavedra García et al. During the decade of the study, home advantage has decreased by 2.15 points. The variation between countries is 6.96 points. In Group C, the percentage rose to 80% and the variation of home advantage is maximum between countries (24.72 points). Home advantage decreased by 1.98 points during the ten years of the study. In Groups D and E, the percentage fell to 40% and 33%, respectively. The variations of home advantage between these countries were high (14.14 and 17.66, respectively). Between 2000 and 2010, home advantage decreased by 0.08 points in Group D and 1.97 points in Group E (Table 4). The top 20 ranked teams in the UEFA Ranking (Groups A and B) have a very similar home advantage, though the dispersion in Group B is greater than in Group A. After the first 20 leagues, the variability in home advantage increases, reaching 58.35 points in Group C and decreases to 52.12 in Group E (Tables 3 and 4, Figure 2) An association exists (p<0.001) between home advantage and the UEFA Ranking, therefore it is very weak (0.056). Home Advantage, Season Rank and Total points Both the classification of a team and the number of points won has a significant association (p<0.001) with home advantage. The points obtained by a team are positively associated with home advantage (0.721). The classification of a team in a league has an inverse association with home advantage and a value of 0.674. North American Soccer, 63.3% for basketball and 63.9% for European soccer. Later on, Pollard (2006b) proved the existence of variations in home advantage in the same sport between different countries, which explains factors such as size of the country, producing longer journeys or factors such as territoriality, defined by Neave and Wolfson (2003). More recent studies, like the one by Pollard and Pollard (2005) establish for football and basketball values of 60% and for ice hockey 55%, values very similar to the ones found in this study (55.6%). Clarke (2005) found a value of 59.9% in Australian soccer, while García et al. (2009) found home advantage of 55.2% in Spanish basketball and finally, Gutiérrez et al. (2012) found a value of 61.3% in Spanish handball. The evolution of home advantage during the first decade of the 21st century decreases by two percentage points. This drop seems to indicate that professional teams are progressively overcoming home advantage, decreasing the disadvantage of playing away. But the tendency of this decrease has been different. In the first fiveyear period of the century, the decrease of home advantage was 1.88 points, while in the second part of the decade, the drop was only 0.2 points, which seems to indicate that home advantage stabilizes at around the value of 54.8. The degree of evolution of home advantage agrees with the conclusions of Pollard (1986), because as the seasons advance, home advantage declines. In the Pollard study, home advantage changes from 67.9% in the first half of the period to 63.9% almost 100 years later, while in the present study, the drop is more or less 2 points, but it is also true that the Pollard's study is of a whole century while this study only considers a decade. For the analysis of home advantage with regard to the quality of teams, 5 groups of 10 teams were established, except for group E, which had 12 teams. To establish this classification, the Country coefficients of UEFA for the 2010/11 season were used: in leagues of group A, the percentage of nations with a home advantage is 100%. In group B, the percentage is 60%, in group C, the percentage increases considerably, reaching 80% of the countries. In groups D and E, which are formed of the 22 countries with the lowest UEFA ranking, there is a low percentage of Discussion The objective of the present study was to analyse the behaviour of home advantage in UEFA-zone soccer in a complete sample: all the leagues in the first decade of the 21st century. According to the results obtained, home advantage of 55.6 has been found in the highest soccer categories of Europe, being significant in every season, except for the season 11, as this last season was made up of only 13 of the 52 countries analyzed, those nations whose seasons coincide with the natural year as opposed to the majority, who play from late summer to the following spring. Pollard (1986) showed values that oscillated between 53.6% in baseball and 65.5% in Calculating home advantage in the first decade of the 21th century UEFA soccer leagues finished in a draw, while in the present study the percentage is 23.9% of the games analyzed. However, other studies have obtained results similar to those of this research. The analysis of the category variable coincides with the conclusions of Pollard (1986), as in both studies, the lower the team's category, the higher the home advantage. This finding could be explained by the fact that teams in lower categories suffer difficulties such as uncomfortable journeys, players having to work or study, lower level of the players in these leagues, or other factors like local pressures. The same conclusion was obtained by Sánchez et al. (2009), who compared home advantage in the two highest categories of Spanish soccer and concluded that home advantage was higher in the first category competition. Finally, similar associations were found by Gutiérrez et al. (2012) in Spanish handball. countries with a significant home advantage (40% and 33%, respectively). Except for group C, there is a tendency towards a decline in the percentage of nations with a significant home advantage in line with the Country coefficients, which is an indicator of the level of competition. If we focus on the analysis of the top five, we can see that the first five countries (England, Spain, Germany, Italy and France) have a very similar home advantage, as their scores hardly oscillate more than 1.3 points. In other countries, the rest of the groups prove to have an important increase in their heterogeneity values, oscillating between 76.10 (Bosnia-Herzegovina) and 50.03 (Republic of Ireland), even reaching negative values in a few countries, which means that for them there is a disadvantage of playing at home. When taking into account the influence of the level of the team, the home advantage shows a significant association as there is a positive relation between the points won by a team and home advantage (0.721). The classification of a team in its league has an inverse association with home advantage (-0.674). These results contradict the study of Morton (2006) in rugby and Jacklin (2005) as both concluded that there were no differences in home advantage and the level of the participating teams. Differences also exist between the results of this study and those of Bray (1999) in ice hockey, as he finds that home advantage is similar for all teams independent of the quality of the team. It is necessary to highlight the fact that in ice hockey, the possibility of obtaining a draw is lower than in football. In the matches analyzed by Bray over 20 years, only 13% Conclusions Fifty-two of the fifty-three countries that make up the UEFA territory have league competitions. Only in 32 of them there was a significant home advantage in league competitions at the highest level. In the first decade of the 21st century it was 55.59±0.20. The countries that made up group A through their UEFA Ranking have a greater homogeneity when taking into account home advantage. In the rest of the groups, home advantage is much more heterogeneous. The teams with a better classification and more points have better home advantage values.

Journal

Journal of Human Kinetics – de Gruyter

Published: Sep 1, 2013

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