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Articles

Who wins the championship? Market value and team composition as predictors of success in the top European football leagues

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Pages 223-242 | Received 19 Oct 2015, Accepted 10 Nov 2016, Published online: 29 Dec 2016
 

ABSTRACT

Processes of commercialisation and globalisation have changed professional football and the composition of football teams fundamentally. Against the background of these shifting conditions we investigate to what extent the success of football teams in their national leagues is determined by: (a) the monetary value of the team expressed in its market value, (b) the inequality within the team, (c) the cultural diversity of the team, and (d) the degree of fluctuation among the team members. The empirical analysis refers to five football seasons, spanning from 2011/2012 until 2015/2016, and includes the twelve most important European football leagues. The findings demonstrate that success in national football championships is highly predictable. The market value of a team is by far the most important single predictor, whereas different features of a team’s composition – inequality, cultural diversity, and fluctuation – are less decisive. However, the market value of a team does not play the same role in all of the leagues. The lower the degree of financial inequality in a league, the lower the impact of the market value on teams’ performance.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Jürgen Gerhards is Professor of Sociology at the Free University Berlin. His research interests include comparative cultural sociology, and sociology of European integration. His most recent publications include European Citizenship and Social Integration in the European Union (Routledge, 2015, with H. Lengfeld); Transnational Linguistic Capital. Explaining English Proficiency in 27 Countries (International Sociology, 2014); European Integration, Equality Rights and People’s Beliefs: Evidence from Germany (European Sociological Review, 2013, with H. Lengfeld) and Free to Move? The Acceptance of Free Movement of Labour and Non-Discrimination among Citizens of Europe (European Societies, 2008).

Michael Mutz is Professor for Sociology of Sport at the Justus-Liebig-University Gießen. His research is focused on sociological issues in the fields of sport, leisure, and culture. His most recent book Jugend, Migration und Sport [Youth, Migration and Sport] (VS Verlag, 2015, with U. Burrmann and U. Zender); Transnational Public Attention in European Club Football (European Societies, 2015); and Mental Health Benefits of Outdoor Adventures (Journal of Adolescence, 2016, with J. Müller).

Notes

1 Authors' calculation, based on data available at www.transfermarkt.de (last accessed 24 July 2016). The leagues in England, Spain, Germany, Italy and France are among the top five European football leagues.

2 In the German Bundesliga, for instance, the Impire AG collects and provides data on every league game and each single player. Bundesliga matches are watched by four trained observers in the stadium. These observers document every incident on the pitch, including passes, tackles, fouls, shots and goals. Moreover, video analyses are used to validate the data after the end of the match (www.bundesliga-datenbank.de).

3 We do not only assume that market value impacts on success, we also expect that success increases a club's revenue, allowing for the purchase of more expensive and thus probably better players for the next season. The increase of a team's market value should in turn manifest itself in their overall success at the end of the season. In the present work we are not able to analyse the long-term interaction between market value and success due to data restrictions. We can however examine for single football seasons to what extent the measured market value at the start of the season determines the result at the end of the season.

4 It can also be assumed that inequality within the team serves as motivational incentive for lower paid players. This hypothesis finds support in the functionalist theory of social stratification emphasising that differences in pay and in social status function as necessary incentives to strive for higher positions (Davis and Moore Citation19Citation45).

5 Moreover, processes of discrimination might also play a role (Kalter Citation2003). Existing prejudices against foreigners decrease the willingness to cooperate with them, which negatively affects team performance. Another argument is derived from group identity theory (Tajfel and Turner Citation1986): Cultural heterogeneity increases the likelihood for the formation of subgroups within larger groups, to the detriment of cooperation between the subgroups and overall identification with the team.

6 For all 18 teams of the German Bundesliga we calculated the sum of all transfers in the 1980s (season 1980/81 to 1989/90). Over the course of these ten years, each club signed or sold 14 players on average. This number has nearly doubled to 26 players over the course of ten seasons (2003/2004 to 2012/2013) suggesting a substantial increase of fluctuation in football squads over time.

7 We are aware that our operationalisation of diversity only comes close to the theoretical construct at best. We also tried to survey several other and better indicators such as mother tongue of the players. However, for too many players this information is not available.

8 This curvilinear relationship is also plausible due to the fact that the score a team can reach in a season is limited and cannot be increased indefinitely. In the 2012/2013 season, for example, FC Bayern Munich won the German Bundesliga and scored 91 out of 102 points. FC Barcelona dominated the Spanish championship scoring 100 out of 114 points. Even if both teams were to invest some more millions in their squads, it is doubtful that another substantial increase in the number of points scored would be at all possible.

9 If each season is analysed separately, the variance of performance explained by market values varies between 68% (2011/2012), 70% (2012/2013), 71% (2013/2014), 70% (2014/2015), and 67% (2015/2016). A series of five years does not, however, allow us to draw any conclusion regarding time trends.

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