Jumping from pond to pond: How the duality of individuals and organizations shapes transfer networks in European professional football
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This document summarizes a study examining how individual reputation and organizational status shape transfer networks in European professional football. The study used event history models to analyze over 15,000 transfers between 1987-2011. It found that for all transfers, as well as for young and senior players separately, higher organizational performance status and downward transfers between clubs were associated with more transfers. However, the effects of individual reputation and career stage mattered - senior players tended to move horizontally while young players moved downward, and big reputation players moved horizontally while little reputation players moved downward. The study provides insights into how the duality of individuals and organizations shapes movement patterns within professional sports leagues.
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Jumping from pond to pond: How the duality of individuals and organizations shapes transfer networks in European professional football
- 1. Jumping from pond to pond: How the duality of individuals and organizations shapes transfer networks in European professional football Thijs Velema National Taiwan University, Department of Sociology Institute of Sociology, Academia Sinica
- 2. Transfer networks in football Period Club Transfer fee 1987 Born 2005 - 2006 Nacional - 2006 – 2007 FC Groningen € 800.000 2007 - 2011 Ajax € 7.500.000 2011 - Present Liverpool € 26.500.000 • Important for organizational survival • Important for individual career success
- 4. What shapes the transfer network? Duality of individuals and organizations Transfer network Reputation individual Status organization Search pool
- 5. ERGMs to analyze transfer network formation Dependent variable: • All transfers • Young/senior players • Big/little fish • Young/senior big fish Independent variables: • Performance status • Status difference • Upwards, downwards, horizontal dyads Control variables: • Network-level • Node-level • Dyad-levelData from transfermarkt.de
- 6. Results – organizational status & career stage All transfers Young players Senior players Coefficient Direction Coefficient Direction Coefficient Direction Performance status .7685*** .5277*** .5234*** Downwards dyads -.0121 .4341*** -.1143*** Upwards dyads -.6720*** -.5054*** -.6646*** *** p < .01 Models run with control variables, which are consistent across models
- 7. Results – individual reputation & career stage Big fish Little fish Senior big fish Young big fish Coeff. Dir. Coeff. Dir. Coeff. Dir. Coeff. Dir. Performance status .4797*** .8368*** .4261*** .9514*** Downwards dyads -.3899*** .2004*** -.4890*** .1707 Upwards dyads -.4076*** -.7814*** -.4497*** -.0588
- 8. Conclusion • Duality shapes transfer network • Organizational status • High status teams • Horizontal and downwards moves • Contingent on individual reputation and career phase • Senior players horizontal – Young players downwards • Big fish horizontal – little fish downwards • Young big fish • Implications for organizations and individuals
- 9. Thank you for your attention!! Questions? www.thijsvelema.com Attribution for pictures Edgar Davids (Juventus F.C., no.26) clashing with Gennaro Gattuso (A.C. Milan), by soccer illustrated Argentina-Holanda_1978, by Archivo Clarin Bundesarchiv Bild 183-N0716-0314, Fussball-WM, BRD – Niederlandse 2-1, by Rainer Mittelstadt Howitis, as appeared on http://www.shof.msrcsites.co.uk Luis Suarez vs. Netherlands (cropped).jpg, by Jimmy Baikovicius
- 10. Type of variable Variable All transfer network Network-level control Edges -6.5724*** (.2212) Reciprocity .6086*** (.0767) Two-path .0080* (.0042) Gwesp (alpha 0.6) .9991*** (.0604) Node-level control Number of players .0154*** (.0040) Stadium capacity .0039*** (.0008) Same country 2.9032*** (.0633) Independent variables Performance status .7685*** (.0888) Downwards dyads -.0121 (.0493) Upwards dyads -.6720*** (.0549) Model fit Log Likelihood -7217.647
Editor's Notes
- It is really cool to have the opportunity to speak here at the statphys conference. The sociological literature on social networks is full of references to the interdisciplinary nature of the field, and especially that the field develops in close relation to people in physics and statics, but being a sociologist speaking at this conference, this is actually the first time that I actually and truly feel that inter-related nature of the field. So, that is pretty exciting. Right, so my name is Thijs Velema, and I am a PhD candidate at National Taiwan University, and have been affiliated in the past year to the institute of sociology here at academia sinica as a doctoral candidate with fellowship I think it is apparent from this slide that I am going to talk about transfer networks in professional football, or what in the US is called soccer. Especially focus on the formation of transfer relations between teams. I try to analyze the formation of these transfer networks with a concept “the duality of individuals and organizations”. I will explain this concept during this presentation
- Let me illustrate transfer relations with an example Luis Suarez. One of the strongest strikers at this moment; integral part of Liverpool team in past season and key player for Uruguay on the world cup. Came heavily in the news due to his biting incident in the game against Italy last week Let’s take a look at his career: Born in 1987 Started in 2005 (when he was 18) in Uruguay playing for one of the strongest teams in the country, he was promoted from the youth team of that club After a year he was transferred to FC Groningen for 800.000, which is a mid-table team in the Netherlands Here, he played for a year, after which he was transferred to Ajax for 7.5 million euro Where he played for 4 years, after which he was transferred to Liverpool for 26.5 million euro one of the giants in English football So, his career would connect Nacional to Groningen, Groningen to Ajax, and Ajax to Liverpool What I want to ask here is how these transfer relations between teams are formed Based on these questions we can argue that how transfer relations are formed is important for organizations and individuals This question is really important for organizations, because organizations in a knowledge economy compete with the human capital. The people an organization hires affects the amount of human and social capital in the firm, and thus organizational survival It is also a really important question for individuals, because the answer affects their career opportunities. With the answer in hand, you might start to think more strategically about your next career move For Suarez’ career, there seems to be some sort of ordering here; from smaller to bigger teams and increasingly valuable transfers. But is this a typical career? Is this a career structure that people should and could aim for?
- So, what I did for this study is collect all transfers between 186 professional teams in the top 7 European football leagues between July 2006 and June 2012 and code them into a network in which teams are the nodes and transfers of players are the relations connecting teams. The figure shows this network, in which node size shows the status of a team, while the color of nodes indicates their country A number of things are immediately apparent from the network: Transfer relations are pretty common; in fact there are 6437 player transfers in total: That is around 1100 transfers between these teams per year In other words, teams transfer around 6 players with other teams within this population Another thing which stands out is that transfers are really clustered per country: most transfers are domestic transfers so that all teams from a country are neatly clustered together When you go into differences between big and small teams, you see that small teams mostly transfer domestically, while big teams are involved in player movements across boundaries So, the puzzle I try to solve is how relations in this transfer network between teams are formed. What factors play a role here?
- So, how is this network of player transfers shaped? Teams need to overcome 2 problems on the market: The market is big Information about talent is difficult and costly to observe I argue that organizations use the ‘duality of individuals and organizations’ to overcome this problem and identify the players for their search pool Duality of individuals and organizations refers to the idea that Group to which people belong is part of their identity How involved a person is within groups is part of their identity In this study I see duality in terms of status of the organization to which people are affiliated and the reputation of individuals within their group Status of organization – signal for the quality of the people affiliated to the organization Hence, status segregates groups of players based on the quality of the group Talents in big teams are favorably evaluated: Januzay at Man Utd Anecdotal evidence in professional football: Big teams refuse to scout in New Zealand, because “they cannot play football over there” Reputation of the individual – expectations for future performances based on past performances Reputation refers to if a player is a big or a little fish within his team Reputation differentiates individuals within a group You might be a talent playing for Man Utd, but there is a difference between someone regularly playing or someone who is on the bench or out of the match day selections all the time How status segregates and reputation differentiates is not uniform over the career of players, but is contingent on the career phase of a player Expectations about reputation differ in different career stages By identifying people through the duality, organizations form some sort of search pool of suitable people for recruitment. Organizations thoroughly scout the search pool in a structured was to select the players they want to hire into the firm, and thus shape the transfer network So, basically, what I argue is that organizations use this duality to identify players within their search pool, and that this shapes the transfer network between teams we observe
- Examine how duality of individuals and organizations shapes transfer networks in European professional football by estimating a number of ERGM’s (exponential random graph models) Exponential Random Graph Models are appropriate for the task at hand: Focus on network formation Incorporate node-level and dyad-level characteristics & control for network-structural processes Dependent variables are a series of nested models All transfer network To examine how team status is contingent on career phase I distinguish transfer networks of young and senior players To examine how individual reputation affects transfer networks I distinguish between big and little fish To examine how individual reputation is contingent on career phase I combine these two categories: Transfer network of young big fish and senior big fish All data for these networks comes from transfermarkt.de German based online database with information about careers of players From that you can extract the transfer relations between teams Also used for data on performance of players Independent variables Absolute status of a team Based on league ranking of a team weighted by the strength of a league in European cup tournaments Status difference Absolute status used to categorize teams in three status categories: high, middle, low Status difference than operationalized as 3 dummies: Horizontal - Teams in same status category Downwards – Higher status sender, lower status receiver Upwards – Lower status sender, higher status receiver Also used a number of control variables: Network level: edges, twopath, reciprocity, gwesp Node level: stadium capacity, number of players in a team Dyad level: teams from the same country
- So what are the results of these exponential random graph models. Let’s first have a look at the network for all player transfers: Performance status is positive and significant Status difference: Downwards dyads show no difference compared to horizontal transfers But transfers in upwards dyads are negative and significant: hence, transfers in these dyads are less likely to occur than horizontal transfers From this, we conclude that: High status teams are more involved in the transfer market: the higher the status of a team, the more likely it is to transfer players Direction of transfers is predominantly horizontal between teams in the same status category, or downwards from higher ranked to lower ranked teams When we than compare young and senior player, we actually see that the effect of team status is contingent on player career: For young players, we see that transfers in downwards dyads are actually more likely than movements in horizontal dyads For senior players, we see that transfers in downwards dyads are actually less likely than movements in horizontal dyads
- Now, if we look at the different ERGM estimations for players with a reputation as a big and a little fish, we see similar differences: Big fish transfers are less likely to occur in downwards dyads, compared to horizontal moves Little fish transfers are more likely to occur in downwards dyads, compared to horizontal moves From this, we conclude that: Big fish transfers take place between teams in the same status category But little fish transfers are de-selected by their higher status team and move to lower ranked clubs Finally, we can compare how individual reputation and career stage work together to shape player transfers: We see that senior big fish transfers have a lower likelihood to occur in downwards or upwards dyads: these players move horizontally between status-similar teams However, when we look at young big fish, we see that they: Have a positive, but non-significant coefficient for downwards dyads And have a slightly negative, and non-significant coefficient for upwards dyads These results are tricky to interpret, because the network for young big fish transfers is really sparse, and standard errors are relatively high But, these results might imply that: There is a chance that young big fish transfer upwards to higher status teams But, they still face the risk of a downwards transfers
- What I hope to show is how the duality shapes the transfer network: So, the dual positioning shapes the network through the status of organizations to which individuals are affiliated: High status teams are more involved We see that moves are either horizontal or downwards This is pretty similar to other studies to high-skilled competitive labor markets, such as academia (Burris 2004) But the effect of organizational status on the transfer network is contingent on individual reputation and the career phase of a transferred player: Senior players transfers are horizontal, while young player transfers tend to be downwards Selection process at the start of the career -> high risk of de-selection and a forced move down After selection process players are accepted as established players within a given status category Big fish transfers are horizontal, but little fish transfers are downwards Good reputation leads to expectations that people are able to repeat their performance at the same level Bad reputation leads to questions about the ability of people to perform at this level Young big fish Good reputation at a young age might be favorably interpreted: maybe these players are not only able to repeat their performance, but even improve as they mature into senior profs Upwards movements are possible Implications for organizational recruitment processes and individual careers Implications for organizations: Hiring/recruitment is necessary limited, but this might lead to biases: focus on big players and big teams Might be able to recruit ‘players with a spot’: talented people who might have personal problems limiting their involvement in the team. If you are able to pick up these players, you might be able to build a really strong team (this is the Brain Clough strategy which made a modest team (Nottingham Forrest) champion of Europe Might also open your eyes for individuals in lower status teams and the reasons why they are there Implications for individuals: Think strategically about your career: there are two viable strategies: Start as little fish in a big pond and hope that you can grow into a big fish: Opportunity: retain place through horizontal transfers Risk: being de-selected and forced to move down Start as a big fish in a little pond and hope that you can move up: Opportunity: to be transferred to a higher status team Risk: Never receiving that opportunity
- With that, I would like to end my presentation and thank you for your attention
- ERGM aims to identify the node-level, dyad-level, and network-structural factors which maximize the probability of the emergence of a network with similar properties as the observed network