2 BACKGROUND 2.1 THE CONTEXT

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1 TABLE OF CONTENTS 1 INTRODUCTION BACKGROUND The Context Previous Researches OBJECTIVES TOPIC ANALYSIS Methodology Market Value and Transfer Price Media Value RESULTS Market Value and Transfer Price Market Value Model Transfer Price Model Media Value Model CONCLUSION BIBLIOGRAPHY ANNEXES ANNEX 1: Transfer Price Market Value ANNEX 2: Ln Transfer Price Ln Market Value ANNEX 3: Ln Market Value Model ANNEX 4: Market Value Model ANNEX 5: Ln Transfer Price Model ANNEX 6: Transfer Price Model ANNEX 7: Correlation Media Value proxies ANNEX 8: GMSV explanatory power ANNEX 9: Facebook Model (logarithms) ANNEX 10: Facebook Model ANNEX 11: Twitter Model (logarithms) ANNEX 12: Twitter Model... 40

2 1 INTRODUCTION Studying football from a statistical point of view is not an easy endeavor. Its unpredictable outcomes make really challenging understanding how all pieces in this world adjust to make football the most popular sport worldwide. Players performance cannot be seen in a vacuum (as it is a team sport), and competition between teams intertwines further the decisions taken in this sport. To make it harder, an inflationary effect is happening in this market and prices are becoming higher season after season, skyrocketing transfer fees to unimaginable quantities. On the other hand, there is an internal battle between sportive success and financial success in football teams. The strong correlation between personnel costs and on-pitch success is obvious. The more a team invests in its players, the more probabilities it has to win. Even so, football teams are nothing more than enterprises, so their financial stability and growth is extremely important. Football Clubs value their football players in different ways. They can do it based on footballers historical cost (valuing the player at the transfer fee paid for them), replacement cost (based on how much it would cost to replace the player in the transfer market), or market based cost (based on how much other clubs are willing to offer in the market). On the other hand, a lot of factors can be taken into account when doing this process: squad status, position, league factor, age, international performance Moreover, the image and media impact of footballers can have an important role in their appraisal. Players that can increase stadium attendance, jersey sales or fans interest for a team are more sought-after than those who can not. The most renowned example is David Beckham, who was acquired by Real Madrid for 36 million euros, and generated more than 440 million in 4 years only in marketing and commercial activities. The interest that drove this research was the possibility of better understanding footballers valuation and objectively create a model that could explain their on-pitch performance, market value and transfer fee, as well as estimate their media impact. The opportunity to combine statistics with football enabled to successfully carry out this investigation. In this research, a different vision of football is studied. An econometric model is created to value disaggregated individual performance data as well as media impact in footballers market value. 1

3 2 BACKGROUND 2.1 THE CONTEXT As we want to analyze how footballers are valued and the impact their performance and their image has in their value, it is important to initially place player transfer market in context. Football clubs, as any other business, are in a continuous battle for lowering costs while they build competitive teams. Their objective is to invest the lowest possible to reach their season goals. On the other hand, we find an increasingly rich and polarized market. Big teams, every season are even bigger than those with less funds, and have more resources to attract good players that can lead them to their goals. Football market and its competitiveness are making that every year clubs spend more on their squads and that transfer prices are becoming soaring. As Stefan Szymansky, Professor of Sport Management in the University of Michigan, said: Without taking economic risks it is difficult to sustain competitiveness: A team that is not indebted can not seriously compete. Moreover, as Mr. Erik van den Assen expresses (in a thesis mentioned below), the strong correlation between team expenditures in personnel and on-pitch success, can suggest that titles can be, in a certain way, bought. And, besides, the decreasing marginal benefit of relative spending and league finish brings us the idea that in top teams, having an on-pitch better performance is relatively much more expensive than in teams with less resources. The Spanish League is one of the Top Football Leagues, with some of the best teams in the world. Transfer prices in it have skyrocketed historical data and every season it has teams contesting the most prestigious competition in Europe, the Champions League. The Spanish Football Market is divided in two periods (summer and winter transfers windows). This last summer, Spanish Clubs recorded almost 600 M euros in transferring, setting a new record that was stablished since The highest transfer fees paid worldwide in history are Gareth Bale (101 M) and Cristiano Ronaldo (94.7), both players bought by Real Madrid. Teams pay these huge amount of money expecting higher returns from those players on-pitch and off-pitch (ads, tours, stadium attendance ) performance. But, there are more factors that can influence transfer prices. Centralized bodies like European (UEFA) or global (FIFA), as well as local league associations, place constraints to favor competitiveness and ethics leagues. 2

4 Some of those restrictions come from minimum salaries, entry territorial restrictions, transfers of youth players and financial fair play. Those kinds of constraints have to be taken into account when valuing football players. In 1995, the Bosman-arrest took place, and the European Court of Justice decided to limit the player s freedom of movement. Later, on 2003 the Monti-Rules (Fees and Muehlheusser, also included in FIFA Regulations) reinforced this trend of liberalization and stated that a player can unilaterally end his contract at a club by paying a fine. These restrictions avoided Football Clubs to feel free to charge any transfer fee and European leagues were prohibited to discriminate in terms of nationality (removing the rule of number of foreign players allowed in the squad). In 2009, the UEFA introduced some laws known as Financial Fair Play that force football teams to pay their debt and to have no losses. Severe sanctions are applied to those who do not accomplish them. 2.2 PREVIOUS RESEARCHES As mentioned before, Erik van den Assem in 2011 wrote a thesis for the Erasmus School of Economics, The valuation of Human Capital in the Football Player Transfer Market, in which he investigated the transfer fees paid and received in the English Premier League during and seasons. The resultant R 2 (more than 0.4) tells us that he developed a fairly good model to explain the different variables that affect transfer fees in the U.K Football Market. The idea he left is that it is extremely difficult to adequately approach the actual inputs into today s transfer decisions and among other considerations, that a media value proxy need to be included. Other researches, such as the Economic Valuation of Football Players through Media Value from Francesc Pujol and Pedro Garcia-del-Barrio (2008) included this media value proxy and tried to explain football player s market value relying just on media value measures, disregarding any kind of direct measures of sport performance. Previously, in 2006, they had developed a really good model to measure the Media Value of professionals in Football in their report: ESIrg Report on Media Value of Professional Football. In fact, they ranked football players based on their media value. In this Report they disaggregated the media value into Popularity (nº of Web Pages referred to her or him) and Notoriety (nº of Press News that each player generates in any given time). Another interesting research to consider is The Impact of the Euro 2012 on Popularity and Market Value of Football Players (Stephanie Kiefer from University of Münster). The purpose 3

5 of the study was to analyze whether the performance at the Euro 2012 influenced the popularity of participating football players and if this was the case, which specific performance indicators in particular. To do so, changes of the logarithms in popularity in three different online media regressed against different performance variable were studied. These three online media measured Popularity Effects (Media coverage from hits in Google, and on the website uefa.com) and Networks Effect (Likes in Facebook s fan pages). Although it is considered a good methodology, it could not be applied on this project as we could not have information about those media at the beginning of the season. 3 OBJECTIVES The aim of this research is to study the variables affecting the most important asset in the football market, individual football players: how they are valued, which are the main variables that influence their assessment and in what degree they do it, how transfer fees paid by teams differ from actual player values, and the influence of media impact in media value of footballers. To do so, different econometric models have been developed to better understand how this intangible assets are rated. Initially, the goal of this investigation was to create a single model that comprehended footballers on-pitch performance, with team characteristics, experience and media value. Notwithstanding this could not be done, and two different kind of models had to be built: the first group explaining how players are valued and how its valuation differs from transfer fees paid; and the second one that measures the effect of media impact in the market value of footballers. A total of more than 70 variables of 170 observations coming from transfers during 2014/2015 and 2015/2016 summer windows of La Liga have been gathered for the first group of models, and data from the 50 most valued strikers worldwide have been used for the second one. 4 TOPIC ANALYSIS From an economic point of view, the market of footballers is a market just like any other. In this case, football players are an intangible asset really difficult to account. In the balance sheet, football clubs identify them at cost price. Any homegrown player (such as Messi) does not have any accounting value, as they have not been acquired. In that case, football clubs can present really different balance sheets. For example, Athletic Bilbao is a team in which only Basque players are bought. Most of them are homegrown, so they have very few accounted assets. 4

6 Therefore, players are capitalized at acquisition cost and depreciated over their useful life in a financial point of view. In the transfer market, the agreed price always is an intermediate quantity between what the buyer is willing to pay and what the seller expects to obtain. Obviously, the buyer team always is willing to pay less and the seller always expects to obtain more for that sale. Whoever has more bargaining power will influence in a higher degree the final decision. The decision making on signings is very different in each case and a lot of variables can come into play. Clubs in need of money, clubs that need to perform better, laws, signings for getting to know some leagues (such as Jackson Martinez to the Chinese football club Guangzhou Evergrandre) can play a key role in signing new players and are really difficult to be considered. As said before, the objective of this research is to analyze the player s market value without taking into account all these variables. Analyze how transfer price of different players differs depending on the buyer team, and figure out how media value affects the market value of football players. It should be taken into account that in this investigation, only past data is considered (player performance, signing price ), but what clubs really pay is the player s expected future performance. As this is totally impossible to know, we all use their past data as a proxy to estimate their future performance and how they can get used to a specific club playing style. Some websites such as Kickdex, Transfermark.co.uk, or CIES Football Observatory Annual Review offer us interesting data of player s performance and market value. In the first case, Kickdex estimates market values that accurately reflect a player s on pitch importance. Using a performance Index (even Page Rank algorithm) the company objectively measures player s performance (defined as how much each player helps his team to win matches). They found out that aggregating player s performance reveals some trends for performance level. With that formula, they can sensibly predict future performance projection. The second case, transfermarkt.co.uk offers a wide range of statistics of almost every professional player in the world. From matches, goals, and minutes in last season, to players with the same characteristics and estimations of market value. In all these cases the methodology used is the same as in this research, an econometric model. As expected, in this analysis we are not able to cope with the same wide range of data that those websites or football team s scouts can have, but the investigation tries to offer an analysis at maximum level of objectivity as possible with data obtained. 5

7 The initial goal of this research was to build a unique model in which we could explain the market value of a footballer based on its on-pitch performance and characteristics of the player and on its media value. At the end, this could not be made. As data from big companies, and football teams is not reachable and we do not have the capabilities to take daily information of each observation during a year (in terms of performance and media value), we were not able to create a single model with both variables. As later will be explained, in this research only transferred players have been taken into account. It is the only way to know how a player is really valued for a club in a specific time. From this (selected) sample we can not estimate their media value in the precise time when they were transferred, as our proxies to estimate the media value only offer us current information. For this reason, more than one model has been generated. 4.1 METHODOLOGY As mentioned before, to study the market value, transfer price and media value of footballers, four different models were regressed. The first model studies the correlation between transfer fees and market value. The second one explains the market value of players (based on its on-pitch performance, experience and characteristics of the player and team). The third model studies the transfer price (based on market value proxies plus the buyer effect in the bargain). To do so, the seller was considered to have some endogeneity with the market value, as it can vary depending on the team the footballer is playing. Therefore, only the buyer power was taking into account as an added significant variable to the transfer price. Furthermore, as said before, some other variables can also influence the transfer price such as laws, necessities of buyer and seller or interests of the teams, but they are really difficult to be measured. In the fourth model, the market value of the most valued football strikers is measured estimating the importance of media value in it. Considering that a player with a high scoring rate has a great media impact every time he scores a goal or performs well, we considered that media value can also be a good estimator of market value of a player Market Value and Transfer Price This first model tries to explain the relation between these two variables, what other variables can affect each of them and to what extend they can do it. 6

8 To do so, a sample of 170 observations 1 from the last two summer transfer windows of the Spanish League (LFP League or La Liga) have been taking into account. Those observations coming from loan players have been rejected, as they can artificially modify the final result. Loan Players are acquired at cost 0 or less cost than transferred players and they stay in the buying team for a specific period of time (and then go back to the prior). Also, those players transferred during winter windows have neither been considered, as only performance data of the first half of the season could be taken. Even though total season estimations could be made, we considered that they could not actually be adjusted to real data. In most cases, winter transferred players are those who are not playing as much as expected in the first half of the season, or they are acquired to substitute some injured players. Those reasons differ too much from the ones that influence summer transfers. Finally, it should be noted that goalkeepers have also been excluded of these models. Goalkeepers are a special case in the football world, and different variables can come into play in comparison with players from other positions, such as conceded goals or matches without conceding goals. Furthermore, these variables were not significant in our research. Therefore, in this paper we will focus on the model without goalkeepers, as we consider it to be more precise, and will allow to better explain the other variables. As expected, it is a totally selected sample. Football transfer data can be compared with housing prices data. From football players (as housing prices) we only have real information of those who have been sold. From players (or houses) not sold, we only can estimate how much should be paid for them, but we actually can not know the real price until someone buys them. Therefore, for using real values (that possibly have external factors that can make them differ from the market value) only transferred players could be considered. Until now, all statisticians that studied the valuation of human capital in football players transfer market had to face this same issue. We can only find one investigator that objectively could take any player into account in his sample. It was Carmichael et al. in 1999, who studied the determinants of any football player value, not only the transferred ones. To correct that selection bias, he used a Heckman two-step procedure, by using the residuals of a probability of transfer equation to correct OLS estimation errors of the fee equation. This method was out of our capabilities and could not be used. 1 Source: 7

9 Identifying the market value determinants of a football player is not really complicated. In fact, it can be intuitively done. The difficult issue of this project is identifying valid proxies that allow to measure those determinants. That is the reason why from the beginning of the project we tried to gather as much data as possible to later play with it and finally select the most appropriate ones Determinants of Market Value To analyze the Market Value of the Spanish footballers, more than 70 variables, and combinations of them, have been gathered for a total of 170 observations. At the end, not all those variables were used in the study, but most of them were studied and compared, or used to create other interesting variables for the investigation. For this research, we considered that the Market Value of any player depends on four main different aspects: Characteristics of the player, the team in which he plays, his performance in the past short-term (last season) and his experience. Thus, the determinants of the Market Value comes from breaking down those variables: - Regarding players characteristics, we used as proxies the Age and Age 2, the Market value (MarketValue and LnMarketValue), nationality of the footballer (expressed with the following dummies: Spain, Brazil, France, Argentina, Colombia, Mexico and Other with Portugal as reference category), and his position in the pitch (Defender, Midfielder, Striker). Using age 2 allowed to introduce an every time less growing variable (age). The impact of age in a player is greater in his first years of career than in his last ones, so the function of the variable could not be lineal. With this new variable, when drawing the age function we obtain an inverted U-Shape. Therefore, we can also know when the age effect becomes a negative effect for the player (Peak of the function). On the other hand, concerning logarithms, they show the increase in percentage of a variable, in this case the market value. - In respect of team characteristics, the following variables were considered: the league in which he played (using the following dummies: LaLiga, LigaNOS, SerieA, Ligue1, PremierLeague) and whether his team played the Champions League or not (ChampionsLeague). - Concerning the short-term performance of the player (last season s performance), we took a lot of variables broken down into league data, cup data and continental cup data that we later could group together to create seasonal total data of players. Thus, we obtained season appearances, season goals, goals per match, season assists, season 8

10 conceded goals (for goalkeepers), season minutes and season goals scored per position (striker, midfielder and defender). From those variables we only took for our models the Season Appearances variable. - Finally, regarding the experience of the footballer, data about Total Minutes played, Total Goals, Total Matches, Total Conceded Goals in his career have been gathered, as well as total data of his national team, such as matches, minutes, goals, and conceded goals in his national team. Thus, from these data only TotalMinutes, TotalGoals (and this variable broken down into DefenderTotalGoals, MidfielderTotalGoals and StrikerTotalGoals), a dummy that captures whether the player has played for his national team or not (International) and International Minutes were used Determinants of Transfer Price: The determinants taken for analyzing the Transfer Price are the same as those for the Market Value, excepting a final added variable: the Buyer (expressed with the following dummies: FCBarcelona, RealMadrid, AtléticodeMadrid, ValenciaCF, VillarealCF, SevillaCF and OtherST, representing all the other Spanish Teams). With it, we wanted to measure how the buyer team affects the transfer price and how big teams can have some controversies in finding cheap players. It has been said that big teams are made to pay bigger amounts of money for players than modest ones, as they are known to be rich Media Value In order to study the media value of footballers, we gathered data 2 from the 50 worldwide most valued strikers by transfermarkt.co.uk. As mentioned previously, we could not estimate the market value and the impact they had on social networks at the time of signing. Therefore, another model has been created taken into account the market value and the proxies estimating the media value in that precise moment. Moreover, as we were dealing with the 50 most valued strikers, we also considered other variables in the study, such as goals, age and age 2. With these data we wanted to analyze whether the performance and characteristics of the players have a greater impact in footballer s market value than media value does or not. Again, a selected sample was used, as players with a high media impact were considered. The aim of this research was to identify how this impact affects to worldwide famous players. Therefore, this part of the research had to be done with worldwide known footballers. 2 Source: 9

11 Determinants of Media Value: To study the media value of footballers we used a similar methodology as the one used in other researches such as ESIrg Report on Media Value of Professional Football from Francesc Pujol and Pedro Garcia-del-Barrio and The Impact of the Euro 2012 on Popularity and Market Value of Football Players from Stephanie Kiefer. We considered that the media value of footballers comes from two different variables: Popularity and Notoriety. The popularity reflects the interest that a footballer creates among general public all around the world (through his presence in personal web pages, social media ). On the other hand, notoriety measures the mass media exposure received by each player. It is related with the news generated, most of them coming from its sport performance. Different proxies have been used to analyze both variables. For the first one, popularity, we used the followers on Facebook 3 (LnFacebookand) and Twitter 4 (LnTwitter) of each player in the precise moment that the market value was gathered. The problem we had to face was that some players only use one of both social networks and, therefore, the model was not precise. To solve it, two models had to be created, one for those who had Facebook and another one for those who had Twitter (independently if they also use the other social network or not) In the matter of notoriety, we wanted to estimate the number of news generated by a footballer and the number of times a player had appeared on internet during the last year. In other words, we wanted to measure the echo a player did during that period. We did not have the tools and were not capable to gather all that information from the beginning of the season, therefore we decided to use as proxies the Google Monthly Search Volume (LnGMSV and GMSVadj that refers to the Google Monthly Search Volume divided by ) from Google Adwords of each observation and the Google Search Interest (GoogleSearchInterest) from Google Trends. The term Google Monthly Search volume of Google Adwords 5, refers to the number of times a word has been searched in Google in a certain period of time. For instance, we could search how many times the word Cristiano Ronaldo (specifying that the research refers to the football player) was searched in Google. The challenge we found with this tool is that some players are used to be searched in a specific certain way and others can be searched in more than one. An example of this is Cristiano Ronaldo and Lionel Messi (or Messi, Leo Messi, Leo,

12 Lionel ). However, as the program allows to identify that what is been looking for is the term Cristiano Ronaldo as a football player, we considered it as a valid way to proxy popularity. In respect of Google Search Interest of Google Trends 6, it refers to an average of weightings during a certain period of time of the number of term searches in Google. It takes into account the term you are looking for and those related with it. For example, if you are looking for the search interest of the term Tokyo, the following words will also be considered: 东京, Токио, Tokyyo, Tokkyo, Japan capital, etc. Taking the peak search of a term as a base, this tool monthly weights the searches and gives an annual average of those weightings. It also allows comparing more than one term, taking as a base the maximum value of all of them. Thus, what has been taken as a base was the term Lionel Messi, which obtained its peak value during these last 12 months on June Then, we compared that term ( Lionel Messi ) with the different players (that also took as a base value the peak value of Lionel Messi ). 4.2 RESULTS Market Value and Transfer Price To get started with the study of these two variables, an analysis of how they are correlated has been done. We must remember that Market Value data come solely from the market value suggested by the website transfermarkt.co.uk. Therefore, this model explains how the market value of this website is compliant with actual transfer prices. As transfermatk.co.uk s model is well accepted worldwide by football lovers, we could consider that the difference between these two variables comes from some external factors that can influence transfer fees paid by teams. As we can find appended in this research 7, the correlation between these two variables is really high (0.918) and the regression line is very tight, with very small deviations Annex 1: Transfer Price Market Value 11

13 The coefficient of determination is 0.842, therefore, the Market Value variable can explain an 84.2% of transfer prices. Analyzing the coefficient obtained for the Market Value variable in this model, and the high significance it has, we can state that an increase in a million in the market value of a player affects in a million the transfer price paid for him. Although the model has an insignificant constant, it could be expressed as follows: Transfer Price = x Market Value Thereby, we realize that the price paid for players (considering this model) is more or less 17.3% higher than the market value thereof. Thus, we might consider that the transfer price is affected, as expected, by some external factors aside from the market value of the player. To explain that difference, we introduced dummies of buyer teams in the model, but all of them were insignificant. When comparing the same model with logarithms 8, we obtain very similar results. As we can see in the R 2, with the logarithm of market value, a 75.8% of the logarithm of transfer price can be explained. The fact of working with logarithms allows to have results in percentage. Therefore, considering the coefficients of this model, a 1% increase in the market value of a player results in a 1.068% increase in the transfer price Market Value Model In order to study this model, as it has been mentioned before, characteristics of the player, experience and short-term performance variables, as well as characteristics of the team have been used. The results enabled to create a model that largely explains the market value proposed by transfermarkt.co.uk. As done until now, we will start focusing on the coefficient of determination of the different models. As in all cases, two models have been built. The first one in logarithms, and the second one in absolute values. The Coefficient of Determination of the first model (with logarithms) 9 is Therefore, a 66.4% of the market value can be explained with the study realized. 8 Annex 2: Ln Transfer Price Ln Market Value 9 Annex 3: Ln Market Value Model 12

14 As shown in the ANOVA table, only 153 of the total 168 observation have been used by the program when carrying out this model. This is due to 14 observations coming from goalkeepers were rejected and one is always dismissed for the program as a degree of freedom. The results from each variable will be discussed grouped together. Regarding player characteristics we can state that: The first variables, age and age 2, as expected, have a high significance in this model. The experience takes an important role in football, and the more experienced is a player the more he is valued. However, as explained before, the age function does not behave as a lineal function, changes in age during the first years of the career have a greater impact in market value than changes at the end of it. For this reason, when we introduce the variable age 2, it also has a really high significance degree and a negative coefficient. Therefore, one year change in age increases the market value in a 0.856% when all the other variables remain the same. But as the player gets older, the negative impact of age 2 (-0.20) is greater. Regarding the position of the player, the results obtained in this research differ from what was expected. In this model, the fact of playing in one position or another does not affect the market value, although the opposite result was expected. After analyzing the short-term performance related variables of a player, the following results were obtained: Seasonal data resulted to have only significance in Season Appearances. As shown appended, every match increases the market value of a player in a 0.27%. As expected, the better a footballer performs, the more he plays, and the more he is valued. As to the experience of the player, the total minutes played during his career is significant when valuing footballers, while the total scored goals is not. The obtained coefficient for this variable is very small (4,644E-005), as it indicates the increasing percentage in market value for each minute played when the other variables remain the same. To contextualize this data, a retired player such as Carles Puyol (a former FC Barcelona captain), played minutes in his whole career. On the other hand, if we break down the Total Goals variable by position, any significance in their coefficients can be found. So, comparing the goals scored by strikers and defender with respect to midfielders does not have any significance, and therefore any conclusion can be taken from this. 13

15 Considering the characteristics of the club we obtained the following results: Firstly, the fact whether the player s club has played the previous year the Champions League or not has a big impact on the market value of players. The Champions League is the most prestigious European competition, and playing it increases the value of the player in a 0.463%. The Champions League is a worldwide high valued competition, and this variable permits to distinguish between those players who play in European/worldwide recognized clubs and those who play in teams with less capabilities. When comparing the most renowned European leagues we can conclude that they significantly do affect the value of footballers. Contrasting La Liga, Liga Nos (Portuguese league), Seria A (Italian League), Ligue 1 (French League), and Premier League (English League) variables with other worldwide leagues where our observations come from, we can observe that all these variables have an important significance (excepting the Portuguese League, Liga NOS). Detailing the analysis, playing in La Liga increases the market value a 0.628%, the Premier League a 0.782%, while playing Serie A increases it a 0.861% and Ligue 1 a 0.677%. A fact that should be reconsidered is the matter that playing in the Spanish League is less valued than playing the other three leagues, when a lot of the most valued players are playing in there. However, La Liga is the actual studied league in this research and which has more observations in our sample (92 observations). Considering this, it is reasonable to think that it has a smaller coefficient than other leagues, as Spanish teams with many resources invest their money in best worldwide players (wherever they play). In contrast, Spanish teams with fewer capabilities often buy players from other Spanish teams, because they can get them cheaper. Following this consideration, it could be fair to think that in this model, footballers coming from abroad are more valued than those transferred between Spanish teams, as they might be better players, recognized across borders. Finally, regarding international caps, the fact of being whether an international player or not, affects, as expected, the market value of the player. It does it increasing in 0.424% the market value of the footballer. However, the variable of International Minutes played, does not have any significance in this model. Thus, big differences are created between those footballers called by their national teams and those who are not. On the other hand, playing for a country or another (compared to playing for Portugal) doesn t have any significance in this model. 14

16 Analyzing the model in absolute values 10, we can study how increases the market value in euros when changing each variable. In this model we do not discuss percentages. As shown in the coefficient of determination, this model explains a 63.7% of footballers market value (a very similar result to the previous one). As in the other model, 153 observations are used. When studying this model we can conclude that: Regarding player characteristics, as we knew, the age affects in a high degree the market value of footballers. Increasing one year the age of a player changes his market value in 7,434 million euros (although it has a corrective effect with the age 2 variable and its coefficient of million). Therefore, we can calculate when the age effect becomes a negative effect for players. As the peak value of a polynomial taking age and age 2 coefficients is in years, we can state that a player that remains exactly the same (the other variables don t change) one year, decreases its value from that point on. Being a defender, midfielder or striker is still not significant and, therefore, we can not draw any conclusion from these variables. If we consider the variables estimating the short term performance of players, we realize that games played during last season affect the market value in 170 thousand euros per match. When studying how the experience increases the market value in absolute terms, the variable Total Minutes Played (with a coefficient of 286 euros per minute played) does not appear to be significant. On the other hand, Total Goals during his career does appear as a significant variable in this model. Each goal increases the market value in euros. Even so, goals broken down by position (striker, midfielder or defender) still does not have any significance. Therefore, in this model we can not draw any conclusion from these variables. Finally, analyzing the collective team data, we find an important distinction between those players that played the Champions League with their respective clubs and those who did not. The fact of playing the Champions League last season increases the market value of footballers in 4,89 million euros. 10 Annex 4: Market Value Model 15

17 Unlike what happened with total data, whether being international or not does not appear to be significant in this model. Contrarily, each international minute played, increases significantly the market value of a player in euros. Finally, the fact of playing for one national team or another does not have any significance in this model, as it happens to be when considering playing in one of the most prestigious leagues in Europe or not Transfer Price Model As explained before, the Transfer Price model is the same as the previous ones with the inclusion of Buyer team dummies to measure the bargaining effect in transferring players. In the Logarithm Transfer Price model 11 an R 2 of is obtained. Hence, 66.5% of the amount paid by teams can be explained. The results obtained from variables explaining short-term performance of players are fairly similar to previous models, attaining significant variables in age (although it is narrowly outside the limits we consider it to be significant), and age 2. Moreover, Season Appearances, whether the player has played Champions League or not, the variable describing internationality, as well as the fact of being Brazilian, and La Liga and Premier League variables are also significant in this model. Analyzing the subject matter in this topic, the Buyer team, we realize that bigger teams in La Liga (and those which we have more observations from), such as FC Barcelona, Real Madrid, Atlético de Madrid, Valencia CF, Villareal CF and Sevilla CF usually pay bigger amounts than modest teams compared with buyers from other leagues. Villareal CF and Sevilla FC variables, as well as FC Barcelona, appear not to be significant in this model (that takes buyers from other leagues as a reference category). Despite not being significant, it is interesting to pay attention to the negative coefficient of Sevilla FC variable, as this team is considered to be one of the best buyers in La Liga (they usually buy really cheap). On the other hand, FC Barcelona (as well as Madrid) is said to be the team that pays higher quantities for acquiring players. The rest of teams studied have significant values in this model. Real Madrid pays for La Liga players 0.651% more than buyers from other leagues. Moreover, Atlético de Madrid pays 0.852% more, while Valencia 1.058%. On the other hand, the other Spanish teams (many of 11 Annex 5: Ln Transfer Price Model 16

18 them more modest than previous ones) pay less for acquiring players than bigger teams. In fact, they pay 0.423% less than those buyers from other leagues. When evaluating results in absolute values 12, a model that explains 57.2% of the transfer price is obtained. Focusing only on the variables of buyer teams, only Real Madrid and Atlético de Madrid appear to be significant in this model, and are the only two teams which we can draw conclusions from. Real Madrid pays 13,731 million euros more than foreign buyers, while Atlético de Madrid does it in 6,403 million Media Value Model As explained before, the original idea for this project was to create a single model that included previous studied models with media value. As it could not be done, the following models were created to explain how media value influences market value of players. Variables to proxy popularity and notoriety, as well as a few to proxy player s performance, were taken into account to contrast on-pitch and off-pitch influence of players in their market value. The fact that some players only had one of both social media challenged the analysis of the media value model. Moreover, the high correlation between the two social media variables and another proxy of notoriety ( Google Search Interest ) prevented to create a single model that explained the significance of media value in valuating footballers. On account of the high explanatory power of Google Monthly Search Volume, and the soaring correlation 13 between Google Search Interest and the two variables estimating Popularity, the second variable mentioned to proxy notoriety ( Google Interest Search ) has been removed for this analysis. Therefore, as mentioned, two models were created. The first one for those players who had Facebook and the second one for those who had Twitter (although some of them were included in both models). Even so, first, to prove the high explanatory capacity of Google Monthly Search Volume (GMSV from now on), a basic model has been built 14. Taking solely into account the adjusted GMSV (units divided by ), and LnFacebook variables for only those observations that had a Facebook account, a coefficient of determination of was obtained. Moreover, the high significance of both variables showed us the huge capacity of both of them to explain the market 12 Annex 6: Transfer Price Model 13 Annex7: Correlation Media Value proxies 14 Annex 8: GMSV Explanatory Power Model 17

19 value of a player. On the other hand, some endogeneity could be found, as the media impact of a player is in large extent a reflection of its performance. In other words, when a player performs well there is a media echo and both market value and media value increase. Starting with the analysis of the first model 15 created to explain the market value through media impact, the following estimators were used: the age, age 2, goals, LnFacebook and GMSVadj. It must be mentioned that for this model a restriction to only those observation that had Facebook was introduced. Therefore, only 40 observations were considered for this study. Similar results as the basic model were obtained, although (positively) the coefficient of determination increased (to 0.792). With this model, market value of players could further be explained. The results obtained were the following: As seen until now in this research, age has a high positive impact in market value, although it has a corrective effect coming from age 2. Goals have an important role in this model, as we are talking about the 50 most valued strikers worldwide. Precisely, in this model each goal increases the market value of a player in %. On the other hand, analyzing the results obtained by media value proxies, LnFacebook reveals how much increases (in percentage) the market value of a player for an increase of 1% in Facebook followers while the other variables remain the same. In this model, an increase of 1% of a player s Facebook followers affects his market value in a 0.104%. Finally, regarding GMSVadj, 0.008% of the market value is increased for every searches of a player in Google. As we also wanted to know how much increases each of these variables the market value in absolute terms, the same model changing the dependent variable to one without logarithms was created 16. The result was better. The R 2 increased to 0.857, therefore an 85.7% of the market value could be explained with this model. Analyzing the coefficients obtained, each year increase in age of a player rises the market value in 19,811 million euros, although it has the corrective effect of age 2. In this model the age effect turns negative after being years old. Each goal boosts the market value in 1,084 million euros. On the other hand, an increase of 1% in Facebook followers, rises the market value in 3,567 million euros. Finally, for each searches of a player in Google, the market value of a player increases 0,844 million euros. 15 Annex 9: Facebook Model (logarithm) 16 Annex 10: Facebook Model 18

20 In relation to Twitter models, both used as estimators the following variables: age, age 2, goals, LnTwitter and LnGMSV. The R 2 obtained for the first model 17 is 0.815, and 39 observations were considered. The results are the following: An increase of a year in a player s age, increases the market value 0.564% in this model, reducing its effect with the age 2 variable (-0.011%). Each goal rises the market value a 0.19% while each 1% growth in Twitter followers represent a boost of 0.59% in the footballer value. Finally, an increase in 1% of the total Google Monthly Search Volume affects the market value in a 0.211%. However, when considering the results in absolute values a quite different model is obtained 18. With a R 2 of 0.795, and 39 observations used, this model does not take the variables age and age 2 as significant. Therefore, all the explanatory power of those variables is transferred to the other. Each goal rises the market value in euros, while each increase of 1% in Twitter followers does it in 3,109 million euros. Finally, a growth in 1% in Google Monthly Search Volume represents a rise of 9,129 million euros. 5 CONCLUSION As a result of this investigation, we can conclude that something else than market value can affect transfer fees paid by football teams. Although there is a high correlation between these variables, transfer fees tend to be higher than market value of players. Therefore, we can consider that some other variables rather than market value affect transfer fees. The bargaining power of each team has an important influence on the negotiation, and bigger teams tend to spend more money in similar players than teams with less capabilities. On the other hand, media impact contains a great explanatory effect of market value, as players that perform better are those who news and social media tend to mention in a higher degree. In this research, different models to explain this results have been created and some conclusions could be taken. The age of the player, although its every time less growing effect, is one of the most influencing variables in explaining market value and transfer fees. Furthermore, minutes played and scored goals, as well as the importance of playing the Champions League or being an international player have an important role in valuating football players. On the other hand, playing for one national team or another does not have any significance. Finally, the fact of playing in one of the most 5 renowned leagues in Europe has a high influence. 17 Annex 11: Twitter Model (logarithm) 18 Annex 12: Twitter Model 19

21 Concluding the study of the media impact when valuing human assets in football, it must be underlined the high explanatory capacity of Google Monthly Search Volume from Google Adwords. As it was expected, best performing players are those in which people are more interested in. Therefore, more searches are made in Google of those players. As suggestions for further researches, a single model that encompasses on-pitch performance and media impact could be done, gathering media proxies data from the beginning of the season. Furthermore, a model without selected samples (such as Carmichael et al. s model) or including projected future player performance could better approach the actual inputs of today s transfer decisions. 6 BIBLIOGRAPHY Brand Finance plc (2014), The Annual Report on the World s Most Valuable Football in BrandFinance Football 50 Football_Finance (2015). How Players Are Valued. November 20 th, Kiefer, Stephanie (2014). The Impact of the Euro 2012 on Popularity and Market Value of Football Players. International Journal of Sport Finance, 9, Poli, Raffaele; Ravanel, Loïe and Besson, Roger (2015). Performance analysis: Best clubs and players of the big-5 league season. CIES Football Observatory Monthly Report, Issue no.5. Poli, Raffaele; Ravanel, Loïe and Besson, Roger (2015). Transfer values and probabilities. CIES Football Observatory Monthly Report, Issue no.6. Pujol, Francesc and Garcia-del-Barrio, Pedro (2006). ESIrg Report on Media Value of Professional Football. Navarra. ESIrg University of Navarra. Pujol, Francesc and Garcia-del-Barrio, Pedro (2008).Economic Valuation of Football Players through Media Value. Gijón. IASE Conference. Solntsev, Ilia (2014). Application of Income Approach for Valuation of Football Club. Moscow, Russia. Plekhanov Russian University of Economics. Szymanski, Stefan (2013). L impacte de la Crisi al Futbol. Estratègies Adaptatives. 1ª Conferència Acadèmica Ernest Lluch d Economia i Futbol. Auditori 1899, FC Barcelona. Van den Berg, Erik (2011), The Valuation of Human Capital in the Football Player Transfer Market. Rotterdam. Erasmus School of Economics. 20