Assessing Professional Football Player Salaries Through the Use of Analytics by Global Urban Lab

Assessing Professional Football Player Salaries Through the Use of Analytics Enrique Walsh 4/24/15

Created for Global Urban Lab Rice University: School of Social Sciences

Executive Summary From basketball to baseball and even horse racing, the use of data and analytics software has reshaped the modern sports world. Where teams had previously relied heavily on scouts and other traditional ways of evaluating players, general managers, team executives and head coaches are now making decisions based on statistical evaluations, revolutionary algorithms and complicated rankings. This paper focuses on football 1, and explores the use of analytics in the sport at a professional level. It presents arguments that support and oppose professional football players earning massive salaries. To have a quantifiable way to solve the debate of overpaying football players, this paper statistically assesses whether Englandâ&#x20AC;&#x2122;s Chelsea Football Club and Turkeyâ&#x20AC;&#x2122;s FenerbahĂ§e Sport Club are overpaying or underpaying their players, according to on-field data. Furthermore, it will evaluate the correlation between having high quality players and team performance.

Note to reader: In this paper, football refers to the sport known in the United States of America as soccer. The word football will be used in the entirety of this paper, as it is the way the sport is known everywhere else around the world.

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Table of Contents

Cover Page ...................................................................................................................................... 0 Executive Summary......................................................................................................................... 1 Table of Contents ............................................................................................................................ 2 Introduction .................................................................................................................................... 3 Issue Statement .............................................................................................................................. 4 Research .......................................................................................................................................... 5 Findings ........................................................................................................................................... 9 Conclusions ................................................................................................................................... 11 Appendix ....................................................................................................................................... 12 Works Cited ................................................................................................................................... 19 Acknowledgements....................................................................................................................... 21

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Introduction Sports Analytics is the use of techniques from academic areas such as statistics, mathematics, game theory, biomechanics and kinesiology to model, rank and predict professional sports performances. Sports analytics was first successfully applied in baseball, when the methods of pioneer Bill James revolutionized Major League Baseball in the United States. The book written by Michael Lewis (and the movie based on that book), Moneyball, depicts James’ strategies with Sabermetrics, the use of sport analytics specifically in baseball, and how a ball club with a restricted budget and limited resources used analytics to build a championship caliber team. Sports Analytics has also made a smooth transition into professional basketball. According to NBA Basketball Research, 26 out of the 30 NBA teams either employ basketball analytics professionals or work with statistical consultants to enhance their decision-making and playing style (“NBA Teams that have…”). Ironically, sports analytics has failed to grasp as much traction in football, the most popular sport on the planet. Football has underutilized the application of analytics due to three main reasons. The first is the lack of publicly available data. The second is the complexity of the sport; the game cannot be systematically broken down as easily as baseball for example, as the results in football are “products of complex and intriguing interactions between performances of individual players and also uncertainties pertinent to specific citations” (Kumar, 1). Football is a very fluid sport, and the scarcity of discrete events makes it very difficult to quantify player performance. The third reason is the culture and effect of a “star player.” Whereby Bill James would not be afraid to hire an athlete no one had heard of but had good on-field production, football clubs tend to go for the players that are red-hot at the moment, often times ignoring past underachievement, injury history and other red flags. However, analytics in football are becoming increasingly more popular. According to an article by Sports Illustrated, United States Men’s National Team head coach Jürgen Klinsmann advocates the use of analytics. “Let’s say, how quickly you release the ball, how quickly you get into transition, how quickly you transition into your defensive shape. How you connect your back line right away with your midfielders. There’s a lot of good data to use,” said Klinsmann (Qtd. in Whal). As a result, the United States Soccer Federation is establishing a data analysis center based at the StubHub Center in Carson, California. Evidently, statistics can be used to model player performance. This paper adapts a method of measuring statistical performance developed by Michael Fotopoulos and Andrew Opatkiewicz, and will creatively rank players for two of the biggest, wealthiest and most popular teams in England and Turkey: Chelsea FC and Fenerbahçe SC, respectively. The aim of this is to apply sports analytics to football, and try to answer a question that is of much debate: do football players really deserve to get paid as much as they do?

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Issue Statement There is much controversy as to whether professional football players truly deserve to be paid astronomical salaries. The proponents of players being overpaid argue that they get paid too much for what they do. A football player’s week consists of daily 2-hour training sessions, a weekly press conference and one or two 90-minute matches. For this, the average Premier League player earns £ 2.3 million - around $ 3.4 million. Conversely, a paramedic’s week is full of emotional stress, traumatic situations, brutally long shifts and life saving opportunities. In 2013, the average annual salary for EMTs and paramedics in the United States was $31,270 (“Parademic: Salary). To put these figures in perspective, it would take the average Premier League player 47 minutes to earn the average paramedic’s weekly salary (“How long would it take…”). In other words, it would take the average paramedic 216 years of work to earn the average Premier League player’s annual salary. If the average paramedic had started working in 1799, he’d almost be even with what the average Premier League player makes in one year. The work – to – earnings ratio in professional football is extremely high, making a lot of people question whether players deserve what they earn. On the other hand, those that say that football players are not overpaid argue that football is just a business, plain and simple. They say that to keep the business running, teams must invest in capital, which in this business happens to be players. According to Chelsea’s website, the club officially reported a total revenue of over $ 486 million coming from TV rights, ticket day sales, merchandise and others sources. At the epicenter of this revenue are the footballer players themselves, as they are the responsible for the club making the majority of this money. Advocates of players not being overpaid say that since the players are responsible for all the money being generated, shouldn’t they not benefit significantly from it? Furthermore, they argue that being a professional player requires an enormous amount of talent, hard work and an even bigger amount of sacrifices. Take Lionel Messi for example. When he was only 13 years old, he left his hometown of Rosario, Argentina and moved to Barcelona’s youth academy, often times crying at night due to homesickness. In an effort to see what local Londoners had to say on the topic, a die-hard Chelsea fan was interviewed. Mr. Jacob Baldwin was asked “Do you think professional football players are being overpaid?” and he said: “Yeah, I think so. If you think about it, Wayne Rooney earns 300,000 pounds a week. I might never earn that in my lifetime. But football is now a business, and like in any business, you have to pay a premium to get the best quality. The bigger the business, the bigger salaries you can afford. If Wayne Rooney played for Crawley (a third division team) he’d earn 2K a week, because the business isn’t that big. But since Manchester United is a massive business, they can afford to keep him. It’s just a business.” (Baldwin, Jacob). He was then asked, “Staying in line with football being a business, based on the amount of money Manchester United makes out of their players, do you still think they are being overpaid?” He responded with, 4|Page

“Well in that case, no, they are not. They are not overpaying them because they are making more money off them than they are losing. If they were overpaying them, they would run out of business. But for what they do, I think they get too much money. If computing was as big as football, and they earned billions of pounds a year for what they do, computer technicians would be on a 300K a week salary, but it’s the fact that football is such an enormous industry that players get these ridiculous wages.” (Bladwin, Jacob). Evidently, it is very hard to say whether football players deserve to be paid a lot of money, exemplified by Mr. Baldwin’s mixed thoughts. This paper attempts to solve this argument in a statistically manner, by statistically analyzing whether professional football players in Chelsea FC and Fenerbahçe SC are being underpaid or overpaid. This analysis is based on data tracked during each of the player’s performances during the current 2014/15 season. It uses statistical data to determine what wage each player truly deserves. The study focuses on Chelsea and Fenerbahçe because they are among the teams in their national leagues that spend the most money on players, thus offering good examples of whether teams are overpaying or underpaying players.

Research Companies such as Prozone Sports and WhoScored.com have transformed football statistics. Gone are the days when only rudimentary statistics like goals, saves and shots were recorded. These companies employ people to watch matches and document every single occurrence. Information such as headers won, types of through balls and amount of shots taken from inside the box are now available. Figure 1.1 in the appendix is an example of what an analyst’s computer screen looks like after an entire game of logging in statistics. For the purpose of the study, 14 statistics were extrapolated for each player 2 of England’s Premier League and Turkey’s Süper Lig. Table 1.2 in the appendix is an excerpt of the entire data set, showing Chelsea FC players’ statistics for each of the 14 categories recorded for each player. Next, a ranking system was created to show which players perform best. This ranking was based on a method developed by Michael Fotopoulos and Andrew Opatkiewicz used to 2

Statistics for a total of 865 players between the two leagues were recorded, for a grand total of 12,110 cells of data. Data for PL players was recorded through the first 30 games played of the current season, while data for Super Lig players was recorded through the first 25 games. The Turkish league starts their season later; hence they have played fewer games. Players on loan were omitted from the data due to their absence from games. Goalkeepers were also omitted since they do not share the same statistics as non-goalkeepers. 5|Page

assist salary allocation strategies in Major League Soccer. In contrast to European Leagues, the United State’s Major League Soccer has a salary structure unique to the football world. With the league constructed as a single entity, clubs receive a share of a common pool of salary cap dollars to spend on their rosters each season (Fotopoulos & Opatkiewicz, 1). While European teams have enormous spending disparities between top, middle and lower tier teams, MLS salary disparities are kept to a minimal. For the 2013/14 season, every MLS team received a salary budget of 3.1 million dollars to be used to build their rosters. The maximum budget charge for a single player is $387,500. However, the Designated Player Rule allows clubs to acquire up to three players whose salaries exceed their budget charges, with the club bearing financial responsibility for the amount of compensation above each player’s budget charge (“Roster Rules and Regulations”). As a result, MLS teams don’t have 100% identical payrolls, but the inequality is astronomically smaller than in Europe. To put it in perspective, the Premier League’s biggest spender for the 2013/14 season, Manchester City, spent approximately $ 350 million on their roster (Arshad, “Man City…”). Conversely, Cardiff City spent the least amount of money, totaling a payroll of 45 million dollars (Arshad, “Cardiff City…”). That is a difference of 305 million dollars. In contrast, MLS’s biggest spender, Toronto FC, spent $15 million while New England Revolution spent only $2.9 million, a difference of around $12.1 million (Rodriguez). While a salary cap promotes league parity, it introduces a new element to teambuilding, and gives clubs with more effective resource allocation benefits over others. (Fotopoulos & Opatkiewicz, 1). Teams that maximize the utility of each salary cap dollar spent will perform better than those who don’t. For this reason, Fotopoulos and Opatkiewicz designed a way to identify player production in three phases of the game (offense, defense, and possession) and to provide a comprehensive view of the individual events that contribute to a player’s on the field performance. By statistically ranking players, MLS teams can make better salary allocation decisions and spend their limited resources more effectively. The ranking system they developed works as follows. A player’s ability to convert offensive events such as goals, assists and shots on target was condensed into a single measure, Offensive Production (OFF). The weights for each variable were obtained by observing the frequencies of offensive events and the corresponding ratios between events. For example, shots on target were 5 times as frequent as goals, so the individual weightings of these events reflect the 1:5 ratio. Fotopoulos and Opatkiewicz used the following equation to calculate a player’s Offensive Production raw score: OFF = 5.000*Goals + 1.003*Shots On Target + 5.784*Assists – 2.482*Offsides Using the same linear weighting approach, this paper came up with its own Offensive Production equations, reflecting on the ratios and total observed frequencies for both the Premier League and the Süper Lig. PL OFF = 3.220*Goals + 0.990*Shots On Target + 4.465*Assists – 2.047*Offsides SL OFF = 3.677*Goals + 1.000*Shots On Target + 5.637*Assists – 2.487*Offsides 6|Page

For example, Chelsea’s Eden Hazard scored 6 goals, had 24 shots on target, gave 6 assists and was caught offside 3 times, so his Offensive Production is equivalent to: PL OFF = 3.220*6 + 0.990*24 + 4.465*6 – 2.047*3 = 76.609 These raw scores were then ranked as a percentile among all of the players in their respective leagues, with the final Offensive Production score for a player recorded as his percentile rank. However, since a player’s value is not limited to his offensive skills, two more measurements were created. Using Prozone and WhoScored.com to isolate player production in the defensive and possession phases of the game, the Defensive Production (DEF) and Possession Production (POSS) measurements were created in a similar way the Offensive Production statistic was developed. These values add to the overall concept that every player contributes to each phase of play over the course of a match. However, for more internal consistency, some adjustments were made to the model to account for infrequent events skewing a player’s raw score. For example, the frequency of possessions gained to total possessions was observed roughly 2.8 to 1 over a much larger sample size than successful headers. A similar ratio to the 2.8 to 1 ratio was also used for the less frequent events such as successful headers as both events led to a similar result, the active taking of possession from an opponent. (Fotopoulos & Opatkiewicz, 2). This paper used the following equations to calculate each player’s DEF and POSS raw scores: PL DEF = Possessions + 2.015*Possessions Gained – 2.015*Fouls + 2.512*Crosses Defended SL DEF = Possessions + 1.998 * Possessions Gained – 2.023 * Fouls + 2.574 * Crosses Defended PL POSS = 1.002*Successful Short Passes + 1.007*Clearances + 2.018*Successful Long Passes – 1.994*Possessions Lost + 2.593*Shots Blocked + 2.802*Successful Header SL POSS = 0.999*Successful Short Passes + 1.041*Clearances + 2.053*Successful Long Passes – 2.124*Possessions Lost + 2.456*Shots Blocked + 2.705*Successful Header Once again, these raw scores are then ranked as a percentile among all of the players in their respective league. With each phase of the game scored as a percentile rank, a summary metric that considers the total output of a player was derived. Former Italian football star and manager Gianluca Vialli provides a simple way to evaluate players geometrically (Qtd. in Fotopoulos & Opatkiewicz, 2), taking each phase of the game and plotting it on an x,y plane. In this case, the 3 rankings create a triangle: AREA = 0.5 * (OFF) * (DEF + POSS)

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For example, if a player scores 61.0 OFF, 63.0 DEF, 82.0 POSS, his AREA score would be: AREA = 0.5 * 61.0 * (63.0 + 82.0) = 4422.5 Once every single AREA score was calculated, they were also ranked as a percentile. Table 2.1 and Table 2.2 shows players with the top 10 highest AREA’s in each league, along with their OFF, DEF and POSS percentile rank scores. Building out from Fotopoulos and Opatkiewicz’s work, this paper used their ranking system to illustrate trends in professional football player salaries. With each player ranked in terms of an AREA percentile rank score, it is possible to determine whether they are being overpaid or underpaid. To do this, each Premier League player’s salary was extrapolated from CelebritiesMoney.com. Table 3.1 shows the ten highest paid players in the Premier League. In theory, the best player should be getting paid the most; the second best player should be getting the second highest salary and so forth. According to Christina Settimi from Forbes magazine, Manchester United’s Radamel Falcao is the highest paid player in the Premier League, making $ 469,775 a week, or $ 24,428,300 a year. After the best players were ranked in terms of their AREA score, they were assigned a Theoretical Weekly Salary, equivalent to the salary of the player corresponding to their AREA rank. For example, Chelsea’s Cesc Fàbregas ranked #1 in AREA score, so he was assigned the #1 salary, which belongs to Radamel Falcao. Arsenal’s Santi Cazorla came in with the second highest AREA score, so he was assigned the second highest salary, which belongs to Manchester City’s Sergio Agüero. Chelsea’s Eden Hazard ranked #6 in AREA score, so he was assigned a Theoretical Weekly Salary equivalent to the 6th highest paid player, which is Manchester City’s Yaya Touré at $300,000 a week. This was done for all Premier League players. Table 3.2 illustrates Chelsea FC player’s weekly and yearly salaries, as well as their Theoretical Weekly Salary and also where they rank in terms of AREA score. The same process of cross-referencing AREA scores to a Theoretical Weekly Salaries was attempted to be done with the Süper Lig. However, due to the lack of publicly available data, it was not possible to find every single Süper Lig salary. For this reason, instead of comparing salaries and AREA scores across the entire league, scores and salaries were compared internally within the club. For example, Dirk Kuyt has the second highest AREA score in the Süper Lig, which ranks first among Fenerbahçe players. As a result, he should be getting the highest salary 8|Page

within the club, which belongs to Moussa Sow. Table 3.3 illustrates Fenerbahçe player’s weekly and yearly salaries, as well as their Theoretical Weekly Salary and also where they rank in terms of AREA score within their club and the Süper Lig. Since it was not possible to cross-reference Fenerbahçe AREA scores with the entire league’s salaries, Chelsea’s internal comparison is illustrated in Table 3.4. This was done in order to conduct an “apples to apples” comparison, as it was not fair to compare Chelsea’s players to 438 other players while only comparing Fenerbahçe players to their own team.

Findings Having conducted the previous research, it is now possible to answer the issue statement: are players being underpaid or overpaid for their efforts? To do this, the following scale between the actual weekly salary and Theoretical Weekly Salary discrepancy was developed: Discrepancy + or - $ 0 - $50,000 + or - $ 50,000 - $ 100,000 + or - $ 100,000 - $150,000 + or - $ 150,000 +

Category Slightly Under / Overpaid Under / Overpaid Heavily Under / Overpaid Severely Under / Overpaid

Table 4.1 and Table 4.2 summarizes under which category each player fell into. The following tallies represent each team’s verdict: Chelsea FC Severely Underpaid Heavily Underpaid Underpaid Slightly Underpaid Just Right Slightly Overpaid Overpaid Heavily Overpaid Severely Overpaid

Fenerbahçe SC 1 2 3 5 5 1 1

Severely Underpaid Heavily Underpaid Underpaid Slightly Underpaid Just Right Slightly Overpaid Overpaid Heavily Overpaid Severely Overpaid

3 4 5 4 2 -

When comparing the two teams at a glance, Chelsea seems to overpay more players than Fenerbahçe. Chelsea has a total of 7 players categorized as being slightly overpaid or worse, while Fenerbahçe only has 6. Although these figures are pretty similar, it is necessary to compare how much each team is overpaying or underpaying their players. When summing everything up, Chelsea is underpaying its players by a total of $408,000 every week, while overpaying its players by exactly the same amount. This means that they have a net “overpaying” of $0, indicating that although they are overpaying 7 of their players, they are 9|Page

underpaying 6 to balance it out. Fenerbahçe is underpaying its players by a total of $204,488 every week, while overpaying its players by exactly the same amount as well. Although both teams have a net “overpaying” of $0, amount by which Fenerbahçe overpays and underpays its players is half of Chelsea’s, indicating that they do a better job at valuating players and salary allocation. Another interesting observation is that Fenerbahçe stays away from the extremes, as none of its players are being severely nor heavily underpaid / overpaid. Conversely, four Chelsea players are being severely or heavily underpaid / overpaid. What do these numbers indicate? They indicate that Fenerbahçe’s decision makers are evaluating and pricing talent fairly accurately, much more effectively than Chelsea. Now that comparisons have been drawn between Chelsea’s and Fenerbahçe’s spending tendencies, it is noteworthy to highlight spending discrepancies between the England and Turkey. Provided by Nick Harris from the Daily Mail, Table 5.1 shows the average salaries in the world’s major football leagues (Harris). As previously stated, Premier League footballers earn an average of £ 2.3 million a year, giving them wages 56% larger than their next competitor, Germany's Bundesliga. When compared to the Turkish Süper Lig, the Premier League average wages are 410% higher. What’s even more surprising is that England's second division, the Championship League, is ranked 8th globally, with its players earning more than most first divisions around the world. The astronomical salaries in England are attributed to its massive television deals. Nick Harris from the Daily Mail reports that the Premier League signed a 5 year domestic TV deal with Sky and BT worth £ 1 billion (roughly $ 1.5 billion) each year, overseas TV deals in 212 countries bringing in £ 733 million (roughly $ 1.1 billion) a year and assorted other highlights, near-live and clip deals bringing in hundreds of millions more. To put the incredible money-making in perspective, the Premier League now earns a similar amount from selling goal clip rights to the UK mobile market alone as Scottish domestic football does combined from all its live television rights - about £ 15 million ($ 23 million) a year (Harris). Clearly, the football business in England is unparalleled anywhere else. Some people would argue that it doesn’t matter if all of the players are being severely overpaid; if the team is winning games, who cares? For this reason, this paper went an even further step and tried to conclude to what extent having higher quality players translates to winning more points3. In other words, to what extent is it justifiable, based on team wins, to overpay players? In theory, the higher the quality of a team’s players, measures in AREA score, the more points a team will win. To do this, the Average AREA of each team was calculated, as well as the number of points each team has won. This can be seen in Table 6.1 and Table 6.2 in the appendix. Total points as a function of average Area was plotted and a linear regression line was ran through these data points – shown in Figure 6.3 and Figure 6.4 – to asses the correlation and develop R and R2 statistics. The Premier League had an R statistic of 0.8324 and the Süper Lig had an R of 0.8105. This means that there is a strong, positive correlation between the Average AREA and points won. R2 statistics of 0.69289 for the Premier League and 0.64946 for the Süper Lig mean that 69.289% and 64.946% of the variation in a team’s In football, a win earns a team 3 points, a draw earns a team 1 point and a loss earns a team 0 points. 3

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performance, measured in points won, can be explained by the scores of the teams’ players, measured in Average AREA. As a result, in general, the higher quality players a team has, the more points the team should win. This does not mean, however, that teams should go ahead and severely overpay every player since there seems to be a positive a correlation between having better players and winning more. Linear regression analysis simply states that likelihood of two events happening; it does not suggest that one event causes the other to occur.

Conclusion In conclusion, this study tackled the much-heated question as to whether football players are being overpaid. It presented arguments for both sides of the debate, and subsequently answered this question through the use of a statistical analysis. Based on 14 different statistics and a creative way to rank each player, it was found that relative to the performance of their teammates, Chelsea and Fenerbahçe both underpay and overpay players. The paper also shows spending differences among the different leagues in the world, showing the power England’s Premier League has over the rest of the world. Furthermore, it showed the there is a strong, positive correlation between having better quality players and winning more points. This study has numerous applications. Agents can reach out to a statistician and pay them to come up with a similar analysis. Afterwards, the agent can leverage contracts and visit team executives and say, “According to his statistics, this is how my player is performing. This is what he is getting paid in relation to his fellow teammates and competitors. There seems to be a discrepancy here that needs to be addressed. If it isn’t, he might just walk out on the club.” Equally, this argument can be reversed, as team executives can show this analysis to the players and restructure their contracts. Another application this study has is that a team executive can see a player’s AREA score before buying him, taking any hype or emotions attached to the player out of the equation. Evidently, football is a business, and like Mr. Baldwin said, “if they were overpaying them, they would run out of business.” In the case of the two specific clubs that were tested in this study, the numbers say that Chelsea tends to overpay its players, while Fenerbahçe has a more balanced payroll. Additionally, the wage of an average American was compared to that of the average Premier League player, and the results were breath taking. Finally, the paper explored the correlation between player quality and points won, showing there is a strong, positive correlation. Do football players truly deserve their salaries? It’s very hard to say. What is known is that the industry is only going to get bigger, as it does so will the wages.

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Appendix Figure 1.1: Computer Screen after an entire game of activity recording.

Table 1.2: All of the statistics for Chelsea’s players. Goals

Shots On Target

Assists

Offsides

Possessions

Possessions Gained

Fouls

Crosses Blocked

Successful Short Passes

Clearances

Successful Long Passes

Possessions Lost

Shots Blocked

Headers Won

1436

1295

1965

1659

144

564

463

1804

152

1376

101

1242

873

103

Oscar

1128

935

César Azpilicueta

1032

104

753

John Terry

1408

1044

149

Willian

871

731

Gary Cahill

1216

827

151

Filipe Luis

460

367

Kurt Zouma

243

158

Ramires

429

331

John Obi Mikel Juan Cuadrado

391

318

Loïc Remy

122

Didier Drogba Ruben Loftus

125

Player Eden Hazard Cesc Fàbregas Diego Costa Nemanja Matic Branislav Ivanovic

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Table 2.1: Top 10 highest AREA’s in the Premier League, along with OFF, DEF and POSS scores.

1 2 3 4 5 6 7 8 9 10

Player Cesc Fàbregas Santi Cazorla Yaya Touré David Silva Jordan Henderson Eden Hazard Christian Eriksen Wayne Rooney Ki Sung-Yueng Branislav Ivanovic

England - Premier League Team AREA Chelsea 99.70 Arsenal 99.50 Manchester City 99.30 Manchester City 99.00 Liverpool 98.80 Chelsea 98.60 Tottenham 98.40 Manchester United 98.10 Swansea 97.90 Chelsea 97.70

OFF 99.3 99.0 95.4 98.4 96.5 98.8 97.7 95.6 88.1 92.2

DEF 99.5 96.8 98.6 95.6 97.2 94.2 92.9 91.5 97.9 92.6

POSS 99.3 96.1 99.0 95.2 96.8 94.0 92.4 94.9 97.4 92.9

Table 2.2: Top 10 highest AREA’s in the Süper Lig, along with OFF, DEF and POSS scores. Turkey - Süper Lig 1 2 3 4 5 6 7 8 9 10

Player Bilal Kisa Dirk Kuyt Selcuk Inan Cicinho Caner Erkin Aurélien Chedjou Fernando Belluschi Emre Carl Medjani Renato Civelli

Team Akhisar Belediyespor Fenerbahçe Galatasaray Sivasspor Fenerbahçe Galatasaray Bursaspor Fenerbahçe Trabonzpor Bursaspor

AREA 99.70 99.50 99.30 99.00 98.80 98.60 98.30 98.10 97.90 97.60

OFF 99.0 98.6 94.4 94.6 95.8 89.7 92.3 91.6 94.1 88.1

DEF 95.3 94.4 99.3 98.3 96.7 98.6 96.0 93.4 87.6 94.6

POSS 95.5 96.5 99.3 98.1 93.4 98.6 92.5 96.0 92.3 97.2

Table 3.1: Top 10 salaries in the Premier League 1 2 3 4 5 6 7 8 9 10

Team Manchester United Manchester City Chelsea Manchester United Arsenal Manchester City Chelsea Liverpool Chelsea Liverpool

Player Radamel Falcao Sergio Agüero Eden Hazard Robin van Persie Theo Walcott Yaya Touré Diego Costa Jordan Henderson John Terry Raheem Sterling

Weekly Salary $469,333 $330,000 $320,000 $317,308 $309,616 $300,000 $296,000 $291,923 $264,000 $253,846

Yearly Salary $24,405,333 $17,160,000 $16,640,000 $16,500,000 $16,100,000 $15,600,000 $15,392,000 $15,180,000 $13,728,000 $13,200,000

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Table 3.2: Chelsea’s Area Score Rank, Weekly Salary, Yearly Salary, Theoretical Weekly Salary Chelsea FC Area Score Rank 1 6 10 13 21 47 58 61 68 104 172 234 254 273 278 339 354 433

Player

Actual Weekly Salary

Theoretical Weekly Salary

Difference

$249,600 $320,000 $104,000 $144,000 $264,000 $112,000 $136,000 $112,000 $136,000 $296,000 $104,000 $120,000 $120,000 $160,000 $136,000 $136,000 $64,000 $56,000

$469,333 $300,000 $253,846 $221,154 $176,000 $126,933 $117,333 $112,500 $107,885 $76,154 $52,658 $37,500 $33,173 $29,333 $28,846 $19,556 $16,923 $4,693

-$219,733 $20,000 -$149,846 -$77,154 $88,000 -$14,933 $18,667 -$500 $28,116 $219,846 $51,342 $82,500 $86,827 $130,667 $107,154 $116,444 $47,077 $51,307

Cesc Fàbregas Eden Hazard Branislav Ivanovic Oscar John Terry César Azpilicueta Willian Nemanja Matic Gary Cahill Diego Costa Ramires John Obi Mikel Filipe Luis Didier Drogba Loïc Remy Juan Cuadrado Kurt Zouma Ruben Loftus-Cheek

Table 3.3: Fenerbahçe’s Süper Lig AREA Rank, Internal AREA rank, Weekly Salary, Yearly Salary, Theoretical Weekly Salary SL AREA Rank 2 5 8 25 36 41 43 50 98 113 134 160 165 190 194 197

Fenerbahçe AREA Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

222 331

17 18

Player Dirk Kuyt Caner Erkin Emre Mehmet Topal Gökhan Gönül Raul Meireles Diego Alper Potuk Moussa Sow Egemen Korkmaz Bekir Irtegün Michal Kadlec Selçuk Sahin Pierre Webó Burno Alves Emmanuel Emmenike Hasan Ali Kaldirim Mehmet Topuz

Weekly Salary $46,666.67 $94,222.23 $24,000.00 $81,777.00 $75,555.56 $66,666.67 $50,000.00 $40,000.00 $125,333.33 $56,000.00 $34,666.67 $27,555.56 $11,733.33 $41,333.33 $39,111.12 $94,000.00

Theoretical Weekly Salary $125,333.33 $94,222.23 $94,000.00 $81,777.00 $75,555.56 $66,666.67 $56,000.00 $50,000.00 $46,666.67 $41,333.33 $40,000.00 $39,111.12 $34,666.67 $27,555.56 $24,000.00 $23,111.12

Difference $-78,666.65 $$-70,000.00 $$$$-6,000.00 $-10,000.00 $78,666.65 $14,666.67 $-5,333.33 $-11,555.56 $-22,933.35 $13,777.77 $15,111.12 $70,888.88

$17,333.33 $23,111.12

$17,333.33 $11,733.33

$$11,377.79

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Table 3.4: Chelsea’s Premier League AREA Rank, Internal AREA rank, Weekly Salary, Yearly Salary, Theoretical Weekly Salary PL AREA Rank 1 6 10 13 21 47 58 61 68 104 172 234 254 273 278 339 354 433

Chelsea AREA Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Player Cesc Fàbregas Eden Hazard Branislav Ivanovic Oscar John Terry César Azpilicueta Willian Nemanja Matic Gary Cahill Diego Costa Ramires John Obi Mikel Filipe Luis Didier Drogba Loïc Remy Juan Cuadrado Kurt Zouma Ruben Loftus-Cheek

Weekly Salary $249,600 $320,000 $104,000 $144,000 $264,000 $112,000 $136,000 $112,000 $136,000 $296,000 $104,000 $120,000 $120,000 $160,000 $136,000 $136,000 $64,000 $56,000

Theoretical Weekly Salary $320,000 $296,000 $264,000 $249,600 $160,000 $144,000 $136,000 $136,000 $136,000 $136,000 $120,000 $120,000 $112,000 $112,000 $104,000 $104,000 $64,000 $56,000

Difference -$70,400 $24,000 -$160,000 -$105,600 $104,000 -$32,000 $-$24,000 $$160,000 -$16,000 $$8,000 $48,000 $32,000 $32,000 $$-

Table 4.1: Table showing which category of Under / Overpaid Chelsea’s players fell into. Chelsea FC Player Cesc Fàbregas Eden Hazard Branislav Ivanovic Oscar John Terry César Azpilicueta Willian Nemanja Matic Gary Cahill Diego Costa Ramires John Obi Mikel Filipe Luis Didier Drogba Loïc Remy Juan Cuadrado Kurt Zouma Ruben Loftus-Cheek

Weekly Salary

Theo. Weekly Salary

Difference

Verdict

$249,600 $320,000 $104,000 $144,000 $264,000 $112,000 $136,000 $112,000 $136,000 $296,000 $104,000 $120,000 $120,000 $160,000 $136,000 $136,000 $64,000 $56,000

$320,000 $296,000 $264,000 $249,600 $160,000 $144,000 $136,000 $136,000 $136,000 $136,000 $120,000 $120,000 $112,000 $112,000 $104,000 $104,000 $64,000 $56,000

-$70,400 $24,000 -$160,000 -$105,600 $104,000 -$32,000 $-$24,000 $$160,000 -$16,000 $$8,000 $48,000 $32,000 $32,000 $$-

Underpaid Slightly Overpaid Severely Underpaid Underpaid Overpaid Slightly Underpaid Just Right Slightly Underpaid Just Right Severely Overpaid Slightly Underpaid Just Right Slightly Overpaid Slightly Overpaid Slightly Overpaid Slightly Overpaid Just Right Just Right

*Juan Cuadrado moved to Chelsea at the middle of the season, so he has played only 13 games for the club.

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Table 4.2: Table showing which category of Under / Overpaid Fenerbahçe’s players fell into. Fenerbahçe SC Player Dirk Kuyt Caner Erkin Emre Mehmet Topal Gökhan Gönül Raul Meireles Diego Alper Potuk Moussa Sow Egemen Korkmaz Bekir Irtegün Michal Kadlec Selçuk Sahin Pierre Webó Burno Alves E. Emmenike Hasan Ali Kaldirim Mehmet Topuz

Weekly Salary $46,666.67 $94,222.23 $24,000.00 $81,777.00 $75,555.56 $66,666.67 $50,000.00 $40,000.00 $125,333.33 $56,000.00 $34,666.67 $27,555.56 $11,733.33 $41,333.33 $39,111.12 $94,000.00 $17,333.33 $23,111.12

Theoretical Salary $125,333.33 $94,222.23 $94,000.00 $81,777.00 $75,555.56 $66,666.67 $56,000.00 $50,000.00 $46,666.67 $41,333.33 $40,000.00 $39,111.12 $34,666.67 $27,555.56 $24,000.00 $23,111.12 $17,333.33 $11,733.33

Salary Difference -$78,666.65 $-$70,000.00 $$$-$6,000.00 -$10,000.00 $78,666.65 $14,666.67 -$5,333.33 -$11,555.56 -$22,933.35 $13,777.77 $15,111.12 $70,888.88 $$11,377.79

Verdict Underpaid Just Right Underpaid Just Right Just Right Just Right Slightly Underpaid Underpaid Overpaid Slightly Overpaid Slightly Underpaid Slightly Underpaid Slightly Underpaid Slightly Overpaid Slightly Overpaid Overpaid Just Right Slightly Overpaid

Table 5.1: Average salaries in the world’s major football leagues Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Country England Germany Italy Spain France Russia Brazil England Turkey Mexico Portugal Switzerland Netherlands Argentina China

League Premier League Bundesliga Serie A La Liga Ligue 1 Premier League Campeonato Serie A Championship League Süper Lig Liga MX Primeira Liga Super League Eredivisie Primera Division Super League

Avg. Wage / Year

Avg. Wage / Week

$3,523,579 $2,257,676 $2,036,206 $1,880,187 $1,531,296 $1,397,480 $904,166 $753,351 $691,387 $411,564 $396,025 $366,488 $356,500 $332,689 $325,350

$67,761 $43,417 $39,158 $36,157 $29,448 $26,875 $17,388 $14,488 $13,296 $7,915 $7,616 $7,048 $6,856 $6,398 $6,257

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Table 6.1 and Table 6.2: Average AREA score and Points won Premier League Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Team Manchester City Chelsea Arsenal Tottenham Manchester United Westham Southhampton Newcastle United Liverpool Stoke Burnley Crystal Palace West Bromwich Sunderland Hull Leicester Aston Villa Swansea Everton Queen Park Rangers

Süper Lig

Avg. AREA 4622.48 4514.87 3607.96 3489.55 3159.59 3133.94 3060.00 3018.45 2939.58 2591.83 2526.57 2416.22 2413.55 2407.57 2352.94 2275.48 2235.71 2056.57 2048.65 1745.68

Points 61 67 60 53 59 42 53 35 54 42 25 36 33 26 28 19 28 43 34 22

Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Team Fenerbahçe Galatasaray Besiktas Bursaspor Mersin Istanbul Basaksehir Trabonzpor Kasimpasa Eskisehirspor Sivasspor Genclerbirligi Erciyesspor Konyaspor Rizespor Balikesirspor Akhisar Belediyespor Karabukspor Gaziantepspor

Avg. AREA 5126.70 3973.85 3669.80 3665.77 3295.49 3068.20 3029.20 2929.28 2746.31 2631.94 2484.01 2453.82 2326.44 2318.92 2135.72 2134.52 2040.10 1850.30

Points 53 55 54 41 32 43 43 29 27 27 32 20 31 28 18 29 18 34

Figure 6.3: Linear Regression: Average Area vs. Points, Premier League 70 60

Points

Chelsea

y = 0.0158x - 3.6046 R² = 0.693

40 30 20 10 0 0

1000

2000 3000 Average AREA

4000

5000

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Figure 6.4: Linear Regression: Average Area vs. Points, Süper Lig 70 60

Points

y = 0.0114x + 1.2955 R² = 0.6495

Fenerbahçe

30 20 10 0 0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Average AREA

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Works Cited Arhsad, Sameer. “Man City Players Salary List 2014.” TSM Plug. 6 Aug. 2013. Web. 16 Apr. 2015. Arhsad, Sameer. “Cardiff City Players Salary List 2014.” TSM Plug. 6 Aug. 2013. Web. 16 Apr. 2015. Baldwin, Jacob. Personal Interview. 9 Feb. 2015. “Chelsea FC announces annual profit.” Chelsea FC. 13 Nov. 2014. Web. 24 Apr. 2015. “Chelsea Players Salaries 2015 (Hazard The Highest Paid).” Total Sportek. 3 Jan. 2015. Web. 27 Feb. 2015. Fotopoulos, Michael; Opatkiewicz, Andrew. “Salary Allocation Strategies for Major League Soccer.” MIT Sloan Sports Analytics Conference. 2-3 Mar. 2012. Web. 3 Mar. 2015. Kumar, Gunhan. “Machine Learning for Soccer Analytics.” KU Leuven. 2012-2013: 1. Web. 3 Mar. 2015 Harris, Nick. “Premier League wages dwarf those around Europe.” Daily Mail. 14 Nov. 2014. Web. 30 Mar. 30, 2015. “How long would it take you to earn a top footballer’s salary?” BBC. 4 Feb. 2015. Web. 30 Mar. 2015. “NBA Teams that have Analytics Department.” NBA Basketball Research. 29 Oct. 2014. Web. 3 Apr. 2015. “Paramedic: Salary.” US News. Web. 24 Apr. 2015. “Premier League Player Statistics.” WhoScored.com. Web. 9-23 March 2015. Rodriguez, Alicia. “Comparing 2014 MLS team spending - teams largely stay in similar positions year to year.” SB Nation. 11 Apr. 2014. Web. 16 Apr. 2015. “Roster Rules and Regulations.” MLS Soccer. Web. 17 Apr. 2015. Settimi, Christina. “The World's Highest-Paid Soccer Players.” Forbes. 5 Jul. 2014. Web. Mar. 23, 19 | P a g e

2015. “Super Lig Player Statistics.” WhoScored.com. Web. 9-23 Mar. 2015. Wahl, Grant. “Jurgen Klinsmann on use of analytics, fitness demands, more dual-nationals.” Sports Ilustrated. 12 Mar. 2015. Web. 23 Mar. 2015. “X Player Salary, Net Worth.” Celebrities Money. Web. 28 February 2015.

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Acknowledgments I thank my professors, collaborators and mentors for their interest in my project, dedication and unconditional guidance throughout the writing of this paper. I appreciate everything Dr. Jeffrey Fleisher did to help me carry out and enhance my study. I would like to extend special acknowledgments to my father and friends for sparking my torrid passion for football. I would like to also thank Dr. James Disch for being an academic authority in the sport analytics field and teaching me most of what I know on this topic. Last but not least, I would like to thank Dr. Clark Haptonstall, Professor Tom Stallings, the Sport Management Department and the Study Abroad Office, especially Abbey Godley, at Rice University for making all of this possible. Enrique Walsh.

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