THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE

DEPARTMENT OF FINANCE

GETTING A KICK OUT OF FINANCE: A STATISTICAL ANALYSIS OF SOCCER’S TRANSFER MARKET

RICHARD JOSEPH BLAIR SPRING 2017

A thesis submitted in partial fulfillment of the requirements for a baccalaureate degree in Finance with honors in Finance

Reviewed and approved* by the following:

Robert Novack Associate Professor of Supply Chain Management Thesis Supervisor

Brian Davis Professor of Finance Honors Adviser

* Signatures are on file in the Schreyer Honors College.

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ABSTRACT

Having grown up playing soccer competitively, employed at the Philadelphia Union’s

PPL Park, and being fortunate enough to travel internationally on multiple occasions and experience the game globally, I have become absolutely captivated by the sport. In an attempt to apply my financial skill-set and background in business, as well as my multi-faceted experiences and passion for soccer, my Schreyer Honors Thesis will focus on the valuation of soccer’s greatest assets: its players.

This thesis attempts to determine if “Moneyball,” an analytical approach utilizing data, can be applied to soccer in an effort to accurately determine player value. There is often a subjective quality to valuing athletes; however, the development of sabermetrics and their application with Moneyball have allowed general managers the opportunity to objectively analyze players. Intrinsic value can be determined therein and roster management decisions can occur without behavioral finance components.

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TABLE OF CONTENTS

LIST OF FIGURES…….……………………………………………………………………..iii

LIST OF TABLES…………………………………………………………………………....iv

ACKNOWLEDGEMENTS…………………………………………………………………...v

Chapter 1 Introduction of Topic ...... 1

Soccer’s Transfer Market ...... 2 Thesis Statement ...... 2

Chapter 2 Literature Review ...... 3

Initial Understandings of Soccer’s Transfer Market and Application ...... 3 Behavioral Finance: Heuristics and Biases in the Transfer Market ...... 6 Talent ...... 6 Position ...... 7 Status ...... 7 Age 7 Injury Record...... 8 Nationality/Adaptability ...... 8 Intangible Qualities ...... 8 My Direction ...... 9

Chapter 3 Data Methodology ...... 10

Challenges ...... 10 EA Sport’s FIFA 17’s Player Valuation Model ...... 10 Data Collection ...... 12 English Premier League Historical Charts ...... 12 FIFA 17 – Player Skills ...... 13 English Premier League Fantasy Soccer ...... 14

Chapter 4 Data Analysis ...... 15

English Premier League’s Clubs ...... 15 Overall Ratings vs. EPL Fantasy Price ...... 16 General Manager – Player Analysis ...... 17 Assumptions ...... 18 iii

Regression Analysis ...... 20 Goalkeepers ...... 21 Center Backs ...... 22 Right/Left Backs ...... 23 Center Defensive Midfielders ...... 23 Center Midfielders ...... 24 Center Attacking Midfielders ...... 25 Right/Left Midfielders ...... 26 Right/Left Wings ...... 26 Strikers ...... 27 Generated Roster ...... 28

Chapter 5 Conclusion ...... 29

Further Considerations ...... 31 FIFA 17’s Player Valuation Model ...... 31 Prozone and Opta Statistics ...... 31 Team Budget ...... 32 Player’s Expected Growth ...... 32

Appendix A English Premier League Club Statistics (1992 – 2016) ...... 33

Overall Statistics – Games, Records, Goals For, Goals Against, Goal Differential, Points 33 Overall Statistics – Position Finishes, Relegations, Avg. Points Per Season, Avg. Finish, Best Finish ...... 35 English Premier League Season Tables ...... 37 1992/93 ...... 37 1993/94 ...... 38 1994/95 ...... 39 1995/96 ...... 40 1996/97 ...... 41 1997/98 ...... 42 1998/99 ...... 43 1999/00 ...... 44 2000/01 ...... 45 2001/02 ...... 46 2002/03 ...... 47 2003/04 ...... 48 2004/05 ...... 49 2005/06 ...... 50 2006/07 ...... 51 2007/08 ...... 52 2008/09 ...... 53 2009/10 ...... 54 2010/11 ...... 55 2011/12 ...... 56 iv

2012/13 ...... 57 2013/14 ...... 58 2014/15 ...... 59 2015/16 ...... 60

Appendix B English Premier League Player Ratings and Prices ...... 61

Field Players ...... 61 Goalkeepers ...... 81 English Premier League Average Player Ratings & Average Finishes By Club ...... 83

BIBLIOGRAPHY ...... 84 v

LIST OF FIGURES

Figure 1: Pre-Tax Profit/Loss by EPL Clubs as a figure of league position between 1993-2012 4

Figure 2: Higher player wages result in higher finishing position for clubs between 2003-2012 5

Figure 3: Player Valuation Model ...... 12

Figure 4: EPL Clubs' Average Player Ratings and Average Finishing Position ...... 15

Figure 5: Regression between Field Players' Overall Rating and EPL Fantasy Price ...... 16

Figure 6: Regression between Goalkeepers' Overall Rating and EPL Fantasy Price ...... 17

Figure 7: 4-4-2 Soccer Formation ...... 20

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LIST OF TABLES

Table 1: Goalkeepers' Regression Analysis ...... 21

Table 2: Center Backs' Regression Analysis ...... 22

Table 3: Right/Left Backs' Regression Analysis ...... 23

Table 4: Center Defensive Midfielders' Regression Analysis...... 24

Table 5: Center Midfielders' Regression Analysis ...... 24

Table 6: Center Attacking Midfielders' Regression Analysis ...... 25

Table 7: Right/Left Midfielders' Regression Analysis ...... 26

Table 8: Right/Left Wings’ Regression Analysis ...... 27

Table 9: Strikers' Regression Analysis ...... 27

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ACKNOWLEDGEMENTS

This thesis is a culmination of the knowledge and experiences I have gained during my time studying at Penn State’s Schreyer Honors College. I would not be where I am today without the love and support of my parents (Richard A. Blair Jr. & Dina M. Blair), sister (Alyssa M.

Blair), and grandparents (Joseph S. Baratta & Dorothy M. Baratta). Many individuals have influenced my thesis and supported my academic career including family, friends, teachers, coaches, and mentors. I would like to thank Dr. Novack for supporting my thesis experience and guiding my journey. Thank you to Professor Davis for inspiring me to pursue a topic of genuine interest.

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Chapter 1

Introduction of Topic

In 2013, Real Madrid paid $117.65 million (€100.8 million) to Tottenham Hotspur for

Gareth Bale, the largest transfer fee in soccer’s history at the time.* Considering Real Madrid won a UEFA Champions League title that same year, the transfer could be deemed successful, but frequently similar transfers do not work nearly as well. Even further, while Bale is an excellent footballer, some may argue he is not worth $117.65 million.

Clearly, there are elements of subjectivity that exist when valuing athletes in every sport or within any type of human capital. However, Oakland Athletics’ General Manager Billy Beane employed an alternative means utilizing statistical analysis to create a competitive baseball team despite a lack of revenue; Michael Lewis chronicled Beane and the Athletics’ in Moneyball: The

Art of Winning an Unfair Game. Other baseball organizations have replicated Beane’s tactics and similar strategies have influenced other sports, but can Moneyball or the use of statistics in valuating players prove useful in soccer?

* Paul Pogba was purchased by Manchester United from Juventus for €105 million on August 8,

2016 ($115,597,650, after adjusted inflation on July 31, 2016) but the success of the transfer cannot yet be determined.

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Soccer’s Transfer Market

Soccer, considered the “world’s game,” dominates global sports with about 43% market share; the sports industry is currently expanding at a rate greater than the GDP in both the fastest-growing economies (BRIC Nations) and mature markets of North America and Europe

(Deloitte, 2016). Thus, with astronomical amounts of financial capital involved, why do tremendous inefficiencies exist in soccer’s transfer markets? Similar to free agency in American sports such as baseball or football, soccer’s transfer markets are highly subjective. Heuristics and biases that affect decision-making exist in any market, however, those that affect soccer vary from financial markets or trades/free agency in other sports due to the globalization of soccer, structure and market regulation. Therefore, a player’s inherent value is based upon numerous factors, both concrete and intangible. However, it is difficult to place an exact financial value upon human capital, and players are frequently over- or under-valued.

Thesis Statement

Through the use of sabermetrics, soccer clubs around the world will be more effective in managing both their financial and human capital. Soccer clubs who utilize empirical statistics and data analytics will acquire high-performing players whom are undervalued by the market, which will translate to on-field success. 3

Chapter 2 Literature Review

Initial Understandings of Soccer’s Transfer Market and Application

“Managers tend to pick a strategy that is the least likely to fail, rather than to pick a strategy that

is most efficient…The pain of looking bad is worse than the gain of making the best move.”

– Michael Lewis, Moneyball: The Art of Winning an Unfair Game

Soccer clubs are not a “for profit” entity; in fact, many of them are not profitable. Many studies attempt to analyze soccer clubs similarly to businesses, but soccer clubs are not businesses. When owners attempt spend rationally and make fiscally responsible decisions, the club suffers both on and off the field. An alternative scheme is to invest in players to drive club success, but still the best teams do not profit. In fact, team success and profit maximization are negatively correlated in forty-five percent of events: higher league position, lower profits or lower league position, higher profits (Soccernomics, 2009). In fifty-five percent of events league position and profits were positively correlated. Thus, club success does not result in profitability.

Those that are profitable are only slightly profitable; these clubs do not experience success and consequently do not retain the best players and potentially face relegation (Refer to Figure 1).

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Figure 1: Pre-Tax Profit/Loss by EPL Clubs as a figure of league position between 1993-2012

Research shows that soccer clubs favor win maximization as opposed to profit maximization; in other words, soccer clubs would rather retain quality players, win matches, and lift championship trophies than fill their stadiums, acquire television contracts and endorsements, and ultimately generate revenue in both the short- and long-term (“Goal! Profit Maximization

Versus Win Maximization in Soccer”, 2006). Between 1978 and 1997, club expenditures on players explained more than ninety percent of the variation in league position, which means high wages help a club much more than do transfers of much grandeur (Soccernomics, 2009). That is not to say ticket sales, exposure, and a soccer club’s sales are not important, but it certainly indicates soccer teams have an underlying motive to be efficient with their expenditures on players. Thus, it is not only important to retain players, but also to select and acquire the right players as well (Refer to Figure 2). 5

Figure 2: Higher player wages result in higher finishing position for clubs between 2003-2012

“While the market for players’ wages is pretty efficient—the better a player, the more he earns—the transfer market is inefficient. Much of the time, clubs buy the wrong players”

(Soccernomics, 2009). Thus, it is rather common for players to be over- or under-valued. Yet, because of the growth of “big data” – one entity estimates the “daily amount of data generated worldwide doubles approximately every forty months” – its sphere has reached the sport of soccer, in which companies such as Prozone and Opta work to analyze the game and its players from an empirical perspective. As the most popular sport in the world, it is natural Big Data has begun to influence soccer club’s front offices and the examination of metrics provides a more comprehensive understanding of the game’s players which should theoretically translate to more wins. 6

Behavioral Finance: Heuristics and Biases in the Transfer Market

Soccer is a fundamentally different than baseball as a sport, but can an analytical strategy employing soccer statistics be advantageous? Research has looked at the effect of economics and psychology on the purchase of players at prices over or under their relative value. It is important to recognize the intangible influences on player valuation. Economics and psychology are factors that influence soccer clubs to purchase players at prices over or under their relative value

How can a specific price be placed upon an athlete? Do managers study player statistics for valuation purposes? Is there a procedure by which managers determine value? “Like any marketplace, a player is worth what the market is willing to pay for him” (Lyttleton, 2015). Yet, as discussed, human behavior influences the transfer markets and managers currently use

“perceptions and precedents” (Arsenal Review, 2009).

Talent

As with many sports markets, the more talented player, the higher he is valued. Because there are elements of subjectivity, this thesis will attempt to valuate players utilizing data analytics. 7

Position

Players who produce goals tend to be most valued by the market, while the individuals stopping goals are most undervalued by the market. Thus, Strikers, Forwards, and Midfielders (Offensive

Players) are considered “Premium” and tend to cost more than Defenders and Goalkeepers.

Status

Beyond position, a player’s status on his current team influences transfer price as well. Is he the key component of the team? Is he a valuable role player? Is he an indispensable backup? A player’s utilization on the selling team influences the price at which he is transferred. The more important a player is to a team, the higher the value of the transfer fee.

Age

Many clubs are willing to purchase players aged 16-20 because their talent can be developed and/or they can be “sold-on value,” although that may not always be the case due to injuries, unfulfilled potential, or distractions on or off the field. Soccer players are typically in their prime between ages 24 and 28, and clubs frequently assign higher valuations. Similar to any asset, players depreciate in value as they age. Thus, after age 28 players typically are not valued as highly as they were in their younger years. As a manager, it is imperative to consider “salvage value” when attempting to sell a top-player aged 28 for maximum return before he settles into his

30s and performance declines. 8

Injury Record

Similar to age, a player’s longevity should be considered in terms of his health. Although a particular player may be a top-performer, if he cannot endure a season without sustaining an injury, he probably should not be valued as highly. Thus, players with complex injury histories pose a risk to a team and their value should reflect this notion.

Nationality/Adaptability

In the English Premier League it is mandated that 8 of 25 players of a squad should be homegrown; therefore, there is a premium on signing English players. English soccer leagues are increasingly becoming more international in terms of their recruiting processes, therein lies an

“international bias” where players from foreign lands are over-priced. French, Italian, and

Brazilian players are also often thought of as “better players” and thus considered more valuable

(although they may not be). Moreover, purchasing players from foreign lands has potential implied risks due to their adaptability to the team, club, or community and the transfer does not work due to forces unrelated to ability.

Intangible Qualities

Players whom possess intangible qualities such as leadership and work-ethic, as well as their ability to provide team chemistry work are highly valued. These talents do not appear statistically. Some soccer clubs also consider a player’s image rights and iconic status in terms of 9 valuation as they can be beneficial for the club by providing commercial activity, increased ticket sales, etc. As research concludes, purchasing a player based on the potential generation of revenues and related activities are irrational.

My Direction

After reading literature on the topic, there has been minimal or almost no research in the area being explored. In other words, practically no one has tried to systematically apply

Moneyball’s principles to soccer to essentially fix a broken market. Other academics have attempted to perform empirical analyses of soccer players and have found soccer transfer markets are inherently inefficient and irrational. Utilizing data and statistics to drive findings, the formation of a statistical and quantitative analysis has been developed. Deloitte’s Annual Review of Football Finance 2016 found correlations exist between clubs, salaries, and performance, but relative value should be explored statistically as higher salaries do not always equate to individual success and vice versa. Additionally, particular clubs have the benefit of paying astronomical fees, and while players may be promising, they are still over-priced. This thesis attempts to look at the translation between on-field success and its relationship to various dependent variables, as well as the valuation of specific players in the transfer market to drive victories.

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Chapter 3 Data Methodology

To best determine player value, the analysis is two-fold. First, it is imperative to understand the soccer clubs for whom the best players play and the skills valued by these clubs for position-type. Second, player valuation can occur when their skills are analyzed with their price to determine inefficiencies.

Challenges

Due to the nature of soccer, it is difficult to directly quantify soccer statistics and weight them accordingly. As recognized as a pitfall of this research, the data surrounding soccer is minimal. Of course, statisticians keep track of generic measurements such as goals scored, time- played, passes completed, and while the sport is undergoing an increased movement favoring data, these metrics do not provide much meaning individually. For example, a player’s time of possession may not translate to productivity, or vice versa, a player may not touch a ball for the entirety of the game, but a single moment of brilliance could alter the trajectory of the match.

Because statistics are minimal and do not provide comprehensive insight, employing “Pure

Moneyball” is rather difficult.

EA Sport’s FIFA 17’s Player Valuation Model

With minimal data, there has to exist some level of subjectivity during the valuation of soccer players. Utilizing EA Sport’s FIFA 17’s valuation model, an individual can assess 11 attributes for determining a player’s overall rating. Based on a scale of 1-100, model users simply insert inputs into the model based upon their subjective interpretation of a player’s particular skills including Pace, Shooting, Passing, Dribbling, Defending and Physicality. These overarching metrics have specific components, including Attacking, Skill, Movement, Power,

Mentality, Defending, Goalkeeping, and International Reputation. They can be further broken down, as well. For example, Attacking includes the skills of Crossing, Finishing, Heading

Accuracy, Short Passing, and Volleys.

It is imperative to note that these metrics will change depending on a particular individual’s interpretation of a specific skill. Additionally, each group of skills is weighted differently based upon position. For example, a general manager will likely not value a goalkeeper’s ability to finish and score goals, but he/she probably will value a goalkeeper’s diving ability and reflexes. Comparatively, a striker’s Dribbling ability may be valued more than his ability to perform a Sliding Tackle. With these assumptions in mind, an Overall Rating is determined based off of the skill inputs (Refer to Figure 3). 12

Inputs Attacking Skill Movement Power Crossing 85 Dribbling 89 Acceleration 92 Shot Power 84 Finishing 82 Curve 75 Sprint Speed 91 Jumping 81 Heading Accuracy 72 Free Kick Accuracy 54 Agility 86 Stamina 80 Short Passing 75 Long Passing 58 Reactions 81 Strength 80 Volleys 74 Ball Control 84 Balance 85 Long Shots 71

Mentality Defending Goalkeeping International Reputation Aggression 62 Marking 35 GK Diving 9 International Stars 3 Interceptions 42 Standing Tackle 39 GK Handling 8 Potential 90 Positioning 80 Sliding Tackle 38 GK Kicking 8 Vision 70 GK Positioning 15 Penalties 71 GK Reflexes 11

Output Overall Rating 83 Right Striker, Left Striker, Striker Attackers 84 Right Wing, Left Wing 83 Right Forward, Center Forward, Left Forward 81 Right Attacking Midfielder, Center Attacking Midfielder, Left Attacking Midfielder Midfielders 74 Right Center Midfielder, Center Midfielder, Left Center Midfielder 84 Right Midfielder, Left Midfielder 62 Right Defending Midfielder, Center Defending Midfielder, Left Defending Midfielder 58 Right Center Back, Center Back, Left Center Back Defenders 65 Right Back, Left Back 68 Right Wing Back, Left Wing Back 20 Goalkeeper Goalkeeper Figure 3: Player Valuation Model

Data Collection

English Premier League Historical Charts

The English Premier League’s clubs have played since the 1992-93 season. Data exists

for wins, loses, draws, points, goals for, goals against, and goal differential. Unique to Major

League Baseball, the National Football League, or the National Basketball Association, the EPL

features a relegation-system in which the bottom three teams are demoted to England’s First

Division while the First Division’s top three teams are promoted to the EPL. Thus, forty-seven 13 teams have played in the EPL since its inception. Collecting historical data shows into clubs’ successes over time and provides insight into their player quality.

FIFA 17 – Player Skills

As mentioned, it is determined as nearly impossible to directly employ pure Moneyball utilizing solely statistics. However, efforts have been made to limit subjectivity and create an empirical science when it comes to the valuation of soccer players. Thus, for 454 Field Players, statistics regarding their skill were collected for attributes including Pace, Shooting, Passing,

Dribbling, Defending, and Physicality. Similarly, Diving, Handling, Kicking, Reflexes, Speed, and Positioning skill statistics were gathered for 54 Goalkeepers in the English Premier League.

These skills are composed by both statistics and subjective analysis (the “Eye-Test”). Data in player’s statistics may not exist, but a tangible value of a player’s ability to perform a particular skill can exist.

Surely, an individual’s interpretation of a player’s skill may vary, but again, some level of subjectivity must exist in soccer player valuation and it is the only data point; which can change based on a particular bias. Michael Mueller-Moehring, a producer at EA Sports whom is responsible for rating FIFA’s players, explains that 5.4 million data points are annually collected for FIFA’s 700 clubs and 18,000 players. With a network of 9,000 coaches, professional-level scouts, and season ticket holders, data reviewers submit feedback through a secure EA Sports

Network (Lindberg, 2016). Advanced statistics are considered as well. 14

English Premier League Fantasy Soccer

Because limited data exists regarding English Premier League clubs’ revenues and spending budgets, as well as players’ salaries, data was collected from the EPL’s Fantasy

Premier League regarding player salaries. Every team is given £100 and must compile a team of fifteen players, which include eleven starters and four substitutes (one for each position type; i.e. goalkeeper, defender, midfielder, striker). Thus, each player is assigned a value based upon their on-field contributions. These numbers serve as player salaries and will be useful in determining if a player is over or under-valued based upon an analysis of their skills.

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Chapter 4

Data Analysis

English Premier League’s Clubs

Unsurprisingly, the clubs in the English Premier League that have performed the best over time have retained the best players. Through an analysis of clubs’ average finishing position in the EPL and their average player rating, it is determined that the best clubs’ have the best players. From here, it is important to look at what skills make the best players. In other words,

what

Figure 4: EPL Clubs' Average Player Ratings and Average Finishing Position 16 skills given a player’s position are most valued. To determine the most valued skills by position, regressions were run between overall rating and each of a player’s skills to analyze which skills are most important (Refer to Figure 4). These results were determined using the data in

Appendix A.

Overall Ratings vs. EPL Fantasy Price

After understanding the inputs that make an overall, quality player, it is important to test the overall rating with their price to determine inefficiencies in the market. As shown in Figure 5, it is determined that Field Players’ Overall Ratings and Price are positively correlated. However, they are not perfectly correlated, which means there are inefficiencies to some capacities in the market for soccer players – players are either over or under-valued. Additionally, regressions from an even stronger relationship between Goalkeepers’ Overall Ratings and Price exists, which indicates there are even smaller inefficiencies for General Managers to manipulate. With that

Figure 5: Regression between Field Players' Overall Rating and EPL Fantasy Price 17 said, General Managers should analyze the skills that make the best players at their position types, determine price inefficiencies relative to other players, and capture those players to develop the best team at the cheapest price. These regressions were run utilizing data from 508 player observations (Refer to Appendix B).

Figure 6: Regression between Goalkeepers' Overall Rating and EPL Fantasy Price

General Manager – Player Analysis

As a general manager, developing a strong roster is contingent on several factors. Player personnel is the most important, but even the best players may not perform effectively if the club utilizes a formation that puts them in unproductive situations. For example, a Striker may perform better if there is another Striker on the field that alleviates pressure. He may also prefer attacking goal from the Right and may be entirely ineffective if he is a lone Striker and is forced to attack from both the Left and Right. 18

Additionally, it is important to recognize that players and their skills can be transferable to other positions on the field, especially when a club’s formation changes. For example, Center

Attacking Midfielders can play the Center Midfielder position depending on if the formation calls for a Center Midfielder. As a Center Midfielder, he will likely retain his attacking tendencies. Similarly, a Left Wing can play the Left Midfielder position, and the high pace and dribbling skills will transfer to that position.

Another variable that determines a player’s realized skill is the coaches’ strategy. Some coaches believe dominating possession will allow their team to continuously probe for goal scoring opportunities. Meanwhile, the other team will have limited chances to score and win.

Thus, retaining players with exceptional passing abilities is imperative. This approach will benefit Center Midfielders as possession will flow through them. On the contrary, another team may be more defensive-minded. They will generally “Park-The-Bus” and withstand the opposition’s attack for long-stretches of the game. Their goal-scoring opportunities will come from counter-attacks. Excellent defenders are required to succeed, as well as Right/Left

Midfielders and Strikers with high pace.

Assumptions

In an effort to make this model as simple and transferable as possible, it will assume the following:

 Described as a soccer coaches’ “Bread and Butter,” the composed team’s formation will

be a 4-4-2 (See Figure 7) 19

 The team will be perfectly balanced in terms of attacking and defensive strategy

 The team’s players are not confined to a single position

 The model will adhere to the rules of English Premier League Fantasy Soccer

o The team has a budget of £100; each player is assigned a value based off of skill

and productivity

o Teams are comprised of 15 players: eleven starters and four substitutes (one for

each position type; i.e. goalkeeper, defender, midfielder, striker)

 The model will maximize player’s skill and productivity while minimizing player cost

o Cost will not be minimized to where budget is leftover (soccer clubs cannot earn a

substantial profit)

 The model assumes all players are in the English Premier League and easily attainable

o The model assumes no contracts or transfer fees

o The model assumes player skill is maximized (there is no growth potential)

o The model disregards heuristics and biases previously discussed

. Player skills and value are the only considerations 20

Figure 7: 4-4-2 Soccer Formation

Regression Analysis

For each position, a regression analysis was conducted to observe the relationship between a player’s overall rating and their individual skill rating. The results from Multiple R, or the correlation coefficient, showed their linear relationship. A value of 1 indicates a perfect positive relationship and a value of zero shows no relationship.

For example, a Center Back’s ability to Defend influences their Overall Rating most

(given the results from the regression analysis), which means a Center Back with high Defense attributes are most desirable by the market. Because the Center Backs with the highest overall ratings are necessarily those with the highest Defense skill (otherwise the regression would yield a perfect 1), there are opportunities to exploit the market. Additionally, a Center Back’s Physical rating is highest after Defense, which indicates Physicality influences their Overall Rating 21 second-most. On the contrary, a Center Back’s Pace is almost irrelevant to their Overall Rating.

This principal is applied to every position type. The results indicate which skill ratings are most advantageous for a particular position. Thus, these regressions provide constraints into the Excel

Solver model to determine an “optimal player” for that position type. The optimal player, given their constraints by position type, in addition to the team budget of £100, is determined and shown by position. Given the assumption that a player is not confined to a single position, the optimal player for the 4-4-2 formation, the results may not yield a position for every position type.

Goalkeepers

Goalkeepers whom are strong at handling are most desired. Additionally, it is important for them to be excellent at positioning themselves to defend against the opposition’s attacking efforts.

They must have strong diving capabilities and reflexes to react and make saves. It is not important for goalkeepers to be good at kicking nor is it important that they have speed to retrieve balls.

Table 1: Goalkeepers' Regression Analysis

Regression: Multiple R Diving 0.933391247 Handling 0.942784935 Kicking 0.621875459 Reflexes 0.928697156 Speed 0.363912709 Positioning 0.935555333

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EPL Overall Fantasy Name Rating Price Position Nation Club Class Diving Handling Kicking Reflexes Speed Positioning Manchester De Gea 90 5.4 GK Spain United Gold 88 85 87 90 56 85 Jordan Pickford 76 3.1 GK England Sunderland Gold 74 76 85 78 48 73

Center Backs

Center backs tend to be the most physical players on the field. Additionally, they must be

excellent defenders. It is important they are good passers and shooters, but their dribbling

capability and pace are largely irrelevant. Their sole responsibility is to defend their goal.

However, they can add value in terms of possession and simple passes in the backfield while

passing lanes develop.

Table 2: Center Backs' Regression Analysis

Regression: Multiple R Pace 0.144559138 Shooting 0.543884095 Passing 0.693247681 Dribbling 0.486632425 Defense 0.973933546 Physical 0.861393322

EPL Overall Fantasy Name Rating Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Vincent Manchester Kompany 86 5.9 CB Belgium City Gold 69 54 62 65 86 81 Per Mertesacker 83 4.8 CB Germany Arsenal Gold 27 41 56 48 88 75 Phil Jagielka 82 4.6 CB England Everton Gold 67 46 58 55 83 81

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Right/Left Backs

While it is important for Right/Left Backs to be excellent defenders, it is important for them to

have a high pace to work the sidelines up and down the field. As such, they find themselves in

attacking positions and must have a decent ability to shoot, which is surprising for defenders.

Their passing capability must be high to assist the offense when forward, but they must also be

physical for both offensive and defensive situations. Their dribbling ability is important but not

necessary.

Table 3: Right/Left Backs' Regression Analysis

Regression: Multiple R Pace 0.384042621 Shooting 0.611868532 Passing 0.843062211 Dribbling 0.799749625 Defense 0.935042742 Physical 0.777045511

EPL Overall Fantasy Name Rating Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Leighton Baines 83 5.7 LB England Everton Gold 75 75 81 77 80 74 Mathieu Debuchy 80 4.6 RB France Arsenal Gold 74 65 73 73 79 77

Center Defensive Midfielders

It is much more important for Center Defensive Midfielders be good defenders than other players

on the field besides defenders. It is much more important that they have the pace to navigate the 24 field and get forward to provide support for their teammates offensively. However, they must be the first-line of defense as well. They must be strong passers and physical. Their dribbling ability does not matter much, nor does their shooting ability given their position’s relative distance from goal.

Table 4: Center Defensive Midfielders' Regression Analysis

Regression: Multiple R Pace 0.297177548 Shooting 0.233256162 Passing 0.54886232 Dribbling 0.534775031 Defense 0.813373056 Physical 0.447718878

Center Midfielders

It is entirely unimportant how quickly Center Midfielders move; however, they must be excellent passers and be physical to maintain possession of the ball for their teams. They must be strong dribblers as well. They must be decent shooters and their defending ability matters, but not heavily.

Table 5: Center Midfielders' Regression Analysis

Regression: Multiple R Pace 0.091670221 Shooting 0.722261134 Passing 0.846139291 Dribbling 0.837535925 Defense 0.578978689 Physical 0.532991771

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EPL Overall Fantasy Name Rating Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Cesc Fàbregas 86 6.9 CM Spain Chelsea Gold 63 77 89 81 61 64 Ilkay Manchester Gündogan 85 4.9 CM Germany City Gold 75 72 84 87 63 72

Center Attacking Midfielders

Center Attacking Midfielders must be excellent passers. It is important they are strong dribblers

as well. It is important that they are good shooters given an opportune moment but it is more

important they are physical to yield defenders and put themselves in threatening positions for

their team. The pace by which they navigate the field and their defending capability is

unimportant.

Table 6: Center Attacking Midfielders' Regression Analysis

Regression: Multiple R Pace 0.024873723 Shooting 0.878390827 Passing 0.966806962 Dribbling 0.96052660 Defense 0.224918841 Physical 0.302996411

EPL Overall Fantasy Name Rating Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical David Manchester Silva 87 8.6 CAM Spain City Gold 68 72 87 87 32 58

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Right/Left Midfielders

Similarly to Right/Left Wings, Right/Left Midfielders must have a high pace. They must also be

strong passers, adding an element of attack from the sides, which include crosses, and they must

be good shooters as well. For attacking minded players, it is imperative they work back and

support their defenders as well, thus a high defense rating is valued. Physicality is valued as well,

while shooting must be average at best.

Table 7: Right/Left Midfielders' Regression Analysis

Regression: Multiple R Pace 0.254782917 Shooting 0.673995318 Passing 0.793963929 Dribbling 0.745024537 Defense 0.199030238 Physical 0.372315292

EPL Overall Fantasy Name Rating Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Eden Hazard 88 10.2 LM Belgium Chelsea Gold 90 81 82 91 32 64 Xherdan Stoke Shaqiri 82 6 RM Switzerland City Gold 86 77 78 84 53 72

Right/Left Wings

Right/Left Wings are amongst the quickest players on the field. That, said their dribbling and

passing ability is not compromised by their pace. Their shooting capability must be better than

average, but their defending capability and physicality are not as significant. 27

Table 8: Right/Left Wings’ Regression Analysis

Regression: Multiple R Pace 0.323930714 Shooting 0.900162206 Passing 0.906057386 Dribbling 0.964411857 Defense 0.35047530 Physical 0.40592101

Strikers

Strikers must be excellent at passing, adding an attacking element to their team’s offense. It is

imperative to be physical to win balls in cluttered areas and position themselves to score goals.

They must be good dribblers and be nimble in tight areas, but surprisingly it is not important that

they have a high shooting capability and pace. Their ability to defend is not highly valued.

Table 9: Strikers' Regression Analysis

Regression: Multiple R Pace 0.202525687 Shooting 0.930087449 Passing 0.770381889 Dribbling 0.78854720 Defense 0.373710943 Physical 0.685802481

EPL Overall Fantasy Name Rating Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Zlatan Manchester Ibrahimović 90 11.5 ST Sweden United Gold 72 90 81 85 31 86 Lucas Pérez 81 7.8 ST Spain Arsenal Gold 76 83 76 80 31 72 Islam Leicester Slimani 83 8.2 ST Algeria City Gold 82 77 80 88 30 58

28

Generated Roster

Given the aforementioned assumptions and utilizing Excel’s Solver to maximize players’

specific skills based off of Significant F’s calculated through Regression Analysis, the following

roster was generated:

EPL Fantasy Name Overall Rating Price Position Nation Club Class Diving Handling Kicking Reflexes Speed Positioning Manchester De Gea 90 5.4 GK Spain United Gold 88 85 87 90 56 85 Jordan Pickford 76 3.1 GK England Sunderland Gold 74 76 85 78 48 73

EPL Fantasy Name Overall Rating Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Vincent Manchester Kompany 86 5.9 CB Belgium City Gold 69 54 62 65 86 81 Per Mertesacker 83 4.8 CB Germany Arsenal Gold 27 41 56 48 88 75 Phil Jagielka 82 4.6 CB England Everton Gold 67 46 58 55 83 81 Leighton Baines 83 5.7 LB England Everton Gold 75 75 81 77 80 74 Mathieu Debuchy 80 4.6 RB France Arsenal Gold 74 65 73 73 79 77 Manchester David Silva 87 8.6 CAM Spain City Gold 68 72 87 87 32 58 Cesc Fàbregas 86 6.9 CM Spain Chelsea Gold 63 77 89 81 61 64 Ilkay Manchester Gündogan 85 4.9 CM Germany City Gold 75 72 84 87 63 72 Eden Hazard 88 10.2 LM Belgium Chelsea Gold 90 81 82 91 32 64 Xherdan Shaqiri 82 6 RM Switzerland Stoke City Gold 86 77 78 84 53 72 Zlatan Manchester Ibrahimović 90 11.5 ST Sweden United Gold 72 90 81 85 31 86 Lucas Pérez 81 7.8 ST Spain Arsenal Gold 76 83 76 80 31 72 Islam Leicester Slimani 83 8.2 ST Algeria City Gold 82 77 80 88 30 58

Average Overall Total EPL Rating Fantasy Cost 84.13 98.2 29

Chapter 5

Conclusion

After developing a model to analyze players’ skills relative to their value, it is certain that there are inefficiencies in the transfer market because overall rating and price would otherwise be perfectly correlated. However, it is difficult to determine where the inefficiencies lie in the transfer market and capitalize as a general manager. Thus, regressions run between players’ overall rating and specific skills show, which skills are emphasized for a particular position. To determine the best players given the skills required by their positions, the constraints added to the model ultimately maximize player skills while minimizing the cost of the players.

The Solver-Generated team has an Average Overall Rating of 84.13, which would be third in the English Premier League, only behind Arsenal and Bournemouth. While this fact is rather impressive, this assumes perfect trading conditions; there are no contracts, transfer fees, or most importantly, competition. While this model optimizes players’ skills given their value, a player’s price or contract cost could be increased if multiple clubs are bidding on him. For example, Manchester United’s Zlatan Ibrahimović has an EPL Fantasy Price of 11.5. Given he is the best Shooter and Passer in the English Premier League at his position, other clubs may be willing to spend more for him. Perhaps, he would be worth less if Manchester United did not value Ibrahimović and his skills so highly.

While a soccer club’s general manager can utilize a similar model for optimization purposes, there is a difference between nominal skill and realized skill. In other words, a player 30 may have the skills necessary to make him a high performer, but unless he is properly utilized those skills will not be recognized. Composing a team with the best players is worthless unless the coach employs their skills properly. As discussed, formation and strategy are important when considering which players are needed. Team chemistry and how the best players work together is vital as well. Simply, an effective general manager works well with the team’s coach.

It is no surprise that the generated-team is comprised of players from some of the best clubs. It was previously determined that the best clubs do in fact have the best players, which makes it understandable why those clubs experience the success at the rate they do. Additionally, higher player wages result in higher finishing position because better players earn more.

However, it is important to note that every club has different spending capabilities. The model assumes a budget of £100 and the club effectively spends as much as possible, but some clubs may have more capital to spend while other clubs may have less. Also, clubs may opt to not spend all of its capital in a given year for a multitude of reasons.

While the current system of valuating soccer players is imperfect, the rise of statistics and data will yield results that diminish over- or under-valuation. The creation of player valuation as a science in soccer will make soccer clubs more effective in creating on-field success. Analyzing players and their skills will allow general managers to acquire players whom are undervalued by perform at a high-level. Theoretically, these clubs will spend the least but experience the most success. A subjective nature will always exist in soccer, but with proper implementation of data analytics as this thesis demonstrates, Moneyball can and should be applied to soccer.

31

Further Considerations

FIFA 17’s Player Valuation Model

The models used in this analysis used FIFA’s basic considerations: Pace, Shooting, Passing,

Dribbling, and Physicality for field players; and Diving, Handling, Kicking Reflexes, Speed, and

Positioning for goalkeepers. While there are variables inputted that determine these skills’ ratings, they are certainly not holistic and provide complete insight into a player’s capabilities.

For example, a defender may have a poor Passing rating because he is not accurate when passing at distance, but he consistently completes short passes to his teammates. It is also important to consider how particular skills combine to create a beneficial output. For example, a Defender may not be considered an excellent shooter, but during corner kicks he utilizes his Physicality to consistently score goals with his head. Ultimately, it is encouraged for future researchers to undergo a “deeper dive” into a players’ skills to see how, if at all, valuation metrics change. For example, consider intangible qualities like leadership and game intelligence. Also, consider international reputation.

Prozone and Opta Statistics

Big data is beginning to industrialize soccer. Prozone and Opta Premier League have emerged as companies that analyze sports statistics and provide live, detailed player and team data. Some of these modified statistics include touches and distance traveled, amongst many others. This data provides a more holistic overview of a player’s impact on the field. 32

Team Budget

As discussed, every team has different spending capabilities. Thus, it would be beneficial to adjust the assumed budget of £100 to yield results that favor teams with a spending ability more or less than the average.

Player Loans, Trades, and Injuries

Unlike most American sports, soccer clubs utilize player loans. Similar to “loans” in finance, a player temporarily plays for a club other than the one who owns his contact. The concept of loans and trades were not considered in player valuation. A mid-season transaction could potentially skew a player’s relative value, especially in circumstances where teams aim to acquire a high-performer late in the season for a championship push. Additionally, injuries were not considered. Injuries, especially long-term, pose a risk and a club may cut even the best players if he may not fully recover.

Player’s Expected Growth

Similar to the valuation metrics, determine how, if at all, a player’s potential growth affects his valuation. For example, if a newly acquired 20-year old shows promising shooting ability, is he over-paid because he is expected to be a world-class striker one day? Future researchers can utilize an Expected Return model with standard deviations.

33

Appendix A

English Premier League Club Statistics (1992 – 2016)

Overall Statistics – Games, Records, Goals For, Goals Against, Goal Differential, Points

Ranking Club Seasons Games Played Won Drawn Lost GF GA GD Points 1 Manchester United 24 924 586 194 144 1802 818 984 1952 2 Arsenal 24 924 502 241 181 1621 867 754 1747 3 Liverpool 24 924 456 233 235 1523 944 579 1601 4 Chelsea 24 924 486 238 200 1560 892 668 1696 5 Tottenham Hotspur 24 924 374 239 311 1320 1205 115 1361 6 Leeds United 12 468 189 125 154 641 573 68 692 7 Manchester City 19 734 304 181 249 1093 886 207 1093 8 Newcastle United 22 844 322 217 305 1168 1140 28 1183 9 Aston Villa 24 924 316 275 333 1117 1186 -69 1223 10 Swansea City 5 190 62 52 76 233 257 -24 238 11 Everton 24 924 332 267 325 1197 1163 34 1263 12 Blackburn Rovers 19 734 262 184 250 927 907 20 970 13 Stoke City 8 304 98 86 120 322 401 -79 380 14 West Ham United 20 768 253 200 315 917 1082 -165 959 15 Sheffield Wednesday 8 316 101 89 126 409 453 -44 392 16 Wimbledon 8 316 99 94 123 384 472 -88 391 17 Fulham 13 494 150 136 208 570 697 -127 586 18 Leicester City 10 384 118 110 156 468 547 -79 464 20 Southampton 17 658 210 177 271 814 918 -104 807 19 Charlton Athletic 8 304 93 82 129 342 442 -100 361 21 Middlesbrough 14 536 160 156 220 621 741 -120 636 22 Bolton Wanderers 13 494 149 128 217 575 745 -170 575 23 Portsmouth 7 266 79 65 122 292 380 -88 302 24 Queens Park Rangers 7 278 81 65 132 339 431 -92 308 25 Birmingham City 7 266 73 82 111 273 360 -87 301 26 Norwich City 8 316 89 92 135 365 510 -145 359 27 Coventry City 8 316 99 112 143 387 490 -103 409 34

28 Derby County 7 266 68 70 128 271 420 -149 274 29 Wigan Athletic 8 304 85 76 143 316 482 -166 331 30 Sunderland 15 570 147 153 270 583 835 -252 594 32 West Bromwich Albion 10 380 94 106 180 401 589 -188 388 31 Nottingham Forest 5 198 60 59 79 229 287 -58 239 33 Reading 3 114 32 23 59 136 186 -50 119 34 Ipswich Town 5 202 57 53 92 219 312 -93 224 35 Bournemouth 1 38 11 9 18 45 67 -22 42 36 Crystal Palace 7 274 74 73 127 279 393 -114 295 37 Sheffield United 3 122 32 36 54 128 168 -40 132 38 Hull City 4 152 32 41 79 144 243 -99 137 39 Watford 3 114 23 28 63 104 186 -82 97 Wolverhampton 40 Wanderers 4 152 32 40 80 156 281 -125 136 41 Bradford City 2 76 14 20 42 68 138 -70 62 42 Burnley 2 76 15 18 43 70 135 -65 63 43 Barnsley 1 38 10 5 23 37 82 -45 35 44 Blackpool 1 38 10 9 19 55 78 -23 39 45 Oldham Athletic 2 84 22 23 39 105 142 -37 89 46 Cardiff City 1 38 7 9 22 32 74 -42 30 47 Swindon Town 1 42 5 15 22 47 100 -53 30

35

Overall Statistics – Position Finishes, Relegations, Avg. Points Per Season, Avg. Finish, Best Finish

Average Points Ranking Club 1st 2nd 3rd 4th Relegated Per Season Average Finish Best Finish 1 Manchester United 13 5 3 1 - 81.33 2.00 1 2 Arsenal 3 6 5 7 - 72.79 3.54 1 3 Liverpool - 3 5 5 - 66.71 4.75 2 4 Chelsea 4 4 4 2 - 70.67 4.92 1 5 Tottenham Hotspur - - 1 2 - 56.71 8.25 3 6 Leeds United - - 1 2 1 57.67 8.83 3 7 Manchester City 2 2 1 1 2 57.53 9.32 1 8 Newcastle United - 2 2 1 1 53.77 9.68 2 9 Aston Villa - 1 - 1 1 50.96 10.13 2 10 Swansea City - - - - - 47.60 10.40 8 11 Everton - - - 1 - 52.63 10.46 4 12 Blackburn Rovers 1 1 - 1 2 51.05 10.47 1 13 Stoke City - - - - - 47.50 11.25 9 14 West Ham United - - - - 2 47.95 11.75 5 Sheffield 15 Wednesday - - - - 1 49.00 12.00 7 16 Wimbledon - - - - 1 48.88 12.25 6 17 Fulham - - - - 1 45.08 12.38 7 18 Leicester City 1 - - - 3 46.40 12.40 1 20 Southampton - - - - 1 47.47 12.88 6 19 Charlton Athletic - - - - 2 45.13 12.88 7 21 Middlesbrough - - - - 3 45.43 13.29 7 22 Bolton Wanderers - - - - 3 44.23 13.46 6 23 Portsmouth - - - - 1 43.14 13.86 8 Queens Park 24 Rangers - - - - 3 44.00 14.00 5 25 Birmingham City - - - - 3 43.00 14.14 9 26 Norwich City - - 1 - 4 44.88 14.25 3 27 Coventry City - - - - 1 51.13 14.38 11 28 Derby County - - - - 2 39.14 14.43 8 29 Wigan Athletic - - - - 1 41.38 14.63 10 30 Sunderland - - - - 3 39.60 14.67 7 36

West Bromwich 32 Albion - - - - 3 38.80 14.80 3 31 Nottingham Forest - - 1 - 3 47.80 14.80 3 33 Reading - - - - 2 39.67 15.00 8 34 Ipswich Town - - - - 2 44.80 16.00 5 35 Bournemouth - - - - - 42.00 16.00 16 36 Crystal Palace - - - - 4 42.14 16.14 10 37 Sheffield United - - - - 2 44.00 17.33 14 38 Hull City - - - - 2 34.25 17.50 16 39 Watford - - - - 2 32.33 17.67 13 Wolverhampton 40 Wanderers - - - - 2 34.00 18.00 15 41 Bradford City - - - - 1 31.00 18.50 17 42 Burnley - - - - 2 31.50 18.50 18 43 Barnsley - - - - 1 35.00 19.00 19 44 Blackpool - - - - 1 39.00 19.00 19 45 Oldham Athletic - - - - 1 44.50 20.00 19 46 Cardiff City - - - - 1 30.00 20.00 20 47 Swindon Town - - - - 1 30.00 22.00 22

37

English Premier League Season Tables

1992/93

English Premier League Season Table 1992/93 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 42 24 12 6 67 31 36 84 2 Aston Villa 42 21 11 10 57 40 17 74 3 Norwich City 42 21 9 12 61 65 -4 72 4 Blackburn Rovers 42 20 11 11 68 46 22 71 5 Queens Park Rangers 42 17 12 13 63 55 8 63 6 Liverpool 42 16 11 15 62 55 7 59 7 Sheffield Wednesday 42 15 14 13 55 51 4 59 8 Tottenham Hotspur 42 16 11 15 60 66 -6 59 9 Manchester City 42 15 12 15 56 51 5 57 10 Arsenal 42 15 1 16 40 38 2 56 11 Chelsea 42 14 14 14 51 54 -3 56 12 Wimbledon 42 14 12 16 56 55 1 54 13 Everton 42 15 8 19 53 55 -2 53 14 Sheffield United 42 14 10 18 54 53 1 52 15 Coventry City 42 13 13 16 52 57 -5 52 16 Ipswich Town 42 12 16 14 50 55 -5 52 17 Leeds United 42 12 15 15 57 62 -5 51 18 Southampton 42 13 11 18 54 61 -7 50 19 Oldham Athletic 42 13 10 19 63 74 -11 49 20 Crystal Palace 42 11 16 15 48 61 -13 49 21 Middlesbrough 42 11 11 20 54 75 -21 44 22 Nottingham Forest 42 10 10 22 41 62 -21 40

38

1993/94

English Premier League Season Table 1993/94 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 42 27 11 4 80 38 42 92 2 Blackburn Rovers 42 25 9 8 63 36 27 84 3 Newcastle United 42 23 8 11 82 41 41 77 4 Arsenal 42 18 17 7 53 28 25 71 5 Leeds United 42 18 16 8 65 39 26 70 6 Wimbledon 42 18 11 13 56 53 3 65 7 Sheffield Wednesday 42 16 16 10 76 54 22 64 8 Liverpool 42 17 9 16 59 55 4 60 9 Queens Park Rangers 42 16 12 14 62 61 1 60 10 Aston Villa 42 15 12 15 46 50 -4 57 11 Coventry City 42 14 14 14 43 45 -2 56 12 Norwich City 42 12 17 13 65 61 4 53 13 West Ham United 42 13 13 16 47 58 -11 52 14 Chelsea 42 13 12 17 49 53 -4 51 15 Tottenham Hotspur 42 11 12 19 54 59 -5 45 16 Manchester City 42 9 18 15 38 49 -11 45 17 Everton 42 12 8 22 42 63 -21 44 18 Southampton 42 12 7 23 49 66 -17 43 19 Ipswich Town 42 9 16 17 35 58 -23 43 20 Sheffield United 42 8 18 16 42 60 -18 42 21 Oldham Athletic 42 9 13 20 42 68 -26 40 22 Swindon Town 42 5 15 22 47 100 -53 30

39

1994/95

English Premier League Season Table 1994/95 Position Club Played Won Drawn Lost GF GA GD Points 1 Blackburn Rovers 42 27 8 7 80 39 41 89 2 Manchester United 42 26 10 6 77 28 49 88 3 Nottingham Forest 42 22 11 9 72 43 29 77 4 Liverpool 42 21 11 10 65 37 28 74 5 Leeds United 42 20 13 9 59 38 21 73 6 Newcastle United 42 20 12 10 67 47 20 72 7 Tottenham Hotspur 42 16 14 12 66 58 8 62 8 Queens Park Rangers 42 17 9 16 61 59 2 60 9 Wimbledon 42 15 11 16 48 65 -17 56 10 Southampton 42 12 18 12 61 63 -2 54 11 Chelsea 42 13 15 14 50 55 -5 54 12 Arsenal 42 13 12 17 52 49 3 51 13 Sheffield Wednesday 42 13 12 17 49 57 -8 51 14 West Ham United 42 13 11 18 44 48 -4 50 15 Everton 42 11 17 14 44 51 -7 50 16 Coventry City 42 12 14 16 44 62 -18 50 17 Manchester City 42 12 13 17 53 64 -11 49 18 Aston Villa 42 11 15 16 51 56 -5 48 19 Crystal Palace 42 11 12 19 34 49 -15 45 20 Norwich City 42 10 13 19 37 54 -17 43 21 Leicester City 42 6 11 25 45 80 -35 29 22 Ipswich Town 42 7 6 29 36 93 -57 27

40

1995/96

English Premier League Season Table 1995/96 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 38 25 7 6 73 35 38 82 2 Newcastle United 38 24 6 8 66 37 29 78 3 Liverpool 38 20 11 7 70 34 36 71 4 Aston Villa 38 18 9 11 52 35 17 63 5 Arsenal 38 17 12 9 49 32 17 63 6 Everton 38 17 10 11 64 44 20 61 7 Blackburn Rovers 38 18 7 13 61 47 14 61 8 Tottenham Hotspur 38 16 13 9 50 38 12 61 9 Nottingham Forest 38 15 13 10 50 54 -4 58 10 West Ham United 38 14 9 15 43 52 -9 51 11 Chelsea 38 12 14 12 46 44 2 50 12 Middlesbrough 38 11 10 17 35 50 -15 43 13 Leeds United 38 12 7 19 40 57 -17 43 14 Wimbledon 38 10 11 17 55 70 -15 41 15 Sheffield Wednesday 38 10 10 18 48 61 -13 40 16 Coventry City 38 8 14 16 42 60 -18 38 17 Southampton 38 9 11 18 34 52 -18 38 18 Manchester City 38 9 11 18 33 58 -25 38 19 Queens Park Rangers 38 9 6 23 38 57 -19 33 20 Bolton Wanderers 38 8 5 25 39 71 -32 29

41

1996/97

English Premier League Season Table 1996/97 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 38 21 12 5 76 44 32 75 2 Newcastle United 38 19 11 8 73 40 33 68 3 Arsenal 38 19 11 8 62 32 30 68 4 Liverpool 38 19 11 8 62 37 25 68 5 Aston Villa 38 17 10 11 47 34 13 61 6 Chelsea 38 16 11 11 58 55 3 59 7 Sheffield Wednesday 38 14 15 9 50 51 -1 57 8 Wimbledon 38 15 11 12 49 46 3 56 9 Leicester City 38 12 11 15 46 54 -8 47 10 Tottenham Hotspur 38 13 7 18 44 51 -7 46 11 Leeds United 38 11 13 14 28 38 -10 46 12 Derby County 38 11 13 14 45 58 -13 46 13 Blackburn Rovers 38 9 15 14 42 43 -1 42 14 West Ham United 38 10 12 16 39 48 -9 42 15 Everton 38 10 12 16 44 57 -13 42 16 Southampton 38 10 11 17 50 56 -6 41 17 Coventry City 38 9 14 15 38 54 -16 41 18 Sunderland 38 10 10 18 35 53 -18 40 19 Middlesbrough 38 10 12 16 51 60 -9 39 20 Nottingham Forest 38 6 16 16 31 59 -28 34

42

1997/98

English Premier League Season Table 1997/98 Position Club Played Won Drawn Lost GF GA GD Points 1 Arsenal 38 23 9 6 68 33 35 78 2 Manchester United 38 23 8 7 73 26 47 77 3 Liverpool 38 18 11 9 68 42 26 65 4 Chelsea 38 20 3 15 71 43 28 63 5 Leeds United 38 17 8 13 57 46 11 59 6 Blackburn Rovers 38 16 10 12 57 52 5 58 7 Aston Villa 38 17 6 15 49 48 1 57 8 West Ham United 38 16 8 14 56 57 -1 56 9 Derby County 38 16 7 15 52 49 3 55 10 Leicester City 38 13 14 11 51 41 10 53 11 Coventry City 38 12 16 10 46 44 2 52 12 Southampton 38 14 6 18 50 55 -5 48 13 Newcastle United 38 11 11 16 35 44 -9 44 14 Tottenham Hotspur 38 11 11 16 44 56 -12 44 15 Wimbledon 38 10 14 14 34 46 -12 44 16 Sheffield Wednesday 38 12 8 18 52 67 -15 44 17 Everton 38 9 13 16 41 56 -15 40 18 Bolton Wanderers 38 9 13 16 41 61 -20 40 19 Barnsley 38 10 5 23 37 82 -45 35 20 Crystal Palace 38 8 9 21 37 71 -34 33

43

1998/99

English Premier League Season Table 1998/99 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 38 22 13 3 80 37 43 79 2 Arsenal 38 22 12 4 59 17 42 78 3 Chelsea 38 20 15 3 57 30 27 75 4 Leeds United 38 18 13 7 62 34 28 67 5 West Ham United 38 16 9 13 46 53 -7 57 6 Aston Villa 38 15 10 13 51 46 5 55 7 Liverpool 38 15 9 14 68 49 19 54 8 Derby County 38 13 13 12 40 45 -5 52 9 Middlesbrough 38 12 15 11 48 54 -6 51 10 Leicester City 38 12 13 13 40 46 -6 49 11 Tottenham Hotspur 38 11 14 13 47 50 -3 47 12 Sheffield Wednesday 38 13 7 18 41 42 -1 46 13 Newcastle United 38 11 13 14 48 54 -6 46 14 Everton 38 11 10 17 42 47 -5 43 15 Coventry City 38 11 9 18 39 51 -12 42 16 Wimbledon 38 10 12 16 40 63 -23 42 17 Southampton 38 11 8 19 37 64 -27 41 18 Charlton Athletic 38 8 12 18 41 56 -15 36 19 Blackburn Rovers 38 7 14 17 38 52 -14 35 20 Nottingham Forest 38 7 9 22 35 69 -34 30

44

1999/00

English Premier League Season Table 1999/00 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 38 28 7 3 97 45 52 91 2 Arsenal 38 22 7 9 73 43 30 73 3 Leeds United 38 21 6 11 58 43 15 69 4 Liverpool 38 19 10 9 51 30 21 67 5 Chelsea 38 18 11 9 53 34 19 65 6 Aston Villa 38 15 13 10 46 35 11 58 7 Sunderland 38 16 10 12 57 56 1 58 8 Leicester City 38 16 7 15 55 55 0 55 9 West Ham United 38 15 10 13 52 53 -1 55 10 Tottenham Hotspur 38 15 8 15 57 49 8 53 11 Newcastle United 38 14 10 14 63 54 9 52 12 Middlesbrough 38 14 10 14 46 52 -6 52 13 Everton 38 12 14 12 59 49 10 50 14 Coventry City 38 12 8 18 47 54 -7 44 15 Southampton 38 12 8 18 45 62 -17 44 16 Derby County 38 9 11 18 44 57 -13 38 17 Bradford City 38 9 9 20 38 68 -30 36 18 Wimbledon 38 7 12 19 46 74 -28 33 19 Sheffield Wednesday 38 8 7 23 38 70 -32 31 20 Watford 38 6 6 26 35 77 -42 24

45

2000/01

English Premier League Season Table 2000/01 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 38 24 8 6 79 31 48 80 2 Arsenal 38 20 10 8 63 38 25 70 3 Liverpool 38 20 9 9 71 39 32 69 4 Leeds United 38 20 8 10 64 43 21 68 5 Ipswich Town 38 20 6 12 57 42 15 66 6 Chelsea 38 17 10 11 68 45 23 61 7 Sunderland 38 15 12 11 46 41 5 57 8 Aston Villa 38 13 15 10 46 43 3 54 9 Charlton Athletic 38 14 10 14 50 57 -7 52 10 Southampton 38 14 10 14 40 48 -8 52 11 Newcastle United 38 14 9 15 44 50 -6 51 12 Tottenham Hotspur 38 13 10 15 47 54 -7 49 13 Leicester City 38 14 6 18 39 51 -12 48 14 Middlesbrough 38 9 15 14 44 44 0 42 15 West Ham United 38 10 12 16 45 50 -5 42 16 Everton 38 11 9 18 45 59 -14 42 17 Derby County 38 10 12 16 37 59 -22 42 18 Manchester City 38 8 10 20 41 65 -24 34 19 Blackburn Rovers 38 8 10 20 36 63 -27 34 20 Bradford City 38 5 11 22 30 70 -40 26

46

2001/02

English Premier League Season Table 2001/02 Position Club Played Won Drawn Lost GF GA GD Points 1 Arsenal 38 26 9 3 79 36 43 87 2 Liverpool 38 24 8 6 67 30 37 80 3 Manchester United 38 24 5 9 87 45 42 77 4 Newcastle United 38 21 8 9 74 52 22 71 5 Leeds United 38 18 12 8 53 37 16 66 6 Chelsea 38 17 13 8 66 38 28 64 7 West Ham United 38 12 14 12 46 47 -1 50 8 Aston Villa 38 12 14 12 46 47 -1 50 9 Tottenham Hotspur 38 14 8 16 49 53 -4 50 10 Blackburn Rovers 38 12 10 16 55 51 4 46 11 Southampton 38 12 9 17 46 54 -8 45 12 Middlesbrough 38 12 9 17 35 47 -12 45 13 Fulham 38 10 14 14 36 44 -8 44 14 Charlton Athletic 38 10 14 14 38 49 -11 44 15 Everton 38 11 10 17 45 57 -12 43 16 Bolton Wanderers 38 9 13 16 44 62 -18 40 17 Sunderland 38 10 10 18 29 51 -22 40 18 Ipswich Town 38 9 9 20 41 64 -23 36 19 Derby County 38 8 6 24 33 63 -30 30 20 Leicester City 38 5 13 20 30 64 -34 28

47

2002/03

English Premier League Season Table 2002/03 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 38 25 8 5 74 34 40 83 2 Arsenal 38 23 9 6 85 42 43 78 3 Newcastle United 38 21 6 11 63 48 15 69 4 Chelsea 38 19 10 9 68 38 30 67 5 Liverpool 38 18 10 10 61 41 20 64 6 Blackburn Rovers 38 16 12 10 52 43 9 60 7 Everton 38 17 8 13 48 49 -1 59 8 Southampton 38 13 13 12 43 46 -3 52 9 Manchester City 38 15 6 17 47 54 -7 51 10 Tottenham Hotspur 38 14 8 16 51 62 -11 50 11 Middlesbrough 38 13 10 15 48 44 4 49 12 Charlton Athletic 38 14 7 17 45 56 -11 49 13 Birmingham City 38 13 9 16 41 49 -8 48 14 Fulham 38 13 9 16 41 50 -9 48 15 Leeds United 38 14 5 19 58 57 1 47 16 Aston Villa 38 12 9 17 42 47 -5 45 17 Bolton Wanderers 38 10 14 14 41 51 -10 44 18 West Ham United 38 10 12 16 42 59 -17 42 19 West Bromwich Albion 38 6 8 24 29 65 -36 26 20 Sunderland 38 4 7 27 21 65 -44 19

48

2003/04

English Premier League Season Table 2003/04 Position Club Played Won Drawn Lost GF GA GD Points 1 Arsenal 38 26 12 0 73 26 47 90 2 Chelsea 38 24 7 7 67 30 37 79 3 Manchester United 38 23 6 9 64 35 29 75 4 Liverpool 38 16 12 10 55 37 18 60 5 Newcastle United 38 13 17 8 52 40 12 56 6 Aston Villa 38 15 11 12 48 44 4 56 7 Charlton Athletic 38 14 11 13 51 51 0 53 8 Bolton Wanderers 38 14 11 13 48 56 -8 53 9 Fulham 38 14 10 14 52 46 6 52 10 Birmingham City 38 12 14 12 43 48 -5 50 11 Middlesbrough 38 13 9 16 44 52 -8 48 12 Southampton 38 12 11 15 44 45 -1 47 13 Portsmouth 38 12 9 17 47 54 -7 45 14 Tottenham Hotspur 38 13 6 19 47 57 -10 45 15 Blackburn Rovers 38 12 8 18 51 59 -8 44 16 Manchester City 38 9 14 15 55 54 1 41 17 Everton 38 9 12 17 45 57 -12 39 18 Leicester City 38 6 15 17 48 65 -17 33 19 Leeds United 38 8 9 21 40 79 -39 33 20 Wolverhampton Wanderers 38 7 12 19 38 77 -39 33

49

2004/05

English Premier League Season Table 2004/05 Position Club Played Won Drawn Lost GF GA GD Points 1 Chelsea 38 29 8 1 72 15 57 95 2 Arsenal 38 25 8 5 87 36 51 83 3 Manchester United 38 22 11 5 58 26 32 77 4 Everton 38 18 7 13 45 46 -1 61 5 Liverpool 38 17 7 14 52 41 11 58 6 Bolton Wanderers 38 16 10 12 49 44 5 58 7 Middlesbrough 38 14 13 11 53 46 7 55 8 Manchester City 38 13 13 12 47 39 8 52 9 Tottenham Hotspur 38 14 10 14 47 41 6 52 10 Aston Villa 38 12 11 15 45 52 -7 47 11 Charlton Athletic 38 12 10 16 42 58 -16 46 12 Birmingham City 38 11 12 15 40 46 -6 45 13 Fulham 38 12 8 18 52 60 -8 44 14 Newcastle United 38 10 14 14 47 57 -10 44 15 Blackburn Rovers 38 9 15 14 32 43 -11 42 16 Portsmouth 38 10 9 19 43 59 -16 39 17 West Bromwich Albion 38 6 16 16 36 61 -25 34 18 Crystal Palace 38 7 12 19 41 62 -21 33 19 Norwich City 38 7 12 19 42 77 -35 33 20 Southampton 38 6 14 18 45 66 -21 32

50

2005/06

English Premier League Season Table 2005/06 Position Club Played Won Drawn Lost GF GA GD Points 1 Chelsea 38 29 4 5 72 22 50 91 2 Manchester United 38 25 8 5 72 34 38 83 3 Liverpool 38 25 7 6 57 25 32 82 4 Arsenal 38 20 7 11 68 31 37 67 5 Tottenham Hotspur 38 18 11 9 53 38 15 65 6 Blackburn Rovers 38 19 6 13 51 42 9 63 7 Newcastle United 38 17 7 14 47 42 5 58 8 Bolton Wanderers 38 15 11 12 49 41 8 56 9 West Ham United 38 16 7 15 52 55 -3 55 10 Wigan Athletic 38 15 6 17 45 52 -7 51 11 Everton 38 14 8 16 34 49 -15 50 12 Fulham 38 14 6 18 48 58 -10 48 13 Charlton Athletic 38 13 8 17 41 55 -14 47 14 Middlesbrough 38 12 9 17 48 58 -10 45 15 Manchester City 38 13 4 21 43 48 -5 43 16 Aston Villa 38 10 12 16 42 55 -13 42 17 Portsmouth 38 10 8 20 37 62 -25 38 18 Birmingham City 38 8 10 20 28 50 -22 34 19 West Bromwich Albion 38 7 9 22 31 58 -27 30 20 Sunderland 38 3 6 29 26 69 -43 15

51

2006/07

English Premier League Season Table 2006/07 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 38 28 5 5 83 27 56 89 2 Chelsea 38 24 11 3 64 24 40 83 3 Liverpool 38 20 8 10 57 27 30 68 4 Arsenal 38 19 11 8 63 35 28 68 5 Tottenham Hotspur 38 17 9 12 57 54 3 60 6 Everton 38 15 13 10 52 36 16 58 7 Bolton Wanderers 38 16 8 14 47 52 -5 56 8 Reading 38 16 7 15 52 47 5 55 9 Portsmouth 38 14 12 12 45 42 3 54 10 Blackburn Rovers 38 15 7 16 52 54 -2 52 11 Aston Villa 38 11 17 10 43 41 2 50 12 Middlesbrough 38 12 10 16 44 49 -5 46 13 Newcastle United 38 11 10 17 38 47 -9 43 14 Manchester City 38 11 9 18 29 44 -15 42 15 West Ham United 38 12 5 21 35 59 -24 41 16 Fulham 38 8 15 15 38 60 -22 39 17 Wigan Athletic 38 10 8 20 37 59 -22 38 18 Sheffield United 38 10 8 20 32 55 -23 38 19 Charlton Athletic 38 8 10 20 34 60 -26 34 20 Watford 38 5 13 20 29 59 -30 28

52

2007/08

English Premier League Season Table 2007/08 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 38 27 6 5 80 22 58 87 2 Chelsea 38 25 10 3 65 26 39 85 3 Arsenal 38 24 11 3 74 31 43 83 4 Liverpool 38 21 13 4 67 28 39 76 5 Everton 38 19 8 11 55 33 22 65 6 Aston Villa 38 16 12 10 71 51 20 60 7 Blackburn Rovers 38 15 13 10 50 48 2 58 8 Portsmouth 38 16 9 13 48 40 8 57 9 Manchester City 38 15 10 13 45 53 -8 55 10 West Ham United 38 13 10 15 42 50 -8 49 11 Tottenham Hotspur 38 11 13 14 66 61 5 46 12 Newcastle United 38 11 10 17 45 65 -20 43 13 Middlesbrough 38 10 12 16 43 53 -10 42 14 Wigan Athletic 38 10 10 18 34 51 -17 40 15 Sunderland 38 11 6 21 36 59 -23 39 16 Bolton Wanderers 38 9 10 19 36 54 -18 37 17 Fulham 38 8 12 18 38 60 -22 36 18 Reading 38 10 6 22 41 66 -25 36 19 Birmingham City 38 8 11 19 46 62 -16 35 20 Derby County 38 1 8 29 20 89 -69 11

53

2008/09

English Premier League Season Table 2008/09 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 38 28 6 4 68 24 44 90 2 Liverpool 38 25 11 2 77 27 50 86 3 Chelsea 38 25 8 5 68 24 44 83 4 Arsenal 38 20 12 6 68 37 31 72 5 Everton 38 17 12 9 55 37 18 63 6 Aston Villa 38 17 11 10 54 48 6 62 7 Fulham 38 14 11 13 39 34 5 53 8 Tottenham Hotspur 38 14 9 15 45 45 0 51 9 West Ham United 38 14 9 15 42 45 -3 51 10 Manchester City 38 15 5 18 58 50 8 50 11 Wigan Athletic 38 12 9 17 34 45 -11 45 12 Stoke City 38 12 9 17 38 55 -17 45 13 Bolton Wanderers 38 11 8 19 41 53 -12 41 14 Portsmouth 38 10 11 17 38 57 -19 41 15 Blackburn Rovers 38 10 11 17 40 60 -20 41 16 Sunderland 38 9 9 20 34 54 -20 36 17 Hull City 38 8 11 19 39 64 -25 35 18 Newcastle United 38 7 13 18 40 59 -19 34 19 Middlesbrough 38 7 11 20 28 57 -29 32 20 West Bromwich Albion 38 8 8 22 36 67 -31 32

54

2009/10

English Premier League Season Table 2009/10 Position Club Played Won Drawn Lost GF GA GD Points 1 Chelsea 38 27 5 6 103 32 71 86 2 Manchester United 38 27 4 7 86 28 58 85 3 Arsenal 38 23 6 9 83 41 42 75 4 Tottenham Hotspur 38 21 7 10 67 41 26 70 5 Manchester City 38 18 13 7 73 45 28 67 6 Aston Villa 38 17 13 8 52 39 13 64 7 Liverpool 38 18 9 11 61 35 26 63 8 Everton 38 16 13 9 60 49 11 61 9 Birmingham City 38 13 11 14 38 47 -9 50 10 Blackburn Rovers 38 13 11 14 41 55 -14 50 11 Stoke City 38 11 14 13 34 48 -14 47 12 Fulham 38 12 10 16 39 46 -7 46 13 Sunderland 38 11 11 16 48 56 -8 44 14 Bolton Wanderers 38 10 9 19 42 67 -25 39 15 Wolverhampton Wanderers 38 9 11 18 32 56 -24 38 16 Wigan Athletic 38 9 9 20 37 79 -42 36 17 West Ham United 38 8 11 19 47 66 -19 35 18 Burnley 38 8 6 24 42 82 -40 30 19 Hull City 38 6 12 20 34 75 -41 30 20 Portsmouth 38 7 7 24 34 66 -32 19

55

2010/11

English Premier League Season Table 2010/11 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 38 23 11 4 78 37 41 80 2 Chelsea 38 21 8 9 69 33 36 71 3 Manchester City 38 21 8 9 60 33 27 71 4 Arsenal 38 19 11 8 72 43 29 68 5 Tottenham Hotspur 38 16 14 8 55 46 9 62 6 Liverpool 38 17 7 14 59 44 15 58 7 Everton 38 13 15 10 51 45 6 54 8 Fulham 38 11 16 11 49 43 6 49 9 Aston Villa 38 12 12 14 48 59 -11 48 10 Sunderland 38 12 11 15 45 56 -11 47 11 West Bromwich Albion 38 12 11 15 56 71 -15 47 12 Newcastle United 38 11 13 14 56 57 -1 46 13 Stoke City 38 13 7 18 46 48 -2 46 14 Bolton Wanderers 38 12 10 16 52 56 -4 46 15 Blackburn Rover 38 11 10 17 46 59 -13 43 16 Wigan Athletic 38 9 15 14 40 61 -21 42 17 Wolverhampton Wanderers 38 11 7 20 46 66 -20 40 18 Birmingham City 38 8 15 15 37 58 -21 39 19 Blackpool 38 10 9 19 55 78 -23 39 20 West Ham United 38 7 12 19 43 70 -27 33

56

2011/12

English Premier League Season Table 2011/12 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester City 38 28 5 5 93 29 64 89 2 Manchester United 38 28 5 5 89 33 56 89 3 Arsenal 38 21 7 10 74 49 25 70 4 Tottenham Hotspur 38 20 9 9 66 41 25 69 5 Newcastle United 38 19 8 11 56 51 5 65 6 Chelsea 38 18 10 10 65 46 19 64 7 Everton 38 15 11 12 50 40 10 56 8 Liverpool 38 14 10 14 47 40 7 52 9 Fulham 38 14 10 14 48 51 -3 52 10 West Bromwich Albion 38 13 8 17 45 52 -7 47 11 Swansea City 38 12 11 15 44 51 -7 47 12 Norwich City 38 12 11 15 52 66 -14 47 13 Sunderland 38 11 12 15 45 46 -1 45 14 Stoke City 38 11 12 15 36 53 -17 45 15 Wigan Athletic 38 11 10 17 42 62 -20 43 16 Aston Villa 38 7 17 14 37 53 -16 38 17 Queens Park Rangers 38 10 7 21 43 66 -23 37 18 Bolton Wanderers 38 10 6 22 46 77 -31 36 19 Blackburn Rovers 38 8 7 23 48 78 -30 31 20 Wolverhampton Wanderers 38 5 10 23 40 82 -42 25

57

2012/13

English Premier League Season Table 2012/13 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester United 38 28 5 5 86 43 43 89 2 Manchester City 38 23 9 6 66 34 32 78 3 Chelsea 38 22 9 7 75 39 36 75 4 Arsenal 38 21 10 7 72 37 35 73 5 Tottenham Hotspur 38 21 9 8 66 46 20 72 6 Everton 38 16 15 7 55 40 15 63 7 Liverpool 38 16 13 9 71 43 28 61 8 West Bromwich Albion 38 14 7 17 53 57 -4 49 9 Swansea City 38 11 13 14 47 51 -4 46 10 West Ham United 38 12 10 16 45 53 -8 46 11 Norwich City 38 10 14 14 41 58 -17 44 12 Fulham 38 11 10 17 50 60 -10 43 13 Stoke City 38 9 15 14 34 45 -11 42 14 Southampton 38 9 14 15 49 60 -11 41 15 Aston Villa 38 10 11 17 47 69 -22 41 16 Newcastle United 38 11 8 19 45 68 -23 41 17 Sunderland 38 9 12 17 41 54 -13 39 18 Wigan Athletic 38 9 9 20 47 73 -26 36 19 Reading 38 6 10 22 43 73 -30 28 20 Queens Park Rangers 38 4 13 21 30 60 -30 25

58

2013/14

English Premier League Season Table 2013/14 Position Club Played Won Drawn Lost GF GA GD Points 1 Manchester City 38 27 5 6 102 37 65 86 2 Liverpool 38 26 6 6 101 50 51 84 3 Chelsea 38 25 7 6 71 27 44 82 4 Arsenal 38 24 7 7 68 41 27 79 5 Everton 38 21 9 8 61 39 22 72 6 Tottenham Hotspur 38 21 6 11 55 51 4 69 7 Manchester United 38 19 7 12 64 43 21 64 8 Southampton 38 15 11 12 54 46 8 56 9 Stoke City 38 13 11 14 45 52 -7 50 10 Newcastle United 38 15 4 19 43 59 -16 49 11 Crystal Palace 38 13 6 19 33 48 -15 45 12 Swansea City 38 11 9 18 54 54 0 42 13 West Ham United 38 11 7 20 40 51 -11 40 14 Sunderland 38 10 8 20 41 60 -19 38 15 Aston Villa 38 10 8 20 39 61 -22 38 16 Hull City 38 10 7 21 38 53 -15 37 17 West Bromwich Albion 38 7 15 16 43 59 -16 36 18 Norwich City 38 8 9 21 28 62 -34 33 19 Fulham 38 9 5 24 40 85 -45 32 20 Cardiff City 38 7 9 22 32 74 -42 30

59

2014/15

English Premier League Season Table 2014/15 Position Club Played Won Drawn Lost GF GA GD Points 1 Chelsea 38 26 9 3 73 32 41 87 2 Manchester City 38 24 7 7 83 38 45 79 3 Arsenal 38 22 9 7 71 36 35 75 4 Manchester United 38 20 10 8 62 37 25 70 5 Tottenham Hotspur 38 19 7 12 58 53 5 64 6 Liverpool 38 18 8 12 52 48 4 62 7 Southampton 38 18 6 14 54 33 21 60 8 Swansea City 38 16 8 14 46 49 -3 56 9 Stoke City 38 15 9 14 48 45 3 54 10 Crystal Palace 38 13 9 16 47 51 -4 48 11 Everton 38 12 11 15 48 50 -2 47 12 West Ham United 38 12 11 15 44 47 -3 47 13 West Bromwich Albion 38 11 11 16 38 51 -13 44 14 Leicester City 38 11 8 19 46 55 -9 41 15 Newcastle United 38 10 9 19 40 63 -23 39 16 Sunderland 38 7 17 14 31 53 -22 38 17 Aston Villa 38 10 8 20 31 57 -26 38 18 Hull City 38 8 11 19 33 51 -18 35 19 Burnley 38 7 12 19 28 53 -25 33 20 Queens Park Rangers 38 8 6 24 42 73 -31 30

60

2015/16

English Premier League Season Table 2015/16 Position Club Played Won Drawn Lost GF GA GD Points 1 Leicester City 38 23 12 3 68 36 32 81 2 Arsenal 38 20 11 7 65 36 29 71 3 Tottenham Hotspur 38 19 13 6 69 35 34 70 4 Manchester City 38 19 9 10 71 41 30 66 5 Manchester United 38 19 9 10 49 35 14 66 6 Southampton 38 18 9 11 59 41 18 63 7 West Ham United 38 16 14 8 65 51 14 62 8 Liverpool 38 16 12 10 63 50 13 60 9 Stoke City 38 14 9 15 41 55 -14 51 10 Chelsea 38 12 14 12 59 53 6 50 11 Everton 38 11 14 13 59 55 4 47 12 Swansea City 38 12 11 15 42 52 -10 47 13 Watford 38 12 9 17 40 50 -10 45 14 West Bromwich Albion 38 10 13 15 34 48 -14 43 15 Crystal Palace 38 11 9 18 39 51 -12 42 16 Bournemouth 38 11 9 18 45 67 -22 42 17 Sunderland 38 9 12 17 48 62 -14 39 18 Newcastle United 38 9 10 19 44 65 -21 37 19 Norwich City 38 9 7 22 39 67 -28 34 20 Aston Villa 38 3 8 27 27 76 -49 17 61

Appendix B

English Premier League Player Ratings and Prices

Field Players

Name Overall EPL Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Rating Fantasy Price Mesut 89 9.4 CAM Germany Arsenal Gold 72 74 86 86 24 58 Özil Alexis 87 11.8 LW Chile Arsenal Gold 86 82 79 88 39 74 Sánchez Santi 86 7 CAM Spain Arsenal Gold 71 78 85 86 57 64 Cazorla Laurent 85 6.3 CB France Arsenal Gold 78 40 62 65 85 78 Koscielny Aaron 84 7.6 CM Wales Arsenal Gold 69 77 80 81 68 76 Ramsey Granit 84 5.2 CM Switzerla Arsenal Gold 51 66 81 72 72 77 Xhaka nd Per 83 4.8 CB Germany Arsenal Gold 27 41 56 48 88 75 Mertesac ker Shkodran 83 5.8 CB Germany Arsenal Gold 70 57 63 60 83 79 Mustafi Theo 81 7.4 LM England Arsenal Gold 93 77 73 80 38 66 Walcott Nacho 81 5.9 LB Spain Arsenal Gold 77 53 72 75 81 73 Monreal Francis 81 4.3 CDM France Arsenal Gold 70 54 70 76 79 81 Coquelin Mathieu 80 4.6 RB France Arsenal Gold 74 65 73 73 79 77 Debuchy Alex 79 5.8 RM England Arsenal Gold 89 71 73 84 42 70 Oxlade- Chamberl ain Héctor 79 6.5 RB Spain Arsenal Gold 95 51 68 77 74 68 Bellerín Gabriel 79 4.8 CB Brazil Arsenal Gold 70 54 65 64 80 76 Gabriel 78 4.8 CB Brazil Arsenal Gold 70 54 65 64 82 76 Mohame 74 4.5 CDM Egypt Arsenal Silver 63 67 72 69 70 77 d Elneny Carl 73 4.8 RB England Arsenal Silver 74 50 66 68 72 74 62

Jenkinson Alex 70 6 LM Nigeria Arsenal Silver 77 59 60 76 28 64 Iwobi Rob 67 4 CB England Arsenal Silver 65 34 52 61 65 72 Holding Ainsley 65 4.5 RM England Arsenal Silver 77 58 59 67 39 49 Maitland- Niles Jeff 63 4.5 CAM France Arsenal Bronze 78 48 57 66 44 54 Reine- Adelaïde Olivier 83 8.3 ST France Arsenal Gold 64 82 69 72 38 83 Giroud Lucas 81 7.8 ST Spain Arsenal Gold 76 83 76 80 31 72 Pérez Danny 80 7.3 ST England Arsenal Gold 86 75 71 80 34 79 Welbeck Yaya 69 4.6 ST France Arsenal Silver 69 65 52 60 28 69 Sanogo Jack 82 5.9 CM England Bournemouth Gold 76 69 81 84 62 73 Wilshere Max 79 5 LW Ivory Bournemouth Gold 88 74 71 82 44 61 Gradel Coast Harry 75 5 CM Ireland Bournemouth Gold 69 72 72 73 63 78 Arter Junior 73 5.1 RM England Bournemouth Silver 84 72 72 75 36 60 Stanislas Andrew 72 4.6 CM England Bournemouth Silver 63 63 75 70 62 70 Surman Marc 72 4.6 LM England Bournemouth Silver 74 66 66 75 39 68 Pugh Dan 72 4.7 CM England Bournemouth Silver 65 68 70 68 67 69 Gosling Charlie 72 5.1 LB England Bournemouth Silver 78 52 71 70 70 70 Daniels Ryan 72 4.8 LM Scotland Bournemouth Silver 90 63 66 75 42 56 Fraser Steve 72 4.9 CB England Bournemouth Silver 58 24 50 60 72 75 Cook Jordon 71 4.9 LW England Bournemouth Silver 86 67 60 75 48 68 Ibe Adam 71 4 RB England Bournemouth Silver 76 53 65 70 68 67 Smith Tyrone 71 4.5 LB England Bournemouth Silver 78 36 59 67 69 77 Mings Lewis 71 4.2 CM England Bournemouth Silver 76 56 66 76 62 70 Cook Baily 65 4 CB England Bournemouth Silver 61 29 47 57 64 65 Cargill 63

Callum 76 6.1 ST England Bournemouth Gold 91 73 63 75 34 78 Wilson Benik 74 5.5 ST England Bournemouth Silver 83 73 60 71 26 72 Afobe Joshua 72 5.3 ST Norway Bournemouth Silver 90 68 60 74 30 62 King Lys 68 5.1 ST France Bournemouth Silver 84 72 39 67 21 69 Mousset Steven 79 5.7 CM Belgium Burnley Gold 68 70 77 76 74 73 Defour Joey 76 4.5 CM England Burnley Gold 55 69 76 73 70 81 Barton Robbie 76 5.5 LM Ireland Burnley Gold 81 69 75 75 63 68 Brady Matthew 75 4.5 RB England Burnley Gold 76 59 66 70 72 75 Lowton Ashley 75 4.5 CM England Burnley Gold 66 53 79 68 64 64 Westwoo d Michael 75 5 CB England Burnley Gold 67 39 56 60 76 75 Keane Ben Mee 74 4.7 CB England Burnley Silver 69 37 60 65 73 77 Jon 73 4.3 RB England Burnley Silver 70 39 59 69 74 75 Flanagan Stephen 72 4.5 LB Ireland Burnley Silver 68 59 67 67 71 73 Ward Scott 72 5.2 LM Canada Burnley Silver 71 68 70 73 63 68 Arfield Jeff 72 5.3 CM Ireland Burnley Silver 68 63 67 67 67 70 Hendrick Jóhann 72 4.9 RM Iceland Burnley Silver 76 72 68 75 49 70 Berg Gudmund sson George 71 5.1 RM Scotland Burnley Silver 67 69 69 72 57 68 Boyd Dean 71 4.4 CM England Burnley Silver 67 65 70 69 68 80 Marney James 71 4 CB England Burnley Silver 68 24 47 61 72 71 Tarkowski Tendayi 70 4.2 RB England Burnley Silver 89 48 58 68 63 76 Darikwa Kevin 66 4 CB Ireland Burnley Silver 54 27 35 50 65 66 Long Andre 75 6.1 ST England Burnley Gold 91 74 60 70 23 75 Gray Sam 73 5.7 ST Wales Burnley Silver 60 72 62 66 40 80 Vokes Ashley 71 4.5 ST Austria Burnley Silver 69 70 60 70 42 78 64

Barnes Diego 86 10.6 ST Spain Chelsea Gold 77 83 63 78 40 89 Costa Eden 88 10.2 LM Belgium Chelsea Gold 90 81 82 91 32 64 Hazard Cesc 86 6.9 CM Spain Chelsea Gold 63 77 89 81 61 64 Fàbregas Willian 85 7 RM Brazil Chelsea Gold 89 74 80 86 50 63 John 84 5.2 CB England Chelsea Gold 34 47 57 51 86 80 Terry David Luiz 84 6.3 CB Brazil Chelsea Gold 73 64 73 71 82 79 Azpilicuet 84 6.5 LB Spain Chelsea Gold 79 56 75 74 84 77 a Nemanja 84 5 CDM Serbia Chelsea Gold 67 70 76 72 81 88 Matić Gary 83 6.5 CB England Chelsea Gold 67 58 52 61 84 76 Cahill Pedro 83 6.9 LM Spain Chelsea Gold 82 76 77 84 37 62 Kurt 80 5.3 CB France Chelsea Gold 73 50 58 55 80 81 Zouma Victor 77 5.6 LM Nigeria Chelsea Gold 82 71 69 81 38 69 Moses Marcos 77 6.7 LM Spain Chelsea Gold 79 66 72 76 73 79 Alonso Kenedy 73 5.1 LM Brazil Chelsea Silver 82 66 65 77 46 64 Nathan 72 3.9 LB Netherlan Chelsea Silver 78 49 65 66 71 73 Aké ds Michy 81 8.5 ST Belgium Chelsea Gold 83 80 59 79 27 73 Batshuayi Nathaniel 68 4.5 CM England Chelsea Silver 73 51 62 69 67 72 Chalobah Ruben 68 5 CAM England Chelsea Silver 70 54 60 67 64 68 Loftus- Cheek Ola Aina 63 4.5 RB England Chelsea Bronze 78 35 46 59 62 62 Dominic 69 5 ST England Chelsea Silver 76 67 62 73 24 60 Solanke Mamado 82 4.7 CB France Crystal Palace Gold 62 33 62 56 81 84 u Sakho Yohan 81 5.6 CDM France Crystal Palace Gold 67 76 82 78 74 73 Cabaye Loïc Rémy 81 6.2 ST France Crystal Palace Gold 89 80 68 74 34 72 Christian 81 7.2 ST Belgium Crystal Palace Gold 77 79 65 75 32 84 Benteke Wilfried 79 5.6 RM England Crystal Palace Gold 89 69 70 86 32 72 Zaha Scott 79 5.2 CB England Crystal Palace Gold 52 37 54 50 81 78 Dann 65

Andros 78 5.8 RM England Crystal Palace Gold 89 75 72 80 32 62 Townsen d James 77 4.8 CDM Scotland Crystal Palace Gold 65 60 72 75 72 75 McArthur Pape 76 4.2 LB Senegal Crystal Palace Gold 81 49 72 73 74 75 Souaré Joel Ward 76 4.8 RB England Crystal Palace Gold 70 54 67 66 78 76 Jason 76 5.2 CAM England Crystal Palace Gold 76 75 76 78 36 68 Puncheon James 76 4.9 CB England Crystal Palace Gold 54 33 53 54 77 75 Tomkins Joe 75 4.4 CM Wales Crystal Palace Gold 67 66 73 71 70 78 Ledley Patrick 75 5 LB Netherlan Crystal Palace Gold 88 62 67 74 71 73 van ds Aanholt Bakary 75 5.3 LM Mali Crystal Palace Gold 81 75 73 74 41 77 Sako Damien 75 4.8 CB Ireland Crystal Palace Gold 45 42 52 38 76 81 Delaney Martin 74 4.2 RB England Crystal Palace Silver 69 45 62 63 77 76 Kelly Luka 74 5 CDM Serbia Crystal Palace Silver 64 62 69 66 71 80 Milivojevi ć Lee 73 4.2 RM Korea Crystal Palace Silver 76 63 71 75 51 64 Chung Republic Yong Jeffrey 71 4.7 LB Ghana Crystal Palace Silver 88 60 61 74 66 77 Schlupp Sullay 66 4.5 LM England Crystal Palace Silver 84 66 62 71 26 52 Kaikai Ezekiel 66 4.5 LB England Crystal Palace Silver 73 37 56 65 64 69 Fryers Connor 74 5.5 ST England Crystal Palace Silver 71 74 61 71 27 77 Wickham Frazier 71 4.4 ST England Crystal Palace Silver 79 70 59 71 34 66 Campbell Romelu 84 9.9 ST Belgium Everton Gold 82 82 66 74 34 84 Lukaku Ashley 83 5.1 CB Wales Everton Gold 70 43 56 62 82 82 Williams Leighton 83 5.7 LB England Everton Gold 75 75 81 77 80 74 Baines Phil 82 4.6 CB England Everton Gold 67 46 58 55 83 81 Jagielka Séamus 82 5.9 RB Ireland Everton Gold 79 68 72 78 81 78 Coleman 66

Morgan 82 4.6 CDM France Everton Gold 66 63 75 75 80 82 Schneider lin James 81 4.8 CDM Ireland Everton Gold 72 67 76 76 78 81 McCarthy Kevin 81 6.1 LM Belgium Everton Gold 88 77 77 83 47 65 Mirallas Ross 81 7.1 CAM England Everton Gold 78 73 76 82 56 75 Barkley Gareth 80 4.4 CDM England Everton Gold 33 65 80 66 80 78 Barry Aaron 78 5.5 RM England Everton Gold 89 65 71 84 38 63 Lennon Yannick 78 5.7 LM DR Congo Everton Gold 89 68 68 84 31 79 Bolasie Ramiro 77 4.7 CB Argentina Everton Gold 72 50 61 65 76 79 Funes Mori Muhame 76 4.3 CDM Bosnia Everton Gold 72 50 64 74 76 75 d Bešić Herzegovi na Idrissa 76 5 CM Senegal Everton Gold 74 58 68 75 78 78 Gueye Tom 64 4.4 CM England Everton Bronze 73 47 59 65 56 68 Davies Matthew 64 3.9 CB England Everton Bronze 58 33 41 48 64 69 Penningto n Mason 61 4.2 RB England Everton Silver 80 42 52 61 64 69 Holgate Jonjoe 57 4 CB England Everton Bronze 62 33 50 51 57 60 Kenny Enner 76 5.2 ST Colombia Everton Gold 85 76 66 74 43 70 Valencia Arouna 75 5.1 ST Ivory Everton Gold 77 73 62 74 35 74 Koné Coast Dominic 63 4.5 ST England Everton Bronze 81 61 47 65 30 59 Calvert- Lewin Ademola 61 5.4 ST England Everton Bronze 84 59 40 65 17 55 Lookman Dieumerci 79 5.4 ST DR Congo Hull City Gold 82 76 61 76 33 78 Mbokani Alfred 79 4.5 CDM Senegal Hull City Gold 69 54 65 67 78 85 N'Diaye Kamil 77 5.5 RW Poland Hull City Gold 88 75 73 76 32 59 Grosicki Curtis 77 4.9 CB England Hull City Gold 68 38 48 54 78 78 Davies 67

Michael 77 4.3 CB England Hull City Gold 32 32 45 37 79 77 Dawson Lazar 76 5.4 RM Serbia Hull City Gold 92 65 68 83 40 54 Marković Markus 76 4.9 CAM Norway Hull City Gold 65 77 70 73 66 81 Henriksen Andrew 76 4.5 CB Italy Hull City Gold 53 32 57 62 79 75 Ranocchia Tom 75 4.6 CM England Hull City Gold 43 71 79 67 70 72 Huddlest one Ahmed 75 4.6 RM Egypt Hull City Gold 78 68 73 73 69 72 Elmoham ady Ryan 75 4.6 CDM England Hull City Gold 68 68 76 73 69 68 Mason Omar 75 4.5 RB Norway Hull City Gold 83 55 71 78 70 70 Elabdella oui Andrew 75 4.3 LB Scotland Hull City Gold 82 60 67 72 71 71 Robertso n Evandro 74 5 CM Brazil Hull City Silver 66 71 75 78 49 60 Shaun 74 4.3 CAM Scotland Hull City Silver 67 68 73 76 38 60 Maloney Moses 72 4.4 RB England Hull City Silver 87 60 62 69 70 75 Odubajo David 72 4.4 CM Ireland Hull City Silver 55 62 69 68 65 80 Meyler Harry 72 4.4 CB England Hull City Silver 47 35 53 51 71 80 Maguire Clucas 68 4.7 LM England Hull City Silver 72 65 65 66 54 69 Brian 63 3.9 RB Ireland Hull City Bronze 63 48 59 59 62 60 Lenihan Josh 58 4 LB England Hull City Bronze 65 28 47 60 56 51 Tymon Abel 76 5.9 ST Uruguay Hull City Gold 87 73 57 75 29 73 Hernánde z Oumar 75 5.6 ST Senegal Hull City Gold 81 74 57 72 30 73 Niasse Adama 72 4.5 ST Norway Hull City Silver 82 71 44 70 24 78 Valentin Diomand e Will 67 4.4 ST England Hull City Silver 76 66 55 68 34 60 Keane Greg Luer 58 4.4 ST England Hull City Bronze 72 59 45 53 22 55 Jarrod 57 4.5 ST England Hull City Bronze 63 53 49 57 26 56 68

Bowen Ben 55 4.5 ST England Hull City Bronze 60 55 46 57 18 47 Hinchcliff e Islam 83 8.2 ST Algeria Leicester City Gold 82 77 80 88 30 58 Slimani Jamie 82 9.6 ST England Leicester City Gold 93 80 63 77 54 79 Vardy Ahmed 78 6.9 ST Nigeria Leicester City Gold 93 76 65 82 28 56 Musa Riyad 85 8.9 RM Algeria Leicester City Gold 82 77 80 88 30 58 Mahrez Robert 80 4.9 CB Germany Leicester City Gold 44 54 49 40 81 86 Huth Danny 80 5.3 CM England Leicester City Gold 68 67 76 75 73 74 Drinkwat er Shinji 77 5.6 ST Japan Leicester City Gold 76 75 65 77 43 74 Okazaki Nampalys 78 4.4 CDM France Leicester City Gold 75 55 72 74 76 80 Mendy Christian 77 5.2 LB Austria Leicester City Gold 67 69 78 70 76 77 Fuchs Wes 77 4.7 CB Jamaica Leicester City Gold 50 30 41 56 78 82 Morgan Marc 76 4.9 LM England Leicester City Gold 78 69 75 77 46 69 Albrighto n Yohan 75 4 CB Tunisia Leicester City Gold 67 39 57 54 75 77 Benaloua ne Molla 74 4.5 CB Mali Leicester City Silver 60 33 41 44 74 81 Wagué Danny 72 4.8 RB England Leicester City Silver 73 38 63 68 72 75 Simpson Andy King 72 4.7 CM Wales Leicester City Silver 67 71 68 70 65 67 Marcin 72 3.9 CB Poland Leicester City Silver 39 55 56 40 71 72 Wasilews ki Daniel 71 4.5 CM Ghana Leicester City Silver 78 60 62 68 71 86 Amartey Bartosz 71 5 LM Poland Leicester City Silver 80 62 68 70 48 55 Kapustka Demarai 70 4.8 LM England Leicester City Silver 89 64 58 78 23 52 Gray Ben 68 4.3 LB England Leicester City Silver 78 38 63 67 65 60 Chilwell Leonardo 74 5.5 ST Argentina Leicester City Silver 54 72 60 67 42 79 Ulloa 69

Onyinye 74 4.9 ST Argentina Leicester City Silver 77 54 61 64 71 77 Ndidi Daniel 84 9.6 ST England Liverpool Gold 89 83 69 81 25 70 Sturridge Roberto 83 8.4 ST Brazil Liverpool Gold 80 80 79 85 47 76 Firmino Divock 79 6.2 ST Belgium Liverpool Gold 86 76 68 76 31 77 Origi Danny 77 6.2 ST England Liverpool Gold 86 74 63 78 35 70 Ings Coutinho 85 8.2 CAM Brazil Liverpool Gold 82 74 82 87 33 56 Joel 83 5.3 CB Cameroo Liverpool Gold 72 46 70 68 85 78 Matip n Dejan 82 4.9 CB Croatia Liverpool Gold 58 40 60 63 82 81 Lovren Adam 82 7.1 CAM England Liverpool Gold 73 72 79 83 47 67 Lallana Nathaniel 81 5.8 RB England Liverpool Gold 86 61 71 78 79 76 Clyne James 81 6.5 CM England Liverpool Gold 67 76 83 77 69 76 Milner Georginio 81 7.5 LM Netherlan Liverpool Gold 82 75 74 84 56 70 Wijnaldu ds m Leiva 80 4.4 CDM Brazil Liverpool Gold 54 39 70 71 80 78 Lucas Jordan 80 6.3 CM England Liverpool Gold 74 70 81 75 71 80 Henderso n Emre Can 80 3.7 CM Germany Liverpool Gold 76 66 78 78 78 82 Sadio 79 9.2 RM Senegal Liverpool Gold 92 73 70 82 35 67 Mané Ragnar 78 4.6 CB Estonia Liverpool Gold 66 48 63 62 79 77 Klavan Alberto 77 4.4 LB Spain Liverpool Gold 89 68 68 79 70 74 Moreno Kevin 72 4.2 CDM England Liverpool Silver 74 54 65 66 71 75 Stewart Marko 70 4.3 CM Serbia Liverpool Silver 71 65 66 63 68 73 Grujić Joe 69 4.8 LB England Liverpool Silver 79 30 53 65 68 71 Gomez Trent 68 4.3 RM England Liverpool Silver 82 56 67 65 60 65 Alexander -Arnold Sheyi Ojo 64 4.7 LM England Liverpool Bronze 84 59 58 66 30 55 Connor 56 3.9 RB England Liverpool Bronze 64 49 53 56 53 62 Randall Sergio 89 12.7 ST Argentina Manchester Gold 89 88 75 89 23 70 70

Agüero City Gabriel 78 9.3 ST Brazil Manchester Gold 86 76 70 85 26 66 Fernando City de Jesus Kevin De 88 10.6 CAM Belgium Manchester Gold 77 83 86 84 40 75 Bruyne City David 87 8.6 CAM Spain Manchester Gold 68 72 87 87 32 58 Silva City Vincent 86 5.9 CB Belgium Manchester Gold 69 54 62 65 86 81 Kompany City Nicolás 85 5.9 CB Argentina Manchester Gold 72 56 56 53 85 82 Otamendi City Ilkay 85 4.9 CM Germany Manchester Gold 75 72 84 87 63 72 Gündoga City n Yaya 84 7.3 CM Ivory Manchester Gold 74 83 81 77 69 84 Touré Coast City Nolito 83 8.4 LW Spain Manchester Gold 77 79 79 84 39 67 City Pablo 82 4.7 RB Argentina Manchester Gold 68 56 70 74 84 83 Zabaleta City Raheem 82 7.8 LM England Manchester Gold 93 71 72 86 46 61 Sterling City Bacary 82 5.3 RB France Manchester Gold 75 53 66 74 82 80 Sagna City Fernandin 81 5.3 CM Brazil Manchester Gold 77 75 76 78 76 78 ho City Jesús 80 6.1 RM Spain Manchester Gold 88 70 75 83 28 50 Navas City Gaël 80 5.2 LB France Manchester Gold 83 43 65 73 79 68 Clichy City Fernando 79 4.7 CDM Brazil Manchester Gold 67 61 68 67 79 82 City Aleksand 79 5.8 LB Serbia Manchester Gold 67 69 78 74 79 79 ar City Kolarov Leroy 79 7.5 RM Germany Manchester Gold 91 75 70 83 34 64 Sané City John 78 4.7 CB England Manchester Gold 78 69 74 82 68 74 Stones City Fabian 78 4.7 CM England Manchester Gold 78 69 74 82 68 74 Delph City Aleix 63 5 CM Spain Manchester Bronze 62 52 64 65 38 47 García City Tosin 59 4.5 CB England Manchester Bronze 60 28 41 47 58 64 Adarabio City yo Kelechi 74 6 ST Nigeria Manchester Silver 87 73 61 74 22 67 Iheanach City 71 o Zlatan 90 11.5 ST Sweden Manchester Gold 72 90 81 85 31 86 Ibrahimo United vić Wayne 84 8.6 ST England Manchester Gold 71 84 81 79 53 86 Rooney United Anthony 82 9.2 ST France Manchester Gold 91 79 70 87 42 76 Martial United Marcus 76 6.4 ST England Manchester Gold 90 73 66 79 31 65 Rashford United Paul 88 8.3 CM France Manchester Gold 77 80 83 87 72 87 Pogba United Henrikh 85 8.9 RM Armenia Manchester Gold 83 77 81 87 55 70 Mkhitary United an Chris 84 5.8 CB England Manchester Gold 77 45 56 62 84 84 Smalling United Juan 84 7.2 CAM Spain Manchester Gold 68 75 84 85 32 54 Mata United Bastian 83 5.1 CM Germany Manchester Gold 51 76 83 75 75 74 Schweinst United eiger Eric Bailly 82 5.3 CB Ivory Manchester Gold 81 41 53 59 82 83 Coast United Ander 82 6.3 CM Spain Manchester Gold 72 72 81 84 64 71 Herrera United Michael 81 4.3 CDM England Manchester Gold 52 66 80 73 76 67 Carrick United Daley 81 5.2 CDM Netherlan Manchester Gold 61 56 77 76 81 76 Blind ds United Matteo 80 5 RB Italy Manchester Gold 80 58 69 76 80 75 Darmian United Antonio 80 5.6 RB Ecuador Manchester Gold 84 65 73 79 76 83 Valencia United Luke 80 5.2 LB England Manchester Gold 82 51 70 78 79 79 Shaw United Phil Jones 79 4.8 CB England Manchester Gold 62 56 65 61 79 80 United Ashley 79 5.1 LM England Manchester Gold 81 72 76 81 52 58 Young United Marcos 78 5.2 LB Argentina Manchester Gold 74 60 69 69 79 83 Rojo United Marouan 78 5.8 CM Belgium Manchester Gold 55 73 69 73 78 90 e Fellaini United Jesse 77 5.6 RM England Manchester Gold 83 68 71 79 49 62 Lingard United Timothy 71 4.1 CDM Netherlan Manchester Silver 80 60 63 63 71 78 Fosu- ds United Mensah 72

Negredo 80 6.3 ST Spain Middlesbrough Gold 66 80 66 73 39 78 Bernardo 78 4.3 CB Colombia Middlesbrough Gold 66 44 60 50 78 79 Espinosa Marten 78 4.4 CDM Netherlan Middlesbrough Gold 66 60 71 67 76 79 de Roon ds Gastón 78 5.2 CAM Uruguay Middlesbrough Gold 74 75 76 80 41 62 Ramírez Barragán 75 4.5 RB Spain Middlesbrough Gold 75 51 64 67 76 77 Stewart 75 4.9 CAM England Middlesbrough Gold 75 70 75 76 40 59 Downing Viktor 75 4.9 LW Denmark Middlesbrough Gold 79 72 71 78 35 66 Fischer Fábio 74 4.4 LB Brazil Middlesbrough Silver 81 55 69 74 71 66 Daniel 74 4.8 CB Spain Middlesbrough Silver 65 37 52 59 75 72 Ayala George 73 4.3 LB England Middlesbrough Silver 74 54 63 71 73 77 Friend Adam 73 4.3 CDM England Middlesbrough Silver 68 63 70 70 66 70 Clayton Calum 72 4.4 RB England Middlesbrough Silver 62 51 66 69 74 74 Chambers Adama 72 4.8 RW Spain Middlesbrough Silver 91 61 61 76 24 67 Christian 72 4.7 RM Uruguay Middlesbrough Silver 70 72 67 69 46 80 Stuani Adlène 71 4.3 CM Algeria Middlesbrough Silver 71 69 67 69 64 81 Guédiour a Grant 71 4.5 CDM England Middlesbrough Silver 66 67 72 65 66 72 Leadbitte r Adam 70 4.5 CM England Middlesbrough Silver 67 63 70 72 53 60 Forshaw Ben 70 4.9 CB England Middlesbrough Silver 57 26 42 41 72 69 Gibson Dael Fry 56 4 CB England Middlesbrough Bronze 64 29 30 32 58 59 Rudy 74 4.7 ST Benin Middlesbrough Silver 72 73 50 65 34 81 Gestede Patrick 71 4.9 ST England Middlesbrough Gold 75 69 60 70 34 62 Bamford Manolo 81 6.5 ST Italy Southampton Gold 82 82 72 79 26 72 Gabbiadi ni Charlie 78 6.4 ST England Southampton Gold 70 79 56 68 40 76 Austin Shane 77 6.1 ST Ireland Southampton Gold 84 74 60 75 37 81 Long Virgil van 82 5.6 CB Netherlan Southampton Gold 75 61 66 68 82 83 Dijk ds 73

Sofiane 80 6.7 LW Morocco Southampton Gold 85 73 72 87 36 49 Boufal Dušan 79 7 LM Serbia Southampton Gold 71 68 80 83 37 66 Tadić Ryan 79 5.4 LB England Southampton Gold 78 46 70 75 78 73 Bertrand Jordy 78 4.6 CDM Netherlan Southampton Gold 66 51 80 76 66 74 Clasie ds Cédric 78 4.8 RB Portugal Southampton Gold 78 68 72 76 75 72 Jay 77 6.2 LM England Southampton Gold 82 77 69 77 32 69 Rodriguez Steven 77 5.1 CM Northern Southampton Gold 67 61 77 77 66 74 Davis Ireland James 76 7 CM England Southampton Gold 68 61 83 74 61 68 Ward- Prowse Nathan 75 5.8 RM England Southampton Gold 86 68 69 79 24 49 Redmond Peirre- 75 4.2 CM Denmark Southampton Gold 68 62 73 77 68 77 Emile Højbjerg Jérémy 75 4.7 RB France Southampton Gold 78 72 75 75 72 73 Pied Oriol 74 4.5 CDM Spain Southampton Silver 60 38 64 61 72 79 Romeu Florin 74 4 CB Romania Southampton Silver 55 48 56 60 75 76 Gardos Maya 73 4.2 CB Japan Southampton Silver 66 41 48 62 74 74 Yoshida Cuco 70 4.3 RB Netherlan Southampton Silver 73 53 57 63 73 74 Martina ds Antilles Harrison 68 4.3 CDM England Southampton Silver 68 51 66 65 62 69 Reed Matt 67 4.1 LB England Southampton Silver 78 37 57 64 65 64 Targett Lloyd 67 4.4 RM Wales Southampton Silver 87 51 58 69 33 60 Isgrove Jack 65 4 CB England Southampton Bronze 62 41 54 56 66 68 Stephens Jake 63 4.5 CAM England Southampton Bronze 68 47 65 66 46 43 Hesketh Sam 63 4.2 LM England Southampton Bronze 76 57 58 66 24 51 McQueen Josh Sims 61 4.2 LM England Southampton Bronze 79 55 56 66 29 50 Wilfried 81 6.9 ST Ivory Stoke City Gold 72 82 59 74 39 85 Bony Coast Mame 78 5.7 ST Senegal Stoke City Gold 87 76 56 68 42 77 Diouf 74

Saido 77 6 ST England Stoke City Gold 82 78 57 76 27 60 Berahino Peter 76 4.9 ST England Stoke City Gold 53 76 69 66 32 70 Crouch Jonathan 75 5.4 ST Ireland Stoke City Gold 68 73 69 73 59 85 Walters Xherdan 82 6 RM Switzerla Stoke City Gold 86 77 78 84 53 72 Shaqiri nd Marko 82 7.1 LM Austria Stoke City Gold 78 75 78 82 51 80 Arnautovi ć Ryan 81 4.9 RM England Stoke City Gold 54 44 52 50 82 84 Shawcros s Giannelli 79 4.5 CM France Stoke City Gold 77 61 72 79 74 77 Imbula Ibrahim 79 5.4 CAM Netherlan Stoke City Gold 77 73 78 80 50 61 Afellay ds Bruno 78 4.9 CB Netherlan Stoke City Gold 61 43 59 58 78 81 Martins ds Indi Geoff 77 4.2 CB United Stoke City Gold 69 58 68 68 78 76 Cameron States Glen 77 4.7 RB England Stoke City Gold 77 65 71 75 75 75 Johnson Joe Allen 77 5.1 CM Wales Stoke City Gold 65 59 74 78 66 64 Glenn 77 4.3 CDM Ireland Stoke City Gold 44 66 72 69 74 77 Whelan Marc 76 4.1 CB Spain Stoke City Gold 62 43 64 66 76 72 Muniesa Stephen 76 4.4 CAM Ireland Stoke City Gold 60 67 73 76 46 60 Ireland Charlie 76 4.7 CM Scotland Stoke City Gold 50 78 81 72 60 76 Adam Erik 76 4.6 LB Netherlan Stoke City Gold 68 40 67 67 78 78 Pieters ds Phil 73 4.3 RB Scotland Stoke City Silver 67 59 56 64 73 77 Bardsley Ramadan 71 4.8 LW Egypt Stoke City Silver 78 62 70 73 43 57 Sobhi Liam 59 4 CB England Stoke City Bronze 55 30 37 39 62 57 Edwards Jermain 80 7.8 ST England Sunderland Gold 77 83 61 79 25 66 Defoe Wahbi 79 6 LM Tunisia Sunderland Gold 76 80 79 79 43 69 Khazri Papy 78 4.3 CB Senegal Sunderland Gold 71 48 54 52 77 82 Mison Djilobodji 75

Lee 78 4.2 CDM England Sunderland Gold 68 58 69 68 76 80 Cattermol e Jan 77 4.3 CDM Germany Sunderland Gold 70 38 68 57 79 76 Kirchhoff Manquillo 77 4.3 RB Spain Sunderland Gold 81 44 65 72 73 74 Darron 77 4.3 CM Ireland Sunderland Gold 59 68 78 71 74 74 Gibson Steven 77 4.8 LM South Sunderland Gold 69 70 78 79 51 63 Pienaar Africa John 76 4.4 CB Ireland Sunderland Gold 33 33 57 54 78 66 O'Shea Lamine 76 4.4 CB Ivory Sunderland Gold 60 38 53 53 74 82 Koné Coast Jack 76 4.3 CM England Sunderland Gold 65 72 72 74 73 76 Rodwell Fabio 76 5.1 RM Italy Sunderland Gold 78 74 70 76 28 69 Borini Adnan 76 5.3 LM Belgium Sunderland Gold 81 64 74 81 21 52 Januzaj Sebastian 75 4.7 CM Sweden Sunderland Gold 53 76 80 74 62 73 Larsson Didier 74 4.8 CM Gabon Sunderland Silver 70 60 66 72 74 78 Ndong Jason 74 4.7 CB Belgium Sunderland Silver 78 42 56 64 74 73 Denayer Paddy 72 4.2 CDM Northern Sunderland Silver 67 45 61 64 73 74 McNair Ireland Bryan 72 4.3 LB Costa Rica Sunderland Silver 76 61 67 75 69 67 Oviedo Billy 71 4.4 RB England Sunderland Silver 67 55 61 69 72 73 Jones Duncan 69 4.7 RM England Sunderland Silver 81 65 60 72 30 58 Watmore Lynden 66 4.2 CAM United Sunderland Silver 77 57 63 68 41 63 Gooch States Thomas 60 4 LB England Sunderland Bronze 60 34 49 58 57 62 Robson George 60 4.5 CAM England Sunderland Bronze 69 53 59 61 45 52 Honeyma n Donald 59 4 RB Scotland Sunderland Bronze 72 31 49 56 56 63 Love Ethan 57 4.5 CM England Sunderland Bronze 64 54 60 60 44 49 Robson Josh 56 4 RB England Sunderland Bronze 58 30 39 55 59 55 Robson Joel 63 4.4 ST Sweden Sunderland Bronze 88 61 51 70 23 53 Asoro 76

Josh Maja 60 4.5 ST England Sunderland Bronze 74 60 50 61 22 51 Borja 80 6.7 ST Spain Swansea City Gold 63 81 59 71 29 74 Bastón Llorente 79 6.2 ST Spain Swansea City Gold 55 75 59 66 28 78 Jordan 77 5 ST Ghana Swansea City Gold 77 76 71 77 31 73 Ayew Gylfi 82 7.5 CAM Iceland Swansea City Gold 68 81 82 78 53 68 Sigurdsso n Federico 79 4.2 CB Argentina Swansea City Gold 53 28 51 50 80 76 Fernánde z Jefferson 79 5 LM Ecuador Swansea City Gold 90 71 73 82 31 67 Montero Jack Cork 78 4.6 CDM England Swansea City Gold 59 54 69 73 75 74 Ki Sung 78 5 CM Korea Swansea City Gold 66 74 81 76 62 71 Yueng Republic Leon 77 4.4 CM England Swansea City Gold 53 53 77 78 69 65 Britton Nathan 77 4.7 RM England Swansea City Gold 90 73 67 81 38 66 Dyer Àngel 76 4.3 RB Spain Swansea City Gold 59 59 71 72 79 75 Rangel Wayne 76 4.6 LM England Swansea City Gold 78 66 70 81 36 64 Routledge Leroy Fer 76 4.8 CAM Netherlan Swansea City Gold 75 75 72 75 70 80 ds Luciano 76 5.5 RW Netherlan Swansea City Gold 93 64 67 77 24 58 Narsingh ds Mike van 75 4.3 CB Netherlan Swansea City Gold 54 45 47 48 75 76 der Hoorn ds Neil 75 4.1 LB Wales Swansea City Gold 72 40 68 72 76 67 Taylor Kyle 75 4.3 RB England Swansea City Gold 75 52 65 67 74 70 Naughton Martin 75 4.5 LB Sweden Swansea City Gold 83 62 67 71 72 76 Olsson Tom 72 4.2 CM England Swansea City Silver 68 58 74 70 52 45 Carroll Stephen 70 3.9 LB Scotland Swansea City Silver 73 37 64 69 69 65 Kingsley Alfie 68 4.5 CB England Swansea City Silver 54 35 41 50 68 69 Mawson Jay Fulton 64 4.4 CM Scotland Swansea City Bronze 68 54 63 65 59 69 Daniel 58 4.5 LW England Swansea City Bronze 71 56 52 62 27 51 James Oliver 62 4.5 ST Scotland Swansea City Bronze 70 62 44 53 21 59 McBurnie 77

Harry 84 11.2 ST England Tottenham Gold 73 84 71 78 42 81 Kane Hotspurs Vincent 78 7.5 ST Netherlan Tottenham Gold 74 80 52 70 24 79 Janssen ds Hotspurs Toby 85 6.3 CB Belgium Tottenham Gold 66 58 70 64 86 79 Alderweir Hotspurs eld Christian 84 8.7 CAM Denmark Tottenham Gold 76 76 84 84 44 59 Eriksen Hotspurs Jan 83 5.5 CB Belgium Tottenham Gold 67 67 70 68 83 81 Vertongh Hotspurs en Moussa 82 5.4 CM Belgium Tottenham Gold 77 72 75 83 73 84 Dembélé Hotspurs Kyle 81 6.3 RB England Tottenham Gold 90 63 72 73 78 81 Walker Hotspurs Erik 81 6.6 RM Argentina Tottenham Gold 79 78 79 84 43 65 Lamela Hotspurs Moussa 80 6.6 RM France Tottenham Gold 81 74 75 78 72 85 Sissoko Hotspurs Danny 80 6 LB England Tottenham Gold 82 63 71 77 78 77 Rose Hotspurs Dele Alli 80 8.8 CM England Tottenham Gold 75 74 74 78 61 77 Hotspurs Kevin 79 4.6 CB Austria Tottenham Gold 67 27 61 64 79 75 Wimmer Hotspurs Heung 79 6.8 LM Korea Tottenham Gold 86 82 71 81 25 62 Min Son Republic Hotspurs Victor 78 4.8 CDM Kenya Tottenham Gold 66 66 67 72 78 89 Wanyama Hotspurs Ben 78 4.7 LB Wales Tottenham Gold 78 50 70 73 77 71 Davies Hotspurs Eric Dier 78 5 CDM England Tottenham Gold 61 60 71 62 79 82 Hotspurs Georges- 78 6.8 LM France Tottenham Gold 92 72 70 79 35 62 Kévin Hotspurs Nkoudou Kieran 76 4.7 RB England Tottenham Gold 79 56 72 71 75 74 Trippier Hotspurs Josh 70 4.2 CAM England Tottenham Silver 81 57 65 73 35 48 Onomah Hotspurs Harry 70 4.5 CM England Tottenham Silver 76 57 72 70 52 63 Winks Hotspurs Cameron 62 4.3 CB United Tottenham Bronze 68 22 42 46 61 66 Carter- States Hotspurs Vickers Nordin 79 5 ST Morocco Watford Gold 86 76 78 84 38 72 Amrabat Stefano 77 5.1 ST Italy Watford Gold 76 74 57 73 26 85 78

Okaka Mauro 77 5.5 ST Argentina Watford Gold 80 79 68 85 33 65 Zárate Troy 76 6.7 ST England Watford Gold 69 78 60 68 31 84 Deeney Robert 81 5.8 CM Argentina Watford Gold 81 66 80 83 67 69 Pereyra Etienne 78 4.6 CDM France Watford Gold 60 62 70 70 76 81 Capoue Abdoulay 78 4.6 CM France Watford Gold 71 71 73 74 71 79 e Doucouré Tom 78 5.1 LM England Watford Gold 70 69 77 78 54 64 Cleverley M'Baye 78 6 LW France Watford Gold 89 74 67 82 26 67 Niang Sebastian 77 4.5 CB Austria Watford Gold 52 36 48 40 78 79 Prödl Younès 77 4.4 CB France Watford Gold 63 44 58 55 76 79 Kaboul Jose 77 4.7 LB Greece Watford Gold 82 69 68 74 75 81 Holebas Valon 77 4.5 CDM Switzerla Watford Gold 67 64 72 71 74 79 Behrami nd Daryl 77 4.5 RB Netherlan Watford Gold 75 63 69 74 74 81 Janmaat ds Christian 76 4.8 CB Belgium Watford Gold 78 52 49 62 76 81 Kabasele Miguel 75 4.3 CB Uruguay Watford Gold 55 34 50 52 77 74 Angel Britos Isaac 74 5.7 RW Nigeria Watford Silver 87 68 64 76 29 71 Success Craig 73 4.3 CB Northern Watford Silver 62 37 54 54 74 72 Cathcart Ireland Juan 73 4.2 LB Colombia Watford Gold 86 65 71 80 67 69 Camilo Zúñiga Ben 73 4.2 CM England Watford Silver 58 68 74 70 68 70 Watson Charlie 55 4 CB England Watford Bronze 57 39 38 43 52 62 Rowan Brandon 54 4 LB England Watford Bronze 78 38 44 55 48 48 Mason Salomón 80 6.7 ST Venezuel West Gold 77 78 56 74 31 80 Rondón a Bromwich Albion Claudio 80 4.4 CDM Argentina West Gold 53 59 66 69 79 76 Yacob Bromwich 79

Albion Jonny 79 4.4 CB Northern West Gold 68 32 57 52 80 73 Evans Ireland Bromwich Albion Allan- 78 4.4 RB Cameroo West Gold 77 47 63 64 78 84 Roméo n Bromwich Nyom Albion Nacer 78 6 LM Belgium West Gold 77 77 75 78 45 77 Chadli Bromwich Albion Jonas 76 4.2 CB Sweden West Gold 52 56 63 52 75 76 Olsson Bromwich Albion Gareth 76 5.1 CB Northern West Gold 42 43 50 42 78 76 McAuley Ireland Bromwich Albion Darren 76 4.5 CM Scotland West Gold 54 68 74 68 81 74 Fletcher Bromwich Albion James 75 4.7 CM Scotland West Gold 68 75 76 77 58 65 Morrison Bromwich Albion Jake 75 4.8 CM England West Gold 68 66 72 72 71 80 Livermore Bromwich Albion Marc 74 3.9 CB Ireland West Silver 67 57 65 65 75 73 Wilson Bromwich Albion James 74 4.9 LM Ireland West Silver 76 67 69 76 58 74 McClean Bromwich Albion Matt 74 5.8 RM Scotland West Silver 89 69 68 75 35 69 Phillips Bromwich Albion Chris 69 5.1 LB Northern West Silver 61 73 79 74 66 62 Brunt Ireland Bromwich Albion Craig 67 4.8 RB England West Silver 67 43 51 54 73 71 Dawson Bromwich Albion Hal 67 5 LM Wales West Silver 75 68 60 68 37 72 Robson- Bromwich Kanu Albion Jonathan 64 4.2 RW England West Bronze 89 63 47 68 23 61 Leko Bromwich Albion Sam Field 62 4.4 CM England West Bronze 64 49 60 62 52 53 Bromwich Albion 80

Andy 78 6.3 ST England West Ham Gold 67 78 62 68 49 86 Carroll Jonathan 77 6.6 ST Argentina West Ham Gold 76 74 61 73 39 74 Calleri Diafra 76 5.8 ST Senegal West Ham Gold 79 73 58 73 31 74 Sakho Ashley 66 5 ST England West Ham Silver 74 64 38 64 24 69 Fletcher José 83 5.3 CB Portugal West Ham Gold 54 35 52 62 84 79 Fonte Winston 81 5.1 CB New West Ham Gold 73 52 49 50 82 81 Reid Zealand Angelo 80 4.8 CB Italy West Ham Gold 72 40 58 59 83 77 Ogbonna Sofiane 80 5 RW Algeria West Ham Gold 88 73 72 84 32 62 Feghouli André 80 7.1 RM Ghana West Ham Gold 78 74 76 81 63 79 Ayew Håvard 79 4.7 CB Norway West Ham Gold 66 53 71 66 79 79 Nordtveit Gökhan 79 5.3 RM Turkey West Ham Gold 86 71 76 85 36 76 Töre Mauel 79 6.3 CAM Argentina West Ham Gold 82 70 76 85 26 53 Lanzini Mark Noble 78 6.2 CM England West Ham Gold 55 67 77 74 71 78 Michail 78 7 RM England West Ham Gold 89 73 69 80 52 82 Antonio James 76 4.2 CB Wales West Ham Gold 40 47 53 45 76 79 Collins Arbeloa 76 4.5 RB Spain West Ham Gold 68 55 67 59 80 74 Pedro 76 4.4 CDM Spain West Ham Gold 68 59 74 72 74 76 Obiang Aaron 76 5.2 LB England West Ham Gold 78 59 71 75 73 67 Cresswell Arthur 75 4.7 LB France West Ham Gold 84 70 70 77 71 73 Masuaku Sam 73 4.2 RB England West Ham Silver 78 61 66 73 69 72 Byram Edimilson 70 4.8 CAM Switzerla West Ham Silver 79 65 66 70 46 64 Fernande nd s Declan 56 4.5 CB Ireland West Ham Bronze 64 29 34 41 56 62 Rice

81

Goalkeepers

Name Overall EPL Position Nation Club Class Diving Handling Kicking Reflexes Speed Position Rating Fantasy Price Petr Čech 88 5.4 GK Czech Arsenal Gold 83 90 77 85 45 85 Republic David Ospina 79 4.7 GK Colombia Arsenal Gold 83 71 78 84 34 77 Artur Boruc 76 4.5 GK Poland Bournemouth Gold 77 72 62 83 41 74 Adam Federici 73 4.4 GK Australia Bournemouth Silver 73 69 78 73 47 70 Ryan Allsop 64 4 GK England Bournemouth Bronze 64 58 65 67 42 60 Tom Heaton 76 5.1 GK England Burnley Gold 76 75 71 78 52 76 Paul Robinson 70 4 GK England Burnley Silver 67 68 78 65 40 73 Nick Pope 64 3.9 GK England Burnley Bronze 67 65 59 66 50 63 Thibaut Courtois 89 5.9 GK Belgium Chelsea Gold 84 91 69 89 46 86 Asmir Begović 83 4.7 GK Bosnia Chelsea Gold 83 81 74 84 52 80 Herzegovina Steve Mandanda 85 4.2 GK France Crystal Palace Gold 86 80 79 85 53 81 Wayne Hennessey 75 4.3 GK Wales Crystal Palace Gold 74 76 82 74 50 76 Julián Speroni 73 4.3 GK Argentina Crystal Palace Silver 75 72 71 74 42 69 Maarten 76 4.8 GK Netherlands Everton Gold 78 73 81 76 35 74 Stekelenburg Joel Robles 75 4.7 GK Spain Everton Gold 76 74 75 75 49 74 David Marshall 76 4.5 GK Scotland Hull City Gold 76 72 67 78 48 77 Eldin Jakupović 68 4.1 GK Switzerland Hull City Silver 69 69 65 71 46 66 Kasper Schmeichel 82 4.9 GK Denmark Leicester City Gold 83 79 86 85 62 79 Ron-Robert Zieler 81 4.4 GK Germany Leicester City Gold 78 82 66 82 46 81 Ben Hamer 67 4.3 GK England Leicester City Silver 70 64 66 67 45 64 Loris Karius 82 4.4 GK Germany Liverpool Gold 84 76 80 85 48 80 Simon Mignolet 78 4.7 GK Belgium Liverpool Gold 79 73 65 84 55 72 Alexander 73 4 GK Austria Liverpool Silver 72 72 53 74 39 72 82

Manninger Claudio Bravo 85 5 GK Chile Manchester Gold 83 85 87 85 58 78 City Willy Caballero 78 4.7 GK Argentina Manchester Gold 79 75 69 80 49 78 City De Gea 90 5.4 GK Spain Manchester Gold 88 85 87 90 56 85 United Sergio Romero 79 4.7 GK Argentina Manchester Gold 76 70 81 82 44 76 United Dimitrios 72 4.2 GK Greece Middlesbrough Silver 72 70 74 70 34 75 Konstantopoulos Brad Guzan 75 4.3 GK United Middlesbrough Gold 75 74 58 77 50 75 States Victor Valdés 82 4.6 GK Spain Middlesbrough Gold 80 80 77 80 36 84 Fraser Forster 78 5 GK England Southampton Gold 75 80 74 82 32 77 Alex McCarthy 76 4.3 GK England Southampton Gold 78 73 61 81 58 74 Mouez Hassen 70 4 GK France Southampton Silver 75 65 64 69 48 70 Shay Given 75 4.3 GK Ireland Stoke City Gold 72 73 72 75 41 79 Jack Butland 74 4.5 GK England Stoke City Silver 77 73 75 81 51 71 Vito Mannone 75 4.3 GK Italy Sunderland Gold 75 72 71 76 59 71 Jordan Pickford 76 3.1 GK England Sunderland Gold 74 76 85 78 48 73 Maksymilian Stryjek 58 4 GK Poland Sunderland Bronze 61 53 51 59 41 53 Mika 71 4.4 GK Portugal Sunderland Silver 73 67 68 73 37 70 Łukasz Fabiański 80 4.8 GK Poland Swansea City Gold 77 84 73 84 50 78 Kristoffer Nordfeldt 73 3.9 GK Sweden Swansea City Silver 73 66 66 78 55 74 Hugo Lloris 88 5.5 GK France Tottenham Gold 87 87 68 90 65 82 Hotspurs Michel Vorm 80 4.8 GK Netherlands Tottenham Gold 79 76 76 84 57 77 Hotspurs Luke McGee 58 4 GK England Tottenham Bronze 67 51 56 65 46 50 Hotspurs Pau López 73 5 GK Spain Tottenham Silver 70 75 68 71 45 74 Hotspurs Costel Pantilimon 77 4.3 GK Romania Watford Gold 74 77 68 78 48 79 Gomes 78 4.9 GK Brazil Watford Gold 83 73 62 82 50 74 Giedrius Arlauskis 69 4 GK Lithuania Watford Silver 70 64 70 77 33 62 Ben Foster 80 4.7 GK England West Bromwich Gold 79 79 73 84 50 77 Albion Boaz Myhill 73 4.2 GK Wales West Bromwich Silver 74 71 69 78 41 70 Albion Jack Rose 60 4 GK England West Bromwich Bronze 62 49 58 57 26 50 Albion Adrián 80 4.6 GK Spain West Ham Gold 80 78 71 86 46 75 Darren Randolph 75 4.4 GK Ireland West Ham Gold 80 76 77 82 45 77 Raphael Spiegel 60 4 GK Switzerland West Ham Bronze 59 58 57 63 56 58 83

English Premier League Average Player Ratings & Average Finishes By Club

Club Average Player Ratings English Premier League Average Finishes Manchester United 75.50 2.00 Chelsea 82.05 4.92 Manchester City 75.65 9.32 Arsenal 86.29 3.54 Liverpool 76.59 4.75 Leicester City 78.18 12.40 Tottenham Hotspurs 70.65 8.25 Crystal Palace 79.25 16.14 Stoke City 74.34 11.25 Swansea City 72.58 10.40 Everton 78.75 10.46 Middlesbrough 77.29 13.29 Watford 66.11 17.67 Southampton 74.94 12.88 West Ham United 60.52 11.75 West Bromwich Albion 65.33 14.80 Bournemouth 84.83 16.00 Hull City 79.77 17.50 Burnley 80.57 18.50 Sunderland 74.18 14.67 84

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CURRENT ADDRESS: PERMANENT ADDRESS: 222 East College Ave. Apt. 6 RICHARD J. BLAIR 4353 Trophy Drive State College, PA 16801 [email protected] | 610.804.0338 Boothwyn, PA 19061 \

EDUCATION The Pennsylvania State University | Schreyer Honors College University Park, PA Smeal College of Business | Bachelor of Science in Finance Graduation: May 2017 Smeal College of Business | Two-Piece Sequence in Accounting

RELEVANT EXPERIENCE Grant Thornton LLP Alexandria, VA Advisory Intern – Public Sector | Grant Thornton Business Consulting & Data Analytics May 2016 – Jul 2016 · Collaborated with Grant Thornton’s National Managing Principal of Revenue Growth to expand the firm’s market share through market sizing, frameworks, and management consulting industry examinations of GT, MBB, Big 4, and Mid-Tier firms · Performed Transaction Valuation Services to evaluate client assets by utilizing Bloomberg, IDC, and pricing models along with preparing Valuation Methodologies for Equities, Convertible Securities, Corporate Bonds, Structured Notes, and Options · Diagnosed the implications of Brexit to mitigate risk on Grant Thornton and the firm’s clients through analysis, case studies, and the attendance of Bloomberg Surveillance with Alan Greenspan

The Baratta Investment Group Incline Village, NV Summer Analyst Intern Jun 2014 – Aug 2014 · Assessed the hedge fund’s $44.8 million assets under management with portfolio manager and prepared materials of potential investments through fundamental analysis, market research, and due diligence · Utilized models for market trends, historical data, and forecasted movement based on macroeconomic indicators using data from Bloomberg, Reuters, CNBC, and independent research institutions to make informed investment decisions

Nittany Consulting Group University Park, PA Consultant | Consultant Training Program (CTP) Sep 2015 – Dec 2015 · Selected from 150+ students for a comprehensive training program to prepare graduates for careers in the financial services industry · Augmented core consulting skills through hands-on case study analyses with industry professionals and by working with a team of four to consult mock clients, construct revenue models, and sensitivity analyses

Penn State Investment Association (PSIA) University Park, PA Analyst | Healthcare Sector Aug 2013 – Dec 2015 · Contributed analytical support and research for an investor-funded $7.0 million student-managed portfolio of equities · Developed valuation skills pertaining to financial analysis, common market metrics, and equity research

LEADERSHIP EXPERIENCE Penn State IFC/Panhellenic Dance Marathon (THON) University Park, PA Local Donor Development Captain | Donor & Alumni Relations Development Sep 2015 – Feb 2016 · Created a solicitation strategy for all current and potential donors in Centre County and promoted THON through local networking events to maximize the fundraising total for the largest student-run philanthropy in the world · Supervised THON’s organizations to ensure their fundraising goals are met through proper fundraising techniques

Dancer Storage Leader | Dancer Relations Committee Member Sep 2014 – Feb 2015 · Monitored 709 dancers throughout THON weekend by providing physical and emotional support to assure safety and well-being during the 46 hour event that raised $13,026,653.23 for the Four Diamonds Fund · Attended weekly meetings/fitness instruction sessions to learn how to handle potential situations in said conditions · Coordinated with Dancer Relations Captain and 38 committee members to create an environment for dancers and volunteers to store equipment and receive medical attention by developing dancer storage themes, décor, and entertainment

Alpha Kappa Psi Professional Business Fraternity University Park, PA Bylaws Chair | Warden Jan 2015 – May 2015 · Oversaw the fraternity's bylaws to promote the guiding regulations of Alpha Kappa Psi and assure proper implementation of rules and order during official parliamentary proceedings · Supervised the Judicial Review Board to deliberate and provide disciplinary recommendations for brothers in bad-standing

Philanthropy Chair | Epsilon Tau Pledge Class Jan 2014 – May 2014 · Cooperated with a pledge class of 23 to plan professional, philanthropic, fundraising, and social events · Met weekly with committee members and co-chair to discuss philanthropic matters to create an atmosphere of charity and humanitarianism, and to organize a philanthropic event with the Ronald McDonald House for the fraternity · Enhanced professionalism through mock interviews, resume workshops, and professional seminars

HONORS, SKILLS, & INTERESTS · Congressional Scholar-Athlete – Presented by State Representative Patrick Meehan, 7th Congressional District of PA · Pennsylvania Sports Hall of Fame – Delaware County Chapter Induction | Danny Murtaugh Award for Most Courageous Athlete · Hugh O’Brien Youth Leadership Seminar (HOBY) · Working knowledge in: Bloomberg, FactSet, Microsoft Office (Excel, Outlook, PowerPoint, Word), Tableau · Interests include: Fantasy Football, history, horse racing, Nittanyville, Philadelphia sports, soccer goalkeeping, Texas Hold ‘Em, and traveling