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.
i 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.
ii 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
vi 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
vii 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.
1 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.
2 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).
4 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.
10 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, timeplayed, 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
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. 13 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.
15 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
22 Name Overall Rating EPL Fantasy Price Position Nation Club Class Diving Handling Kicking Reflexes Speed Positioning De Gea 90 5.4 GK Spain Manchester 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 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
23 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 Overall Rating EPL Fantasy Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Name 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
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 24 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
EPL Fantasy Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Overall Name Rating Cesc Fàbregas 86 6.9 CM Spain Chelsea Gold 63 77 89 81 61 64 Ilkay Gündogan 85 4.9 CM Germany 25 Manchester 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 Overall Rating EPL Fantasy Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Name David Silva 87 8.6 CAM Spain Manchester City Gold 68 72 87 87 32 58
26 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 Fantasy Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Overall Name Rating 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 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 Overall Rating EPL Fantasy Price Position Nation Club Class Pace Shooting Passing Dribbling Defense Physical Name Zlatan Ibrahimović 90 11.5 ST Sweden Manchester 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: Name Overall Rating EPL Fantasy 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 Name Overall Rating EPL Fantasy 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 David Silva 87 8.6 CAM Spain Manchester City Gold 68 72 87 87 32 58 Cesc Fàbregas 86 6.9 CM Spain Chelsea Gold 63 77 89 81 61 64 Ilkay Gündogan 85 4.9 CM Germany Manchester 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 Ibrahimović 90 11.5 ST Sweden Manchester 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 Rating Total EPL 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
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 40 Wolverhampton 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 34
Overall Statistics Position Finishes, Relegations, Avg. Points Per Season, Avg. Finish, Best Finish 35 Ranking Club 1st 2nd 3rd 4th Relegated Average Points 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 15 Sheffield 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 24 Queens Park 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
32 West Bromwich 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 40 Wolverhampton 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 36
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