the other 50%: technology

and analytics in soccer

jason heckendorn

w.p. carey school of business

honors thesis project

director: john eaton

second reader: amy ostrom

1

special thanks to

amy ostrom, arizona state university

john eaton, arizona state university

jonathan lofrisco, arizona state university

chris anderson, cornell university

eddie kendralla, phoenix suns

coleman bessert, exos

craig friedman, exos

dan burns, exos

darcy norman, exos

dave schifrin, exos

nick winkelman, exos

amanda carlson-philips, exos

roy sugarman, exos

joel mcfadden, fan interactive

claudio romano, fifa

michael crowley, infomotion

peko hosoi, massachusetts institute of technology

fergus connolly, sport science consultant

erik duhaime, mit sloan sports analytics conference

matia kostakis, mit sloan sports analytics conference

tatiana mendoza, mit sloan sports analytics conference

jeff agoos, mls

geir jordet, norwegian school of sport sciences

blake wooster, prozone

ravi ramineni, seattle sounders fc

bekir sirin, sentio sports

john brenkus, sport science

andrew opatkiewicz, stats llc

brian kopp, stats llc

paul robbins, stats llc

2

Table of Contents

Special Thanks To .............................................................................................................. 1

Abstract ............................................................................................................................... 3

My Journey ......................................................................................................................... 5

Methodology and Research Methods ............................................................................... 7

Industry Overview ........................................................................................................... 10

Sports Science, Analytics, and Technology ................................................................. 10

Industry History and Transition .................................................................................. 12

The Other 50% .............................................................................................................. 17

In Sports ........................................................................................................................ 19

In Business ..................................................................................................................... 25

Technology and Analytics in Soccer Performance ........................................................ 29

Technical Abilities and Tactics .................................................................................... 30

Physiology...................................................................................................................... 42

Neuroscience ................................................................................................................. 52

Moving Forward .............................................................................................................. 61

Ethical Discussion ......................................................................................................... 61

Implications ................................................................................................................... 69

Recommendations and Conclusion ............................................................................. 78

References ......................................................................................................................... 84

3

Abstract

The beautiful game is unpredictable. Arguably half of soccer is entirely out of our control,

instead being determined by a simple concept: luck. But what of the other 50%? Ultimately, the goal

of the rapidly-advancing technologies and analytics in on-field sports performance is to maximize

the elements that we – the coaches, players, decision-makers, and analysts – truly control. Once

perceived as too mathematical and systemized, contradicting coaches’ intuitions, sports sciences are

burgeoning in the sports arena both in applied and mainstream popularity. While the industry has

its critics and is far shy of its pinnacle, its advancements and successes cannot be ignored. From the

training ground to match day decision-making, analytics are embedded in soccer and sport.

Technology and analytics are vastly utilized throughout sporting organizations across a

myriad of sports and purposes: scouting and drafting, fan experience, ticketing, etc. However, while

these areas must be addressed in discussing the success of analytics in assessing situations and

reducing uncertainty, my central thesis relates to the technological capabilities and corresponding

analytical tools utilized to identify, assess, and improve on-field soccer performance: match analysis.

This paper’s core focuses on optimizing performance in soccer players in three specific areas of

performance: technical abilities and tactics, physiology, and neuroscience.

After dozens of interviews and expansive secondary research, my findings are composed in

three central areas: industry analysis, soccer performance analysis, and additional commentary. The

industry analysis will comprehensively address and analyze the industry’s current position across

4

soccer performance, allowing insight into the methods and practices of the industry. Further, by

integrating case studies to highlight numerous stories of teams, players, and sports entities that have

found success with analytics, I demonstrate the profound impact an analytical strategy can have to

amplify the decision-making process. Lastly, I provide unique commentary on the ethical dilemmas

associated with rapidly-advancing technologies and big data, the overall implications of technology

and analytics in soccer and sport, and future simple four key recommendations that clubs and the

industry as a whole should consider: 1. Have a plan, 2. Understand the process, 3. Find harmony,

and 4. Impact soccer’s culture.

As opposed to utilizing current data to assess a team’s situation or providing new analytical

models, this triangulation of research methods incorporates facets of quantitative and qualitative

analyses, allowing for a strengthened understanding of the industry, its successes, and its future

direction. Ideally, this will be used as an educational tool for aspiring professionals in the sports

analytics industry, yet its holistic perspective also provides value for players, teams, coaches,

analysts, fans, and decision-makers.

5

My Journey

Sports are my true love; since I was five, soccer has been my favorite pastime, conditioning

workout, and coping mechanism. Although my dreams of goalkeeping for Manchester United have

long passed since the glory days of high school and ASU club soccer – not to mention that I don’t

even eclipse six feet – my aspirations of living through sports were realized at ASU with the Sports

Business Association. I joined the Sports Business Association during my freshman year at ASU,

eager to discover my passion and pursue a career in sports; three years later, I was elected as the

organization’s president. My leadership in planning strategic and tactical initiatives, managing

internship fairs and a prominent speaker series, and demonstrating a firm commitment to

community outreach ultimately led to SBA winning W.P. Carey’s Organization of the Year.

My journey towards the sports technology and analytics space began two years ago when

SBA facilitated my introduction to Athletes’ Performance, the “industry leader in integrated

training, nutrition, and physical therapy for elite and professional athletes.” Initially, the company

seemed fascinating – yet not overly extraordinary nor a company for which I envisioned working.

When the company’s CEO, Dan Burns, hosted our organization for a tour and question/answer

session, my main curiosity centralized around the adidas miCoach team system and how the

company used data analytics with its athletes, namely the soccer players. At that point, MLS recently

began using the system as a unique performance (and marketing) tool, illustrating live statistics,

heat charts, and so on. While this was merely the cusp of what the industry was capable of, it

6

enraptured me. Responding to my question, Burns shocked me when he explained that the

company worked extensively with adidas on the miCoach system. At that point, I was determined

to develop or market or sell or simply be involved with team systems, and Athletes’ Performance

was the ideal destination.

(MLS, Adidas)

Eventually, my ardent pursuit towards a sports analytics career intensified with an array of

internship and part-time job opportunities, including with AdSport, the Phoenix Suns, and

Navigate Research. Most notably, I interned with Athletes’ Performance in the summer and fall of

2013. As a member of the marketing team, I was given the opportunity to partner with the

performance innovation team to further its sports science programs. For my capstone project, I

focused on the market opportunity of a proprietary athletic assessment tool by understanding the

product, performing market research, and presenting a marketing analysis with actionable

recommendations. Ultimately, this invaluable experience sharpened my skills, expanded my

network, and furthered my passion. Likewise, after attending the MIT Sloan Sports Analytics

7

Conference in 2013, my ultimate goal has been to maximize athletes’ potential and usher in a new

era of sports technology and analytics by developing team system and player-tracking products and

analyzing sports performance data, allowing teams and players to succeed.

In essence, this thesis project has enabled the ardent pursuit of my passion for sports

technologies and analytics. Throughout my expansive research, I’ve furthered my knowledge of the

industry and hopefully positioned myself successfully for a post-graduate career. As Chris Anderson

would state in The Numbers Game, I am an iconoclast of the soccer reformation.

Methodology and Research Methods

I am a student under the direction of Professor John Eaton in the W.P. Carey School of

Business and Barrett, the Honors College at Arizona State University. This thesis paper is

constructed as an industry assessment; as opposed to using the traditional scientific research study

format or analyzing a particular element of the industry or proposing a new analytical model with

which to assess soccer, this is my attempt at analyzing the current industry as it stands. Simply put, I

hope to identify the current industry landscape – first with brief summaries of other uses of

technology and analytics across business and other sports. The core area and purpose of my thesis

paper focuses on an in-depth discussion of technology and analytics uses in relation to soccer

technical and tactical, physiological, and neuroscientific performance, including case studies to

further validate my support and passion for the industry. Lastly, I will consider ethical situations

8

affecting the sports science industry as a whole (including discussion perhaps more relevant to other

sports) and provide implications and recommendations going forward.

Considering this format, I will not necessarily infer solutions or make concrete, quantitative

conclusions; instead, I will conclude with personal commentary concerning implications for the

industry over the next several years, ethical questions related to rapidly-advancing technologies and

the availability of data and analytics systems, and lastly recommendations that I can provide based

on my knowledge and limited experience. Whereas the remainder of the paper is a compilation of

available data and professional insights, this last section will allow me to add my opinions and

insights on more qualitative subjects.

Concerning research methods, I collected data via several primary sources: primary research

(interviews), secondary research (literature, online sources, conferences), and personal experience. I

conducted over 10 individual interviews, each approximately an hour in length, over the course of

several months – several of which included a follow-up interview for more questions and

clarification. While Institutional Review Board guidelines prohibit me from including names and

directly attributing quotes to each individual throughout my paper, I can briefly summarize the

individuals with whom I interviewed. These individuals work for a multitude of leading companies

in the analytics and technology spaces in soccer and sport. The following is a list of generalized job

titles and industry experience:

Business development and marketing, 5+ years

9

League director and former player, 15+ years

Neuroscientist, 30+ years

Nutritionist and researcher, 10+ years

Performance innovation and athletic trainer, 15+ years

Performance innovation and athletic trainer, 20+ years

Professor and soccer analytics consultant, 2+ years

Professor and sports psychology consultant, 15+ years

Professor and team leader, 2+ years

Soccer performance and analytics consultant, 15+ years

Sports performance consultant, 15+ years

My primary literature sources are the following: The Numbers Game: Why Everything You

Know About Soccer Is Wrong; Sports Analytics: A Guide For Coaches, Managers, and Other

Decision Makers; andThe Handbook of Soccer Match Analysis (also featured: Moneyball; and The

Signal and the Noise). While there are a myriad of other essential books to further understand the

industry, these were the several I chose to aid in my analysis. Online sources were primarily used for

more specific areas of interest that required more depth. As previously mentioned, I also attended

the 2013 MIT Sloan Sports Analytics Conference, and I will be incorporating video and notes from

the event. Lastly, my personal experiences during my internships and general discussions with

sports professionals will round out all of my sources and methods of research.

10

Industry Overview

Sports Science, Analytics, and Technology

My passion lies at the intersection of sports technology and analytics – an industry

commonly synonymous with (or heavily related to) the popular term “sports science.” Before

delving into an industry analysis, it is essential to understand several critical introductory ideas and

terms, including the following: what is data analytics? what is sports science? and how does

technology relate to each?

Analytics can be defined simply: the discovery and communication of meaningful patterns

in data (Analytics). Businesses harvest massive amounts of information and use “statistics, data

management, data visualization, and several other fields” to assess and view the data in meaningful

ways to simplify and reduce risk in the decision-making process (Alamar 1). When looking through

the sports frame, the idea is the same, although Benjamin Alamar defines it slightly differently,

stating that sports analytics is “the management of structured historical data, the application of

predictive analytic models that utilize that data, and the use of information systems to inform

decision makers and enable them to help their organizations in gaining a competitive advantage on

the field of play.” While businesses use analytics to increase their competitive edge in the

marketplace, teams not only use analytics to gain that same competitive edge but also to succeed on

the field. Elite teams such as England’s Chelsea FC, for instance, have accumulated roughly “32

million data points from something like 12,000 or 13,000 games” (Anderson, Sally 6). In sum, “the

11

ability to understand data, work with data, and think analytically about sports – that is what created”

this field of sports analytics (Alamar ix).

Sports science is slightly different although equally as important. It is “a discipline that

studies the application of scientific principles and techniques with the aim of improving sporting

performance” (Sports science). Sports science is more biologically-related than sports analytics,

including elements of physiology, biomechanics, and even psychology, yet this definition is

relatively loose and depends on who is providing it. Often times, sports science is all-inclusive,

including all forms of analyzing sports performance; throughout this paper, this is how I will define

sports science, unless otherwise noted. Sports science has become a more buzzworthy and

mainstream term as evidenced by the ESPN television show Sport Science which takes a look at the

scientific elements of modern sports marvels, such the analytics of saving a penalty kick or delving

into Marshawn Lynch’s “Beast Mode” run.

Technology’s role in analytics and sports science is constantly evolving and integrating itself

into the sports world – soccer included. From consumer-facing products (e.g. adidas miCoach

Speedcell) to technologies that influence the way the game is played (e.g. goal-line technology –

Germany’s GoalControl GmbH, which will be used in Brazil for the 2014 World Cup) to

technologies that allow for data collection and analysis (e.g. SportVU, Prozone3), technology’s

impact on the beautiful game cannot be ignored (FIFA). When it comes to sport sciences, these are

the rapidly-advancing products that enable the back-end analytics to happen. Companies like

12

Prozone/AMISCO, STATS, and Match Analysis are spending millions to ensure that their camera

and tracking technologies and analytics programs are top-notch and allow for teams to maximize

player performance on the pitch.

Despite the relatively clear idea of what sports science is and how technology and analytics

influence the game, the industry itself still lacks definition and clarity. As described by one analyst:

“an industry in the making, comprised of a number of people information, handling, collecting,

disseminating, communicating, and implementing information.” Another: it is a “situation where

there are a number of domains that can be very beneficial to sporting performance and

entertainment, but we struggle with A) trying to figure out how to use them to deliver meaning,

and B) how to integrate them.” A third leader in the industry definitively states that, “in our sport, it

is a burgeoning and useful way to look at the game,” and that it adds objectivity to an extremely

subjective sport. Ultimately, all of these are true – yet partial – definitions of an evolving industry in

which the rules have yet to be established in their entirety.

Industry History and Transition

Everyone credits the analytics revolution to the Bill James, the baseball statistician who is

widely considered one of the most influential men in baseball and sports history – and perhaps

rightfully so. From 1977 onwards, James published articles and an annual book titled The Bill James

Baseball Abstract, redefining baseball analytics as a whole (Bill James). James fathered sabermetrics

in baseball, and his ideas have reverberated for decades, most notably with Billy Beane and the 2003

13

Oakland Athletics –the epic story of Moneyball and how the A’s won the most games in the MLB

that year in spite of having one of the lowest cumulative team salaries. However, despite his

brilliant work, he is not the first person behind sports analytics.

The first analyst – in soccer, at least – was Wing Commander Charles Reep. After a military

career in the early 20th century, Reep combined his interests in accountancy and soccer to become

the first soccer analyst: the Soccer Accountant. “The continuous action of a game is broken down

into a series of discrete on-the-ball events, such as a pass, center, or shot,” precisely how he recorded

a game’s events with his notational analysis system. In total, Reep analyzed and recorded over 2,200

games, devoting about eighty hours to each. While Reep’s decades of conclusions were not

necessarily the most accurate or valid (e.g., long balls – aka efficiency – is the best way to score and

win), his work prefaced the start of a new science and industry (Anderson, Sally 16). Until the

technological revolution and movement towards motion analysis, soccer data analysis primarily

relied on visual identifications and notational analysis, as exhibited below.

Sample of notational analysis (Carling 4)

The industry truly emerged in soccer in the late 1990’s with the arrival of Opta Sports. The

company compiled an “index of player performance in soccer” as a means to “get the brand into the

14

public eye,” yet it soon realized the vast worth of such a legion of performance data to both the

media and, eventually, clubs (Anderson, Sally 10). As the industry progressed and the technology

used to capture game footage and record/analyze data became more intricate, other firms leaped

into the cauldron: Prozone, Catapult, AMISCO, STATS LLC, Match Analysis, StatDNA, and others

now compete (or collaborate) to continually advance sports analytics and provide clients with a

competitive advantage. The goal of this billion dollar industry: to maximize on-field performance of

soccer players in every way possible – player and team performance optimization.

In the modern game, there has been a transition from gut-instinct and tradition to one of

objective measurement and data analysis to support decision-making, not be the decision-making

tool (this must be clearly established: analytics and science only aid in the process as a means to an

end; they are not the ends themselves). Formerly, managers didn’t buy into the modern way of

thinking; many still don’t. Whether it is because of skepticism, fear, or simply because stats “can’t

measure the size of a player’s heart,” there are still counters of this movement (Anderson, Sally 14).

Yet others – such as Everton’s Roberto Martinez or decision-makers as FC Köln or Sunderland –

believe wholeheartedly in the opportunities that technology and analytics provide. Across the

board, managers and teams are largely using analytics for the same purposes – technical, tactical,

and behavioral analysis – and they’re doing it pre-game, in-game, and post-game, using the

information to better plan and prepare for future matches. The coaching cycle’s six steps

15

(performance, observation, analysis, interpretation, planning, and preparation) demonstrate the

recurring pattern of analytical assessment in sport.

The coaching cycle (Carling 10)

At the 2013 MIT Sloan Sports Analytics Conference, Chris Anderson asked an interesting

question: is the sports analytics glass half empty or is it half full? Several key elements suggest that

it’s half full: decreasing cost of data, increasing accessibility of data, developments of fan-oriented

data, and the exponential growth of sports analytics blogs across the blogosphere. Yet despite these

vast improvements and the apparent upward trend of sports analytics, the industry is still

unformed. As another interviewee stated:

It’s accelerating towards a peak on the hype cycle – becoming a very buzzworthy term with

lots of marketing and traction around it. Look at Chip Kelly’s “Mystery Man” [the former

Oregon coach utilizes analytics and technology with the Eagles, and he was responsible for

the NFL’s first-ever “sports-science coordinator” (Vrentas)]– their first ever head of sports

OBSERVATION

16

science, looking at data to improve performance. Eventually the hype is going to peak and

fall off a little bit. There’s also a level of incompetence concerning how to use the data; it’s

such a new field, people don’t know how to interact with it yet. Hype plus inexperience

equals a really interesting time for innovation. Many people are putting a lot of energy into

this, leading to new, brilliant, innovative thoughts. At the same time, though, many people

will abandon it until it’s more usable.

Since Reep, the industry has evolved and adapted and continues to grow at an exponential rate – yet

it is still a relatively young concept in a sport deep-rooted in tradition and subjectivity. Further,

there’s little impetus for many managers to convert to an analytics approach. Whereas the average

NBA manager serves for about three years, the average EPL (English Premier League) manager is

only around for 1.7 years, and 79% of all managers on the elite level will manage less than 75 games

in their career (SSAC Soccer Analytics). Given the short-term winning orientation, how do

managers take a leap of faith towards a new winning approach when their careers and relegation

are at stake? Currently, the sports analytics industry is at a pivotal stage, and it’s our job as analysts

to ensure that its burgeoning success continues. In order to do so, Prozone’s Blake Wooster argues

that “we use data to win, and unless we can demonstrate value in relation to winning…we’re failing,

really” (SSAC Soccer Analytics).

Despite these obstacles, more teams in the modern game are continuing to adopt analytics as

a new approach for success – sometimes a managerial decision, other times from ownership, yet

17

virtually always a top-down decision considering the uncertainty and the necessary investment. The

depth of the technology and analytics utilized at each club varies tremendously, but three things

remain relatively constant for those who have successfully made the plunge: they perceive it as an

investment, they’ve created a plan, and they perceive the true value and opportunity available.

“Analytics are helping players become better athletes, manager win more games, and owners cut

better deals” (SSAC Soccer Analytics), and there are numbers and case studies to validate that.

The Other 50%

The preface of my title, The Other 50%, owes largely to The Numbers Game. Simplified, the

concept is that soccer is a very unpredictable game that can really be decided by a coin toss on any

given day. Goals and results often come down to chance or sheer luck, not necessarily decided

entirely by the physical, technical, and tactical abilities of players and teams .Several statistics that

validate this idea are featured here:

The success rate of a pregame favorite to win is merely just over 50%.

(Anderson 53)

18

The underdog is likely to win approximately 45.2% of the time (sample size: 43,000+

games). This was the lowest amongst all major sports, with strong favorites likely to win

only 65% of the time (Anderson 57).

Soccer, by far, has the largest betting spreads of any major sport.

(Anderson 54)

The team that shoots more only wins the game between 45-49% of the time (sample size:

8,232 matches across four major leagues). 50-58% for more shots on target.

(Anderson 63)

19

Approximately 44.4% of goals are attributed to some bit of fortune in the final build-up play

or the shot itself (sample size: 2,500+ goals) (Anderson 62).

There are more statistics and relevant points to make here supporting this idea that soccer – more

than any other sport – is random and a fifty-fifty game, but my thesis does not pertain to this idea in

full. Instead, assuming that 50% of soccer is based strictly on luck and fortune, my aim is to examine

and analyze the market that enables teams to improve the elements of soccer that are within their

control. That other 50% is up for grabs for each team, and, utilizing technology and analytics, teams

can maximize their control, eliminate fortune’s hand, and earn all-important victories on and off the

pitch (Anderson).

In Sports

While my thesis is concerned with technology and analytics and their roles in soccer, I

believe that it is essential to at least briefly mention their roles and progression throughout other

major sports of interest, including the following: baseball, basketball, American football, and rugby.

The general consensus is that soccer is near the bottom of this list in regards to its relative sports

science integration, yet the past decade has seen a surge for the beautiful game’s analytical

perspective. Of these sports, baseball is widely-regarded as the most analytically-oriented sport –

especially considering the one-on-one elements – followed by basketball, yet the rest is very

subjective and depends on which elements of analytics and technology to which we refer. Most

sports utilize technology and analytics across the board – both on the business and performance

20

ends – ranging from scouting to player development to ticket sales to revenue generation. Across all

sports, however, there still exists problems with making data actionable – identifying how to

accurately and effectively utilize the data.

Baseball: As briefly discussed, sabermetrics is widely considered to be one of the first official

uses of analytics in major sports. Sabermetrics is the “term for the empirical analysis of

baseball, especially baseball statistics that measure in-game activity,” and the term itself

comes from SABR, the Society for American Baseball Research (Sabermetrics). Unique

statistics such as WAR (wins above replacement), win shares, and runs created are all

evidence of the analytical elements of baseball. More than other sports, baseball’s reliance on

metrics has enabled its growth in the analytics space. Technology has also enabled further

progression into the analytics space. A primary analytics company in baseball, SportVision,

created PITCHf/x – a tool that allows for the tracking and analysis of virtually every

element of a pitch (since then, the company has also introduced other products, such as

HITf/x). Pitchf/x made its MLB debut in the 2005 World Series, and it has enhanced the

viewing experience while also gathering mounds of data on almost a million pitches

throughout a regular season (Braley). SportVision’s Fieldf/x, however, is at risk against a

brand-new, yet-to-be-named MLB Advanced Media technology that does a superior job of

identifying and measuring defensive performance, as exhibited below (Budway).

21

Likewise, in 2008, MLB sanctioned the use of instant replay on home run calls and is further

expanding its use this season and beyond. Overall, baseball is on the forefront of

incorporating technology and analytics into the game both on the consumer and backend

sides of the sport.

Basketball: Basketball is fascinating to me because of its similarities to soccer and ability to

effectively utilize technologies and analytics to create advanced metrics and ways to measure

performance. For years, the most basic of statistics were the only ways to measure a player’s

on-court production: field goals made/attempted, assists, rebounds, etc. Most of these

metrics lack substantial value; to counteract this, ESPN’s John Hollinger created the Player

Efficiency Rating (PER). PER is a “rating of a player’s per-minute productivity,” and while it

was once foreign to the league, it’s now widely embraced as one of the better ways to

measure player value and production (Hollinger). Similarly, Grantland article Databall

introduces a new metric: expected possession value (EPV). For example:

22

Imagine LeBron James holding the basketball completely unguarded underneath the

basket. We would expect him to score two points. The EPV at that moment would

be very close to two. Conversely, imagine Dwight Howard holding the ball 40 feet

from the hoop with one second remaining on the shot clock and three defenders in

his face. It’s highly unlikely that Howard would score. That moment would be

ascribed an EPV very close to zero (Goldsberry).

With the new league-wide deal between the NBA and STATS LLC, the league now has

access to the company’s product, SportVU – sets of cameras that track every movement and

action on the court. The graphics below demonstrate some of the technological powers of

SportVU’s data visualizations, enabling teams to understand player performance on both the

technical and physiological sides of the game. SportVU identified that in 2012, for instance,

Kevin Durant earned a rebound 73% of the time while he was in rebound range, a league

best (XY Panel, SSAC).

23

When combined with physiological tracking chips (e.g. heart rate, accelerometers, etc.), this

allows for massive amounts of data collection and analysis. Like with other available

technologies ranging from different sports, SportVU also allows for incredible consumer-

facing transparency.

American Football: For a long time, performance analytics have dominated football – yet

never through a direct route, and that’s evident considering football’s relatively lacking

position in sports science. Quality control coaches have been commonplace in football for

years, their job being to analyze game footage of upcoming opponents and relay information

to the head coach and coaching staff. However, even today, some NFL teams – including the

St. Louis Rams – do not have sports scientists or performance analysts, and some only have

analysts focusing on the salary cap element and other topics to a minute degree. While the

Eagles are credited with the first sports scientist, other teams have long used sports analytics

as early as 2001 with the 49ers. For the most part, it appears that football analytics

centralizes around talent evaluation and strategic decision-making. Examples: Jim Harbaugh

and his analytics focus took a 6-10 49ers to the NFC Championship in one year and a Super

Bowl in two, and the New England Patriots have excelled at drafting and signing

unconventional players such as Julian Edelman, Danny Woodhead, and Wes Welker (SSAC

Football Analytics). Other hot topics in football are related to the significance and

practicality of the NFL Combine and play calling (e.g. going for it on 4th down or two-point

conversions). Most technological advancements in football surprisingly focus on what the

24

players themselves wear. Concussion and sub-concussive hits are becoming major issues and

factors into the sport as a whole, prompting amazing progressions in the equipment being

used by athletes to protect the brain. Similarly, another unique advancement is in the actual

uniforms, such as the Nike uniforms below, making them lighter, more liquid-resistant, and

more breathable.

Lastly, in-game audio technology generated new opportunities for fans to experience the

game and feel stronger connections to teams and players (Leap).

Others: Team tracking systems largely surfaced in Australia with both rugby and Australian

rules football and have since expanded to Europe and America, the former being the current

dominant market. Further, like with football and hockey, both rugby and rules football have

experienced concussion issues, accelerating the movement towards technology aimed at

protecting players’ heads (Australians assessing).

25

In Business

I will focus less on the business technology and analytics that facilitate success from the off-

field and business standpoints, although their uses are immensely important to teams, leagues, and

external organizations. These innovations are enabling teams to increase their revenue streams,

appeal to and satisfy fans, and enable organizations to be more efficient across the board. External

organizations and end consumers are also maximizing the value of analytics in sports through

gambling, fantasy sports, and a variety of other opportunities.

Front Office Decision-Making: While there are a myriad of utilizations of analytics across

the business side of sports, one of the more interesting and discussed is that of player

scouting: talent evaluation and identification. Teams across virtually all sports are using

analytics to assess talent and find diamonds in the rough (e.g. Swansea City’s acquisition of

Michu for a mere £2M – and he proceeded to be one of the English Premier League’s top

goal scorers by the end of the season with 18), identifying the most important metrics to

predict current and future value. This is the information that general managers utilize to

make trades, choose and sign draft picks, and so on. However, talent identification programs

(talent ID) are also prevalent on the youth level throughout major sports countries (notably

England and Australia) as they attempt to uncover homegrown talent (Vaeyens). Further, as

discussed at the 2013 SSAC, one of the major – and elementary – purposes of analytics in

26

major sports organizations (namely basketball and football) is how to most effectively

distribute salaries: the salary cap.

Sports Betting: The sports betting industry is enormous: each year, illegal wages alone

consist of approximately $380 billion (yes, billion) dollars (Sports Wagering). Companies

such as CantorGaming continue to pioneer this space, creating databases and models that

reduce uncertainty in gambling by analyzing statistical data (Predictive Sports Betting). Like

performance analysis, this was once a very subjective industry, yet pattern recognition,

predictive modeling, and a further quantification of sports has made this industry a

quantitative, objective game – yet still a gamble altogether.

Revenue Generation: Ticket sales, media, and sponsorship deals are critical to revenue

generation – and all of which rely heavily on analytics. Dynamic ticket pricing – a system in

which ticket prices vary per game reflect real-time pricing based on expected supply and

demand – is implemented amongst teams (such as the Phoenix Suns) as a means to boost

revenue maximization, maintain more real-time pricing, and simplify logistical processes

(Rishe). CRM systems, which burgeoned in sports in the early part of the century, are

further enabling teams to better understand and reach their target market segments.

Similarly, any media and/or sponsorship contracts are all carefully scrutinized by calculating

the true return-on-investment for each property involved. Companies such as Navigate

Research, for instance, examine a company’s ROI on a given sponsorship based on

27

consumer awareness, purchase intentions, emotions, and so forth; while these analytical

models are challenging to objectify entirely, their sophistication continues to grow.

Fan Experience: Technology continues to

enable fans to get closer to the game via brand

activations in spite of the reduced fan/player

physical interaction of the 21st century. For

instance, a German company installed a

“Twitter Wall” in FC Schalke’s locker room

so the players could read motivational tweets

directed to them by fans (Wiltshire). Further,

the in-stadium experience continues to be

improved by teams, counteracting the trend of viewers opting towards the less-expensive

route of watching from home. The 49ers are one of the teams ahead of the game in this

respect as they’ve developed a mobile application that enables fans to monitor beer lines and

watch instant replays from their phones – all while in the stadium (Boebel). With improved

Wi-Fi infrastructures throughout modern stadiums, this is an enormous push towards more

technology-friendly sports.

Academia: Sports analytics and technologies continue to adapt and evolve in the

marketplace, yet new ideas and innovations derive from higher education and research.

Sports science previously was an unimportant term to universities, and now there are

28

hundreds of undergraduate, masters, and doctorate programs available for students

passionate about the field, not including the other burgeoning degrees available in data

analytics, big data, and so forth. My limited industry interactions have fostered connections

with sports-science-driven professors from Cornell, MIT, and the Norwegian School of

Sport Sciences, and the impact of these researchers on the technologies and analytics of this

space will continue to influence the industry.

29

Technology and Analytics in Soccer Performance

More than most sports, soccer is extremely subjective. While the outcomes are concrete –

wins, draws, and losses – objectively evaluating player and team performance is extremely

challenging. In contrast to baseball, in which the game is largely one on one performance, soccer is

fluid and always moving, teams are never truly in possession, and goals are so few (roughly 2.66 per

game on the professional level) that identifying correlations is an arduous, uncertain task. As

discussed, elite soccer results are largely based on luck, chance, and error, but there is, of course,

massive room for teams to improve and differentiate themselves across physical, technical, and

tactical areas of play – and teams are constantly fighting to do so. After all, in the modern game,

“clubs don't just want a competitive advantage, they need it” (Medeiros).

These next sections offer a comprehensive analysis of the roles of technology and analytics

across three core areas of football: technical and tactical, physiological, and neuroscientific

performance. Using industry expert insights, critical data and graphics, and key examples, I hope to

provide a holistic view of the industry differently than what is currently available. Ultimately, the

purpose of these tools and methods is simple: add objectivity to soccer for coaches to better prepare

teams, or simply put, win.

30

Technical Abilities and Tactics

Technical and tactical analyses are very different elements of performance. The technical

side is concerning the quality of the soccer player: ball control, shooting, dribbling, tackling, and so

forth. Players such as Andres Iniesta and Andrea Pirlo don’t overwhelm with size or speed or

strength; instead, each player’s absolutely dominant soccer skill allows them to compete at the

highest levels in spite of their lack of physical attributes. Conversely, tactics are the team’s overall

style of play – how the eleven on the field will effectively defeat the opposing eleven. It is the head

coach’s job to not only train and prepare players for games but also make tactical decisions such as

team formation, passing style, conservativeness, and so forth. As American football coach Knute

Rockne once stated, “As a coach, I play not my eleven best, but my best eleven,” suggesting that the

most talented or physically overpowering players aren’t always the ideal choice based on a team’s

tactics. When examining player and team performance, whether it be from the physiological,

technical, or tactical side of the game, we can look to several different analyses: player vs. team

(individual, head-to-head, team unit [e.g. midfielders], team); time (different periods of a game,

across games, throughout seasons); and match type (training, friendly, competitive) (Carling 61).

Further, data can be represented in charts, graphs, spatially (on the pitch), or with data

visualizations. As is the case with any analysis, the data is virtually limitless – it’s just a matter of

how it’s utilized.

31

Before delving into the data analysis space, it is critical to understand the data collection

process. Generally with technical and tactical performance, data is either collected via manual

notational analysis, tracking monitors, or video and computerized match analysis technologies.

Notational analysis, in a nutshell, consists of monitoring and manually tracking/notating

information as needed, as illustrated with the example below. Notational analysis is still very

prevalent, especially within clubs that have yet to adopt technology and more advanced analytics.

Notational analysis tally sheet to identify successful/unsuccessful match actions (Carling 20)

Tracking monitor systems are more critical and telling for the physiological elements of soccer, yet

they can be valuable for technical and tactical purposes as well (such as identifying patterns,

connecting players, and generating heat maps) when analyzing player positioning. Like the adidas

miCoach or the system from German company Cairos AG, the microchip transmitters are used on

player jerseys and/or cleats and can even be used on the balls to determine three-dimensional

positioning up to 200 times per second, with a range of mere centimeters (Carling 42). Generally,

these chips are equipped with GPS and/or accelerometer capabilities, yet new technologies such as

RFID (radio frequency identification) chips are now emerging in the sports arena as well. However,

32

tracking chips have several drawbacks, including the following: 1. Installation is expensive, 2. Chips

are generally fragile and/or cannot endure heavy contact, 3. Battery life can be low, and 4. Some

governing regulations prevent micro transmitters (exemptions being the MLS, Brazil, Norway,

etc.). Lastly, the most prominent tools for currently analyzing the technical and tactical sides of the

game are video-based match analysis technologies, such as those used by Prozone and AMISCO

(recently merged), Opta Sports, and Match Analysis, among many others.

Sport Direct video analysis software (Carling 34)

33

Prozone’s Prozone3 product (Prozone)

In essence, these video-based player tracking systems – which are comprised of multiple permanent,

highly-calibrated cameras throughout an arena – use advanced mathematical algorithms and video

processing to identify player positioning and movement at literally every second of the game. As of

the mid-2000’s, the AMISCO system analyzed movements “ten to twenty-five times a second during

the whole 90 minutes…containing around 4.5 million positions as well as 2,500 ball touches”

(Carling 39). Simplified, video-based analysis identifies “what [the players] are doing and how

they’re doing it” (Interview). These systems track everything from the interactions between player

and ball, average velocity of passes, direction of most runs, average shape of the team, and so on.

However, there are several disadvantages of video analysis, yet they haven’t necessarily stopped

clubs from using it. First, unless your opponent has the same technology from the same company,

or perhaps there is an arrangement to share data, you will not be able to collect away game data

since it requires permanent set-up. Also, for the most part, data is not available until post-game,

except for smaller details – although clubs seem okay with this. Lastly, video-based systems are

generally only used during games, so critical data from training sessions is not collected (explaining

why the MLS partners with both adidas [for the tracking chips] and Match Analysis [for video

analysis]). Despite the drawbacks, video analysis has a myriad of huge benefits, especially

concerning technical and tactical analysis, whereas tracking chips cannot assess technical ability,

awareness, or any finer, more subjective elements of the game. Many managers, notably Everton’s

34

Roberto Martinez, thrive by watching game footage side-by-side with data analysis, as is offered by

many video-based companies. Another unique offering is from US firm Match Analysis, which,

unlike most other competitor products, offers a panoramic video. The K2 Panoramic Video displays

end-to-end video of a game, displaying the entire field at once (see the picture below).

When assessing player technical ability and a team’s tactics, there are a variety of metrics

available, and although the universal and prevalent metrics are sometimes not as telling as others,

new metrics are continuously being added to team’s repertoires. On the offensive end of football,

teams do their best to assess and analyze specific elements, including the following: playing style and

team shape, attempts on goal, entries into the attacking third, build-up play, free kicks, corners,

crosses, one-on-ones, support play, and space created. Conversely, there are also unique actions that

analytics attempt to identify on the defensive end: playing style and team shape, free kicks, corners,

goalkeeping, tackles, one-on-ones, support play, and closing space (Carling 74). Some, such as shots

and team possession, are easy to identify and interpret, as demonstrated by the graphic below.

35

Team possession and shots analysis throughout a game (Carling 69)

On the other hand, some of these actions are ultra-challenging to measure, namely concerning both

movement and defense. Prozone’s Blake Wooster explains the challenge in analyzing these pieces of

game intelligence:

“What does the center midfielder do, just because he doesn’t register a stat, his subtle

movement to actually stop a transition. The center back that never gets himself into a

position that he has to do a sliding tackle…that stat, that event never occurred. The

goalkeeper that didn’t make that last ditch save because his starting position was such that

he was able to prevent that shot from ever taking place” (SSAC Soccer Analytics).

It is extremely challenging to identify what the most crucial metrics are in assessing quality. Of

course, scoring the most or having the most clean sheets doesn’t point to a specific player being the

best because there are so many other variables that teams haven’t quite assessed or analyzed. The

simple piece is collecting the data, like a player’s movement throughout a match; the complex

element is determining what it means.

36

Player movement via data tracking (Carling 72)

Despite these difficulties, there are certainly key performance indicators available that give

us a general sense of quality and overall ability – yet their uses are still limited. In 2012, Manchester

City FC released a year’s worth of Opta data, allowing the general public to have an analytics field-

day, and some of the results are worth sharing. For example, one user charted the top ten English

Premier League strikers based on 1. Time efficiency, and 2. Conversion rates.

On the left, these strikers produce the lowest minutes per goal, scoring more efficiently while on the

field. Conversely, the strikers on the right (already reduced to the top ten shooters by volume) are

measured by their goals to shots ratio. Which is better? Answer: it just depends. To make the

37

“depends” answer more clear, using the same source, there is a data visualization showing catches by

goalkeepers (right). Is Wayne Hennessey better than Petr Cech, Michael Vorm, or David De Gea

because he made more catches (and more saves)?

Not at all; as a matter of fact, he had to make 169

saves compared to 84, 136, and 102, respectively –

so he was just arguably busier because of poor

defending, perhaps due to a lack of holdup play

from the strikers, both, or something else entirely.

While the KPIs are extremely valuable in assessing

and comparing players, they generally mean little

without further validation and understanding of what all is happening (MCFC).

I’ve collected and noted several interesting case studies involving the use of technical and

tactical data, including Stoke City, Santi Cazorla, and home/away tactics:

Stoke City: As noted in The Numbers Game, teams such as Stoke City have largely

abandoned the idea of tiki-taka, beautiful football that Barcelona and Spain often dazzle with

in exchange for a style of efficiency: long throws, corner kicks, and so on. Why? They’ve

used analytics to build teams that are less technically dominant yet fulfill niche roles at a

fraction of the cost. Looking at the numbers, they’re a statistical anomaly; whereas the

average side scores two of every three goals from open play (i.e. not a penalty, corner, free

38

kick, etc.), Stoke scores only half from open play. Only about one in ten possessions for the

team consists of more than three passes, and only four percent have seven or more

completions. They consistently lead the league in long throw-ins (550 in 2012), and the time

that the ball is in play and moving during a Stoke game can be as low as 45 minutes or so.

Stoke’s secret lies in its tactics due to the team’s general lack of technical ability. First, the

team revels in not having possession because possession does no good for such a poor

passing team; instead, it relies on the long ball and set pieces to steal goals and wins. To add

to that, the team ensures the opposition has fewer chances to score by having the ball in play

as little as possible; by keeping the ball out of play or at set pieces, the team starves the

opposition of possession. All in all, as former manager Bob Paisley once stated, “It’s not

about the long ball or the short ball, it’s about the right ball” (Anderson 175).

Santi Cazorla: Current sports scientist and performance analyst for Seattle Sounders FC,

Ravi Ramineni, operates a blog: onfooty.com. Using Opta data, Ramineni analyzed the

impact of Santi Cazorla on his former club, Villareal, and what to expect going forward with

his new club, Arsenal, based on team KPIs across two years. Overall, the team’s performance

dipped substantially without him from 2010/2011-2011/2012, averaging .49 less points per

game on .32 fewer goals per game. Conversely, his then-new team, Malaga, surged to a

fourth place finish in La Liga, securing its first Champion’s League berth. Further, lower-

level statistics dropped off: shots (5.4 4.6), successful dribbles (4.62 3.50) and pass

percentage in the final third (72.76 69.59), and ball recoveries in the final third (3.13

39

1.79) were all statistically significant and lower, suggesting Cazorla’s substantial impact on

the team’s attack. Likewise, Santi’s heat/movement map across both years suggests his

dynamism and ability to play a variety of roles in the midfield and attack.

While there are a myriad of additional variables to consider with each statistic, their

aggregation – including his other findings – suggest that Cazorla had a substantial impact on

the team’s most important key performance indicators as well as lower-level stats

(Ramineni). By performing analyses both with/without Cazorla across years and within

years, this analysis gives a perspective of what value he adds to the team – a key resource in

assessing a player’s performance and overall quality.

Home and Away Tactics: Team formations and tactics have evolved substantially over the

past several decades; as early as the late 19th century, teams were documented using radical

formations such as a 1-2-7, but the modern game is largely dominated by the 4-4-2, 4-3-3,

4-5-1, 3-5-2, and etc. In turn with the normalization of playing formations, teams have

apparently also adopted a universal similar strategy: win at home, draw away. Recent

research by Disney Research identified that this was indeed the case using automatic

40

formation analysis (while video-based systems have identified individual players, they

previously were not capable of automatically recognizing a specific playing formation).

As can be seen with the data on the above, points, goals, shots on target, and final third

possession time were all statistically significant and reduced away from home. The visual on

the right also shows that team aggregate position – while consistent on the vertical axis –

was farther from the opponent’s goal on the horizontal axis, suggesting that teams sucked in

and played more defensively away from home. In general, teams played the same formation

both at home and away, meaning that the tactics of each formation were different. Using

this type of post-match automatic formation analysis, teams can better prepare for away

games and perhaps alter their tactics to reflect the more positive, threatening approach that

they utilize at home (Automatic Formation).

Interesting and unsurprisingly, leagues are also particularly interested in the tactical data of

teams, namely in regards to tendencies and overall entertainment value provided. For instance, the

41

MLS has the Attacking Index which measures shot position and how a player got into that specific

position prior to shot. Another telling gauge is the Entertainment Index, a dynamic formula to

measure shot quality and match variation. Leagues are consistently measuring clubs against each

other while simultaneously gauging domestic versus international performances. These indices,

among others, are continually recalibrated to ensure that the right metrics are being measured,

ultimately helping smaller leagues like the MLS understand how to compete based on both quality

and consumer value and entertainment.

Ultimately, analytics and technology are continuing to pave successful roads for soccer

teams, creating a competitive advantage that was previously unavailable. Teams have the data, and

now the challenge is to analyze it and produce meaningful results. In terms of the technical and

tactical pieces, “football analytics is a discipline in which the way a team plays dictates which

statistics are significant. The challenge is to find out which” (Medeiros).

42

Physiology

Physiology is the scientific study of function of living organisms. To tailor this more

appropriately, sports physiology can be defined as an “aspect of performance science concerned with

the assessment of how the body responds to single or repeated bouts of exercise” (Physiology).

Unlike the tactical and technical side of performance, physiology is very consistent across sports,

and although there are different means of preparation, physical training, and injury prevention,

analytical tools and technologies used for measuring human performance are largely universal. In

this section, I will focus on several core areas of soccer-related physiology and how technology and

analytics are impacting the way we prepare, replenish, and recover athletes in three areas: training;

health, injury, and recovery; and nutrition.

The physiological performance industry is massive in scope, ranging from simple heart rate

monitors to advanced tools for urine testing to muscle biopsies. There are hundreds of ways that

teams are attempting to get the slightest competitive advantage, ensuring that its players are always

at their peak physical performance levels. Of course, the industry also has two unique markets:

general end users and elite athletes/teams. I will not focus on the general consumers – you and me,

the ones purchasing a Garmin watch to track our mileage and times – yet there are several

interesting elements about this market. The adidas miCoach, for instance, is both an individual

consumer product (with limited capabilities at a substantially reduced price point) and an elite team

tracking system used by all of the teams in the MLS to gauge more advanced real-time metrics such

43

as power and field position. Similarly, a competitor such as Nike uses its market power to

commercialize products like the Fuel Band rather than focus on elite use, yet it also produces

equipment-related products at both elite and basic levels.

Like with the technical and tactical sides of soccer, there are a variety of means to measure

physiological data in training and games. Video-based technologies and data tracking sensors (used

on jerseys or in cleats) are the primary tools being used to measure movement and the physical

elements of the game. On the video analysis side of soccer, pioneering firms such as

Prozone/AMISCO, Opta Sports, StatDNA, Apollo MIS, and Match Analysis are innovating and

dominating the market. Meanwhile, firms such as adidas, Catapult, GPSports, Inmotio Cairos AG

are market leaders in the team tracking systems market. Tracking devices generally use

accelerometer

(measures rates of

acceleration, translates

into force) and/or GPS

(positioning)

technologies, yet they

can also use

gyroscopes (measures

angular velocity, multiaxis movement) and magnetometers (measures direction). Whereas the

44

video-based systems can measure information such as distance, speed, and acceleration, tracking

chips offer more physiologically-oriented metrics. For example, the adidas miCoach (displayed

above), which is largely used in the MLS, not only measures simple metrics like distance and speed

but also measures heart rate, power output, and work response, and the data visualizations allow

easy interpretation and dissemination of information. Others, such as Catapult, focus on injury,

performance, and tactical analysis with its GPS tracking systems. Using advanced hardware and

software systems, these companies manage virtually all measurable metrics and offer personalized

feedback and training recommendations.

When discussing physiology in training and in-game performance, the general metrics are

simple and relate to work-rate, such as speed, distance covered, acceleration, and so on. The two

charts below, total distance covered and percentage of time moving at various paces, reflect a

standard of physical assessment in the football world.

Analysis of total distance covered by players (Carlson 67), percentage of time moving at various

paces (Carlson 91)

45

However, at the most elite levels, virtually all players are extremely fit and can run endlessly; with

general measurements like these, their significance is more vital as you move to lower levels of

professional soccer as well as the implications across positions in relation to technical play (e.g.

forwards receive the ball more frequently on sprints, midfielders turn and cut more frequently on

the ball). Key performance indicators also vary more greatly across positions as midfielders

generally run at least 10% more than their defensive counterparts. To supplement these general

KPIs, there are a variety of unique means to attain telling metrics that are emerging to become more

substantial in gauging physiological performance, such as the following: functional movement

screen, physical therapy assessment, metabolic response, strength testing, linear and lateral

movement skills, and training loads. The graphs below, for instance, display the physiological

responses (energy cost, blood lactate) when running with and without the ball at various speeds,

suggesting the benefits of using the ball in drills when possible (Carling 93).

46

Further, as discussed in the Handbook of Soccer Match Analysis, work-rate is influenced by several

factors: positional role (goalkeeper, defense, midfield, forward), playing style (possession, direct,

counter-attacking, etc.), overall fatigue, and the environment (weather, altitude, location).

A vast variety of other unique tools and tests exist in the marketplace to gauge fitness,

measure readiness, and prepare elite athletes for desired work-rates. Tensiomyography, or TMG,

was a unique topic of discussion in one interview: an immediate and noninvasive technological

procedure that essentially diagnoses muscle status, type, and symmetry. TMG has a variety of

purposes, including “injuries prevention and detection, rehab, speed development, sport training

optimization, and fatigue index” (Tensiomyography). Relating back to injury prevention and

detection, TMG can assess whether a muscle is fully rehabilitated; contrary to what an athlete may

want to believe, the procedure can identify that a specific muscle is still not ready for contact or

intense movement. Another test is the urine specific gravity (USG) test which helps determine

hydration levels. Hydration has one of the highest correlations to soccer performance, yet during an

interview, a source cited that a major client’s team was largely dehydrated during training and

games – USG testing enabled the consultants to identify that as a major issue. Another measure,

briefly noted above, is blood lactate, which is “used to indicate anaerobic glycolysis” – the

breakdown of glycogen for energy. While most of a soccer match uses aerobic energy (in the form

of running and jogging), some sprints and high-exertion actions will require high anaerobic output

(Carling 101). Lastly, and arguably one of the most significant measures of game fitness, is the V02

47

max test. V02 max measures the “maximum rate of oxygen consumption as measured during

incremental exercise,” which helps to determine cardiovascular endurance during long, sub-

maximum level exercises – such as a soccer match (V02 Max).

Ensuring that athletes are healthy, actively on the pitch, and not injured is one of the most

substantial investments that clubs make. There are several influences factors that lead to player

injury: player factors (previous injuries, strength, technical skills, etc.), load (season planning,

training dose, recovery and rest, and number of matches), and club factors (playing tactics, club

philosophy, medical services, etc.) (Ekstrand). When assessing the injury economics of soccer, the

following was identified: 2.7 injuries/player/season; 14.7 absent days/injury; 39.6 absent

days/player/season; and 54 injuries/squad/season. For a “top ten” team in the English Premier

League, just one prevented injury can save a team approximately 80-450 thousand pounds

(Norman). According to another study by Ekstrand, for a team of 25 players, there are

approximately 50 injuries per season with 8-9 being severe (see severe injury chart below).

48

Simply put: teams are willing to invest substantial amounts of money to maintain player health and

prevent injury (note: injury prediction is sometimes referred to and studied in soccer – sources

claim it is “laughable to be involved with” as it cannot be predicted without it happening). In

response to how vital healthy players are to a club, there are a substantial amount of technologies

and analytical processes to assess the health, injury, and recovery of elite athletes. AC Milan’s

MilanLab – which is built on the idea of injury prevention – is a prime example of how dedicated

teams are to sports science. With its partner Microsoft, the MilanLab, founded in 2002, analyzes

“aspects of a player's medical, psychological, sporting, and scientific profile” in order to optimize

player health and team success (Meersseman). Jean-Pierre Meersseman is the man responsible for

MilanLab and the club’s movement towards a health and medicine in the late ‘80s. Since then, AC

Milan has won seven Serie A titles and five European championships, and many club players have

performed above expectations into their late 30s, including Clarence Seedorf, Paolo Maldini, David

Beckham, and Gennaro Gattuso. Humbly, Meersseman stated the following concerning Italian

legend Maldini:

“I could never say it was us that were the reason Paolo Maldini played until he was 41. Paolo is

simply an exceptional individual. But when he was about 32 or 33, he would admit himself

that his career was going slightly downhill. Paolo is extremely complementary about the work

we did with him, and I hope we were a helping factor in kick-starting his career again in his

early thirties” (Meersemanlab).

49

Now, Meersseman has a lab located in London providing the same services in healthcare and injury

prevention to elite players in the Premier League.

Hundreds of unique companies and startups have occupied this fitness and health industry

in an attempt to satisfy evolving market needs. Omegawave, for example, has been around for

fifteen years and strives to identify “optimal types and intensities of training and recovery, to

improve athletic performance and help avoid injury” in both elite soccer players such as Mario

Balotelli, Lionel Messi, and Steven Gerrard, and non-elite athletes. The company’s products use

electrocardiogram sensors to assess cardiac, metabolic, and central nervous system readiness,

providing the necessary fitness recommendations. (Omegawave). Another company related to

nutritional health is MuscleSound. Like Omegawave, MuscleSound is targeted towards elite athletes

and aims to improve athletic performance and reduce injury, yet its methodology and focus are

entirely different – it uses non-invasive ultrasound to analyze muscle glycogen storage, the body’s

energy levels. In approximately 15 seconds, an athlete can gauge his/her energy levels and alter

personalized performance and nutritional inputs/outputs to maximize performance (MuscleSound).

Applied, if an athlete enters a game with half of the required levels of glycogen, he/she will run out

of fuel or get “gassed.” In other sports, decreased glycogen levels are proven to be directly related to

performance, including decreased maximum sprinting speeds and shooting percentages in

basketball (Interview). Another company is Sano Intelligence, a yet-to-be-launched startup. Sano is

developing a small (think nicotine patch small), painless patch that automatically transmits

information about your overall metabolic health from your bloodstream to your mobile device.

50

While this product has a limited lifespan and is currently being aimed towards a new generation of

consumers fascinated with the idea of quantifying their individual performances, it’s not hard to see

parallels in what can be achieved within the professional sports world (Schwartz). Others include

Breezing, which measures metabolism via any mobile device simply by breathing; AlterG, which

developed the first anti-gravity treadmill, helping to

shorten recovery times and increase mobility (image

to the right); and a to-be-named South African

startup, which created a tracking device that reads

interstitial fluid, effectively predicting calories

consumed (Interview). In essence, whether marketed

at elite or non-elite athletes, this market is burgeoning, making medical diagnostics accessible, and

clearly demonstrating the capabilities that will soon be available to our most elite footballers.

The nutritional element of the physiological equation is intriguing (some of the tools and

tests already being briefly discussed), and this market is generally broken into four steps or

categories: assessment, prescription, application, and tracking and monitoring. It should be noted

that many elements of these steps are universal across different processes and purposes in sports

(e.g. most team analytical processes begin with a baseline assessment of physiological and athletic

abilities). Nutritional assessment is the baseline understanding of how to tailor personalized

nutritional prescription and is conducted either via self-reporting or blood marking, the latter

enabling the availability of real-time nutritional data. A nutritional aptitude screen can also be

51

utilized here, which assesses consumer eating behaviors and knowledge. The prescription element

is fairly straight forward in that an athlete is given a specific regimen to follow in order to achieve a

specific goal. According to a source, there is “not of new science” in the area of prescription. Next,

the application phase has substantial and vast opportunities to connect different data pieces. The

example my interviewee gave me is simple yet displays the opportunity present: “if an athlete knows

that he needs to consume 500 calories at lunch (based on my personalized prescription), what are

we going to do with that information? How are we going to marry the data points of the

individualized needs and food options available?” Last is the tracking and monitoring element in

which the major questions are “what are we tracking?” and “why?” During our interview, my source

cited a newly piloted dietary product that sends a dietician a daily consumption report based on self-

reporting. In sum, if the end consumer follows the regimen and collects “points” as needed, there is

a guarantee of losing at least 7% body weight over a four month period. Ultimately, there is a

plethora of uses and applications of analytics and technology in nutrition – and physiological

processes as a whole – within the soccer arena, allowing sports scientists and performance coaches

to optimize human performance and potential.

52

Neuroscience

Neuroscience is a fascinating subject to me, one that I was introduced to via secondhand

studying. While its role in soccer and sport is still relatively limited (although sources claim that

virtually everyone uses it to some extent) – often either perceived as a fad or not consistently

validated by research – I consider it to be a differentiator in sports performance, and the research

I’ve gathered (which is relatively limited in scope and depth compared to the other sections) offers

extraordinary insight into its purposes, methods, and future direction. As one source stated, it is the

“last frontier of sport.”

To start, what is neuroscience? Neuroscience is “any or all of the sciences, such as

neurochemistry and experimental psychology, which deal with the structure or function of the

nervous system and brain” (Neuroscience). The sports neuroscience industry is largely unformed

currently, yet its goal is ever-present: increase the speed and accuracy of brain, thus making it (and

therefore in-game decision-making) more efficient. Solutions to topics such as learning and player

development, performing under pressure, and optimizing decision-making are all highly relevant

and sought after in sports, and both neuroscience and sports psychology research continue to offer

gradual answers. At the moment, neuroscience does not appear to offer solutions that are specific or

functional enough, yet this is rapidly changing. Whereas my focus on the physiological and

technical/tactical sides of technology and analytics were more well-rounded and holistic, my

53

emphasis in neuroscience will revolve around three different perspectives: neuroscience in sports,

sports psychology, and computerized cognitive training.

The sports neuroscience industry is highly unregulated; as a matter of fact, about one

hundred new competitors enter the industry per year, ranging from those offering neurotracking

devices (to identify visual and spatial awareness) to neurocognitive training – yet their role is still

highly commercial and ineffective. For the most part, there is no evidence as to the value and

effectiveness of these offerings as many companies do not have legitimate data, published papers, or

university research or backing. However, there are some – such as my sources (one neuroscientist,

one professor and sports psychologist) – who are pioneering a new industry with valid, scientific

findings that will enable technologies and analytics to further enhance the power of the athlete’s

brain.

The human brain is malleable and plastic. A song bird creates a new song every single day,

effectively changing its brain tissues by about 1% each day. While human brains don’t change that

substantially on a day-to-day basis, they are constantly changing and evolving. As a matter of fact,

“the structure of our brain, from the details of our dendrites to the density of our hippocampus, is

incredibly influenced by our surroundings,” suggesting how malleable it is (Sing a New Song). This

vital piece of knowledge is critical in understanding how an athlete’s patterns, habits, and even

cognition can be altered and enhanced via biofeedback.

In order to understand how to optimize the performance of the brain under load (for

instance, during a soccer match), we use biofeedback, which is the “process of gaining greater

54

awareness of many physiological functions primarily using instruments that provide information on

the activity of those same systems, with a goal of being able to manipulate them at will”

(Biofeedback) – think Pavlov’s theory. However, neurofeedback measures brain activity and

rewards particular things via two forms of feedback: “EEG (electroencephalography) neurofeedback

uses sensors that are placed on the scalp to measure brain waves, while HEG

(hemoencephalography) neurofeedback uses infrared sensors or functional magnetic resonance

imaging (fMRI) to measure brain blood flow” (Neurofeedback). Simply put, these tools allow us to

alter the brain’s performance and achieve desired cognitive states of an athlete – flow, focus, and

calmness – while under load or pressure. Another form of neurofeedback is heart rate variability

(HRV), measured by the time in between breaths as you breathe in and out. HRV has become a

standard in the sports science industry as it helps prevent overtraining, and a “high HRV [is linked]

to good health and a high level of fitness, whilst decreased HRV is linked to stress, fatigue and even

burnout” (Fletscher). Companies like BioForce are optimizing athletic performance and leading the

way in HRV training.

Measuring the brain is also achieved by using neuropsychology tests, which measure

physiological functions in relations to the brain. These are often related to memory, language,

learning, and executive function, among other areas, including aptitude in job functions. One

example of a neuropsych test is the ever-popular Wonderlic Test, a common aptitude test

frequently associated with the NFL and player cognition and mental capabilities. While there have

55

been many attempts to establish relationships between a player’s Wonderlic (namely quarterbacks)

and their future performance, no positive correlations have been established.

Sports analytics and technology are also emerging in the area of sports psychology as it

continues to blend with neuroscience. Sports psychology is predominantly related to identifying and

enhancing mindset and motivation, by finding that “it” factor that enables athletes to perform at

their strongest mental and spiritual state. While motivational interviewing is still critical to the

field, questionnaires and computer-based assessments (often related to dual-test tracking, social

acuity, and executive function) are beginning to help understand visual perception, performing

under pressure, and player learning and development.

One fascinating neuroscience study was performed by Geir Jordet of the Norwegian School

of Sport Sciences. In The hidden foundation of field vision in English Premier League soccer

players, Jordet attempted to determine the relationship between visual processes and player

performance. Gordet quotes Barcelona’s Xavi:

“The difference between them and us is we have more players who think before they play,

quicker. Education is the key. Players have had 10 or 12 years here. When you arrive at

Barça the first thing they teach you is: think. Think, think, think. Quickly. [Xavi starts doing

the actions, looking around himself.] Lift your head up, move, see, think. Look before you

get the ball.”

56

By using advanced split-screen player broadcasts, Jordet

and his team were able to watch a soccer game while

simultaneously focusing closely on one specific player at

a time (see image). In all, they watched 64 games, 118

players, and over 1,000 relevant situations pertaining to

his study. In doing so, his goal was to observe player “visual explorations,” which he defined as “a

body and/or head movement in which the player’s face is actively and temporarily directed away

from the ball, with the intention of looking for information that is relevant to perform a subsequent

action with the ball.” Ultimately, how did players use their visual senses, and was there some

relationship between visual explorations and performance? The results are astonishing.

After breaking down the numbers into numerous categories (including by position, pass

completions, areas of the field, etc.), perhaps Jordet’s most telling piece of evidence supporting his

hypothesis (that greater “visual exploratory frequency” was indeed linked to better performance)

was the chart above. In essence, this chart is midfielder pass completion percentage in the

57

opponent’s half, with the vertical axis is pass completion percentage, and the horizontal is visual

exploration frequency. Overall, there is a substantial (and statistically significant) difference

between the passing of those who are more visually aware than those who exhibit fewer visual

explorations. This finding – although not all-telling – allows coaches to better prepare players by

mentally training them to be more visually and cognitively aware during all drills and functions.

One company that is beginning to make inroads into the brain analytics space is Axon

Sports. Axon focuses on a new technology, computer based augmentation (brain games, to a

degree), using neuroscience and computer analytics to assess how athletes use their brain on the

field based on the context of sport and position. As demonstrated in the image below, Axon

centralizes around three areas of performance (with subsets of tools and loads for each):

fundamental cognitive processing, athletic cognitive skills, and athletic cognitive skill consolidation.

58

Currently, the company only works with football and baseball athletes. In football, for example,

running backs are briefly shown a glimpse of their field vision (what they perceive behind the

quarterback) on a large screen or handheld device, and they must quickly identify the defensive

formations and where to make their runs. Similarly, with baseball, batters view the windup of a

pitcher and, before the release of the ball, the batter must identify the pitch type. By “training above

the neck,” the company hopes to empower athletes to be more cognitively aware and garner virtual

reps without the physical wear on their bodies (Axon).

Other technologies and means of neurocognitive analytics are present in the marketplace,

yet their successes are not as qualified or documented. For instance, German team Borussia

Dortmund – an excellent passing team and winners of the Bundesliga in 2011 and 2012 – was

notably using a new technology to not only boost its team’s technique on the ball but also to

improve players’ vision, awareness, and cognitive abilities on the pitch: the Footbonaut.

This unique device puts a player in the middle of a cage-like arena in which a ball is randomly

passed to the player, and he must react to a light at some other area and pass to the flashing light.

59

The ability to be cognitively aware during a football match is critical, as established with Jordet’s

study; as Dortmund’s chief scout stated, “We are convinced that at the very least the Footbonaut

will improve technique but will also benefit spatial awareness and peripheral vision” (Footbonaut).

While new devices like the Footbonaut are paving the way for modern uses of technology and

analytics (e.g. how quickly a player reacts to the light, the time it takes to release the ball, etc.), their

uses are considered somewhat of a fad, and their effects not validated by scientific research as of yet.

Ultimately, neuroscience has a simple goal in sports: improve the athletic brain. Research

findings are critical, yet the most essential element of the neuro process in relation to sports is

communicating key findings to coaches. Although this part of the process is more subjective, its role

shapes how neuroscience is truly applied on the training ground and beyond. For instance, a

neuroscientist’s report on an athlete, which used “subtests that sample speed, accuracy, learning, and

emotional identification” and providing a coaching tip for each element, included the following

information concerning information processing:

“Information Processing: Her immediate and delayed recall are high scoring, with 75th and

87th percentile results. The ability to screen out interference is however not as impressive,

getting worse across time, from the 42nd percentile initially which is average, down to the

19th percentile which is low.

Coaching Tip: Although she focuses well, there is a drive to perform which may be creating

some distraction from coaching input over time, resorting to other influences. Coaching

should be clear, and repeated, so that she complies well with coaching without other

60

influences intervening unintentionally. Contextual boundaries break down over time, so she

may not attribute exactly what information came from what source, disturbing her clarity”

(Gogo 43).

In sum, neuroscience’s role in sports in continuing to expand, and its influences on both subjective

focuses like motivation as well as objective focuses like HRV will perpetuate its use in coming years.

“If you can measure the brain, you can manage it” (Sugarman).

61

Moving Forward

Ethical Discussion

When initially choosing the topic of sports analytics, the idea of discussing ethical play was

nonexistent. As I continued to delve deeper into the subject matter, however, it was evident that

leagues, clubs, and decision-makers face superfluous amounts of ethical dilemmas on a regular basis,

many of which won’t be resolved for years. Some of these illustrations of ethics focus on hard

sciences whereas others centralize around vague, unestablished concepts. Ultimately, these

questions – among others – must be addressed before more adverse consequences result as the

industry continues to progress.

Medical Advancements: What is – and is not – allowed on the physiological side of sports

science going forward? With medical advancements flourishing in the new century, creating

and rebuilding athletes, ethical dilemmas are already present and under scrutiny. To start,

look at one of the most notable surgical procedures affecting sports: Tommy John surgery in

baseball (and other similar procedures across other sports). Currently, Tommy John surgery

reconstructs elbow ligaments, primarily in cases of overuse by pitchers throwing too hard,

too frequently, or with improper throwing technique. However, within the past several

years, harrowing questions have arisen from the current generation’s youth: “If

reconstructive elbow surgery were performed on his healthy throwing arm, might he gain

some speed on his fastball?” Well, according to virtually all doctors and surgeons, the

62

answer is a resounding no. Instead, in most cases, it allows players to return to their pre-

injury form – giving the misconception that the elbow is stronger and enabling a higher

level of play as well as one that the surgery is a cure-all, preemptive way to get better

(Longman). My question, however, is with the advent of new technologies and surgical

procedures, will surgeries eventually lead to improved on-field performance? If a young

athlete with naturally weaker ligaments, tendons, or muscles were to be a recipient of a

procedure leading to enhanced abilities, is that cheating, or is it fair game? While the ones

performing the operations claim that its effects are non-advantageous, both one of my key

interview sources and I contend that this could change in coming years, provoking serious

internal discussions amongst governing bodies.

Another hot topic in medical advancements is cyborg enhancements, such as the

artificial limbs of athletes like Oscar Pistorius (one of the most renowned – and

controversial – Paralympic athletes). The argument is simple: modern technology and

engineering can build better limbs than people can grow. Initially, Pistorius was banned

from competing in able-bodied events with evidence citing that he had a significant

advantage over able-bodied runners; however, the ruling was reversed:

Initially, they “tested Pistorius' biomechanics only at full-speed when he was running

in a straight line (unlike a real 400-metre race).” Further, “the report did not

consider the disadvantages that Pistorius suffers at the start and acceleration phases

63

of the race, and that overall there was no evidence that he had any net advantage

over able-bodied athletes” (Oscar Pistorius).

Pistorius was eligible and competed in the 2012 Olympic Games, and although he did not

medal, it set a precedent for other Paralympic athletes to compete with able-bodied athletes.

Ultimately, will technology eventually cultivate stronger, faster, and better athletes than

natural processes? Once again, I argue yes: “technology will keep producing better and better

athletes.”

Performance Enhancing Drugs (PEDs): It is not a surprise that doping is not highly

prevalent (or at least publicized) in soccer. This is typically due to a lack of testing, yet FIFA

has been diligently working with the World Anti-Doping Agency code for almost a decade.

The minimum first-year penalty for violating this PED policy is a two-year ban from

competitive soccer – with exceptions, of course (World Cup). However, PEDs largely affect

other sports, namely baseball, football, MMA, and cycling.

Baseball and football are extremely relevant to the modern PED discussion as athletes

continue to get stronger and faster, seemingly defying natural limits. I will not delve too

deep into either case considering how well they are documents and discussed, yet new

means of doping are complex to the point that a drug’s presence is unidentifiable, as was the

case with Alex Rodriguez who tested negative on numerous occasions. Words like

Biogenesis and human growth hormone are becoming commonplace as fans almost accept

PED presence in sports. MLB is notorious for frequent drug testing, using analytics to assess

64

a player’s biological status (e.g. higher concentrations, foreign chemicals), and they have

even been known to suspend players with “non-analytic proof” (Keri).

Doping is increasing under the microscope in mixed martial arts. While many

fighters face suspensions for violating doping policies, one of the industry’s most winning

fighters, Georges St. Pierre, claimed that UFC’s stance on the issue was not strict enough.

Yet in a move to continue its stance against performance-enhancing drugs, the Nevada State

Athletic Commission (and, consequently, UFC) banned testosterone replacement therapy

(TRT) in the form of therapeutic use exemptions (TEU) in February of 2014. Several top-

tier fighters used TRT to aid in testosterone deficiencies, yet, on eliminating the exemptions

and advancing the industry’s no-drug policy, UFC President Dana White stated, “We

believe our athletes should compete based on their natural abilities and on an even playing

field” (Helwani). However, some fighters, like Chael Sonnen, claim that TRT is necessary

for them to fight at normal levels – prompting thoughts of retirement – and even to live.

Lastly, blood doping in cycling was exposed with the recent Lance Armstrong

scandal. Concerning blood doping in cycling, one analyst had this to say:

“With the Tour de France, you have physiological data about cyclists with

corresponding physical output capacity, then actual physical output; if they don’t

match [or resemble each other closely], something else is happening. We continue

to gather more data (such as biological passports – [an individual, electronic record

for professional athletes, in which profiles of biological markers of doping and

65

results of doping tests are collated over a period of time]) to prevent unethical

behavior. It can be advantageous and add to the fairness of sport” (Biological

Passport).

One PED in particular that is notorious in cycling is Erythropoietin (EPO), a glycoprotein

hormone; yet, like many illegal methods of enhancing performance, it is extremely

challenging to detect. One interview source simplified the process for me: “In the offseason,

athletes take out blood, centrifuge it, freeze the platelets, and inject prior to competition,

effectively increasing the capacity to carry oxygen,” thus enhancing athletic performance. Of

course, this is just one of dozens of PEDs currently in use across the industry, and as both

analytical and biological detection processes improve, doping should see continued

decreases across all sports.

Data Ownership: When asking industry analysts about ethical dilemmas facing the sports

analytics space, the most frequent response was simple: who owns the data? This might

seem like an easy question to answer, but its complexity is infinite. One source used a

metaphor to describe the current trend: if a photographer or artist creates a piece, they own

it and can license. Not with athletes because they create the data and currently own no stake

in it, so who really owns it?”

Technically speaking, the data companies own the big data and sell subscriptions for

the data’s use to clubs. This includes technical (ball event) data and physiological data (from

providers like Prozone). Clubs also own their own data – data they collect by hand or with

66

the help of software and hardware like SportsCode or miCoach. Then there is a grey zone in

that there is a fair amount of publicly available data on the internet; clubs can download and

use these data sets. Likewise, a source claimed that data tracking devices have not been

approved by FIFA and other governing bodies due to the issue of data ownership. Before

these devices make major breakthroughs in world football, governing bodies are compelled

to possess a controlling stake in data ownership, an issue that collective bargaining

agreements will likely settle in the near future. Under current collective bargaining

agreements between leagues, teams, and players, the players currently do not own any of the

date – yet this could also change as players unions try to determine what data is available to

whom and as sports become more analytics-oriented.

Another implication with data ownership involves decision-making. Who makes

the decisions based off the data? With a team system, for instance, a performance coach may

see discrepancies in force production on the right and left sides of the body, perhaps a 10%

asymmetry, suggesting that the player could get hurt; in this case, they must assess the

situation and determine if there is an issue or if it is an anomaly. Conversely, if an opposing

coach gets ahold of that same athlete’s data, he could try to exploit it. Data ownership is a

hotbed of contestation as leagues, teams, and players vie for a stake in the data. Overall, it

boils down to a debate of products versus people and the question of who is truly profiting

from the data.

67

Unfair Advantages: Cue the capitalism versus communism debate, the notion of the haves

versus the have nots, and the fairness of inequality. Are sports like soccer in Europe or

baseball unfair in which the most elite teams – Manchester United, the Yankees, Real

Madrid – have substantially more resources (and can therefore afford more advanced

analytical systems) than their counterparts? According to most industry analysts, no, and

although greater access to resources will always create some advantage, it is those who

optimize those resources who will truly win. “Sport is about creating competitive

advantages through any means,” and oftentimes those without superfluous assets best

optimize their limited resources. As was the case with the Oakland A’s or Stoke City or

2004’s Greek national soccer team, good coaches, scouts, strategies, and so forth create

advantages – not necessarily money and the best players. Simply put, as long as there is no

exclusivity (e.g. if a technology company signed an exclusive deal with a team), meaning that

everyone has equal access opportunities, and as long as the means are legal, it is fair.

Naturally, as technology and analytics allow us to identify more effective ways to measure

and assess soccer, the teams with the most resources will have an inherent advantage, yet

their counterparts will always discover different, more efficient means to win.

Art vs. Science: Soccer is the beautiful game. According to most players, coaches, and fans,

there is a “right” way to play the game, and some would rather lose playing an attractive,

quality style of football than win in poor fashion. In a sense, there is an art to winning – and,

to some, analytics’ role in sports is threatening that very art. While data is giving the coach –

68

the artists and practitioners of soccer – information that can be vital to success, some

(namely those on the analytics side) perhaps lose sight of true “art” of sport and winning,

instead focusing too much on numbers. In a sense, this leads to coaches and decision-makers

no longer perceiving players as humans but rather as mechanical cogs in a system. In truth,

“playing the numbers game is not really about numbers first and foremost” (Anderson, Sally

309). Instead, it is a way to perceive things differently and combine science and art in the

most optimal way possible to create an attractive brand of football while optimizing player

and team performance.

Overall, we must understand both sides of each conversation and make accurate, balanced decisions,

oftentimes with insufficient or incomplete data and stories. Decision-makers cannot be quick to

condemn new ideas and innovations; instead, they must have serious conversations with engineers,

doctors, and ethicists to determine the path of this industry. While these issues are far from being

all-inclusive, they demonstrate the substantial obstacles and threats that the future of sports

technologies and analytics still pose – and we must identify and resolve them before their effect on

the game is too great.

69

Implications

Sports analytics and technologies have advanced at exponential rates. According to Moore’s

Law, technological capabilities (measured in computer processing power) essentially double every

two years. At this point, the industry still seems to be at a fraction of its potential, yet this

generation is facing a critical tipping point with the effectiveness and success-rate of analytics being

used to assess and predict. As one industry expert claimed, if we continue progressing at this rate

without seeing substantial results, the industry will lose all of the credibility it has established. This

same professional frequently asks the same question to teams: “If your sports scientist was shot in

the morning, would it make any difference?” In essence, is sports science making a difference to the

bottom-line or scoreboard? If not, we’re not doing our job, and the industry lose that credibility. So,

with that, what does all of this mean for the sports analytics and technology space? In their book,

The Numbers Game, Chris Anderson and David Sally make several notable forecasts for the future;

I’d like to highlight three of theirs and add several predictions of my own for soccer as a whole.

1. “The biggest analytical breakthroughs will not occur at Manchester United, Manchester

City, Real Madrid, Barcelona, or any of the twenty richest clubs in Deloitte’s Football

Money League” (Anderson, Sally 298). While some of these clubs have adopted analytics to

varying and powerful degrees, none of them have a strong impetus to invest wholeheartedly

into an analytical approach. The purpose of using analytics is investing in a different way to

win football games; if these clubs are already winning, why would they alter their ways

70

drastically? Similar to the approach of the New York Yankees in baseball (spend massive

amounts of money on new talent acquisitions rather than focusing heavily on developing

homegrown talent), these major clubs have tradition and winning seemingly runs in their

veins. What they lack, however, is the desperation and necessity to differentiate and identify

new ways to win. As Billy Beane stated on his Oakland A’s “moneyball” team, “We had

nothing to lose” (Anderson, Sally 299). But I’d argue it won’t be the teams at the bottom of

the table making these breakthroughs, either – at least not without ownership buy-in.

Unlike baseball, soccer has relegation and promotion, the difference between thousands and

millions of dollars in revenue. As previously stated, managers don’t last long in soccer; on

average, less than two years. Given this, the managers on the hot seat won’t be the ones to

make these drastic moves, as their jobs – and potentially careers – are in jeopardy. Instead, it

will be the teams with several things: ownership buy-in to the analytical approach, a modest

financial allowance, and young, innovative, risk-taking managers.

2. “The volume of soccer data will increase by at least thirty-two times” (Anderson, Sally 302).

Whereas there have been gigantic improvements to sports analytics since Charles Reep or

even Bill James, imagine where we could be in ten years from now? With the abundance of

new means to collect and access data – video and GPS chips, for example – it wouldn’t be a

stretch to suggest that the amount of data available to leagues and teams could continue to

evolve at exponential rates to this point. In addition to just games and practices, teams will

soon have a grasp on players’ nutritional, sleeping, and neuroscience data – and that could

71

just be the start of it. While this will bring up questions of who owns the data (see ethical

discussion), its impact on the game should be huge, and it will be largely up to the number

crunchers and data analysts to figure out what to make of it all.

3. “The reformation of the counters will in turn be countered” (Anderson, Sally 314). There’s a

chance that the movement towards an emphasis on soccer analytics and the impact of

technology on sports could eventually fade away, giving into the hype cycle notion – but I

don’t buy it. While the idea of sports analytics has evolved into an industry-wide fad, its

impact and contributions will make a lasting impression. As StatDNA CEO Jaeson

Rosenfield stated:

“There is a system in place, existing power structures, ways that things have been

done that need to adapt. That doesn’t happen overnight. There are a lot of barriers;

they have seen what happened at Liverpool [the team spent millions on three

players based on advanced analytics – and it failed miserably], so they say, hey,

Moneyball doesn’t work in soccer. You never have an immediate success case. The

things we’re analyzing now, it’ll take a long time to figure out if we’re right, and

when someone does, it’ll take time to see if it’s right. Once there’s a bona fide

success, they’ll rush in” (Anderson).

The success of the sports analytics industry won’t be instantaneous; the data is long-term

oriented and longitudinal, and it won’t give us immediate answers. However, as teams

continue to find success with it, building stronger case studies for the usefulness of what we

72

do, others will make the leap of faith towards using numbers add objectivity to a subjective

sport. The existing mindset of most coaches must die off first, figuratively and literally. One

sports scientist and analyst claimed: “sciences advance one funeral at a time.” While it’ll take

a while, the successes of the industry will soon speak volumes for the industry.

4. All first-flight/major teams will have someone internally responsible for sports sciences – at

least to some degree. While I don’t argue that all teams will pursue soccer analytics as a chief

means to winning, they will certainly find some valuable uses for data, analytics, and

technology on the soccer side. Whether that be from the technical or tactical (head coach),

physiological (performance coaches), or neuroscience side (team physician/psychologist), or

all of the above, teams will undeniable accept the value of analytics to some degree. Teams

like Everton and Chelsea may lead the way in pioneering human performance from an

analytical approach, but others are present, and soon virtually everyone will be in some

regard. As one of my interviewees claimed, those with backgrounds across multiple fields,

including kinesiology, biomechanics, data management, data optimization, leadership,

project management, and soccer knowledge will prevail and fulfill numerous valuable cross-

functional roles.

5. Data chips, both on and off the pitch, will be equally as, if not more, valuable as video

analysis tools. Thank you, nanotechnology. I love team tracking systems; as I explained,

adidas’ miCoach was the first product with which I came in contact. Whereas video tracking

and analysis technologies are invaluable – look at Match Analysis’ K2 Panoramic camera, for

73

instance, giving you a full view of the field, allowing you to visually assess things that

numbers sometimes can’t – the amount of data that they can collect is limited and

incomplete. Video is, of course, best for tactics and technical assessment – but the

physiological data that chips can collect adds one extra element that nearly completes the

puzzle. Imagine having access to video, interpreting players’ performance, while

understanding their work load, power exertion, heart rate, and acceleration – and

potentially even nutritional, sleep, and neuroscience data. I and several industry analysts

assert that the innovations in the nanotechnology space will facilitate microchips to be used

on/in shirts, shoes, balls, and even the body to track and garner positioning and

physiological data. While the current landscape of major soccer leagues and governing

bodies prohibits the use of tracking chips during game play, this will soon change once data

ownership is established, opening the floodgates for opportunity to collect mass amounts of

new data. Traditionalist managers may strictly use video analysis, but others will begin

collect data from all realms of sports performance.

6. New soccer metrics will be born and spurn out – and then more will come. In baseball, the

rise of sabermetrics has been impossible to ignore, and numbers such as WAR and VORP

are regularly being used by teams to assess player performance on more holistic levels than

batting average, runs, and steals. The same thing is happening in basketball with PER.

Despite this movement, soccer is still largely dominated by ancient statistics: goals, assists,

clean sheets, passes, and so forth. In truth, there are no overarching statistics that judge a

74

player’s overall value. Even worse, where are the defensive metrics? Soon, I argue that

metrics will emerge that begin to judge the most subjective elements of soccer (perhaps in

an attempt to solve the Ronaldo/Messi debate) – yet, due to the unpredictability of soccer

and the infancy of this industry with the sport, it won’t work. But, in time, as we continue

to gather data and find correlations that relate to those most important metrics – goals,

assists, and clean sheets – new metrics will emerge that will help us better understand the

game, both for internal player assessment and the external consumer experience.

7. Youth soccer clubs and academies will adopt analytics on smaller scales to both have better

objective measures of talent identification and to perform longitudinal assessments. As I

briefly discussed, talent identification is a huge element of future success; after all, scouting

and player development are what make teams like baseball’s Tampa Bay Rays so successful.

However, to have player and team data – especially within Europe’s youth academies – years

before the collegiate or high school level is a massive advantage. Clubs are already testing

youth athletes regularly (I witnessed youth soccer physical tests at EXOS during my

internship), yet advanced metrics will soon make their ways into the ranks, perhaps just at

the elite clubs and academies at first. With the rapid advancement of technology (cue

Moore’s Law again), technology and data costs will decrease, facilitating their use in smaller

organizations. Teams will invest in GPS positioning chips to continue to assess

physiological performance and growth over spans of years. The power of identifying an

objective, numbers-driven blueprint of expected progression – both from physical and

75

technical standpoints – to aid in decision-making with player development would be

colossal. Further, as coaches continue to use the data and become smarter at assessing the

game (developing players has stagnated due to poor coaching, according to one source),

their improvements will translate directly to crafting better players.

8. The MLS will fulfill its goal of being one of the best leagues in the world by 2022 with

helping hand from its partnerships with adidas and Match Analysis as well as its progressive

approach to soccer. Further, the US will flourish as a soccer nation. “We're operating now

with a clear vision in mind. The vision that has been articulated by our board is that by 2022

soccer is to be a pre-eminent sport in North America and MLS is to be among the best

leagues” – MLS executive vice president Nelson Rodriguez” (Lewis). Despite the somewhat

negative perception of soccer in North America relative to other nations, this dream could

very well become a reality with both the quality of players (both homegrown and transfers

from abroad) and general play increasing substantially over the past several years. According

to one source, this goal can be achieved through success in several pillars: passion of fans,

relevance of clubs in the marketplace, value of club enterprises (fact: the 20th team in NY

had a $100M expansion fee compared to a $5M fee in 1996), and the overall quality of play.

Further, MLS continues to live on the forefront of innovation and technology. In

2012, the league launched its “Smart Soccer” initiative with adidas’ miCoach Elite System – a

game-changing experience for teams and fans. Likewise, in 2013, MLS extended its

partnership with Match Analysis to collect panoramic game video across all of the league’s

76

stadiums. Mark Brunkhart, president of Match Analysis, believes that the “MLS’s

commitment to incorporating the most advanced products is another example of how the

clubs in North America are charting new territory” (Kennedy). These league-wide deals are

giving clubs access to massive amounts of performance data in every capacity, enabling each

to use analytics in different ways, spurning more growth and innovation. Dissimilar to the

trend abroad, coaches and teams stateside seem more interested and eager to embrace

technology and apply a more objective approach. While the US is historically not known for

developing the world’s elite players, this trend could change as technology and analytics

continue to be embraced by the league and its constituents.

Players such as Robbie Keane, who have experienced success abroad, state that the

level of play is high yet not world class – not yet. “There are a lot of players in England that I

speak to now, youngish kind of players, that want to come over here and play over here.

There are certainly a lot of players who look at this league now with certainly a different

opinion than they did a few years ago,” Keane stated last year – and the league continues to

improve since then (Straus).

Lastly, soccer will continue to flourish in the United States. The “David Beckham

effect” certainly aided the growth of a plateaued sport in the US, increasing overall

attendance, teams, and league awareness. Before Beckham’s arrival, the league averaged

about 15,000 fans per game; at the end of his stint, that average jumped by 3,000 and the

Sounder “twice had crowds of over 67,000 and the club set the MLS attendance record for

77

the fifth consecutive season, averaging 44,038 last year, according to MLS figures. That

average attendance would rank sixth in the Premier League, just behind Liverpool” (Tasch,

Red). The league’s growth – in conjunction to the recent successes of both the men’s and

women’s national teams – has continued to attract fans of the beautiful game. Perhaps

equally as telling, US fans purchased twice as many tickets as the next nation for the

upcoming 2014 World Cup (sans hosts Brazil) with over 120,000 (Braley). Ultimately, with

the quality of soccer in the United States continuing to improve and the sport’s forward

momentum, it will soon be one of the world’s leading soccer leagues. An MLS team will not

win the UEFA Champion’s League within a decade, but, as a whole, its quality will be

consistent with the other elites.

78

Recommendations and Conclusion

Technology and analytics have effectively integrated themselves into the fabric of sports,

and their roles in soccer are becoming more and more prevalent. From the technical and tactical

spaces of soccer to physiological and neurological processes, soccer is en route to optimizing human

and team performance in an attempt to gain competitive advantages and win. However, despite the

mounds of data currently available, the lack of inspiring, impactful results in soccer analysis may

lead to decreased growth in an otherwise burgeoning industry. In order to move forward using

match analysis systems to their full potential and achieve results, teams must follow several crucial –

yet simple – recommendations: 1. Have a plan, 2. Understand the process, 3. Find harmony, and 4.

Impact soccer’s culture.

1. Having a plan: Despite being a disciple of the soccer analytics movement, I would not

urge any teams to invest in new technologies without a clear plan and purpose. Just

because Manchester United is one of the wealthiest teams in England doesn’t mean it

should invest in analytics, as perhaps it won’t use it properly or doesn’t have the proper

top-down buy-in from the ownership and coaching staff. Instead, teams must

strategically determine how to spend resources and how analytics and new technologies

will help the team grow. A fringe team like Fulham, for example, might have a league

worst defensive line, especially in its own half, so it could invest in a video-based system

to help assess and analyze team tactics and improve defensive formations. Perhaps

79

Juventus is experiencing injury and fatigue-related problems and might invest in

tracking devices to monitor players’ metabolic levels. In all, a significant internal audit

must be completed to determine the team’s needs and in which capacities new

technologies and analytical processes can facilitate better results. “Don’t go in search of

finding just answers, go in search of answering your questions!” (Norman). Beyond

understanding team needs, there must be top-down buy-in, facilitating substantial

monetary investments into a product that may not produce immediate results.

2. Understanding the process: Before any work with analytics or match analysis is

undertaken by a team, teams must understand the purpose and process of match

analysis. Unfortunately, many managers and coaches expect immediate results with

newly-implemented systems; soccer analytics won’t provide immediately results,

especially without the proper staff in place to comprehend the data and disseminate it in

meaningful ways to coaches and players. From the data analytics side, the process for

both electronic tracking devices and video-based match analysis is virtually the same:

match observation/recording, analysis, and results (Carling 37). While the technology

will facilitate step one, there must be someone within the team capable of

understanding, processing, and analyzing what the data is actually telling them. Beyond

80

analysis, someone with both analytical and soccer backgrounds must then interpret the

results and actually apply them to training, match preparation, and fitness. For example,

video analysis is used to capture a Real Madrid vs. Barcelona

game, and thousands of data points are collected. At that

point, focusing on Karim Benzema (Real Madrid striker) in

particular, a data analyst determines that his left-footed

shooting was off: his shots were significant slower and his

shot location was poor. While it may simply appear that his

left foot is just a weaker foot, perhaps past data demonstrates a

higher shot velocity and more accurate shot placement;

instead, perhaps he has a subtle injury or was not fully fit for

the game. In essence, there are a multitude of possibilities to

explain the behaviors, suggesting that someone must be

qualified to interpret the key results. Further, when

interpreting and presenting the results to the coaches and

other decision-makers, there are several forms of presentation: computer or database,

raw data, spatial, graphical, match video, or match reconstructions (Carling 62).

Illustrating data visually is critical in communicating data and achieving results, and

once coaches receive and interpret these forms of communication, then it is their

81

responsibility to provide individualized and team feedback and advice, preparing the

team for the next match.

3. Finding harmony: Expanding upon the idea of internal collaboration and buy-in, the

concept of backroom bandwidth suggests that “everyone is operating and

communicating at different frequencies,” resulting in a frayed analysis process. While

many teams have invested in brilliant physiologists, psychologists, and data analysts, it is

critical that they are not only communicating amongst each other but operating in

harmony – especially in the final communications with the head coaches and athletes.

According to one source, when evaluating talent, a specific team’s strength and

conditioning coaches were most concerned about running speed, yet the technical

analysts and coaching staff were more interested in space and perception. Similarly,

another team’s analytics department was measuring several metrics directly correlated to

winning games, and the sports science department was measuring entirely different

metrics. Internal silos destroy efficiencies and prevent the proper information from

being communicated to coaches and players. In all, functional units within teams must

understand their roles (medical staff – pre/post, return to play; coaching staff –

education and training efficiency; performance staff – player profile and development;

team personnel – player management and decision-making), yet each function must be

interdependent and work as a unified team (Norman).

82

4. Impacting soccer’s culture: As I’ve noted throughout this paper, despite the rapid growth

of technology and analytics in soccer, the significant use and emphasis on analytics is

still foreign to most teams and “old school” coaching staffs. However, society largely

affects how soccer evolves from here on out; teams and leagues integrate elements of in-

game technologies into sports as a whole to satisfy increasing consumer demands, such

as goal-line technology and instant replay, effectively impacting how fans view and

perceive games. While perhaps only analytics-driven results and successful case studies

will impact the way baby-boomers and Generation Xers perceive soccer, the analytics-

friendly Millennials (1980-2000) and Generation Zers (2001-present) crave instant

feedback, data visualizations, and new technologies. By consistently impacting consumer

experience with game analytics and graphics via social media, media coverage (e.g.

SportsCenter), and sponsorship activations, soccer fans will embrace and demand more

emphasis on modern approaches from those teams hesitant to make the necessary

financial and systematic investments.

Rapidly-advancing technologies and various means of data analytics are pioneering the way

that we watch, play, and coach soccer. Their roles impact all facets of soccer and sport from talent

identification and evaluation to fitness levels to game performance. As a soccer player, consumer,

and future employee in this industry, I hope to further optimize player and team performance by

educating coaches, decision-makers, players, and consumers on the roles and impacts of technology

83

and analytics in soccer. In an industry and sport dominated by “that’s the way it’s always been done,”

it’s my responsibility to challenge the current guard and continue to facilitate the reformation in

soccer (Anderson 1).

84

References

Agoos, J. (2013, August). Thesis Project Interview: MLS. (J. Heckendorn, Interviewer)

Alamar, B. C. (2013). Sports Analytics: A Guide For Coaches, Managers, and Other Decision

Makers. West Sussex: Columbia University Press.

Analytics. (n.d.). Retrieved from Wikipedia: http://en.wikipedia.org/wiki/Analytics

Anderson, C. (2013, August). Thesis Project Interview: Cornell University. (J. Heckendorn,

Interviewer)

Anderson, C., & Sally, D. (2013). The Numbers Game: Why Everything You Know About Soccer Is

Wrong. New York: Penguin Books.

Anderson, C., Agoos, J., Wooster, B., Larcada, A., & Stein, M. (2013). Soccer Analytics - Presented

by Prozone. MIT Sloan Sports Analytics Conference. Bostn.

Axon Sports. (n.d.). Retrieved from Axon Sports: www.axonsports.com

Bell, J. (2013, April 22). Footbonaut: Dortmund's Secret Weapon. Retrieved from NY Times:

http://goal.blogs.nytimes.com/2013/04/22/footbonaut-dortmunds-secret-

weapon/?_php=true&_type=blogs&_r=0

Bialkowski, A., Lucey, P., Carr, P., Yue, Y., & Matthews, I. (2014). "Win at Home and Draw Away":

Automatic Formation Analysis Highlighting the Differences in Home and Away Team

Behaviors. Retrieved from MIT Sloan Sports Analytics Conference:

http://www.sloansportsconference.com/wp-content/uploads/2014/02/2014_SSAC_Win-

at-Home-Draw-Away.pdf

Bill James. (n.d.). Retrieved from Wikipedia: http://en.wikipedia.org/wiki/Bill_James

Boebel, R. (2013, July 30). The New 49ers Stadium App Will Monitor Beer, BathroomLines,

Stream NFL Redzone and Replays - Also Available: Watching the Game. Retrieved from

Sports Grid: http://www.sportsgrid.com/nfl/the-new-49ers-stadium-app-will-monitor-

beer-bathroom-lines-stream-nfl-redzone-and-replays-also-available-watching-the-game/

Braley, C. E. (2014, February 21). Americans tops among non-Brazilian ticket purchases for World

Cup. Retrieved from Sports Illustrated:

http://sportsillustrated.cnn.com/soccer/news/20140221/world-cup-tickets-brazil-usa.ap/

85

Budway, I. (2014, March 4). Baseball Find the Holy Grail for Measuring Defense (Again). Retrieved

from Bloomberg Businessweek: http://www.businessweek.com/articles/2014-03-

04/major-league-baseball-unveils-an-even-newer-player-tracking-system

Carlson-Philips, A. (2013, October). Thesis Projects Interview: EXOS. (J. Heckendorn, Interviewer)

Cohn, N. (2013, February 1). Fourth Down? We're Going for It!: How advanced statistics could

transform the NFL. Retrieved from New Republic:

http://www.newrepublic.com/article/112300/football-sabermetrics-how-stats-could-

transform-nfl#

Connolly, F. (2014, January). Thesis Project Interview: Consultant. (J. Heckendorn, Interviewer)

Dalip, M., & Aday, B. (n.d.). Use and Benefits of Computer Match Analysis. Retrieved from

elingueiusmlearn:

http://www.elingueiusmlearn.it/tempus/images/stories/balkansport/presentations/TEMP

US_METIN_METIN_DALIP_MATCH_ANALYSIS_FIN_2.pdf

Ekstrand, J. (n.d.). Injuries and their prevention at professional level.

Fletcher, E. (n.d.). The Science behind HRV. Retrieved from ithlete: http://myithlete.com/the-

science-behind-hrv.html#.UyuwqvldWSp

Fletscher, E. (n.d.). The Science Behind HRV. Retrieved from ithlete: http://myithlete.com/the-

science-behind-hrv.html#.UyeYH_ldWSo

Friedman, C. (2013, October). Thesis Project Interview: EXOS. (J. Heckendorn, Interviewer)

Fry, B., Ward, J., Hinkie, S., & Wattenberg, M. (2013, March). Data Visualization. MIT Sloan

Sports Analytics Conference. Boston.

Goldsberry, K. (2014, February 6). DataBall. Retrieved from Grantland:

http://grantland.com/features/expected-value-possession-nba-analytics/

Goldsberry, K., Zander, R., Kopp, B., Pavlidis, H., & Morey, K. J. (2013). XY Panel: The Revolution

in Visual Tracking - Presented by Sportvision. MIT Sloan Sports Analytics Conference.

Boston.

Helwani, A. (2014, February 27). Dana White says UFC will no longer allow TRT, 'couldn't wait

for that garbage to go away'. Retrieved from MMA Fighting:

http://www.mmafighting.com/2014/2/27/5454410/dana-white-ufc-will-no-longer-allow-

trt-couldnt-wait-for-that-garbage

86

Hollinger, J. (2011, August 8). What is PER? Retrieved from ESPN:

http://sports.espn.go.com/nba/columns/story?id=2850240

Hosoi, P. (2013, August). Thesis Project Interview: MIT. (J. Heckendorn, Interviewer)

Inside AC Milan's Lab. (n.d.). Retrieved from Meersseman Lab:

http://www.meerssemanlab.com/Meersseman_Lab/Inside_AC_Milan_Lab.html

Jordet, G. (2013, August). Thesis Project Interview: Norwegian School of Sport Sciences. (J.

Heckendorn, Interviewer)

Jordet, G., Bloomfield, J., & Heidmerikx, J. (2013). The hidden foundation of field vision in English

Premier League (EPL) soccer players. Norway.

Kennedy, P. (2013, October 3). MLS signs deal to use Match Analysis tools. Retrieved from Soccer

America: http://www.socceramerica.com/article/54080/mls-signs-deal-to-use-match-

analysis-tools.html

Keri, J. (2013, January 30). Baseball's PED Problem Won't Go Away. Retrieved from Grantland:

http://grantland.com/the-triangle/baseballs-ped-problem-wont-seem-to-go-away/

Leap, M. (2013, October 8). The Top Five Technologies that Forever Changed the NFL. Retrieved

from SportTechie: http://www.sporttechie.com/2013/10/08/nfltechtop5

Leap, M. (2013, October 8). The Top Five Technologies that Forever Changed the NFL. Retrieved

from SportTechie: http://www.sporttechie.com/2013/10/08/nfltechtop5/

Lewis, M. (2004). Moneyball. New York: W. W. Norton& Company, Inc.

Lewis, M. (2011, August 12). MLS Has A Bold 20122 Vision. Retrieved from US Soccer Players:

http://www.ussoccerplayers.com/2011/08/mls-has-a-bold-2022-vision.html

Longman, J. (2007, July 20). Fit Young Pitchers See Elbow Repair as Cure-All. Retrieved from NY

Times:

http://www.nytimes.com/2007/07/20/sports/baseball/20surgery.html?pagewanted=all&_r

=1&

Marathe, P., Demoff, K., Pioli, S., Schatz, A., & Kremer, A. (2013). Football Analytics. MIT Sloan

Sports Analytics Conference. Boston.

MCFC OPTA Analytics Project. (n.d.). Retrieved from

https://efex.bimeapp.com/dashboard/MCFCAnalytics

87

Medeiros, J. (2014, January 23). The winning formula: data analytics has become the latest tool

keeping football teams one step ahead. Retrieved from Wired:

http://www.wired.co.uk/magazine/archive/2014/01/features/the-winning-formula

Miller, S. (2013, March 4). War Is the Answer. ESPN: The Magazine, pp. 51-56.

MLS, adidas to launch first "smart soccer league" in 2013. (2012, July 19). Retrieved from

www.mlssoccer.com: http://www.mlssoccer.com/news/article/2012/07/19/mls-adidas-

launch-first-smart-soccer-league-2013

Neuroscience. (n.d.). Retrieved from Google:

https://www.google.com/search?q=neuroscience&oq=n&aqs=chrome.3.69i60l3j69i59l2j69i

57.3912j0j4&sourceid=chrome&espv=210&es_sm=122&ie=UTF-

8#q=neuroscience+definition

Newcomb, T. (2013, October 9). Sports and Technology: How digital scouting is changing baseball.

Retrieved from Sports Illustrated:

http://sportsillustrated.cnn.com/mlb/news/20131009/mlb-digital-scouting/

Norman, D. (2013). Performance Training Systems: Using Monitoring Systems to Enhance your

Team.

Norman, D. (2013, October). Thesis Project Interview: EXOS. (J. Heckendorn, Interviewer)

Omegawave. (n.d.). Retrieved from Wikipedia: http://en.wikipedia.org/wiki/Omegawave

Opatkiewicz, A. (2013, April). Thesis Project Interview: STATS LLC. (J. Heckendorn, Interviewer)

Oscar Pistorius. (n.d.). Retrieved from Wikipedia: http://en.wikipedia.org/wiki/Oscar_Pistorius

Physiology. (n.d.). Retrieved from Sports Institute of Northern Ireland: http://sini.co.uk/what-we-

offer/performance-science/physiology/

Plafke, J. (2013, October 29). How the NBA's SportVU ball and player tracking tech changes the face

of sports. Retrieved from ExtremeTech: http://www.extremetech.com/extreme/169775-

how-the-nbas-sportvu-ball-and-player-tracking-tech-changes-the-face-of-sports

Predictive Sports Betting Analytics. (2013, March). Retrieved from MIT Sloan Sports Analytics

Conference: http://www.sloansportsconference.com/?p=9607

Press, T. A. (2006, June 9). World Cup at a glance. Retrieved from TribLive:

http://triblive.com/x/pittsburghtrib/sports/s_457259.html#axzz2v47N6aq7

88

Press, T. A. (2013, March 9). Australians Assessing Injuries to the Head. Retrieved from NY Times:

http://www.nytimes.com/2013/03/10/sports/concern-rising-over-sports-concussions-in-

australia.html?_r=2&&version=meter+at+5&region=FixedCenter&pgtype=Article&priority

=true&module=RegiWall-Regi&action=click

Ramineni, R. (2013). Analyse Football. Retrieved from www.analysefootball.com

Ramineni, R., & Rudd, S. (2013). OnFooty. Retrieved from www.onfooty.com

Rapp, J., McIntosh, D. M., Hanna, T., Vincent, B., Hosoi, P., & McClusky, M. (2013). Stronger,

Faster, Better: Technology Analytics - Presented by Ropes & Gray LLP. MIT Sloan Sports

Analytics Conference. Boston.

Rishe, P. (2012, January 6). Dynamic Pricing: The Future of Ticket Pricing in Sports. Retrieved

from Forbes: http://www.forbes.com/sites/prishe/2012/01/06/dynamic-pricing-the-

future-of-ticket-pricing-in-sports/

Sabermetrics. (n.d.). Retrieved from Wikipedia: http://en.wikipedia.org/wiki/Sabermetrics

Schwartz, A. (2012, June 9). No More Needles: A Crazy New Patch Will Constantly Monitor Your

Blood. Retrieved from Fast Company: http://www.fastcoexist.com/1680025/no-more-

needles-a-crazy-new-patch-will-constantly-monitor-your-blood

Sports science. (n.d.). Retrieved from Wikipedia: http://en.wikipedia.org/wiki/Sports_science

Sports Science. (n.d.). Retrieved from ESPN : http://search.espn.go.com/sports-

science/videos/college-football/6-28

Sports Wagering. (n.d.). Retrieved from American Gaming Association:

http://www.americangaming.org/industry-resources/research/fact-sheets/sports-wagering

Staff, E. (2013, October 10). FIFA confirms goalline technology for 2014 World Cup. Retrieved

from ESPN UK: http://www.espn.co.uk/football/sport/story/246433.html

Straus, B. (2013, February 22). Can the MLS be among the world's best soccer leagues by 2022?

Robbie Keane thinks so (poll). Retrieved from Oregon Live:

http://www.oregonlive.com/mls/index.ssf/2013/02/can_the_mls_be_the_worlds_best.html

Sugarman, R. (2013, July). Brain Baseline Report.

Sugarman, R. (n.d.). Things to remember in neuroscience.

Tasch, J., & Red, C. (2014, February 8). The David Beckham effect: Can soccer icon extend legacy to

Miami, where MLS hugely failed before? Retrieved from NY Daily News:

89

http://www.nydailynews.com/sports/soccer/becks-effect-icon-extend-legacy-miami-

article-1.1606999

Tensiomyography. (n.d.). Retrieved from Wikipedia:

http://en.wikipedia.org/wiki/Tensiomyography

The Athlete Biological Passport (ABP). (n.d.). Retrieved from Doping:

http://www.doping.chuv.ch/en/lad_home/lad-prestations-laboratoire/lad-prestations-

laboratoire-passeport.htm

V02 Max. (n.d.). Retrieved from Wikipedia: http://en.wikipedia.org/wiki/VO2_max

Vaeyens, R., Lenoir, M., Williams, M. A., & Philippaerts, R. M. (2008). Talent Identification and

Development Programmes in Sport: Current Models and Future Directions. Retrieved from

Academia:

http://www.academia.edu/667981/Talent_Identificantion_and_Development_Programmes

_in_Sport_current_models_and_future_directions

Vrentas, J. (2013, July). Chip Kelly's Mystery Man. Retrieved from The MMQB:

http://mmqb.si.com/2013/07/24/chip-kellys-mystery-man/

Wiltshire, L. (2012, September 27). @LewisWiltshire. Retrieved from Twitter:

https://twitter.com/LewisWiltshire/status/251391546988060672