Abstract We Analyze the Careers of Esports Professional Players

Paper to be presented at DRUID19 Copenhagen Business School, Copenhagen, Denmark June 19-21, 2019

Esport Superstars

Michael R. Ward University of Texas at Arlington (UTA) Economics [email protected] Alexander S. Harmon University of Texas at Arlington Economics [email protected]

Abstract We analyze the careers of Esports professional players. The Esport industry has been able to monetize growing spectator demand to offer tournament prize money amounting to $113 million in 2017. A few hundred players, out of tens of thousands, earn enough to remain professional gamers exclusively. We examine three aspects of professional Esport player careers. First, a ?superstar’ effect leads to increases in the top prizes drawing amateurs into the professional ranks. Next, while age and experience affect player performance, player ability remains difficult to assess. Finally, career exits reflect a quick resolution regarding the uncertainty in player ability. Preliminary - Please do not quote

ESport Superstars

I. Introduction

Esports refers to competitive video gaming at the professional level. Esports emulates traditional sports with player endorsements and sponsorships, spectators packing into arenas for tournaments, lucrative broadcasting deals, and recruiting from the college ranks. What had started as friendly competition and socialization events just over two decades ago, currently has a

226 million viewer global audience generating $696 million in revenue that is expected to reach nearly $1.5 billion USD by 2020.1 Total prize money across all games played totaled $113 million in 2017, with top earner Kuro Takhasomi (aka KuroKy) grossing $2.44 million.

However, payouts are heavily skewed with a median annual earnings of less than $1,000.2 The size and skewness in earnings suggest a market for superstar talent (Rosen, 1981).

Rosen posited that some labor markets that can be characterized by superstars have some distinct properties. In any profession, workers are heterogeneous with higher ability workers tending to be paid more than less able workers, however, the dispersion is distinctly larger in superstar markets.3 This can occur when higher quality for a service increases both the price and the quantity demanded. When coupled with ex ante ability uncertainty, these labor markets become a sort of “ability lottery.” Entrants are willing to accept much lower earnings to start, i.e., the price of a lottery ticket, if doing so makes them eligible for a chance at the top earnings.

1 http://www.businessinsider.de/esports-popularity-revenue-forecast-chart-2017-3?r=US&IR=T 2 (https://www.esportsearnings.com/history/2017/top_players) By way of comparison, the top male golfer in 2017, Patton Kizzire, won $2.96 million out of a total of $344 million in PGA prize money (http://www.espn.com/golf/moneylist). 3 The annual earnings at the 90th percentile-10th percentile normalized by dividing by the median annual earnings is less than one on average but exceeds four for actors and athletes. (https://www.bls.gov/careeroutlook/2015/article/wage-differences.htm) 1

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We show that many of the features of the growing Esports labor market are consistent with

Rosen’s model.

We exploit earnings data available for nearly every professional Esport player for nearly every tournament they entered. This allows us to track entry into the professional ranks over time, by game and by country. It allows us to determine how earnings vary with player age and experience and how earnings affect the timing of player retirements. We show that, 1) while mean earnings are relatively unchanged, growth in top prizes has fueled entry, 2) a player will experience considerable earnings uncertainty conditional on age and experience, and 3) consistent with idiosyncratic uncertainty being resolved through play, retirements are postponed for top earners. Before we develop our tests of hypotheses, describe our data, and present our results, we first present a brief outline of the Esports phenomenon.

Improvements in communications technology may create more winner-take-all outcomes through a superstar phenomenon. The key model requirement is that increasing worker quality generates ever greater increases in worker revenue. The typical example is better performers reaching ever larger audiences, all of whom are also willing to pay more for the performance.

The digital economy has the potential to multiply audiences for a wide variety of products and services. University instructors are now reaching thousands of students per course through online instruction. “Killer Apps” are downloaded onto millions of smartphones. Previously unknowns have become YouTube, Instagram, and Twitter celebrities and are influencing pop culture. Given how quickly digital communication has evolved, it is difficult to imagine which industries will be transformed into superstar markets in the future.

II. A Short History of Esports

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Depending on your point of view, Esports may or may not be considered a “sport.”

Hallman & Geil (2018) note that it is recreational, competitive, and has organizational structures similar to sports but requires little physical activity and is only slowly gaining general acceptance. Pizzo et al. (forthcoming) notes that Esports spectators appear to have similar consumption motives and attendance frequencies as traditional sports. We take no stand on this issue but instead exploit it as a laboratory for understanding economic phenomenon (Kahn,

2000).

Video games emerged in the 1970s as coin operated machines in arcades that allowed only a single player to compete against a preprogrammed algorithm. With the decline in computing costs, console based games came into homes beginning in the 1980s. By the 1990s, games emerged in which two individuals could compete via dual controllers connected to the console operating movement on a single split-screen. With Local Area Networks (LANs), players gaming on proximate personal computers (PCs) could compete across separate screens.

Gaming network developments on the Internet meant players could remain at home to compete against players virtually anywhere on the globe. Most games adopted this multiplayer, networked paradigm often pitting one squad of usually four players against another. One direction video game development took was the development of Multiplayer Online Battle Arenas (MOBAs) that have since become the mainstay of Esports.

MOBAs typically have a standardized setting in which two teams, of usually four or five players, try to defend their base on a stylized map while destroying their opponents’ base.

Different players take on different characters with different complementary roles. Individual matches usually last 20-40 minutes. The proliferation of MOBAs is due in part to their low graphical requirements and free-to-play marketing models. These games can be played on almost

Preliminary - Please do not quote any modern personal computer. Since game publishers earn much of their revenue from in-game purchases by players, they have become major sponsors of tournament series as a way to induce more players to adopt their games. The features that seem to make games more popular are: scope for creative strategy and tactics, the need for team cooperation, and an advantage for speedy reaction times. At the highest level of play, each player is making hundreds, if not thousands, of decisions and moves per minute in concert with his teammates.

The Red Annihilation tournament for the game “Quake” in 1997 with 2,000 participants is widely considered to have been the first real instance of Esports. While the first tournaments featured “First-Person Shooter” (FPS) games, games based on “Real-Time Strategy” (RTS), such as “Starcraft,” “Dota,” and “League of Legends” soon came to dominate tournament play.

Tournaments were often sponsored by game publishers but various leagues also formed that attempted to create continuity across tournaments. While tournaments were increasingly organized throughout the 1990s, and 2000s, the growth of Esports’s viewership and prize money accelerated tremendously after 2010. This acceleration in the growth of viewership and prize money was largely caused by the 2011 launch of Twitch, an online streaming platform routinely used to stream live Esports competitions. Twitch has also become a popular way for advanced players to upload their gameplay with tips and tricks in “walk through” and so gain a large following thus more firmly establishing a spectator base.

Esports become more professional as it grew. Game makers sponsor leagues, various commercial enterprises sponsor teams and put players on salary. While model player contracts exist, the actual player compensation is subject to negotiation and is often not publicly revealed.

It is reported that, typically, in addition to salaries, team players share prize money equally.

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Some teams keep 10 to 20 percent to defray operating costs (Taylor, 2012, p. 176). The number of teams competing at a tournament can vary but a 16 team format is most common.

As might be expected with such a new phenomenon, not much economic analysis of

Esports has been conducted as yet. Parshakov and Zavertiaeva (2015) find strong country effects among the final standings of Esports tournaments. They further provide evidence that success is related to country tendencies toward masculinity, a long-run orientation, and the level of health and education. Coates and Parshakov (2016) analyze the incentives in Esports rank order tournaments with team production. They find that prizes are consistent with tournament theory, implying that tournament organizers appear to be interested in maximizing the participants’ effort and productivity. We are unaware of any analyses of Esports superstars.

III. Superstar Economics in Esports

The common characteristic of superstar markets is the extreme skewness in earnings.

Rosen (1981) shows that this is an equilibrium result when labor is imperfectly substitutable and the return to talent is convex, or that the marginal return to talent increases as talent increases. In entertainment industries, not only does demand shift out for more talented performers, but performances are easily scalable to audience size. This is primarily due to a single performance being non-rivalrous to some degree. A performer can entertain an audience of 10,000 almost as easily as 1,000 with only minor degradation of quality. Hence, the more talented can command both a higher price and serve larger audiences with each performance. Rosen shows that the return to talent becomes even more skewed as either demand shifts out or costs fall.

Rosen does not consider talent uncertainty, but the extensions are intuitive. Labor supply into an occupation is usually assumed to be upward sloping. Potential workers are drawn into a

Preliminary - Please do not quote profession from other endeavors by the expectation of higher compensation levels. For superstar occupations, a few of the most able individuals are compensated at a much higher level than others.4 Often, however, the specific abilities inherent in superstardom are intangible or difficult to assess. Those contemplating entering the occupation are uncertain if their abilities are sufficient to achieve superstardom. Their entry decision criterion, however, is to compare the expected value of pursuing the superstar occupation with the expected value of their next best alternative. Since achieving superstardom is a high reward event, potential entrants are willing to accept temporary compensation well below the value of the next best alternative in order to pursue the possibility of superstardom. This will tend to magnify the range of earnings observed in occupations characterized by superstardom. An entrant’s own ability is revealed only by attempting the pursuit of superstardom. Most will discover they will not achieve superstardom and exit to pursue their next best alternative career path.

Superstars have been detected in a number of contexts. For professional basketball players in the NBA, Hausman & Leonard (1997) and Jane (2016) find that the appearance of stars increases home and road game attendance creating a positive star externality. For Italian professional soccer players, Lucifora & Simmons (2003) show that soccer players’ earnings are highly convex in performance measures. Jewell (2017) finds that the few marquee signings in

Major League Soccer (MLS) drove higher attendance which produced a superstar externality.

Frank & Nüesch (2012) find support for both talent (Rosen-style) and popularity (Adler-style) effects in German soccer superstardom. Furthermore, Brandes et al. (2012) find that, in the

German soccer leagues, national superstars attract fans based on performance while “local heroes” increase fan support based on their popularity. Candela et al (2016) estimate that the

4 Adler (1985) presents a model of superstars in which artist talent is unimportant so long as consumers have increasing marginal utility for a specific artist. In his model, stardom is primarily the result of luck. 6

Preliminary - Please do not quote elasticity of earnings with respect to talent for modern and contemporary artists is greater than one. Porter and Scully (1996) report how various factors affect the skewness of earnings distributions for a variety of professional sports.

The key prerequisites of superstar markets are: substantial heterogeneity in ability and increasing returns to ability at the highest levels of talent. In entertainment professions, spectators typically greatly prefer the performances by the very best actors, musicians, or athletes. Ticket and broadcast revenue can be an order of magnitude higher for first-tier than for the second-tier entertainers. Thus, movie studios, concert promoters, and sports teams are all willing to pay considerably more for the best talent than even mediocre professionals. This creates large disparities in earnings between first-tier and second-tier entertainers even though the differences in talent may be small. This seems to describe Hollywood stars versus character actors, Grammy award winning musicians versus session musicians, and MLB baseball players versus those in AAA leagues.

In addition, entertainment markets are typically characterized by substantial uncertainty regarding one’s ability. Promising young performers usually know that they are more talented than their local peers, but may not know if their talent will develop enough to reach the level of a superstar. This uncertainty can only be resolved by attempting to enter the professional ranks.

Those considering entering the profession know there is a probability of becoming a superstar but that it is more likely that they will not. Still, the expected compensation from entering the profession is the weighted average of all of the possible compensation levels due to talent. Thus, many young performers are willing to start out earning less than their potential in other careers so that they have the opportunity to become a superstar. Budding baseball prospects start in the minor leagues for low pay, aspiring actors wait tables in restaurants in New York or Hollywood

Preliminary - Please do not quote so as to answer audition calls, and musicians start out playing small clubs on the road to hone their sound. In essence, the income forgone while pursuing an entertainment career is the cost of the ‘lottery ticket’ to becoming a superstar. Eventually, the talent, or luck, will become revealed and the lottery will be won or not.

Rosen points out that superstardom in entertainment professions is largely a consequence of recording and broadcast technology. These technologies decrease the price of entertainment services and dramatically increase audience scope. At the beginning of the 20th century, performers could only entertain the people who came to the venue, perhaps as many as 50,000 but more often a smaller audience. This left plenty of demand to be met by performers across a large spectrum of talent levels. But with the development of sound and picture recording and with the radio and television broadcasting of sporting events, top-tier performers could reach audiences beyond the immediate venue. The potential audience for any one performance could be orders of magnitude larger. Since more audience members could now more easily experience top-level talent, the demand for their performances skyrocketed. This tended to crowd out demand for performances by the second tier and lower. The increment in talent between top-tier performers and second-tier performers may have gone unchanged but the difference in compensation, keeping pace with the difference in demand, increased dramatically. Earnings for top actors increased dramatically first with the advent of motion pictures and later with television programming. Beginning with vinyl records, the top musicians’ earnings came mostly from record sales rather than performances. The radio broadcasting of baseball games opened a new revenue stream for club owners and now NFL television rights are the dominant revenue source.

In each case, the increased earnings to top actors, musicians, and athletes led multitudes of prospective entrants to be willing to “pay their dues” in order to “make it big.”

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In the Esports context, the increase in the compensation of superstars was also fueled by an innovation in communication technology, Internet streaming, which led to the huge increase in prize money being offered to tournament winners. Video game makers have found that the costs of supporting tournaments can be monetized both directly through audience admission and broadcasting rights and indirectly as they encourage additional in-game purchases. To compete for spectators, tournament promoters seek to attract the best players with ever increasing potential prize money. The prize money available to professional video game players differs across games and across tournament location, but in almost all cases, it has grown tremendously over the past decade. Table 1 demonstrates the dramatic growth in both the number of professional Esports players and the prize money available, usually in less than a decade.

This simple model of superstar talent uncertainty has a number of characteristics that that we seek to observe in the Esports setting. First, the equilibrium entry relationship suggests how entry and prize money are related. A prospective superstar will enter ‘the lottery’ if and only if the expected value of the payoff exceeds her alternative. At the same time, more entry reduces the likelihood of winning the lottery. For simplicity, suppose that upon becoming a superstar, a player will earn 푊푆 but that entering and not becoming a superstar the would-be superstar earns only 푊푁푆. These values could include prize earnings, expected team salary, costs or benefits such as equipment purchases or sponsorships, and the utility from pursuing one’s interests. There are N total entrants, out of which, 푁푆 will become superstars so that the ex ante probability of becoming a superstar is 푁푆/푁. Potential entrants have alternative sure income of 푊퐴푙푡. Then, in equilibrium we have

푊푆(푁푆/푁) + 푊푁푆(1 − 푁푆/푁) = 푊퐴푙푡.

Solving for N yields

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(푊푆 − 푊푁푆) 푁 = 푁푆 . (푊퐴푙푡 − 푊푁푆)

A number of simple comparative statics are evident. First, the number of entrants, N, is proportional to the number of superstars, 푁푆. Second, the number of entrants, N, increases with the compensation to superstars, 푊푆. Third, the number of entrants, N, decreases with the compensation to alternative careers, 푊퐴푙푡. The second leads to testable hypotheses:

Hypothesis 1: Increases in the top prize money available to tournament winners will draw more amateurs into professional Esports.

We seek to link the number of new professionals for a game in a year and country to the amount earned by the top players globally, within the region, and within the country.

Note that this is an equilibrium relationship. So long as the value of alternative careers,

푊퐴푙푡, remains constant, the expected value of pursuing the professional ranks should remain constant. This means that with increases in the compensation to superstars, 푊푆, that is with a rising value of the winning “lottery ticket,” there must be a corresponding decrease in the expected value of losing. This could be due to a lower value of 푊푁푆 or a higher probability of losing, (1 − 푁푆/푁).

A natural addition to the superstars model is that there is great uncertainty surrounding a player’s actual ability. Specifically, the players themselves are uncertain about their own ability, at least initially. This is because ability is the product of many specific performance characteristics, which may be difficult to measure and which combine in a complex manner.

Upon entry and engaging in competitions, we expect players to become more aware of their true ability and whether they will achieve superstardom. If so, they will remain professionals and if not, they would exit. This way 푊푁푆 would be earned over fewer years than would 푊푆. Evidence for this uncertainty would be volatile prize money earnings from year to year among those who

Preliminary - Please do not quote continue as professionals. However, the important volatility is the residual uncertainty conditional on the information the player has about variation in her own ability.

Hypothesis 2: Player success in Esports is highly uncertain even after accounting for observable factors affecting player ability.

Within player variability could come from effects observable by the researcher, such as age and experience, but also effects known to the player but unobserved by the researcher, such as being rested or interpersonal conflicts within a team. It is likely that players are aware of information unobservable to the researcher that could account for some share of earnings variation. However, if they could predict their outcomes precisely, they would likely not enter tournaments with low expected payoffs.

Two observable factors that could affect player productivity and are observable are age and experience. For most sports, performance improves with physical maturity and degrades with physical decline. The peak age may differ across sports with a female gymnast’s peak performance tending to occur in her late teens while tennis players play into their thirties and golfers sometimes past their forties. The age of the peak usually depends on the sport’s demands on physical strength, stamina, agility, reflexes, etc. Fried & Tauer (2011) examine the effects of age and experience on professional golfers and find that peak performance occurs at age 36.

Hakes and Turner (2011) find that the best MLB baseball players peak about two years later than marginal players, at 28 versus 26. The physical characteristic most important to playing professional Esports is likely quick reflexes to react quickly to changes in tactics. Since reflexes are known to diminish in early adulthood, all else equal we expect to find diminished performance before a gamer reaches thirty.

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Countering the physical deterioration with age is improved skills with experience.

Professional gamers often practice up to eight hours per day in order to understand the best strategies and tactics to employ and to develop better understanding of the roles each team member plays to carry out these strategies.5 Moreover, competing and training in one year serves to improve skills in subsequent years. Essentially, this is a form of learning-by-doing in which current ‘production’ is increased by the accumulated past ‘production.’

A third testable feature of superstar models is learning one’s own ability through attempting a career. MacDonald (1988) models how information revelation about one’s own talent leads to different career lengths. Often, it is only by pitting oneself against ever better opponents that one discovers if their innate ability will carry them into superstardom. After multiple attempts, a player will become better aware of the likelihood of achieving superstardom.

Those with more success early on will have expectations of reaching the higher echelons of the game and will continue to pursue this goal. Those with more limited success will eventually come to realize that greater success is unlikely and will ‘retire’ from their professional careers earlier.

Hypothesis 3: Players with greater success will continue their professional careers for longer.

Those who succeed discover that their ability is high and that earnings from Esports may dominate alternative job prospects. Those who earn less in prize money will tend to choose to retire from professional play.

Below we develop tests for each of these hypotheses from data on earnings of Esports professionals. Along the way, we uncover some stylized facts about where Esports professional

5 https://www.businessinsider.com/pro-gamers-explain-the-insane-training-regimen-they-use-to-stay-on-top-2015-5 12

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IV. ESports Earnings Data

The data used in this study comes entirely from http://www.esportsearnings.com/. This website is a community-driven compilation of publically-available data dedicated to showcasing the growth of competitive video games. Webpages feature information on Esports-related players, teams, and tournaments verified by published sources.6 The website identifies 100 games in which players have competed for prize money, although many are later versions in a game series, e.g. FIFA 18, FIFA 17, etc. These data are crowd sourced and may be incomplete.

Tournament organizers and players have incentives to make as much information as possible available, as evidenced by data being available for even small tournaments. Still incompleteness could be a source of measurement error.

Collecting relevant data from this website is a relatively straightforward process. After a desired game has been selected, multiple Application Programming Interface (API) calls are made in order to return basic information on players of that specific game. Each player is provided with a unique ID that allows a second API call to that player’s own page. Here information can be collected such as the player’s handle, name, date of birth, and nationality as well as player’s participation in all Esports tournaments, including tournament title, date of occurrence, podium position, and prize money awarded. Of course, this entire process can be automated using a programming language. An example of such code, using Python 2.7, is

6 For more information see https://www.esportsearnings.com/dev/terms_of_use (accessed 12 Aug 2016) 13

Preliminary - Please do not quote available on the author’s Github page.7 For tournaments awarding non-USD denominated currency, the prize amount was converted into US dollars using the appropriate conversion rate from the ending date of the tournament.

For the purposes of this study, the top ten games, by measure of total prizes awarded, were selected for analysis. These ten games are “Dota 2,” “League of Legends,” “Counter-Strike;

Global Offensive,” “StarCraft II,” “Heroes of the Storm,” “Hearthstone: Heroes of WarCraft,”

“Counter-Strike,” “Smite,” and “StarCraft: Brood War.” These games represent over 90% of

Esports activity. While players usually specialize in a single game, players will sometimes enter tournaments for different games. For purpose of our analysis, we only considered participation in these ten games. A few players played two different top 10 games, most notably “Counter-Strike:

Global Offensive” which is the successor to “Counter-Strike.” In our analyses below, we considered these as distinct entries.

Table 1 reports general information about the ten games we study and figure 1 displays total prize money over time calculated from these data. The currently most actively played games emerged after 2010 with the most popular at the end of our sample being “Dota 2,”

“League of Legends,” and “Counter-Strike: Global Offensive.” Two of the top ten games,

“Starcraft II” and “Counter-Strike: Global Offensive,” are successors to two other top ten games,

“Starcraft: Broodwar” and “Counter-Strike,” however, some “Starcraft: Broodwar” tournaments are still being held. Players tend to specialize in a single game but a few players have entered tournaments for different games. Within these 10 games, there is considerable variation in the number of players, tournaments and prize money. Prize money did grow between 2000 and 2010 but accelerated tremendously afterward.

7 https://github.com/alexanderharmon 14

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The data include over 141,000 player-by-tournament observations for over 24,000 players. On average, players entered six tournaments but about 40% of players enter only one tournament. The time between tournaments for players entering multiple tournaments has a median of one month and a mean of about three months. The distribution of time between tournaments is highly skewed but 95% of these spells are less than a year. Most of our analyses aggregate player information into calendar years. For what follows, we define the entry year as the year of the player’s first tournament and exit year to be the date of the player’s last tournament if it was before the last year in our sample, 2017. Otherwise, they are considered still active. It is possible that some players that we have labeled as exiting may return after a long hiatus and that some we label as currently active have recently retired.

Since all of these players have won some prize money, technically they are all professionals. There are millions of players of each MOBA game who never enter a professional tournament. Those who reach tournament play, tend to be among the best players. However, it is likely that some players will enter small tournaments just for the fun of it without a real hope of making a career out of Esports. It is difficult for us to determine which tournament players are mere enthusiasts and which have larger ambitions. To account for this, in some of the analyses, we perform analyses separately by the size of the player’s earnings.

We hypothesize that, just as with actors, musicians, and athletes, the superstar effect was largely the result of the emergence of “new media.” This time the new media was Internet streaming, predominately over Twitch.8 Twitch enabled viewers with an Internet-enabled computer to watch matches from anywhere in the world. Table 2 attempts to estimate the effect

8 https://newzoo.com/insights/articles/esports-drives-21-3-of-twitch-viewership/ 15

Preliminary - Please do not quote of Twitch usage on prize amounts and provides some support for this conjecture.9 We aggregated tournament prize money in a year and country but, unfortunately, we do not have data on Twitch viewership of tournaments. Instead, we collected the number of Google searches for the word

“Twitch” by year and country as a proxy for Esports streaming activity from Google Trends.10

The Google Trends data report the number of searches for a specific term over time and by country but normalize each country’s values to a 0-100 scale and so require a country fixed effects specification. In alternate specifications we include year fixed effects to account for secular growth in both searches and prize amounts. Because effects may span years, this relationship may be non-stationary. To address this possibility, we estimate the model for first- differences in prize and search variables as well as levels. In all cases, there is a positive and statistically significant effect of our proxy for Twitch usage on Tournament prize amounts. We take this as suggestive evidence consistent with the increased ability to monetize the revenue streams from streaming funded a competition between tournament organizers to attract the best talent by increasing the prize money available. Moreover, the estimates are greater than one indicating an elastic effect of viewership on revenue as predicted by Rosen (1981). We infer that the emergence of Twitch represents an exogenous productivity shock that increased the audience reach, and thus compensation, for top players.

Players come from 116 different countries. Table 3 reports the number of players and their earnings for the 25 winningest countries. The most glaring anomaly is South Korea which has embraced Esports more than most countries. The size of the rest of the world category comprising all non-top 25 countries indicates widespread global adoption of Esports. Except for

9 More recently, Huya has become the Esports streaming platform of choice in China https://seekingalpha.com/article/4175878-huya-twitch-china-bet-gaming-e-sports. 10 https://trends.google.com/trends/ 16

Preliminary - Please do not quote the largest tournaments, participation in a tournament is mostly from teams from the host country or nearby countries.

Finally table 4 reports trends in the flow of players into and out of professional Esports.

Each column reports the number of players from that start year who are still active in tournament play. The patterns indicate a considerable attrition from tournament play. It is likely that many players who enter a few small, local tournaments have only the faintest hope of becoming a professional Esports player. If so, their participation might inflate the reported rate of attrition of true entrants into the professional ranks.

V. Results

Before we present the formal tests of hypotheses, we show that a number of the features of professional Esports conform to the model of superstars. First, we can calculate that the mean earnings of Esports professionals have remained roughly constant. While total prize money, and top prize amounts, have skyrocketed, the mean earnings have remained nearly constant in the range of $4,000 to $8,000 for almost two decades.11 That is, despite the growth in top prize pools, the expected return to entering has remained relatively unchanged. The flood of entry has counterbalanced the growth in total prize money to make the expected earnings from entering a professional Esports career remain roughly constant.

Second, as predicted by Rosen, prize earnings are highly skewed. The distribution of annual prize earnings in USD resemble a log normal distribution with a mean of 6.2 and a standard deviation of about 2.0. With multi-million dollar contracts for top players, baseball and football have often been considered superstar markets. However, logarithms of both MLB and

11 Specifically, we can reject the null that mean earnings vary by year. 17

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NFL players’ salaries have a standard deviation of about 1.0.12 As indicated above, many Esports enthusiasts who enter a small tournament may not consider themselves professional players. If so, their inclusion artificially extends the range of annual earnings. However, if we define professionals as players with $10,000 or more in annual income, the standard deviation in log earnings is 1.0. Or Esports has at least as much variation in earnings outcomes as major sports leagues.

Third, these highly skewed earnings are becoming more skewed over time. Figure 2 displays histograms of the logarithm of annual player earnings for six different three-year time periods. While the mean shows no substantial changes, the standard deviation increases monotonically over the time periods from 1.4 to 2.2. If we constrain this to those with $10,000 or more in annual earnings, the standard deviation rises from 0.5 to 1.1. This also implies that as prize money has risen, ever more Esports professionals are earning below the mean. This is consistent with entrants forgoing income initially in order to buy their “lottery ticket.”

These features of the distribution of earnings are consistent with the superstar model. The model requires a wide distribution of earnings in which there are many “losers” to counterbalance the “winners.” However, it has a number of other assumptions and implications.

Below we report various tests of hypotheses emanating from the model.

A. Entry

We test hypothesis 1 by relating the number of new professional Esports players as a function of top earnings globally, regionally, and nationally. We do this by aggregating the data by game, country and year to form a three-way unbalanced panel. We identify players as entrants

12 MLB salary data are from http://www.spotrac.com/mlb/rankings/ (accessed 7 Feb 2017) and NFL salary data are from https://www.pro-football-reference.com/players/salary.htm (accessed 7 Feb 2017). 18

Preliminary - Please do not quote during the first calendar year for which we observe their earnings data. Our outcome variable, the number of entrants, 푁푢푚푁푒푤푔푐푡, identifies the first year of competition, t, of a player in country c for game g. For all years, we simply count the number of such players. Since players’ first tournaments can occur in any month of the year, those who started in December will be newer players than those who started in January. It is possible that for a few players’ the first actual professional interaction was not recorded in our dataset. Our measure of entrants will be measured with error to the extent that this first interaction occurred in a prior year. It is also possible that some tournaments are excluded from the data set altogether again rendering our count of new entrants downward biased. However, we expect these miss-measurement issues to be uncorrelated with our explanatory variables and so are likely not to bias estimates.

Our first hypothesis relates entry to the top prizes available upon entry. To account for possible locational constraints, we calculate the highest annual earnings for a player for each game and year globally, by region, and by country. Some players may consider themselves contenders for the top earnings globally while others are constrained to tournaments closer to home. While players are identified by their country, they often play in not-too-distant, international tournaments. We define six regions based on perceived transactions costs to players from participation in tournaments within and between them. In the end, they are somewhat arbitrarily defined as The Americas, Europe, Africa, Oceania, the Middle East, with the remainder labeled Asia. Our three measures of “lottery winnings,” 푇표푝퐸푎푟푛푔푡, 푇표푝퐸푎푟푛푔푟푡, and 푇표푝퐸푎푟푛푔푐푡 are the maximum value of all players’ earnings prize earnings in year t, for game g, and, for globally, for region r, or for country c. However, both the number of players and prize money are correlated with game popularity which varies systematically across games, over time, and across locations. Without an accurate measure of game popularity, omitted

Preliminary - Please do not quote variable bias would likely lead to spurious positive coefficient estimates. To account for popularity, we include various sets of fixed effects. We always include fixed effects for year, game, and country, but in some specifications, we also include game by year interactions to capture differences in the changes in the popularity across games.

These considerations lead us to the following specification:

푁푢푚푁푒푤푔푟푡 = 훽0푇표푝퐸푎푟푛푔푡 + 훽1푇표푝퐸푎푟푛푔푟푡 + 훽2푇표푝퐸푎푟푛푔푐푡 + 훽3푋푔푟푡 + 휀푔푟푡 (1)

Our data source contain up to 16 years of player data for 10 different games and for over a total of 100 countries. The data set represents an unbalanced panel since most games have had tournaments for far fewer than 16 years and most countries do not have entrants every year. Our control variables, 푋푔푟푡, include the sets of fixed effects discussed above.

The results are presented in table 5. Columns (1-3) include fixed effects for each year, game, and country. Columns (4-6) add the fixed effects for each game by year combination.13

These fixed effects are meant to capture any differences in game popularity across the sample.

Within each panel, we add top earner information for more local tournaments from left to right.

In all cases, the number of new entrants responds positively to the winnings of top players. The larger effects tend to be for earnings more local to the entrants. In fact, the coefficient on global top earners is no longer significantly different from zero in two specifications in the right panel.

This could be because it does not vary across countries and most of the variation is taken up by the nearly fully-saturated interactions.14 Despite these qualifications, these results indicate that higher earnings for top earners is associated with more entry.

13 Our tests for entry responding to lagged top earning effects yield a zero or small negative effect from past top earnings. However, the estimates for concurrent top earnings are qualitatively similar. 14 We also tested whether the effect of top earnings on entry differs across the 10 games. F-tests usually rejects that the interactions of top earnings with game are the same. They also reject that these interactions are all non-positive which supports our hypothesis. 20

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B. Uncertainty and Productivity

Earnings dispersion may primarily be a cross-sectional characteristic of those who have entered. The model assumes, however, that it is difficult for an individual to know his expected earnings from entering a prioi. If there is still considerable dispersion in earnings conditional on a player’s ability, then players are likely to be uncertain about earnings before their entry. To examine this, we estimate a standard earnings regression to understand the effects of age and experience on player productivity.

퐿푛(퐸푎푟푛푖푡) = 훾0퐴푏𝑖푙𝑖푡푦푖 + ∑푎 훾푎퐴𝑔푒푖푎 + ∑푦 훾푦퐸푥푝푖푦 + 휈푖푡. (2)

Many individuals only participate in a single tournament. For what follows, we are constrained to those individuals, i, who continue to multiple years, t. The model is fully saturated with dummy variables for each integer value of age, a, and years of experience as a professional, y.

As is frequently the case, the omission of unobserved Ability of a player is likely to lead to omitted variable bias. This is because more able players are professionals for more years and may begin their careers at a younger age. We attempt to deal with omitted ability bias, by estimating the model with individual fixed effects. This would capture any non-time-varying differences in ability. However, this alone may not completely resolve the omitted variable bias.

This is because more able players are likely to continue their professional careers longer. Of course, since we do not observe player productivity for players’ post-retirement, our sample will be biased toward these more able players, especially in later years. If the marginal productivity of age or experience is correlated with ability, then estimates based on a sample of these players may not generalize to the universe of all professional players.

Another complication occurs with the calculation of player age. The birth date is missing for more than half of the sample. Because of the nature of the data collection at the

Preliminary - Please do not quote esportsearnings.com website, birthdates are more likely to be collected for more successful players. This too is likely to bias coefficient estimates upward if higher ability players have a higher marginal productivity of age or experience than players as a whole. Players with birthdate information tend to be young while competing. The mean age at first appearance is 20 years, with a 10-90 percentile range of 17 to 24 years. The mean age at last appearance is 23 years, with a 10-90 percentile range of 19 to 30 years. In this sample, age and years of professional Esports experience have a 0.42 correlation coefficient.

Results are presented in table 6 for an unbalanced panel of players active in two or more calendar years. The first year of activity often constitutes a fractional year as players commence their professional careers at different times of the year. Of the 25,000 players in total, about

15,000 are active in only one year and about 5,500 are active in three or more years. Birthdate is available for less than half the sample. Figure 3 represents a histogram of age from this subsample and shows that the model gamer age in the sample is 20 years old and almost all are under 30. Figure 4 represents a histogram of years of experience and shows a rapid decline in professional participation.

Column (1) reports the coefficients for experience alone for the full sample of players.

The initial jump is partly due to the first year being fractional. After that, it shows a slight increase in player productivity with more experience for the first few years and a decline thereafter. The spike at year 11 is due to a small number of exceptional earnings. Columns (2) and (3) include only the players with age information, the first with only experience variables and the second with experience and age. The difference between columns (1) and (2) are due solely to the more select sample. While there are differences across the two, the pattern of coefficients is qualitatively similar. This suggests that the sample with birthdate information does

Preliminary - Please do not quote not differ greatly from the sample without. Finally, column (3) reports experience and age effects for the more select sample with birthdate information. These are also reproduced in figures 5 and

6. From these, it seem that there is an inverted “U” shape for both age and experience although the effects are weaker and marginally significant for age. The peak age for performance appears to be 25. Note that age, experience and the player fixed effect account for less than 15% of the total variation in an individual’s earnings over time. This unaccounted for variation indicates a great deal of uncertainty about a player’s earnings across tournaments. This is consistent with potential players being quite uncertain about the payoff to turning professional prior to doing so.

C. Resolution of Uncertainty and Exit

Finally, we examine the speed at which players exit from the professional ranks. While we expect that players are uncertain about their abilities before entering, their early experience will inform them of their true ability. As this uncertainty is resolved, those with weaker earnings will exit sooner. This would be the case if past earnings strongly influence a player to remain active professionally. We test this by estimating a Cox proportional hazard model of quits.

ℎ(푡) = ℎ0(푡)exp (휁퐸퐸푎푟푛𝑖푛𝑔푠푖푡 + Σ푔(휁푔퐺푎푚푒푖푔) + Σ푦(휁푦푌푒푎푟푖푦))

In all specifications, we model heterogeneity in exit probability by game and year by including game and by year fixed effects. We include various measures of earnings to better understand the retirement choices of players. We account for endpoint censoring since many players are still active at the end of our sample period and will not have “failed” yet. Since these players are still competing professionally, we apply the standard correction for censored data. As discussed above, we define exits as the lack of any tournament activity for over one year. Figure 7 displays

Preliminary - Please do not quote the Kaplin-Meier survivor plots by various percentiles of annual player earnings. For most players, retirement comes quickly. Nearly half of those earning below the median exit after one year. Most of these are likely to be enthusiasts with little expectations of an Esports career.

However, the career paths of the top 95th percentile ($20,504+) and 99th percentile ($111,504+) earning quintiles appear to distinctly separate from even those in the 75th percentile ($2,000+).

Table 7 reports coefficient values for three specifications of the proportional hazard estimation. The first column simply includes the logarithm of the player’s annual earnings with the game and year fixed effects. Higher earnings are strongly related to a lower probability of retiring. This specification implies that a doubling of prize winnings reduces the probability of retirement in any year by about 17%. The second column adds the logarithm of the player’s home country GDP per capita. To a first approximation, the value of players’ alternative career opportunities should be proportional to GDP per capita of their home country. Differences in

GDP per capita will be roughly proportional to differences in labor income available to retired players. The positive coefficient indicates that a 100% increase in the value of outside opportunities increases the probability of retirement in any year by 10%. Finally, since figure 7 suggests that the effect of earnings on retirements may not be linear, in column 3 we replace the logarithm of earnings with dummy variables for various percentiles of earnings. All dummy coefficient estimates are significantly different from each other. In all cases, being in a higher quintile significantly decreases the probability of retirement than being in a lower quintile.

However, estimates for the last two categories indicate that, relative to those below the median, a player in the top 95% ($20,504+) is 8 times more likely to play another year and a player in the top 99% ($111,600+) is 17 times more likely to play another year.

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VI. Conclusion

Esports represents a set of video game competitions with fast growing audiences, tournaments numbers of participants, revenue streams, and prize pools. Because it is largely

Internet mediated, much of the information on Esports is readily accessible to researchers. We exploited one set of data on players and their prize earnings to demonstrate that Esports represents a superstar market. Potential entrants are motivated by the opportunity to win substantial prizes, they are uncertain as to their ultimate success, this uncertainty can only be resolved through entry, and is quickly resolved by entry.

As a superstar market, Esports may develop the features of other superstar markets. Most notably, when the financial stakes are high, a number of related institutions tend to develop. For example, in the sports, acting and musical industries, there has developed a role of talent scouts who arbitrage their superior insight into matching talent to teams, producers, or studios. Also, these high stakes professions are notorious for unscrupulous money managers offering services to participants not used to large amounts of money. Finally, many of those who commit to seeking superstardom and fail may have failed to develop other marketable skills.

As its fan based broadened, Esports likely would have grown even without Twitch. But because of Twitch, it grew even faster. Twitch, is merely one among the string of innovations recently emerging from the technological revolution in the communications industry. Future, yet unforeseen, innovations are likely to transform other industries similarly. That is, they will magnify the reach of the highest quality products and services. This will have the potential of creating superstars in industries where they have previously not been seen.

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Figure 1 Annual Esports Prize Money

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Figure 2 Histograms of Esports Annual Player Earnings over Time

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Figure 3 Histogram of Player Activity by Age

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Figure 4 Histogram of Player Activity by Years of Esports Experience

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Figure 5 Estimates of the Effect of Age on Esports Earnings

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Figure 6 Estimates of the Effect of Experience on Esports Earnings

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Figure 7 Kaplan-Meier Survival Estimates by Prize Earnings

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Table 1 Characteristics of the Most Popular Esports Games

t Prizes

ments

Game

Number of of Number Tourn of Number Professional Players First Year Most Recen Year Median Mean Std. Dev Largest Counter-Strike 524 2,593 2000 2014 $423 $940 $1,428 $14,000 Counter-Strike: Global 1,342 9,093 2012 2017 $176 $985 $4,236 $160,000 Offensive Dota 2 530 2,303 2011 2017 $638 $8,684 $68,974 $2,172,537 Hearthstone: Heroes of 434 1,588 2013 2017 $804 $2,697 $9,106 $250,000 WarCraft Heroes of the 142 955 2014 2017 $441 $2,145 $5,267 $100,000 Storm League of 686 4,993 2010 2017 $350 $1,961 $10,646 $338,000 Legends Overwatch 258 2,075 2015 2017 $136 $694 $1,675 $16,667 Smite 39 464 2013 2017 $1,248 $4,835 $19,255 $261,226 StarCraft II 1,281 1,700 2010 2017 $250 $1,196 $4,919 $280,000 StarCraft: 310 614 1998 2017 $372 $2,162 $5,331 $54,505 Brood War

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Table 2 The effect of Twitch on the Logarithm Esports Tournament of Prize Money

Levels First-Differences

Logarithm of “Twitch” 1.057*** 1.440*** 1.406*** 1.459*** Searches (0.053) (0.059) (0.052) (0.049)

FE Country X X X X FE Year X X

Observations 338 338 283 283 R-squared 0.584 0.735 0.760 0.808 Number of Countries 55 55 53 53 Data are for years 2010-2017 and the top 55 countries as identified by Google Trends. Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 3 Players and Prize Earnings by Country

Country Players Prize Money China 1,895 $64,195,093 Korea, Republic of 1,971 $52,736,079 United States 2,715 $27,480,092 Sweden 1,368 $20,273,920 Denmark 804 $13,816,659 Germany 1,285 $10,461,820 Ukraine 297 $10,082,351 Canada 741 $10,081,252 Russian Federation 1,052 $9,343,763 France 1,246 $7,752,825 Brazil 941 $6,937,333 Poland 918 $6,810,331 Finland 673 $6,088,961 Malaysia 265 $5,051,537 Taiwan, Republic of China 378 $4,954,316 Australia 704 $3,956,502 Philippines 236 $3,634,050 Jordan 35 $3,555,400 Bulgaria 182 $3,516,878 Pakistan 4 $2,685,001 Lebanon 34 $2,553,049 United Kingdom 819 $2,407,243 Singapore 292 $2,305,848 Romania 184 $2,123,021 Norway 399 $2,071,552 Rest-of-World 6,303 $24,149,770

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Table 4 Flow of Players into and out of Esports

Start Years Active Year 1 2 3 4 5 6 7 8 9+ 1998 8 4 1 1 1 1 0 0 0 1999 36 8 3 1 1 1 1 2 1 2000 100 56 29 21 13 8 8 1 5 2001 193 72 47 37 21 19 9 9 7 2002 232 71 59 38 30 15 14 11 9 2003 218 87 75 69 35 25 21 20 8 2004 226 107 81 41 37 29 23 14 8 2005 326 150 96 97 74 61 40 22 1 2006 343 120 102 77 78 45 25 3 8 2007 228 88 73 68 44 28 3 4 4 2008 247 90 95 61 23 6 6 13 8 2009 235 75 62 32 1 1 1 1 1 2010 713 302 217 137 88 67 48 41 2011 994 469 302 209 171 121 111 2012 1,660 769 571 490 381 308 2013 2,052 934 791 581 475 2014 2,763 1,277 983 756 2015 5,081 1,954 1,432 2016 5,499 2,105 2017 5,005

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Table 5 The Effect of Top Tournament Earnings on Entry

(1) (2) (3) (4) (5) (6)

Ln Top Earnings 0.339*** 0.217*** 0.161*** 0.347*** 0.195 0.067 Globally (0.024) (0.032) (0.031) (0.134) (0.136) (0.128) Ln Top Earnings 0.134*** 0.047** 0.135*** 0.052** in Region (0.023) (0.023) (0.024) (0.023) Ln Top Earnings 0.173*** 0.174*** in Country (0.011) (0.011)

FE Game X X X X X X FE Year X X X X X X FE Country X X X X X X FE Game×Year X X X

Observations 2,169 2,169 2,167 2,169 2,169 2,167 R-squared 0.545 0.552 0.600 0.584 0.591 0.637 Adj. R-squared 0.512 0.520 0.571 0.543 0.550 0.601 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 6 The Effect of Age and Experience on Ln(Annual Earnings)

Full Sample Non-Missing Age Non-Missing Age 1 Year Pro 0.866*** (0.194) 0.660*** (0.234) 1.816*** (0.541) 2 Year Pro 1.605*** (0.194) 1.694*** (0.234) 2.674*** (0.501) 3 Year Pro 1.825*** (0.194) 1.844*** (0.234) 2.646*** (0.457) 4 Year Pro 1.837*** (0.195) 1.846*** (0.235) 2.493*** (0.413) 5 Year Pro 1.735*** (0.196) 1.763*** (0.237) 2.274*** (0.372) 6 Year Pro 1.514*** (0.199) 1.451*** (0.240) 1.844*** (0.337) 7 Year Pro 0.988*** (0.204) 0.867*** (0.247) 1.176*** (0.309) 8 Year Pro 0.656*** (0.219) 0.582** (0.265) 0.791*** (0.294) 9 Year Pro 0.0536 (0.253) -0.121 (0.304) -0.098 (0.314) Age 14 -1.534 (1.162) Age 15 -0.886 (1.108) Age 16 -0.614 (1.065) Age 17 -0.070 (1.025) Age 18 0.183 (0.988) Age 19 0.320 (0.950) Age 20 0.415 (0.916) Age 21 0.571 (0.883) Age 22 0.778 (0.852) Age 23 1.013 (0.823) Age 24 1.107 (0.798) Age 25 0.960 (0.775) Age 26 0.772 (0.755) Age 27 0.905 (0.743) Age 28 0.917 (0.726) Age 29 0.846 (0.737) Age 30 0.524 (0.719) FE Game X X X FE Player X X X

Observations 46,566 7,876 7,876 R-squared 0.113 0.131 0.147 Adj. R-squared -1.029 -0.248 -0.227 Number of Players 26,239 2,413 2,413 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The omitted category is players over 30 years of age with 10 or more years of experience.

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Table 7 The Effects of Earnings on Esports Retirements

(1) (2) (3) Ln Earnings -0.253*** -0.251*** (0.008) (0.008) Ln GDP Per C apita 0.162*** 0.167*** (0.031) (0.031) Earnings > 50 Percentile -0.235*** (>$300) (0.037) Earnings > 75 Percentile -0.511*** (>$1,000) (0.041) Earnings > 90 Percentile -0.862*** (>$3,000) (0.057) Earnings < 95 Percentile -1.421*** (>$6,250) (0.068) Earnings < 99 Percentile -2.382*** (>$25,000) (0.132) FE Game X X X FE Year X X X

Observations 13,925 13,925 14,014 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1