2 Outline of the E-Sports Research
4.1 Mapping E-Sports Success: A Country-Level Analysis
4.1.1 Historic Total Yearly E-Sports Earnings
4.1.2 Historic Yearly E-Sports Earnings by Country
4.1.3 Historic Normalized Yearly E-Sports Earnings by Country
4.1.4 Historic Yearly E-Sports Earnings by Game
4.1.5 Historic Normalized Yearly E-Sports Earnings by Game
4.1.6 Countries by Normalized Overall Rankings across Genres
4.1.7 The Ten most Successfull Countries of each Genre by Total Earnings
4.2 Determinants of E-Sports Success: Analysis of Correlations
As a by-product of the global spread of information and communication technologies, video games have found their way into the cultural identity of modern societies. The former dominance of sport, film and music as cultural commodities has come under increasing pressure from digital games on smartphones, computers and consoles. In 2023, the gaming market is estimated to be worth USD 385 billion [1], with a global average of 7.11 hours of gaming per week [2].
With so many eyes watching for so many hours, there must be a cultural, economic and ultimately political resource behind the screens. In the face of nuclear parity and economic interdependence, international regimes have increasingly shifted to non-military channels of conflict. Instead of tanks, missiles and aircraft carriers, contemporary definitions of power must measure success in sporting events, literary awards, music charts and box-office sales. After all, even the physical dominance of the US could not be sustained without an elaborate narrative of cultural and political legitimacy. However, the South Korean entertainment industry and the sports management of the Arabian Peninsula have demonstrated that any state with a mere regional claim to power is now both able and obliged to actively produce cultural goods or at least stimulate a space in which these goods can be developed by non-state actors.
Global elo rankings and historical medal tables already provide a comprehensive portrait of the international distribution of success for many of these cultural assets. Video games, however, have not yet been thoroughly mapped in their political dimension. One reason for this is that many game architectures deliberately avoid objective evaluation. In order to bypass this limitation, we will confine ourselves to e-sports in the following. This is in line with our research objectives, as the artificial attribution to monolithic countries itself bears a competitive quality. Secondly, gaming is a complex sphere that is distributed and broadcasted across many platforms. Metrics are therefore often subject to blind spots for specific regions, genres and venues. To address this complexity, we use the website esportsearnings.com, which describes itself as a “community-driven competitive gaming resource based on freely available public information [that] allows esports fans and researchers alike to easily access the history of competitive video games”. [3] The site has two advantages over its competitors: Firstly, it offers the functionality to list earnings by country, as each registered prize must have a geographical data point. Secondly, the site is used relatively evenly across the globe.
Historical prize money between 1998 and 2022 and its specific metadata are first scraped, cleaned and visualised. Based on these results, the second section of the analysis will focus on the determinants of national success in e-sports. For this purpose, a multi-factor analysis will be applied, testing different indices and socio-economic indicators for correlations with tournament success. The corresponding research questions are:
RQ 1: Which countries are particularly successful in e-sports?
RQ 2: How is success distributed across different genres?
RQ 3: Which factors correlate most strongly with successful e-sports?
E-sports is the inevitable progression of the global trend of video games. Anecdotal evidence often points to the 1972 Pong tournament held on the campus of Stanford University as the first e-sports event. [4] Fifty years later, we have the computing power of NASA servers in our pockets, the prize money has gone from a year’s subscription to the roling stones to more than $200 million, and instead of the few hundreds that fit in the auditorium, 540 million people are watching. [5] This increase is all the more remarkable because it took place almost entirely after 2010.
The timing of the breakthrough of e-sports can largely be explained as the free-riding on three trends. Firstly, the global triumph of full-display smartphones and personal computers in the late 2000s provided the technological basis for watching and playing video games. Software quickly adapted to the new affordances and “With the launch of Twitch in 2011 e-sports truly began to thrive, as players could interact with their viewers in real time.” [6] Secondly, e-sports benefits from global gentrification like no other industry. Not only can people around the world afford the aforementioned e-sports media, but with prosperity comes the time and leisure to play games once reserved for children. Finally, e-sports owes much of its success to the advertising industry. In 2023 alone, advertisers could earn more than $9 billion from sponsorships, media rights and merchandise in e-sports. [5]
Although the recent success convinces with great momentum, the research is still stuck in the deep ruts of path dependency. The methods that exist for longer-established cultural goods have only been partially transferred to the new industry, and so the internal structures of the market remain largely undiscovered. Results from related disciplines offer little explanatory transferability. For instance, determinants of Olympic success show a clear correlation with cultural-historical peculiarities or climatic conditions [7] – factors that are non-existent or negligible for such a young discipline represented in all climatic zones. Existing research on the determinants of esports points to the pervasive explanatory power of GDP and the degree of infrastructural development. It also suggests that economies that are or have been under central government control outperform their liberal counterparts. [8]
As mentioned above, all graphs and calculations are based on data from esportsearning.com. The site is designed for research purposes and is therefore easily scraped. We used Python’s beautifulsoup, which navigates through the HTML structure of the simple and coherent website via slicing, indexing and iteration. In addition to aggregated earnings for countries, years and games, we also included genres and rankings. The scrapes were formatted with Pandas and in many cases manipulated with regular expressions, for example to handle redundant units of measure, separators and blanks as required. An additional 12 socio-economic indicators and indices were scraped for RQ3. As 283 country names (e.g. ‘Korea’, ‘South Korea’, ‘Republic of Korea’, ‘Korea, Republic of’) were used for 207 unique countries across the 13 indices, dictionary mappings were used to homogenise the labels. The data was stored in csv files and visualised with the Pythons Plotly.
import requests
from bs4 import BeautifulSoup
import pandas as pd
# URL of the Website
url = 'https://www.esportsearnings.com/history/2023/games'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
table = soup.find('table', class_='detail_list_table')
rows = table.find_all('tr', class_='format_row highlight')
# Extract datapoints by indexing cells of each row
data = []
for row in rows:
cells = row.find_all('td')
rank = cells[0].text.strip()
country = cells[1].text.strip()
earnings = cells[2].text.strip()
players = cells[3].text.strip()
tournaments = cells[4].text.strip()
data.append([rank, country, earnings, players, tournaments])
# Convert list to pandas DataFrame
df = pd.DataFrame(data, columns=['rank', 'country', 'earning', 'players', 'tournaments'])
# Save DataFrame to csv
df.to_csv('games_2023.csv', index=False)
Some of the graphs work with ranks because measuring success in terms of earnings would make all the small games, countries and genres virtually invisible in relation to the rewards of Dota 2, Fortnite and League of Legends, as well as the countries that receive these rewards on an annual basis. In this way, the factor between the two values is limited to the number of countries and can still be displayed. In addition, for some of the graphs, the values are normalised to minimise dimensionality. This means that the highest value is indexed to one and the lowest value is indexed to zero. All other values are then assigned in proportion to their distance from these two values.
Here you can see a line graph showing the development of the total prize money of e-sports tournaments from 1998 to 2022.
The temporal distribution confirms the results of the previous research in that e-sports experienced astonishing growth in the last decade. However, the growth rates have not been higher than in the decade before. Since 1998, an average growth rate of 80% has been maintained, except for the macroeconomic shocks of the global financial crisis in 2008 and the Covid pandemic in 2020. The increased visibility since the 2010s can be sufficiently explained by the low base effect. The data are neither geographically nor temporally adjusted for purchasing power, but one can assume an average loss in value of 70% between 1998 and 2022. [9]
Here you will find a line graph showing the yearly total prize money for each country over time. Lines that appear or disappear abruptly indicate the absence of prize money for a country within a year, as blanks are not treated as zeros.
A first intuitive step for a country-level analysis is the breakdown of total earnings by country. Two categories of observations can be made here. On the one hand, regardless of specific countries, a high fluctuation of earnings can be observed. Even outside the global purchasing power collapses, it is not atypical for countries to record a growth or decline by a factor that is far above that of the total earnings. What becomes clear here is the fact that we are dealing with the zero-sum game of an ever-growing pie. This is not a normative assessment, but an internal quality of any comparative research design.
Here you will find a stacked bar graph showing the shares of different countries in the total prize money within single years. To make the early years of esports visible, all bars are stretched to a uniform height of 1. New countries are added to the bars from above, so that countries that had participated early in e-sports are always found at the bottom.
Normalising the values by stretching each year as a bar across the entire Y-axis neglects the development of the size of earnings, but allows for a more accurate tracking of the international distribution. The order of the countries within the bars is determined by when they first received a payout. The countries at the top are therefore always those that participated in earnings for the first time in the year in question.
Whereas at the beginning of the record a handful of states shared the earnings, the tournament winners in 2022 represent 138 different states (some regions have no status under international law). Not only has the number of states grown, but the share of states outside the aforementioned 4 big players (RU, US, CN, KR) has also increased. In 2022, they reached a low of 42% of the gains. Among the biggest losers in recent years are wealthy and less populated countries. They already had the means to successfully compete for funds at the beginning of the phenomenon, but are now being left behind by populous emerging economies.
Sweden, Denmark, Germany and (former) heavyweight South Korea are on the same level as Brazil, Vietnam, Indonesia and the Philippines in 2022. Similar to economics, this movement shouldn’t be mystified as a remarkable rise, but rather as an inevitable rectification of an industry that has long been dominated by OECD countries. thus, this is not an Asian rise or a Western decline, as occasionally claimed. [10] In fact, with 27% (Russia is fully included), Europe still overperforms when compared to its population and GDP. The US, on the other hand, has recently underperformed with 13%. The argument that Europe, with its many countries, has a systematic advantage over the USA, which can only send one team to tournaments, may be valid in traditional sports, but in e-sports it is almost exclusively teams, not countries, that compete. It is therefore not atypical that countries are able to claim 90% of the earnings of a game for themselves. The increasing correlation of GDP and population, which is known in economics for many years, is also reflected in these statistics. Latin America, with 7.06% of the earnings, is rising just as much as China. The African continent is still far behind with 1.59 million USD or 0.67%. In fact, a large number of the countries that have not yet been included in the list of prize money recipients are located on the African continent (along with the poorly populated island states). However, the sharp increase in gains for Egypt, Morocco and Algeria suggests that the rebalancing trend will not stop in Africa. However, given the high standard deviations, looking at just a few years exposes one to a certain risk of formulating hypothesis based on outliers.
Here you will find a line graph showing the yearly total prize money for each computer game over time. Lines that appear or disappear abruptly indicate the absence of prize money for a game title within a year, as blanks are not treated as zeros.
Since the Pong tournament in 1972, not only prize money and audiences have changed, but also the games themselves. To expand our understanding of success beyond earnings, we should consider the diversity of genres and games.
Here, as in the analysis of earnings by country, there is a strong fluctuation. Thus, the variance in the earnings of countries cannot be adequately explained by the performance in the major tournaments. Equally inadmissible is the assumption of the zero-sum game mentioned for countries within the gaming landscape. In fact, it seems reasonable to assume that success and failure transcend the industry. For example, after strong growth in the first half of the 2010s, Dota 2, Counter-Strike and League of Legends all experienced a minor plateau, which was interrupted but not ended by the pandemic. In addition to these consistencies, there are also outliers such as Fortnite in 2019. With almost $80 million prize money paid out in 470 tournaments, Fortnite overtook the top dog, Dota 2, just two years after its release. However, the profits contracted as quickly as they emerged, and the fact that in 2022 the total payout was as high as in the previous year is only due to a large number of smaller games.
Here you will find a stacked bar graph showing the shares of different games in the total prize money within single years. To make the early years of esports visible, all bars are stretched to a uniform height of 1. New games are added to the bars from above, so that older games are always found at the bottom.
The normalisation illustrates that the phenomenon of multipolarisation, which we already know from the comparison of countries, also applies to video games. Thus, with one exception in 2010 (48.42%), the three largest games accounted for more than 50% of the earnings until 2017. In 2018, this limit was again undercut and after the exceptionally high prize money from fortnite in 2019, it consolidated between 27.37% (2020) and 34.88% (2022). Compared to the performance of other video games, the decline of Dota 2, League of Legends and Counter Strike: Global Offensive is even more drastic than in 4.4. The aforementioned plateau in payout thus becomes a rapid decline in terms of overall share. It is notable that most of the new games that have emerged in recent years show a lopsided regional trend. Rocket League, Call of Duty: Vanguard, Valorant and Fortnite are published by US companies and are played almost exclusively in OECD countries, with the US leading by a wide margin in terms of earnings. Only Apex Legends breaks this pattern, with high rankings for Brazil and Saudi Arabia, despite being published in the US. In line with their target audience, all five were computer games originally, some of which have been or are being made available as mobile versions.
On the other hand, there are games designed primarily for smartphones. With RiotGame, Epic Games and TiMi Studio, three of Tencent’s subsidiaries produced League of Legends: Wild Rift, PUBG: Mobile and Arena of Valor. Since working with a Chinese company is both the legal condition and institutional support for publishing in the Chinese market [11], it is hardly surprising that American dominance has been broken in these cases, with China bringing in more than 50% of the earnings in all three cases. It is also interesting to note that Malaysia, Thailand, Vietnam, Indonesia, the Philippines and Brazil constitute a completely new distribution among the top rankings. There is still some room for interpretation in the data. This circumstance can be explained primarily by the medium of gaming. In highly developed industrialised countries, too many people have computers and consoles for their competitive needs to be satisfied in front of a small mobile phone screen. However, it is also plausible that geographic and cultural proximities cause tournaments to take place within an exclusive sphere. China’s latest crackdown on the gaming industry prevented even Epic Games, which is 40% held by Tencent, from launching a Chinese release of its already highly successful Fortnite. [12] Based on this data, we can assume a further global division along the lines of the cleavages already known from other conflicts: The division between the global North and the global South.
This is a so-called radar chart, which assigns 12 points to each country based on historical earnings within a particular game genre. The rank is used to prevent high prize payouts in a single game from drowning out the figures for all other games. An average rank is calculated for each country, with countries that have never won any prize in a game placed one place behind the lowest payout. The values are then normalised. This means that the lowest (inverted) average rank is set to one and the highest average rank is set to zero. Any number of countries can be stacked on top of each other. With a double click, you can either isolate a country or display all countries. Countries with small participation in diffrent games were not included because they are not suitable for radar charts.
This chart shows how well a selection of countries perform in the 12 game genres. It should be noted that the number of games, tournaments, players and, of course, prize money can vary considerably. For example, even countries that are successful in total earnings, such as China, have not yet been able to produce players who can make money in Third Person Shooters (TPS) and Fighting Games (Fighting). In a direct comparison, emerging countries consistently underperform developed countries with comparable total earnings. Thailand ($25m) is surpassed by the Netherlands ($15m), Brazil ($51m) by France ($49m) and Vietnam ($14m) by Spain ($12m). A look at the composition of these winnings shows that Thailand’s 61 games are just under a third of the 197 Dutch games, Brazil’s 154 games are compared to France’s 260 and Vietnam’s 57 games are far behind Spain’s 174. Whether these countries actually have a broader e-sports landscape that can rank well across many games and genres remains to be seen, as it is also possible that outside the OECD it is more likely that prize money is not registered on esportsearning. If this is not the case, or not enough to explain it, it could be speculated that the time advantage of highly developed countries has brought more opportunity to saturate the market. Communities, advertisers, e-sports organisations and their gamers were able to diversify and specialise in niches for at least one more decade. In many emerging markets, gaming only became a cross-societal phenomenon in the late 2000s, and in that short time, large organisations have focused predominantly on games with high payouts and advertising revenues.
The aforementioned trend towards mobile video games in emerging and developing countries is also evident. Although the genre names do not define the medium on which the games are played, the good performance in puzzle games, strategy games, racing games and MOBAs by emerging countries in Asia could be explained by the large number of video games in these genres that can be played on smartphones. This contrasts with the clear dominance of developed countries in classic computer and console genres such as RPGs and sports games.
This bump chart shows the ten most successful countries of each genre. As in the radar chart of 4.6, success is defined as the lowest average rank among all games in a given genre. Countries are connected by a transparent line to give a rough sense for the number of occurrences.
The rankings once again highlight the dominance of the OECD countries in the niches of e-sports. The USA is in fourth place in its worst genre. 86 of the 120 nodes represent one of the 38 states in the Organisation for Economic Co-operation and Development. Besides China, Russia and Brazil as well as the OECD confederation, India and ASEAN countries stand out. In FPS, three countries of the Southeast Asian alliance – the Philippines, Vietnam and Thailand – are in the top ranks. Despite its poor performance in the e-sports top dog for fighting games “brawlhalla”, India has landed in first place of the genre because it is well positioned in smaller fighting games. The fact that India would not even be listed among the top 10 countries using total winnings as a measure of success shows once again how the use of ranks influences the representation of performance. Other notable examples are Azerbaijan, with two appearances in Battle Royale and Music, but also Saudi Arabia and Puerto Rico, which do well in strategy games are rare names in e-sports coverage so far. The metrics, however, should be taken with a grain of salt. Just as measuring winnings in USD cannot provide a reliable reflection of the shape of a genre, measuring average rankings is not free of problems. Many small games with few active e-sports players in a geographically limited area are not an arguable sign of success.
In this scatterplot, all countries (and regions) that appear at least three times in the 13 socio-economic indicators and in the list of e-sports earnings are shown on the x-axis. The countries are sorted from left to right by total E-sports earnings and alphabetically in the event of a tie. In the legend, below the earnings, the 12 socio-economic indicators are shown, which can be made visible by clicking on them. A double click makes all indices visible at the same time. The values on the Y-axis are normalised so that the highest value is set to 1 and the lowest value to 0. The Gender Inequality Index was inverted so that all indices follow a uniform normative dimension of development.
Here you can see the correlation of the twelve socio-economic factors with the total earnings in esport. The correlations are based on Pearson’s correlation coefficient, where a dimensionless value of one testifies to a clear linear correlation and a minimum value of negative one expresses a negative correlation between the dependent and the independent variable. The value zero, on the other hand, expresses that there is no linear correlation.
All indices are analysed individually to identify patterns, trends and significant outliers.
Average Broadband Internet Speed: As packet loss and a high ping will almost always be a disadvantage in gaming, this score has been scraped from Ookla’s “Speedtest Global Index”. [13] The pattern of internet speed shows a clear upward trend. Apart from small and rich dwarf countries such as Liechtenstein, Andorra and Monaco, there are no countries in the upper left quadrant. At the same time, Indonesia, Russia, Australia and Pakistan are some countries with comparatively low broadband speeds that are still competing in terms of earnings. This could be explained by topographical and demographic characteristics that don’t (economically) allow for high overall internet speeds, while at the same time having digital hotspots such as Jakarta, Moscow or Sydney. The large standard deviation compromises the explanatory power of internet speed as a determinant of e-sports revenues. In addition to population and other key figures, which are also taken into account (and therefore do not have to contribute to the explanation of the distribution here) one possible reason could be that internet speed only improves performance up to a certain benchmark, after which higher speeds lead to diminishing improvements. Another reason could be that boradband speeds are measured as an average, hence we do not necessarily know the difference across social strata. This is important however, as indivudual wealth affects ones ability to meet the requirements of professional e-sports.
Average Mobile Internetspeed: A high internet speed is also advantageous for mobile games, which is why average mobile internetspeeds were scraped from Ookla. [13] The fact that a clearly different pattern emerges here can be explained mainly by the high speeds of the united Arab emirates and qatar, which push the entire scatter plot somewhat to the lower edge due to the normalisation. Otherwise, however, the values for broadband expansion correlate mainly with those for mobile internet connections. However, it can be noted that outliers and thus standard deviations are even more pronounced than with Broadband speeds. In the bottom right corner, for example, there are some top performers. Even Vietnam, Mexico, the Philippines and Indonesia, which have done well in genres dominated by mobile games, can be found here.
Digital Competetiveness Index: This index consists of three factors with apparent relevance for e-sports. Under the umbrella term knowledge, human resources from the digital sector and scientific output are assessed. For technology, political frameworks, household budgets and private venture capital are measured, and for future readiness, attitude surveys are conducted, economic agility, and technological integration are evaluated. [14] It is immediately evident that there is a clear bias, which creates a knowledge gap for the countries on the left half of earnings. Latin American countries were rated particularly weak here. Venezuela defines the lower limit by some distance, and with Chile even the best-rated country in this region is in the international lower midfield. This rating contrasts with the good results that Brazil, Argentina and Peru can show in e-sports. While it is always easy to find elements that complicate a hypothesis in such a large amount of data, the number of exceptions and the size of the standard deviations make it impossible to speak of a clear linear dependence.
Freedom House Index: Regime types influence a variety of areas of society, the economy and culture. The Freedome House Index maps countries and regions on a one-dimensional scale from Not Free to Free. The data are based on 22 questions on political rights and civil liberties. [15] It is immediately clear that the freedome house index barely correlates with e-sport earnings. The entire graph is populated by data points. Especially the population of the corners, which should be separated by a regression line, denies any correlation between dependent and independent variable. With China and Russia on one side and some island states on the other, the resulting distribution is nearly homogeneous, broken only by an increased concentration in the second quadrant. The Freedome House Index does not seem to be the right metric, because the questions cannot distinguish between different types of authoritarianism. While democracies do indeed offer some systematic overlap from an international perspective, Somalia, Saudi Arabia and China have little in common politically beyond the scores measured.
Internet Freedome Index: This index measures barriers to access, content limits, and violations of user rights [16], and tests the assumption that e-sports revenues correlate positively with the accessibility of online content outside the observed country. The distribution of scores reveals that this is not the case. In addition to the left-sided knowledge gap we already know about, we see that China, which has the lowest Internet Freedom Index score of 10 in the graph, earns the most across all countries. Another example is the Russian Federation, which earns the fourth most with a Internet Freedome Score of 16. One reason for these countries’ success despite their low scores on both Freedom Indices could be the region’s own video game ecosystem, which aligns with the prevalence of mobile gaming in China and the APAC region in general. Another explanation could be the success of players playing for an international team or teams from other countries. Making foreign content inaccessible sometimes even functions as protectionism of the domestic market. Digital barriers could stimulate earnings insofar as domestic games advantage their compatriots by imposing costly requirements such as travel and visa requirements onto foreign gamers or embedding qualification processes in regional leagues.
Human Development Index: Based on the assumption that a high standard of living satisfies people’s physiological needs, leaving more resources for culture, play and entertainment, the HDI was also included in the multi-factor analysis. The HDI evaluates societies according to health, knowledge and standard of living. [17] A first look at the data shows enormous standard deviations. While there is a clear correlation trend, the average area within which the majority of the data resides covers well over half of the total range. The only remarkable feature is that the lower right corner is scarcely populated. This observation speaks to a point of human development that must be breached in order to participate in the e-sports culture.
Inverted Gender Inequality Index: By taking the gender inequality index and inverting it, we can measure the relative gender equality per country instead. This value seems to correlate incredibly little with a country`s earnings and the main reasons becomes quite clear when looking at the players earnings. Esports itself does not have gender equality. This is easily visible when checking for the highest earning player in the female pro player category on the esports earnings website, [18] named Scarlett. If we look at Scarlett’s ranking in the highest Overall Earnings ranking, we can see that she takes rank 482. This means that there are 481 men ranking higher than the highest earning women in esports. The second highest ranked woman, takes rank 1096 in the overall earnings, meaning that she will not even show up in the list on the esports earnings website, as it only goes to rank 1000. [19] Furthermore, according to Statista 48 percent of people who play videogames in the USA are female. [20] The USA has an inverted gender inequality index of 0.82, which is comparable to Chinas 0.81 and Russia’s 0.8, but still lower than many other high earnings countries. If we assume similar rates of female players for countries with a comparable and higher index, it opens the question as to why there is such little successful women in esports. If we follow the words of voices like the American psychology association[21], saying that women’s and men’s cognitive skills are equal, we can rule out biological differences as an explanation, this indicates that Esports has a structure that promotes and upholds gender inequality. While proving this claim would need further research into the way Esports is organized, it seems as though being male is currently one of the main factors that correlate with esports success.
Total Olympic Medals: Second to Nominal and PPP GDP, we can see that the amount of Total Olympic Medals correlates quite well with esports earnings. This opens the question whether the factors leading to Olympic success are similar to those that lead to success in esports. Looking only at the factors that correlate the highest with earnings in esports, we are left with the nominal GDP, PPP GDP and population size. This falls very well in line with findings in regards to Olympic success in multiple publications, such as an article by Johan Rewilak [22] and a publication by Jacob Volz and Zeb Millslagle [23]. Both publications come to find clear correlations regarding GDP and Olympic success. Regarding to Rewilak population is also a significant factor in Olympic success. Olympic success is however also influenced by more factors, such as hosting country and female population. Rewilak finds that an increase in population has a high impact on female success in the Olympics. Measures like this are currently out of the scope of this work and would need more advanced research. However, since the Olympics are attaining higher percentages of female participation every year, with the Tokyo Olympics reaching 48 percent participation [24], comparing such an increasingly equal field to esports, which has barely begun moving towards gender equality, would be most ambitious.
GDP: The Nominal GDP and the PPP GDP are the values with the highest correlation to total esports earnings. These strong correlations are most likely, due to numerous advantages, which people and organizations in High GDP countries might possess. People in these countries are not only more likely to have the financial means to afford gaming hardware, but also to invest time into competing, as living in a high GDP country often brings an increased amount of leisure time. Furthermore, financial stability also leaves room for more risk in career taking, as more people have access to backup funds. Another point of profit is the increased number sponsors and organizations that are able to invest in and organize tournaments, but also the increased number of organisations that finance professional players, lowering their financial risk. Increased access to money, also gives these organisations larger access to coaches, healthcare professionals and training facilities, which in return leads to further professionalization of competitive gaming. Better IT infrastructure in high GDP countries also leads to easier tournament organization and more access to online game services. While Nominal GDP and PPP GDP seem to be the two top correlators, GDP per capita seems to barely make an impact in comparison. This could in big parts be due to low GDP per capita countries like China, Brazil, Ukraine and the Russian Federation taking top spots amongst the earnings, while countries like Ireland, Singapore, Luxemburg and Liechtenstein are placed amongst the lowest earners. Reasons for this might be the countries smaller population, but also their inhabitants higher access to more expensive ways of spending their leisure time. The increasing popularity of mobile games and their respective esports also play a part in this development, as mobile phones are a more financially accessible platform, increasing esports participation in countries with a lower PPP GDP per Capita in recent years.
Population: Population is a considerable factor regarding competitiveness, as a higher amount of people should logically also come with more discoverable talent and an increased number of people that can organize gaming groups, bring forth a scene and therefore also increase a regions professionality in terms of esports. The effect of population size is, while still measurable with a correlation value of 0.553, decidedly lower than values like the nominal and PPP GDP, as most high-ranking countries like France or South Korea do not have a population size that exceed the size of most lower ranking countries. This in combination with the fact that low population countries like Denmark and Sweden are top earners, while China not only the highest in earnings but also in population, makes population size a vastly inconsistent measure of a country’s competitiveness.
RQ 1: Which countries are particularly successful in e-sports?
It’s evident that rich developed countries have had a head start in creating a professionalised and internationally competitive e-sports scene, as demonstrated by the fact that European countries still accounted for around 27 % of all earnings in 2022. This is still an incredibly high percentage, given the growing participation of population-rich developing countries, particularly from the APAC region. This phenomenon should not be mystified as a surprising shift towards Asian countries, as with the increasing accessibility of ICTs, esports will naturally gravitate towards regions with higher population densities. One should keep in mind that esports is still a young and rapidly changing scene, meaning that winnings and dynamics can strongly fluctuate between each year. In particular, in the statistics prior to the last five years, the total amount of money derived from esports winnings was low enough for a single big payout to skyrocket a country in terms of percentages.
RQ 2: How is success distributed across different genres?
In contrast to casual gaming, esports currently consists mainly of the categories FPS, MOBA and Battle Royale. The main genres represented in esports have always been subject to change, as strategy games have gone from high popularity in the early years to almost non-existence in recent years. The dominance of the aforementioned genres can be explained by the dynamics of online popularity, but also by their inherent competitiveness, which allows for easier tournament organisation and better balancing. Another factor is the superior viewability of the genres, which is particularly evident in the MOBA genre, as most popular games in this genre feature a top-down camera, providing spectators with a comprehensive view of whats happening in the game. In terms of average rankings, OECD countries remain dominant. The US leads seven out of twelve genres, and OECD countries together occupy an average of 7.2 positions in the top ten.
As e-sports in APAC seems to be gravitating towards mobile games, countries in the region are naturally more represented in genres that do well on the mobile platform, such as puzzle games, strategy, MOBAs and battle royale games. Many games seem to have a regional bias, for example, Fortnite is most successful in OECD countries, and Arena of Valour is most popular in APAC countries. There are games that do not see this effect and are popular cross-regionally, but one should acknowledge the fact that this trend seems to affect a large number of games in e-sports, creating many scenes featuring a severe regional bias. The origin of this trend can be classified as a combination of several possible factors, such as the dominance of certain publishing companies, such as Tencent, which control a big part of the Chinese gaming market and therefore have a big influence on what games are played in their publishing area.
RQ 3: Which factors correlate most strongly with successful e-sports?
With a Pearsons r of 0.90, nominal GDP is able to explain most of a country’s e-sports success. Not only does it predict e-sports earnings, but it can also explain the low correlation values of most other socio-economic factors. Using the Freedom House Index as an example, we can see that top earners such as China, which not only ranks first in total esports earnings but also features a high GDP, do not necessarily have a high value on the Freedom House Index, directly contradicting its impact on esports success. Like the extensive research body surrounding the distribution of Olympic medals, the results proof that (historic) GDP and population are overarching factors of competitiveness beyond traditional sports. Gender equality, human development, regime type or average internet speed positively effect earnings per capita, but they can’t make up for differences in population size. In that sense e-sports follows a more broad tendency towards multipolarization. This phenomenon shouldn’t be confused with the romantic idea of a multicultural online community, that would stimulate much needed cultural exchange. However, the trend of the last years appears to be heading towards a stricter regional separation preventing such dialogue.
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