The other research by A. A. Kurnia was aimed on studying the phenomenon of microtransactions within video games and how it impacts customer satisfaction. A. A. Kurnia was using survey with 138 respondents who were answering on questions about games and microtransactions in them. First of all, it was found out that there is a correlation between type of game and satisfaction level of players - respondents who used to play and make microtransaction in free-to-play games had much higher level of satisfaction comparing to respondents who were playing pay-to-play games. Thus, respondents who were playing and making in-game purchases within free games had 4.66 level of satisfaction in average while pay-to-play players had only 4.04 average level of satisfaction. This results shows that microtransaction for some reasone are much more suitable and pleasant within free-to-play model. Moreover, the expectation level was also higher within free-to-play gamers comparing to pay-to-play players - 6.00 in average against 5.62 in average for pay-to-play players. What is playing major role in these results is that the survey showed that free-to-play respondents had higher average level of intention of future purchases in games comparing to pay-to-play respondents - 4.14 against 3.43. Moreover, avid gamers had higher average level of satisfaction from performing an action of microtransaction comparing to casual gamers - 4.49 versus 4.04. Avid gamers also showed higher average level of intention of future purchase comparing to casual games - 3.92 versus 3.46. Further these results of the survey showed that respondents from 18-35 years old had higher level of satisfaction after performing an action of microtransaction comparing to respondents under 18 years old and those who are above 36 years old - 4.45 comparing to 3.1 and 4.17. The difference between age periods from 18-35 years old and those who are under 18 years old is significant. Therefore, in the discussion section authors is coming to bunch of conclusions such as the fact that microtransaction practice is not working that effectively in pay-to-play games as it does within free games and while some free-to-play games can gain more players because of microtransactions, pay-to-play games would lose significant amount of gamers, because they are dissatisfied with such monetization form. Also, the expectation from microtransaction itself are higher within free-to-play games, because in pay-to-play games players already used to pay for the game and are expecting that in-game content without different loot boxes and other types of microtransaction will be enough to get the most from the game. In the end, this work leads to the conclusion that within free games players are just ready to purchase different in-game stuff while those who bought game via real money are not expecting to waste some more money on different cosmetics or gambling as loot boxes or other types of in-game microtransaction (A. A. Kurnia, 2013).

Within the study by Marc von Meduna, F. Steinmetz, L. Ante, J. Reynolds and I. Fielder the concept of loot boxes was researched from the point of pay-to-win mechanic and the gambling addiction was studied at the same time. For this purpose, the survey was done with the sample of 1508 pay-to-win users and 586 of those users used to purchase loot boxes in games. Authors of the study divide respondents on two groups: those who used to purchase loot boxes within games and those who used to play in pay-to-win games, but never purchased any loot box. With the aim to measure level of gambling addiction of respondents, authors used PGSI gambling index. According to the research nearly half of respondents who purchased loot boxes have gambling addiction (48.3 percent) and nearly two-thirds of those respondents are problem gamers. However, study showed that respondents who never purchase loot boxes in games, but still play in pay-to-win games have much lower percentage of gambling addiction (only 18 percent comparing to 48.3). At the same time, 73.3 percent of those respondents are problem gamers. Moreover, within this work sever hypotheses were tested by authors and one of them was rejected, because it appeared that the income is unrelated to the purchase of loot boxes in general and loot boxes purchasing frequency. Study showed that unemployed people used to buy the same amount of loot boxes as employed players. In discussion section authors claim that study showed that younger people in general used to purchase loot boxes in games and also hypotheses about relationship between gambling addiction in online casinos and tendency to buy loot boxes were proved, because it was shown that different sport bettors and other online casino users tend to purchase loot boxes within games. Finally, in the very end of the discussion section authors discuss the problem of limitation of loot boxes in several countries and why it is done. Authors are claiming that if players did not get anticipated result after purchasing a loot box, they would buy more and more until they won't get what they want. It leads to the real problem of gambling and differen loot boxes can cost like 0.5 dollars and at the same time some of them can cost like 100 dollars what is hard to describe as a term of “microtransaction” at this point. Authors are claiming that according to the findings in this work the term of “microtransactions” in games should be reconsidered these days (Marc von Meduna, F. Steinmetz, L. Ante, J. Reynolds and I. Fielder, 2019).

Fresh study that was done by Kim Sinja Wolfarz in 2019 compares life cycle of real, physical items and life cycle of skins (in-game items) in Counter Strike: Global Offensive. With the aim to compare these different items, author used graph with 4 stages of “life cycle” of every type of item. The life cycle of physical was pretty expected, because in the very beginning the demand was low, but on another stage the rapid growth was happening in titanic amounts, on the third stage of life of physical items usually the peak of demand was happening and after this stage the final period of decline was going on. What is playing a key role within this study is a fact that in-game items have completely different life cycle comparing to physical items. Results of study by K.S. Wolfarz showed that in the very beginning stage that is called the “Introduction” by the author of the work, items cannot be received by players on the market. It means that if it the weapon skin, then the player can receive it only by playing opening a case with this new item or if it is a case, then the player can receive it only by playing the game. Such system leads to the rapid growth in the beginning and this is why after the “Introduction” immediately the rapid growth in demand is happening and at the same time it reaches its lifetime peak. Therefore, the period of “growth” has the peak period in demand and after this cycle of in-game items the “maturity” period is happening what also differs from physical items, because demand for items is staying the same for some time and then super slowly start to drop without dramatic changes comparing to the peak period. The final period of “decline” consist of slow decline in demand and in the end the demand becomes absolutely constant without any radical changes. In the conclusion, it can be stated that this research showed that the life cycle of physical and in-game items is different in many ways and they have completely another demand within its period (K.S. Wolfarz, 2019). 

3. Statement of the research question

This research has the main goal of studying the concept of microtransaction as a form for monetization in free-to-play online games. The research question can be stated as: How microtransaction affects the monetization in free-to-play online games? This research question has to be explained in details, because the semantics of it can be differently understood. Thus, the monetization in online free-to-play games is not only raw money from the microtransactions, it is also the amount of players and the level of their motivation to buy these microtransactions. It means, that within this work, it is needed to test different hypothesis and to find if amount of players online actually correlates with amount of sold items. At the same time, it is needed to estimate if new offers of micrtotransaction brings more people to the game and, therefore, leads to higher monetization and, finally, if the majority of players are purchasing in-game items - performing or used to perform at least once the action microtransaction in DOTA 2 and Counter Strike: Global Offensive. Within formulated research question about the effect of microtransaction on the monetization in free-to-play online games, it implies that how micrtotransaction also affect amount of players online and how this amount of players affects the generation of profit. Therefore, following hypothesis will be tested within this research with the aim to answer on the research question in the end:

H0: there is no difference between the means of players online in months when there was no microtransaction and when there was a microtransaction

H1: there is a difference between the means of players online in months when there was no microtransaction and when there was a microtransaction

H2: more than 50 percent of players used to make micro purchases at least once in DOTA 2 and Counter Strike: Global Offensive

H3: there is a correlation between players online and number of sold in-game items in DOTA 2 and Counter Strike: Global Offensive

The null hypothesis is going to be tested by collecting a data from “Steam” about players online in different months for both DOTA 2 and Counter Strike. Then the information about different events that are happening in different months will be collected and the fluctuation in players online will be analyzed by using different tests in SPSS. However, it is hard to tell which test will be conducted, because first of all, it should be checked if the researched data had normal distribution or not - therefore, the parametric or nonparametric test will be used for analysis. The first hypothesis is an alternative one and it means that if the null hypothesis will be rejected then the first hypothesis (alternative) will be accepted. The second hypothesis is going to be tested by conducting an online questionnaire, where one of the question will consider if respondents used to make microtransactions at least once in DOTA 2 or Counter Strike. The final third hypothesis is about finding a possible correlation between such variables as players online and number of sold in-game items. It means that if the correlation will show either positive not negative correlation then the hypothesis will be proved.

4. Methodology and data description

4.1 Data description

In order to study the monetization through microtransaction in free-to-play online games, it was needed to analyze different data. Hopefully, “Steam” is a quite public video game digital distributor and at the same time it is created by Valve same as DOTA 2 and Counter Strike which are studied within this research. Therefore, first of all, the data from “Steam” is going to be collected in order to check the fluctuation of players online and check the first hypothesis about relationship between players online and new offers of microtransactions in games. Secondly, the monetization itself will be analyzed in DOTA 2 and Counter Strike: Global Offensive by collecting a data about profit that was generated by different types of microtransactions. The key role at this point plays the understanding of what type of microtransaction generates the most quantity of profit within the game. With the aim to meet this goal the trustworthy data is needed and the best data source in this case would be a game itself or a game distributor on which this game is working. Therefore, the data about profit that was generated by different types of microtransaction will be collected from the game directly. Unfortunately, in the case of DOTA 2 it is possible to do only for “The International” battle pass, because within this type of microtransaction all the players can see the amount of money that was collected from the battle pass and what part of money goes to the prize pool of tournament called “The International” directly, but there is no data about generated profit from other types of microtransactions and Counter Strike: Global Offensive has the same issue also. Thus, the survey is going to be conducted with the aim to avoid the obscurity of secondary data and the primary data will be collected at this point. The data that is going to be collected from the survey will help to clear the point of how many loot boxes (treasure in case of DOTA 2 and cases in Counter Strike: Global Offensive) were purchased by respondents and the average amount of it will be calculated. This action will provide this study with the unique primary data about the average “check” in both DOTA 2 and Counter Strike: Global Offensive and at the same time, this primary data will clear the point of what type of microtransaction is the most profitable by analyzing the quantity of purchases of every type of microtransaction that the respondents will mention.

At the same time, within the results the review of several “The International” battle passes will be reviewed (from 2014 to 2019 inclusively) and the graph of the revenue growth will be done for each battle pass and with the aim to describe reasons of rapid growth or a decrease, it is needed to mention all days when new events within battles passes used to come out and when new treasures were realized. Moreover, in the case of battle pass that goes from 2016, there were such unique offer as “bundle” which consisted some immortal treasure from battle pass and about 100 level of battle pass which were significantly cheaper to buy comparing to the separate purchase of battle pass levels. The table 1 shows results of collected data from the official site of “The International” battle pass which shows in what day every event or treasure within battle pass used to come out for battle passes in different years:

Table 1

Events within battle passes 2014 - 2019*

* Source: Data received from the DOTA 2

Thus, this data provides this research with the opportunity to analyze in future section the reasons of possible growth in battle pass's revenue at some specific period of times. It is needed to be done with the aim to understand if more microtransactions within one microtransaction stimulate more revenue generation. Thus, such motivators to buy more in-game items via real money as “bundle” within “The International” battle pass will stimulate people to waste more money in order to get more in-game items from the battle pass.

Secondary data that are going to be collected from the DOTA 2, Counter Strike: Global Offensive and Steam itself will be divided in different sections. The data about revenue from such type of microtransaction as battle pass will be collected directly from DOTA 2, but data about “trading” which is possible within Steam will be collected in a quite different way. Steam provides the data about the quantity of items which were traded. This type of microtransaction are performing every day and there is data for different items. Such type of data needed to be collected for some specific period of time and for specific items. Then the overall amount of trades of items are going to be analyzed and the fee of 15 percent will allow to calculate the profit from this type of microtransaction in some period of time.

These items need to be specified in order to understand what type of data will be collected within this research for both DOTA 2 and Counter Strike: Global Offensive. In the case of DOTA 2 the data from such types of items as “Arcana” will be collected, because they are the most unique within the game. First of all, it is only items that should be purchased directly within the game without purchases any treasure first (loot box). Also, the cost of all items with the rarity of “Arcana” is the same at the very start - 34.99 dollars if players want to buy it in the game. However, prices on the community market are slightly lower - thus, items with rarity “Arcana” costs from 25 to 30 dollars. However, the data about 3 different “Arcana” items is going to be collected, because there is no point in collecting data from all 13 “Arcana” items and for every item the graph will be done with the aim to analyze the collected information. Furthermore, this type of item was chosen for analysis, because it the most anticipated item among DOTA 2 players. For instance, annually players who purchased “The International” battle pass can pick the hero on which the new item with the rarity of “Arcana” will be. What is more important, such types of items come only once a few times a year these day in DOTA 2. Moreover, “Arcana” has the lowest price fluctuation comparing to other in-game items what also makes it unique and more “reliable” in terms of price. Therefore, as it was already stated, Steam community market provides all necessary information about amount of trades per day on every in-game item. With the help of this data, the information will be collected for a period of one month and the overall amount of sales within this period of a 1 week will be calculated and the fee of 10 percent will be provide the information about profit generation from this particular type of microtransaction in DOTA 2. Finally, as it was already stated only 3 items with “Arcana” rarity will be analyzed and it will be: “Manifold paradox” item for such hero as Phantom Assassin, “The Magus Cypher” for Rubick and “Great Sage's Reckoning” for Monkey King. They were chosen randomly, but at the same time it is needed to review not the oldest “Arcana” item and not the newest one with the aim to make results of research as representative as they can be. The price of new “Arcana” items and the old ones can depend on the date of their release - the old one will have lower price, because players are tired from this in-game cosmetics, while new item can have higher price only because it recently was released within DOTA 2.

On the other hand, Counter Strike: Global Offensive does not have such unique types of items as “Arcana” in DOTA 2. However, Counter Strike is famous within gaming community for completely another type of items which and this is ordinary cases (loot boxes). In fact, these are not usual loot boxes, because players can get them for free by just playing a game. At the same time, when a new case comes out, it is impossible to buy it directly within the game and players have a choice: either to buy it from the community market for extra charge (because the offer is low, but the demand is immense) or to play a game and hope that they will get this type of item. Thus, Steam community market has all necessary data about sales of all types of cases in this game and as in the case of DOTA 2, 3 random cases will be chosen as an object of analysis, but these cases have to be not the oldest one and not the newest one in terms of release within the game. Therefore, such cases in Counter Strike: Global Offensive as “Glove case”, “Spectrum case” and “Danger Zone case” will be researched in the period of a week - from April 28 to May 4 and the fee of 10 percent will once again help to understand how these “free” cases can generate profit for Counter Strike: Global Offensive.

4.2 Methodology

First of all, this study is quantitative, because all the results will be described from the point of statistical and numerical data. The percentage growth will be analyzed for different types of microtransaction for specific period of time. Results will be divided on different sections: within first section, the analysis of profit generation will be done for different type of microtransactions in DOTA 2 and Counter Strike: Global Offensive. Within second section, there will be testing of hypotheses and finding different correlations, relationships between some variables. However, both of these sections consist of quantitative data. In the end it will be needed to answer the previously formulated research question about how microtransaction affect the revenue in online games and therefore different tables and graphs with all necessary data will be provided with the aim to estimate the monetization from different types of microtransaction in DOTA 2 and Counter Strike: Global Offensive. A key role plays not only gathering of the data about revenue generation of some particular microtransaction within online free-to-play games, but also the analysis of the data which will be collected directly from the respondents within two surveys among DOTA 2 and Counter Strike players. For data analysis will be used different mathematical formulas, but they won't be complex, because all that will be needed is to find diverse percentages, distinction between some variables and excel have all necessary formulas which will be needed and some of them will be described in this section of the work.

Even the data from the survey will be analyzed in numbers and the structure from the survey will be taken from another popular survey about microtransaction which was made by Andrew Rombach and published on site LendEDU. Within the Internet immense number of articles and reports reference this survey with aim to point out the influence of microtransaction in games these day. However, some questions will be changed and the overall amount of questions also will be changed, because the topic of survey of Andrew Rombach was about DLC majorly and within our study loot boxes and battle passes are more preferable. Moreover, because of the fact that within this study two surveys should be conducted than the same structure will be both for Counter Strike: Global Offensive players and for DOTA 2 players. Nevertheless, because of the fact that the overall amount of DOTA 2 players are approximately 10 million of players what means that the population is pretty high and within the survey the confidence level is 95%, then according to the formula, it is needed to calculate needed sample:

.

where: SS- sample size

Z - Z value (1,96 for the confidence interval of 95 percent)

p - percentage picking a choice

c - confidence interval

According to this formula, sample size for DOTA 2 survey will be 96, because the confidence interval will be 10%. It means that for DOTA 2 survey 96 responses are needed with the aim to have a representative analysis of the data about microtransaction in DOTA 2. At the same time, Counter Strike: Global Offensive's survey will have the same confidence level of 95% and the population is approximately the same (the overall amount of Counter Strike players are also about 10 million), then the same amount of respondents is needed with the aim to gain a representative data. The survey will consist from questions about the average “check”, sum of money in general that respondents waste on microtransaction within DOTA 2 and the survey will specify what type of microtrnasaction is generating the most amount of profit. Also, the first question within the both surveys will help to test the first hypothesis and to estimate if more than half of respondents used to make a microtransaction within DOTA 2 and Counter Strike: Global Offensive at least for once. Nevertheless, the average sum of money can be not representative, because few respondents can donate immense amount of money comparing to others and it will make the average “check” for in-game purchases way higher than it should be, therefore, at the same time the table with percentages will be included in the results section with the aim to see what percent of respondents donate specific amount of money. For instance, it will turn up that 45 percent of respondents are wasting about 10 thousand rubles per year and only 1 percent of players wastes more than 100 thousand of rubles annually. Furthermore, the MEDIAN() function will be used with the help of excel with the aim to divide the answers of respondents in a half and understand what amount of money in average will be within first half and what average number will be within the second half. Another question within online questionnaire will consider if respondents are purchasing in-game items either through Steam market or from the game directly. That will help to analyze if the game receives all sum of money for in-game items or just a fee in 10 percent from every purchase within community market. The overall amount of questions within both surveys in DOTA 2 and Counter Strike: Global Offensive will consist of 7 questions in general. Moreover, all the respondents will be Russians, because it is easier to find necessary amount of respondents within Russian audience comparing to others. Thus, all the numbers such as average annual “check” within DOTA 2 and Counter Strike: Global Offensive will be in Russian currency - rubles. However, it is a quite challenging to find 96 gamers at least for one survey with the aim to gain all necessary data. Thus, different tools and social platforms will be used in order to reach needed amount of respondents. The survey will be published within Steam community, on different forums which are related to the DOTA 2 and Counter Strike thematic, also survey will be distributed through social networks such as VKontakte, Telegram and even within in-game chat in DOTA 2 because within this game there is a global chat where players can chat with the people from their city in the game. Same will be done for Counter Strike: Global Offensive, but, unfortunately, there is no global chat and it means that the survey will be distributed through the chat during the game itself.

After the collection of data from survey different types of in-game purchases will be divided in different groups. These groups will be both for DOTA 2 and for Counter Strike: Global Offensive. Such microtransactions will be divided in groups as “A” type of microtransaction, “B” type of microtransaction, “C” type of microtransaction and so on. The “A” type will be the most successful which generates the highest amount of profit for the game. In fact, the calculation of this will be quite simple. In future section there will be a table with results of both surveys for both research online free-to-play games and the formula for calculation will look like this: amount of money on this type * the average annual “check” for this type (in rubles). It will be done for every researched type of microtransaction within both surveys and each calculated number will be compared to another one. Thus, the highest number will be the “A” type monetization, the second from the top number will be the “B” type of microtransaction and so on. Therefore, this analysis will provide this research with the conclusion which type of in-game purchase generates the most amount of profit in DOTA 2 and Counter Strike and in conclusion the least profitable types of microtransaction will be discussed.

Also, it is needed to note that in the analysis of 3 “Arcana” items the calculation will be little more complex that it might seem, because first of all the data of average price of sold items per day will be collected for the period of a week. Then, the quantity of sold items per day will be collected, then with the aim to calculate necessary fee in 10 percent from every sold item which transfers to DOTA 2 directly and 5 percent more additional fee which transfers to Steam, it is needed to find specific percentage from the overall amount of sold items in dollars. Some mini calculations will be done within some columns with the aim to estimate the monetization and it is needed to visually present how it will look like. For instance, table with collected data will look like this:

Table 2

Monetization from trades on Steam market

Day	Price of sold item, $	Quantity of sold item	Amount of sold items in $	DOTA 2 fee
April 28	XXX	XXX	= price of sold item, $ * quantity of sold item	= 0.1 * amount of sold items in $
April 28	XXX	XXX	= price of sold item, $ * quantity of sold item	= 0.1 * amount of sold items in $
April 30	XXX	XXX	= price of sold item, $ * quantity of sold item	= 0.1 * amount of sold items in $
May 1	XXX	XXX	= price of sold item, $ * quantity of sold item	= 0.1 * amount of sold items in $
May 2	XXX	XXX	= price of sold item, $ * quantity of sold item	= 0.1 * amount of sold items in $
May 3	XXX	XXX	= price of sold item, $ * quantity of sold item	= 0.1 * amount of sold items in $
May 4	XXX	XXX	= price of sold item, $ * quantity of sold item	= 0.1 * amount of sold items in $
		= SUM(quantity of sold item)	=SUM(amount of sold items in $)	= SUM(DOTA 2 fee)

Therefore, different simple calculation will be done within the table for every reviewed “Arcana” item with the aim to have more representative analysis. Thus, sum of quantity of sold item within reviewed

Moreover, the correlation between two variables will be found out. These variables will be players online and amount of sales of particular items on the community market. Thus, if there will be a correlation, it means that the growth of players online leads to the growth of sold items on the community market. The correlation will be done in Excel by using such formula as CORREL(). Moreover, the data will be supported by some visual results such as graphics of players online and amount of sales for “Arcana” items in DOTA 2 and cases in Counter Strike: Global Offensive. However, the period of time will be not within one month, but within several years, but the data will be collected on a monthly basis and it will provide this study with the opportunity to find the correlation between these variables. Items will be absolutely the same for DOTA 2 and for Counter Strike: Global Offensive, besides one “Arcana” item from DOTA 2 and one item for Counter Strike, because “The Magus Cypher” cannot be analyzed in the needed period from 2018 to 2019 inclusively, because this “Arcana” item was released after 2018 within the game and the “Demon Eater” item will replace this “Arcana”. The same situation with the “Danger Zone case” from Counter Strike - it was released a””fter January 2018 and “Gamma 2” case will replace this item. All other items will be the same as they were in the case of finding monetization through trades on the Steam market - 3 “Arcana” items from DOTA 2 and 3 cases from Counter Strike: Global Offensive. If the correlation will show -1, it means that more players online lead to the least amount of sold “Arcana” items within the Steam market. For more representative results, the data for 2 years is going to be collected - for all months in 2018 and 2019 about players online per month and amount of sold items per month. If correlation will be +1, then there is a positive relationship between these variables and the growth of players online leads to the growth of sold “Arcana” items. Moreover, in this work, all the charts and tables will be also done in Excel and Word.

Also, in the case of analysis of players online and new offers of microtransaction in DOTA 2 and Counter Strike: Global Offensive, another tools will be used. First of all, data about players online will be collected on a monthly basis and the fluctuation in players online will be analyzed. Each month will be compared to the previous one and a special attention will be done on months where some new microtransaction used to come out. However, the collected data should be analyzed with the aim to understand if there is a normal distribution or not. This information is needed for analysis of the means between two groups of dependent variables - between the first group when there was no microtransaction and the second group where there was a microtransaction. All months in 2018 and 2019 will be checked for players online in DOTA 2 and Counter Strike: Global Offensive and the information about new microtransaction within these two years also will be collected with the aim to divide this data into 2 groups that were stated previously. SPSS will help to check if there is a normal distribution in the researched variable. Therefore, the 1 - sample Kolmogorov - Smirnov test will be used on the collected data with the aim to check if there is a normal distribution and it will provide this work with the information if parametric or nonparametric test should be used with the aim to compare means of two researched groups. With the help of SPSS, the 1 - sample Kolmogorov - Smirnov test showed that the researched variable has normal distribution (the result was 0.06 what is higher than 0.05) what means that there is a normal distribution. It means that any parametric test can be used with the aim to study the difference in means between two researched groups. However, because of the fact that there are only two groups - the first one if there was no microtransaction and the second group of there is a microtransaction, the best solution will be a conduction an independent t - test which can be done by using SPSS once again. The data which will be research by independent t - test in SPSS looks like this in the case of DOTA 2:

Table 3

Researched variables in DOTA 2

Researched months	Dependent variable (players online from Jan 2018 to Dec 2019)*	Independent variable (factor)
Jan 2018	486861	1
Febr 2018	438847	0
March 2018	437262	0
April 2018	430340	0
May 2018	474325	1
June 2018	473900	1
Jule 2018	441714	1
August 2018	476101	1
September 2018	466470	0
October 2018	431173	0
November 2018	461073	1
Dec 2018	438367	0
Jan 2019	475747	1
Febr 2019	564909	0
March 2019	586505	1
April 2019	520219	0
May 2019	548523	1
June 2019	507528	1
Jule 2019	464787	1
August 2019	467148	1
September 2019	421971	0
October 2019	388355	0
November 2019	401931	0
december 2019	384179	0

* Source: SteamCharts, retrieved 18th April, 2020

At the same time, the same table, but with different data will be in the case of Counter Strike: Global Offensive:

Table 4

Researched variables in Counter Strike: Global Offensive

Researched months	Dependent variable (players online from Jan 2018 to Dec 2019)*	Independent variable (factor)
Jan 2018	383 030	1
Febr 2018	382 457	1
March 2018	354 270	0
April 2018	289 076	0
May 2018	262 170	0
June 2018	266 862	0
Jule 2018	273 307	0
August 2018	283 531	1
September 2018	333 164	1
October 2018	325 907	1
November 2018	310 085	0
Dec 2018	395 509	1
Jan 2019	401 366	1
Febr 2019	371 359	0
March 2019	390 240	1
April 2019	351 989	0
May 2019	364 417	0
June 2019	389 376	1
Jule 2019	393 782	1
August 2019	415 097	0
September 2019	410 925	0
October 2019	408 995	1
November 2019	426 080	1
december 2019	456 701	1

* Source: SteamCharts, Retrieved 19th April 2020

Therefore, there will be two independent t - test for two different variables that were collected from the Steam charts. In the case of DOTA 2 both groups consist of similar sample size (12 months for both groups) and in Counter Strike: Global Offensive there is a slight difference between sample sizes for both researched groups - 11 months where there was no microtransaction and 13 months where there was a new offer of microtransaction. The analysis can be conducted in SPSS for these table with the aim to check the null hypothesis if there is no difference in means within players online for months when there was no microtransaction and months when there was a microtransaction in both DOTA 2 and Counter Strike: Global Offensive. However, this analysis itself will be conducted in future section where results will be described.

5. Description of results

Now, in this section the collected data with results will be shown and analyzed by using different tools which were described in previous sections of this work. First of all, it is needed to start with description of collected data from “The International” battle passes from 2014 - 2019 inclusively. Also, it is crucial that the collected data will be about the growth of prize pool within different battle passes. It means that the total overall profit which is generated from each battle pass will be much higher, because prize pool on “The International” is only 25 percent from the profit in battle pass. Moreover, battle pass consists of very complex structure and at the very start not all treasures and cosmetic items are available within it. For instance, it is completely hidden for players and battle pass owners what “Arcana” or in-game cosmetic skin on some hero will look like, it just shows a fact that if a player will have level 300 of battle pass, then he will receive the “Arcana” item, but he or she will receive this item only when this item is going to be officially available. Therefore, the graph 1 shows the prize pool which was generated in general:

Figure 1. Prize pool for battle pass 2014-2019

In this graph, it is seen that with every year “The International” had much higher overall prize pool due to battle pass and the last battle pass within the sample used to generate much more profit comparing to other ones, however, tendency with the growth of revenue for every new battle pass is clear. The number in this graph shows not only contributed prize pool, but also they consist of base prize pool with 1.6 million of dollars for every “The International”. Now it is substantial to analyze the rapid growths within battle passes revenue and the best idea to start with the battle pass 2019 - the last one and the most profitable one. According to the graph 1 the rapid growth happens at the start of this battle pass between day 0 and the very first day of battle pass - from 1.6 million of dollars to 7 419 228 dollars what means that at the first day battle pass 2019 generated about 5 819 228 dollars to the prize pool only. It means that the overall amount of money which was collected in only one day from this type of microtransaction in 2019 was 23 276 912 dollars. Such growth is absolutely understandable, because it was the first day after the release of battle pass. However, future observation shows dramatic increase in revenue from battle pass 2019 between 50th day and 54th day - from 19 967 292 to 25 264 012 dollars. In order to see how the prize pool used to increase in numbers, it is needed to construct another graph with only these 4 days and only for battle pass 2019:

Figure 2. International 2019

Simple calculation helps to estimate that in only 4 days about 5 296 720 dollars were contributed to the prize pool of “The International” and it equals to 21 186 880 dollars of the overall profit which was generated in these 4 days. The reason in this case is not clear, because such dramatic increase in revenue in the middle of battle pass has no logical explanation, but with the help of previously mentioned table 1, it is possible to see if something new were releases within the battle pass 2019 what motivated players to waste more money on the battle pass. According to the table 1, it is seen that on the 51st day of battle pass 2019. The battle level bundle was released with the aim to motivate players to purchase more levels for much more affordable price. It worked perfectly, because the growth in revenue was incredibly high for this period of time. Another growth within battle pass 2019 is seen in the period between 66th day and 70th day - from 26 612 383 to 29 244 429 dollars. About 3 million dollars were generated in another 4 days only for the prize pool. The reason once again is the release of a unique treasure that can be available for anyone, but it costed 2.49 dollars per treasure. Once again, 25 percent of every purchase of this treasure used to go directly to the prize pool of “The International”, but in this case even players without battle pass could purchase this item for 2.49 dollars. In the case of the previous battle pass of 2018, the fluctuation was not so rapid, however, it is seen that extremely high jump between period of 50th day and 54th day is happening as it was in the case of battle pass 2019. The jump was from 15 662 939 to 19 210 938 dollars in only 4 days what means that 3 547 999 dollars were contributed to the prize pool in this period and about 14 191 996 dollars were generated from battle pass in general within these 4 days. Figure 4 will help to visually represent in details information about changes in prize pool in the case of battle pass 2018:

Figure 3. International 2018

The reason is the same as it was in this period for battle pass 2019 - according to the data from table 1, on the 50th day the battle level bundle was released within battle pass what motivated people to purchase it with the aim to get more items from battle pass. In the case of battle 2017 the battle level bundle also used to increase the prize pool - from 16 992 629 on the 49th day to 18 292 293 on the 50th day. However, within battle pass 2017 fluctuation was even lower comparing to the year of 2018 and 2019 and because of this fact there is no solid reason to construct a graph like it was in the case of battle pass 2018 and 2019. At the same time, in the case of 2016, the battle level bundle used to come out on the 44th day after the release of battle pass and it was the reason of a sharp increase in the prize pool in several days - from 14 262 327 dollars on the 44th day to 16 641 896 dollars on the 48th day. In the case of battle pass 2015, increase in growth was on the 34th day and the main reason was the unique treasure (loot box) - collector cache and it led to the growth from 10 596 326 dollars to 11 054 546 dollars. This change is not significant in real number, but it is needed to understand that the overall prize pool was way lower in the case of battle pass 2015 comparing to future years. Finally, battle pass 2014 had new loot box within it on the 21st day and it also was the reason of a sharp increase in revenue from battle pass this year - from 7 149 563 to 8 311 263 dollars on the 23rd day. Moreover, it is needed to analyze the overall revenue and prize pool for all researched years of “The International” and analyze what amount of money was contributed by players via battle pass and what amount of prize pool was the base one - was contributed by the Valve directly. Thus, the table 1 is done with the aim to analyze the prize pool of “The International” 2014 - 2019 prize pool with more details:

Table 5

The International 14-19 prize pools *

	Base prize pool	Contributed prize pool	Final prize pool	Overall profit generated	Percentage change in the overall profit **
The International 2011	1 600 000 $	0 $	1 600 000 $	0 $	-
The International 2012	1 600 000 $	0 $	1 600 000 $	0 $	-
The International 2013	1 600 000 $	1 270 000 $	2 874 380 $	5 080 000 $	-
The International 2014	1 600 000 $	9 331 105 $	10 931 105 $	37 324 420 $	734, 73 %
The International 2015	1 600 000 $	16 829 613 $	18 429 613 $	67 318 452 $	180, 36 %
The International 2016	1 600 000 $	19 170 460 $	20 770 460 $	76 681 840 $	113,91 %
The International 2017	1 600 000 $	23 187 916 $	24 787 916 $	92 751 664 $	120,96 %
The International 2018	1 600 000 $	23 932 177 $	25 532 177 $	95 728 708 $	103,21 %
The International 2019	1 600 000 $	32 730 068 $	34 330 068 $	130 920 272 $	136,76 %

* Source: DOTA 2 Prize Pool Tracker, Retrieved 20th April 2020

** Estimated value

The table divided in several sections: base prize pool which is contributed from Valve funds directly; contributed prize pool which is 25 percent of the overall profit generated from “The International” battle pass; final prize pool which is a sum of base and contributed prize pool; the overall profit which is generated with the help of battle pass and the final section which shows the percentage change of the overall revenue between the battle pass and the battle pass from the previous year. Therefore, it is seen that first two “The International” did not have battle pass at all and this is there was no contributed prize pool. Nevertheless, the constant growth in the overall profit is noticeable and with the help of table 1 all other 75% of pure revenue from such type of microtransaction as “The International” battle pass is seen and the revenue in 130 920 272 dollars only from battle pass which was available about 4 months is immense. It shows the invisible revenue that free-to-play online games can have only from microtransactions.

Now it is time to review and analyze data about sold in-game items of “Arcana” rarity in DOTA 2 and about cases in Counter Strike: Global Offensive for the period of 1 month. First of all, “Arcana” items will be reviewed first. There are 13 items with “Arcana” quality on different heroes, but as it was stated previously only 3 of them will be analyzed within this work. The graph shows results of collected data about such item with “Arcana” rarity as “Manifold Paradox”:

Figure 4. Manifold Paradox's data

The graph consists of 2 lines: the blue one shows the data about average price of all sold “Manifold Paradox” items on the market per day. Second orange line shows results of data bout quantity of sold items per day. As it is seen, in the case of “Manifold Paradox” the fluctuation is not extremely high, because within price of in-game items, it could be much higher, but changes are only within 1 dollar. Moreover, the graph helps to calculate that the overall amount of sold items per week was 366. However, the overall amount of sold items was equal to 10 262.93 dollars from 28th of April to 4th of May. Now, according to the fact that 10 percent from this sum goes to the DOTA 2 budget directly means that DOTA 2 has 1 026. 293 dollars of pure revenue in the period of the week only from amount of sold “Manifold Paradox” on the community market. Also, 5 more percent goes to the Steam directly and it equals to 513.15 dollars. Now the table which was described in methodology section will be used with the aim to visually represent collected data:

Table 6

Manifold Paradox's data

Day	Price of sold item, $	Quantity of sold item	Amount of sold items in $	DOTA 2 fee
April 28	28. 71	42	1205. 87	120. 59
April 29	27. 32	45	1229. 53	122. 95
April 30	28. 46	61	1735. 91	173. 59
May 1	27. 35	46	1258. 00	125. 80
May 2	28. 37	63	1787. 35	178. 73
May 3	28. 18	54	1521. 49	152. 15
May 4	27. 72	55	1524. 80	152. 48
		366	10262. 93	1026. 29

The other “Arcana” item for which data is going to collected and analyzed is “The Magus Cypher” for such DOTA 2 character as Rubick.

Figure 5. The Magus Cypher's data

Once again the slight fluctuation is seen in the graph. However, if “Manifold Paradox” item had fluctuation in its price every day, “The Magus Cypher” has steady downfall and then increase in the price once again. The fluctuation is completely insignificant for in-game item, but at the same time it has fluctuation in about 2 dollars while “Manifold paradox” had fluctuation in no more than 1 dollar. Now it is needed to construct a table and analyze how profit that this particular in-game item generates for DOTA 2.

Table 7

The Magus Cypher's data

Day	Price of sold item, $	Quantity of sold item	Amount of sold items in $	DOTA 2 fee
April 28	27. 01	...

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