Deconstructing Fame

An Analysis of eSports Brand Value. The Case of Dota 2

Master Thesis (15 cr.)

Department of Informatics and Media Media and Communication Studies

Ilya Musabirov

May 2016 Abstract

This work is dedicated to studying pricing and valuation mechanisms of eSports brands in free-to-play game Dota 2. The object of the study are virtual autographs of Dota 2 professional players, which are traded on the market but, at the same time, do not provide buyers with any functional advantage in the game. The principal aim of the study is to reveal factors influencing the value of autographs and understand how the community of players evaluate them.

Theoretical framework of current study borrows from economical sociology, in particular, approach of Patrik Aspers who argues that standard and status markets have distinct forms of goods’ evaluation. Special attention in this work is focused on conception of status market on which there is no unified quality scale for evaluation, and all quality judgements are deeply entangled with market actors. Kornberger’s treatment of brands as ‘organising devices’ sup- plements the framework and supports the focus on devices of brands’ value construction.

Mixed research strategy, combining qualitative, and quantitative methods is used to make sense of the data extracted from Steam community market and discussion from networking service Reddit.

Results show that among the most influential factors explaining the price of autograph there are personal player performance statistics, team tournament performance, and media coverage of player’s performance. The more detailed analysis of discussions on Reddit illustrates how evaluation devices are performed by the community of players and spectators.

KEYWORDS: free-to-play, status market, eSports, evaluation, autographs, brand value, sport brands, netnography, conditional tree model

2 Preface

For such a short paper this thesis has indecently not a straightforward history. My sympathy to the original topics was on its roller coaster ride for months. It was growing to breathtaking peaks during the periods of ideas being discussed with friends and colleagues, new relevant concepts being found in books and articles, and connections being made with problems in hand of other people. It was plummeting to the jelly-like abyssal swamps of apathy during the periods of data gathering opportunities being missed, communication-based approaches being lost, and time passed.

Then the current topic emerged, from pieces, crumbs, and bits of talks, papers, walks and code. This part is not for a reader. It is for me-after to remind that I was ok with the topic in hand and this thesis – at least for a bit. Another year had passed and another loop with an adjacent, but new topic is on its way. Hopefully, for the better.

For helping me get to this point I would like to thank a lot of people. My parents. My teachers. My friends. My relatives. Some of them are already not with us, others will, hopefully, hang around for decades to come. My students, who happened to be my teachers as well. In a humble hope that life will lavish you with people who will be pains in your arses to the same extent you are in mine.

For making this work possible I would like to thank:

• Department of Informatics and Media, Uppsala University, all teachers and my fellow students, for being open and friendly • Swedish Institute for supporting my studies via Visby Scholarship • Sociology of Education and Science Laboratory, Higher School of Economics St.Petersburg, and personally Daniel Alexandrov and Valeria Ivaniushina for making a stranger feel like home • Paul Okopny, Denis Bulygin, Stas Pozdnyakov, Vadim Voskresenskii, Viktor Karepin (especially for drawing beautiful tree diagram used in this thesis, replacing quite fright- ening native R picture), Ksenia Tenisheva, Natalia Dmoshinskaya, and Valery Nechay for their support, patience, and friendship.

3 4

• Participants of ‘Money and Games’: 12th Game Research Laboratory Seminar in Uni- versity of Tampere, for the comments, discussion, atmosphere, and, the most important, providing an outsider with a snapshot of ‘what is going on here?’. Contents

Preface 3

1 Introduction 7

2 Background 11 Gameplay ...... 11 Monetization strategies. Virtual items trading ...... 11 F2P and Cosmetic Items ...... 11 Trade Platforms ...... 12 eSports ...... 13 Teams and Organisations ...... 13 Streaming ...... 13 Virtual Autographs ...... 14

3 Positioning and Theoretical Framework 15 Autographs ...... 15 Sport Brands ...... 16 Theoretical Framework ...... 17 Research Questions ...... 18

4 Market Pricing Model: Trading Autographs 19 Market Snapshot ...... 19 Virtual Autographs as an Object ...... 20 Autographs as a Brand’s Imprints… ...... 21 … and Dimensions of Their Value ...... 21 Personal playing performance ...... 22 Tournament performance ...... 22 Media saliency ...... 22 Team Roles ...... 23 Data, Method, and Model ...... 24

5 CONTENTS 6

Market in Action ...... 26 Price-Predicting Factors … ...... 26 … and Alternative Explanations ...... 28 Model Quality and Validity ...... 29 Chapter Discussion. Status Market, Brands, Rankings and Media ...... 29

5 Capturing The Process: Market Construction on /r/dota2 31 Data and Method ...... 32 Autographs ...... 33 Player and Team in the Eyes of Spectators ...... 36 Personal Performance Statistics ...... 36 Levels of players’ and teams’ evaluation. Player-team matching ...... 37 Community oriented performance and lifestyle ...... 39 Players and Teams Brand Singularity ...... 39 Chapter Discussion. Organising Devices and Algorithms ...... 40

6 Concluding Notes 42 Practical and Theoretical Implications ...... 43

List of Source References 44

List of Figures 45

List of Tables 46

References 47 Chapter 1

Introduction

This paper is an effort to explore intersections of some topics and subjects which are of huge interest to me in the last couple of years, namely raising popularity of eSports (Hamari and Sjöblom 2015; Adamus 2012) phenomenon, virtual goods and game markets (Hamari and Keronen 2016; Lehdonvirta and Castronova 2014), and the increasing role of online reputation devices (Masum and Zhang 2004; Kornberger, Justesen, Mouritsen, et al. 2015), e.g. rankings, reputation systems, and reviews.

It is difficult to find an object to study where all the topics you like are intersecting. Even more difficult if you want your study to remain readable, detailed enough, deep and scientific. Yet, over a past year, readings and discussions, especially with Denis Bulygin, who I like to thank again here, drove my attention to the strangest segment of virtual items market I have ever seen – market of autographed cosmetic items in Dota 2.

My thesis is about one particular aspect and approach of this market – valuation of eSports brands on the case of Dota 21. Dota 2 is a free-to-play game with a monetization system based on the real money trade and the secondary market trade (Lehdonvirta 2008). This means that you can buy items for real money, and you can sell them to the other players. From a research perspective it is hard to overestimate the meaning of this: you can study a market, and the market means action. There are prices, and supply, and they can draw a picture of people preferences, which is usually much harder to find.

Moreover, the goods traded on this market are mostly cosmetic items, which influence player’s game interface and character appearance, but do not provide a player with any functional advantage in game. Among this peculiar kind of goods, I choose even more peculiar – virtual autographs of Dota 2 professional players. I should mention here that Dota 2 is one of the trending disciplines of the eSports domain, gathering attention of millions of players and as

1http://dota2.com

7 CHAPTER 1. INTRODUCTION 8 of 2015, having the biggest prize pool on its main tournament series2.

In this small market segment of the much bigger Dota 2 eSports picture, sketched in Chapter 2, as I show, all topics of interest, mentioned earlier, come to play. I treat virtual autographs as imprints of eSports brands. The secondary market trade in this particular case, supplemented with an access to market snapshots – data on what items are on the market, how many and how they are priced, provides us with an opportunity to go beyond studying purchase motivations and intentions (cf. Hamari and Keronen 2016), and to see what factors associated with personal and team sport brands do actually contribute to the price which people are ready to pay, i.e. how they together construct the notion of what is valuable, and to see how the value is constructed.

In doing so I start from a theoretical tool box, set forth in Chapter 3, which combines different approaches to the value construction on markets. As I am mostly interested in the social side and the role of different kinds of media in this process, I focus less on economical approach, which tends to be more concerned with utility and calculation, and inclines more to the framework, provided by modern economic sociology, mostly as developed by Jens Beckert and Patrik Aspers (Aspers 2009; Aspers and Beckert 2011). In particular Aspers’ concept of ideal-type standard and status markets as having distinct mechanisms of evaluation is used, where standard markets with a scale of quality serving for comparison of goods based on some objective criteria contrasted to status markets, where quality judgements are deeply entangled with market actors, and no unified quality scale exists (Aspers 2009).

As players’ autographs are an ideal case status market goods, which is discussed in theoretical part of the thesis, I proceed further to look at how entanglement between market actors works in creating evaluation. Here virtual autographs are similar to goods with purely status value, more specifically, a kind of brand imprints, encapsulating the value of personal and team sport brands (Yang, Shi, and Goldfarb 2009; Yang and Shi 2011; Filo, Lock, and Karg 2015).

Brands then are treated as ‘organising devices’ (Kornberger 2015, 108), ‘semantic spaces’, resulting and performed by competing evaluation devices, multidimensional and incommensurable ‘singular goods’ (Karpik and Scott 2010), evaluation of which is possible only via ‘judgement devices’ (ibid.).

This framework is then applied in Chapters 4 and 5 to empirical data on two different levels, supplementing each other in the spirit of mixed methods approach (Sudweeks and Simoff 1999), argued for in game, business and media studies (Hazan 2013; Siitonen 2007; Kozinets 2015a).

In Chapter 4, using Steam Community Market for Dota 2 data snapshot, capturing the market

2http://www.pcgamer.com/the-international-2015-prize-distribution-announced/ CHAPTER 1. INTRODUCTION 9 pricing of professional players autographs, and additional data sources, I build a quantitative Conditional Inference Tree model (Hothorn, Hornik, and Zeileis 2006) and Conditional Ran- dom Forest model (Strobl et al. 2008), showing how different dimensions of brand value, and media coverage of the player, are connected to the autograph price and discuss the results in relation to branding and status market concepts.

Then in Chapter 5 I dive deeper in an attempt to see how different media, resources, concepts, and rankings are working in the process of emergence of value orientations, how they are assessed and re-assessed in the community reflections. In doing so, I analyse discussions data from Dota 2 subreddit on Reddit3, which is arguably the largest open platform for Dota 2 community, combining qualitative and quantitative text analysis and usage of computational tools in the spirit of Netnography (Kozinets 2015a; 2010). Findings of this chapter partially explain and extend the results of quantitative analysis.

This thesis is somewhat different from traditional approach studies of virtual goods economy and consumption (Hamari and Keronen 2016; Lehdonvirta and Castronova 2014), and brand communities from economics and media perspectives (Muniz and O’Guinn 2001; Kozinets 2015b). By focusing on the ‘social devices’ of evaluation (Kornberger, Justesen, Madsen, et al. 2015) I tried to go further than focus only on a fan community, or brand construction by professional players. My goal is to show how all market participants on different trade and media platforms interact in co-creation of decisions what is good and what is not, use, reflect on and participate in continuous development of these social devices. It is left to the reader to judge whether the attempt was worthy.

On a methodological level, this thesis employs a mixed methods approach in a broad sense, i.e. a combination of qualitative and quantitative methods.

Greene, Caracelli, and Graham (1989) summarise different reasons to use mixed-methods, two of which are the most important for my study:

• complementation , i.e. elaboration outputs from one method using other method, • expansion, i.e. extending a number of research questions answered by applying different methods for different questions.

In the course of the discussion ‘digital vs. virtual methods’ in contemporary literature, widely adopted etnography-based methodological mix to study virtual context, proposed by Kozinets (2010) under self-coined name ‘Netnography’, was opposed by some researchers, most notably Rogers (2013), who stressed out the division between “natively digital” and “digitalised” data and, hence, between methods for such data (Ibid, 19). In a recent update (Kozinets 2015a)

3http://reddit.com/r/dota2 CHAPTER 1. INTRODUCTION 10 to his book, Kozinets expands on the omnivorous nature of netnography when it comes to ‘digital’ methods, providing some practical examples.

Regardless of details of particular methods provided in each of the empirical chapters, it is worth noting here, that the mix of methods and parts used, in a sense, may serve as an illustra- tion of this discussion. Chapter 4 is methodologically founded on a theory-based explorative approach of contemporary machine learning techniques, while Chapter 5 is methodologically close to ‘core’ methods of netnography, though augmenting usage of ‘digital’ ways of getting and navigating the data. Chapter 2

Background

In my study I focus only on a very small part of the whole Dota 2 landscape. In this chapter I briefly introduce the main concepts related to Dota 2 in general such as virtual items trade, eSports and streaming, in order to provide the reader with the necessary foundation to understand the layout of related studies and theoretical framing, and then engage into empirical analysis.

Gameplay

Dota 2 is a multiplayer online battle arena (MOBA) video game developed by Valve and is a sequel of Warcraft 3 map Defence of the Ancients (DotA).

MOBA is a sub-genre of real-time strategy games, in which two teams of players compete within a virtual environment. In Dota 2 each player controls a single character. There are more than 100 characters (“heroes”) available1 and each of them has unique abilities and associated playing strategies.

Monetization strategies. Virtual items trading

F2P and Cosmetic Items

Dota 2 is based on free-to-play (F2P) model of monetization. The game is available for everyone for free, but variety of virtual goods and extra services are provided. Free-to-play is a highly discussed and criticized model in game studies, as it introduces inequality based on the

1http://www.dota2.com/heroes/

11 CHAPTER 2. BACKGROUND 12 ability to acquire in-game advantage for real money ‘donations’, and, as Lin and Sun (2010) argue, causes community members perception shift in identification from ‘player’ to ‘consumer’. Term ‘pay to win’ was coined as a community reflection on game disbalance in early attempts of mass F2P games. F2P consequences are still discussed in the research community today, with generally unfavourable view of the model, which was discussed broadly during the 12th Game Research Laboratory Seminar, which was dedicated to connection of money and games. However, it seems that the discussion goes from critique of the model in general, to detailed analysis of the most topical issues (Alha et al. 2014), for example involvement of children into real-time purchases. Professional game designers are gathering experience and practices of F2P design, which is less destructive and biased towards paying players.

One of such practices is used by Valve in Dota 2. In June 2012 Dota 2 online store was introduced, providing cosmetic items for real money, i.e. items that do not affect the game balance. Developers declare that “Dota 2 will not be a pay-to-win game. All the items in the store are cosmetic, and don’t affect gameplay. All of the heroes will be available free of charge. We believe restricting player access to heroes could be destructive to game design, so it’s something we plan to avoid.”2 While there are some discussion on to what extent cosmetic items model is ‘fair’, it is out of scope of this study.

Trade Platforms

Trade in Dota 2 is organised via different platforms, introduced sequentially. Dota 2 Store3 is an in-game shop where a player can purchase cosmetic items. Prices in the Store are regulated by Valve, and several revenue share schemes were introduced for Dota 2 item designers and autographs.

The younger major trade platform is Steam Community Market for Dota 2,4 where players can purchase and sell items. On this platform, a seller establishes a price of a lot, and a buyer chooses among the lots available with a particular item. Hence, price of items on the market is regulated by players (or, to be more precise, is formed as a result of market equilibrium) and conforms, in general, to Supply and Demand Law.

2http://www.dota2.com/spoilsofwar/faq 3https://www.dota2.com/store/ 4https://steamcommunity.com/market/search?appid=570 CHAPTER 2. BACKGROUND 13 eSports

Competitive nature of the game predetermined an important part a professional scene has been playing in game’s community. In August 2011 the first tournament of The International series was organized with the prize pool of 1.6 million USD. Closed beta testing cycle took place in parallel with a series of eSports tournaments. By now, more than 700 tournaments of all categories from local weekly to premier have been organized, gathering millions of viewers and a recent total prize pool of $25.9 million.5

Teams and Organisations

As in more traditional professional sports, organisational entity, connected to actual playing – ‘team’, does not always operate as an independent business entity.

Teams with good tournament performance could sign contracts with organisations which consolidate teams, representing different eSports disciplines. A role of such organisation may include establishing corporate sponsorships and partnerships.

An example of such an organisation is ‘NAVI’ or Natus Vincere6. The organization declares ownership of teams in 7 eSports disciplines, including Dota 2, and 13 officially announced partners.

While studying this organisation as brand owners would be valuable future addition to this study, I leave it out of the scope for the time being. It is worth noting, that such study would capture some organisations in their formation or even emergence, as eSports field is extremely young if compared to some traditional sports brands.

Streaming

Professional tournaments are covered by live broadcasting platforms such as Twitch.tv and Hitbox. Livestreaming of The International final match in 2015 year gathered several millions concurrent viewers.

Successful teams, as their traditional sports counterparties, receive sponsorship and advertis- ing contracts.7 Professional players, in turn, are engaged in livestreaming as a way of gaining popularity and earnings by gameplay streaming (Kaytoue et al. 2012). On Twitch.tv League

5https://www.superdataresearch.com/blog/spring-2016-esports-report/ 6http://navi-gaming.com/ 7http://www.engadget.com/2015/07/31/the-business-of-esports-in-numbers/ CHAPTER 2. BACKGROUND 14 of Legends (LoL) and Dota 2 are considered among the most popular games, with the number of average concurrent viewers in 2015 being 550,000.8

Virtual Autographs

In July 2014 Valve released a new feature for items in store — ‘autograph runes’. A rune contains a signature of a known Dota 2 person, allowing a player to make one of his cosmetic items “autographed” by augmenting the rune, resulting in changed appearance and descrip- tion. The author of the autograph earns a share of revenues from autographs sold via Store.

8https://www.twitch.tv/year/2015 Chapter 3

Positioning and Theoretical Framework

In this chapter I: (1) discuss relevant research in two areas – studies of autographs and brand valuation, (2) set forth theoretical framework and theory-motivated research questions.

Autographs

While autographs (in a broader sense, as original handwriting of a famous person, not only a signature per se) are of interest on their own as cultural and historical objects (e.g. King 1952; Cox and Hamilton 1983), I focus here on two types of empirical studies: (1) connected with approaches to price autographs, usually using hedonic regression approach (Edmonds 1984), (2) discussing their value, especially in the sports context.

Autographs are an object of trade, possessing value and price, and in this status are discussed both in popular press (Brown 1993), and in science.

Pradier et al. (Pradier et al. 2015), studying pricing of French autographs in the period from 1960 to 2006 focus mostly on the dynamics, and show that the market reacts to changes in supply asymmetrically. In addition, sensitivity of the price with respect to the signers’s ‘relative nature’ is discovered, though, possibly due to heterogeneity of the sample, researchers do not go further into details of discriminating different types of signers.

Collins et al (2006) in earlier work focuses on the status of a signer, operationalised via no- tions of ‘rising stars’, ‘superstars’, and ‘dying stars’, and show price effects for autographs, belonging to live or passed away people, and their profile. They, in addition, explore the price effects of the artefacts, containing the autographs.

This and similar studies, grounded in cultural economics framework, and armed with hedonic regression, usually gather data from large market segments, as it is necessary, for example, to

15 CHAPTER 3. POSITIONING AND THEORETICAL FRAMEWORK 16 obtain reliable coefficients estimates. However, this also inevitably leads to very broad classification of people, as different ‘spheres of fame’ (ibid.) require different sub-measurements of this ‘fame’, i.e. crtiteria and dimensions of success for film actors would significantly differ from ones for singers and sportsmen.

This is unavoidable for economical studies, looking for reliable estimates. However, to understand virtual autographs in multidimensionality of their value, I am more interested in orders, ranking and local effects. In the methodological chapter I will discuss further the implications of this goal and proposed approach to achieve it via combining machine learning techniques and netnography.

In relation to sports, autographs are usually discussed in the context of community and media relations (J, Stephen, and William 2014, 318; Gibson, Willming, and Holdnak 2003), as a way for supporters to solidarise with their teams (Giulianotti 2015, sect. “Sport, social solidarity and religion”), or in the context of commodification of sport (Giulianotti and Walsh 2001). Another aspect of the phenomenon is further commercialization of “authenticity”, which is discussed by Morones (2004) in her study of exclusive autograph deals, where “memorabilia companies” stratify market, providing wealthy collectors with a highly priced authentic autographs via exclusive channels.

In this study, commodification of eSports is acknowledged, but the main focus is on the connection of valuation of autographs and associated sport and personal brands. In chapter 5 I will also return to the notion of autograph as a way of showing support by fans in the context of Dota 2.

Sport Brands

Branding of athletes is another topic relevant for this work. Most of the research in this area (for now?) is concerned with formation of brands and reputations in traditional off-line sports, but their relevance, as will be demonstrated, is undoubtful.

Firstly, I focus on works, studying the value of athletes’ brands. Yang and Shi (2011), on the basis of the National Basketball Association (NBA) data, measured the strength of brand value using the number of all-star votes an athlete received, and studied the dynamics of professional athletes’ statuses and connections between personal and team brands. Scholars observed that the number of all-star votes is directly connected with physical abilities of athletes and success metrics of their teams. In (Yang, Shi, and Goldfarb 2009) scholars also study alliance formation patterns between personal sportsmen and teams brands. Researchers analysed different value combinations and found that a combination of high-valued athletes and CHAPTER 3. POSITIONING AND THEORETICAL FRAMEWORK 17 mediocre teams is associated with optimal selling rates.

Arai, Ko, and Ross (2014) propose their own model of personal sportsmen brand construction. They argue, that brand image of sportsmen consists not only of their athletic performance, but also of attractive appearance and marketable lifestyle, so brand value optimisation requires from managers or agents helping sportsmen to holistically work on improving all of the factors .

In the study of models’ personal brands in their connection to organisational fields (Parmen- tier, Fischer, and Reuber 2013), non-specific, but relevant to the sports domain, researchers argue, that successful positioning depends on the combination of two principles: “standing out” and “fitting in”. “Standing out” requires from a person acquiring more specific cultural and social capital in the field than his or her competitors. At the same time, the model has to “fit in”, in other words, his or her behaviour should be with respect to norms, accepted in the particular field.

Theoretical Framework

This section is dedicated to explaining and motivating an overall theoretical approach I use in this study.

There are different approaches to the value construction on markets. Economical approach tends to be more concerned with utility-based calculations, when it comes to value and pricing, and uses hedonic price models (Edmonds 1984) as its tool. While more practical for quantitative modelling and theoretical analysis with mathematical tools, it tends not to answer to the question “how value is constructed”, especially on markets where goods are complex, multidimensional in terms of aspects of value, and no direct calculation of utility is possible, e.g. aesthetic markets.

Such markets, however, became of much interest for economic sociology, in particular for Jens Beckert and Patrik Aspers (Aspers 2009; Aspers and Beckert 2011).

Aspers (2009) develops ideal-type concepts of standard and status markets as having distinct mechanisms of evaluation. In his terms, standard markets are characterised with an existence of scale of quality. Such scale serves for comparison of goods based on some objective criterion, and allows to compare different goods directly in accordance to this criterion. However, this type of market does not include product differentiation or positioning.

Of larger interest to this study is another type of market: a status market. For this is a type of market where quality judgements are deeply entangled with participating market actors, CHAPTER 3. POSITIONING AND THEORETICAL FRAMEWORK 18 and no unified quality scale exists.

As virtual autographs clearly can not be assessed and ranked according to quality criteria of some kind, and their value is completely defined by the status of a signer with respect to status position of a buyer, in my opinion, they are traded on an ideal type of status market, even more so than traditional aesthetic markets of different kinds, e.g. fashion and antiques.

Moreover, autographs, are connected to personal and team brands, and, if no additional influence of an object which contains an autograph is present, which is the case for virtual autographs, the autograph value reflects the brand value.

Brands, in turn, are treated as ‘semantic spaces’ (Kornberger 2015, 108), resulting and performed by competing evaluation devices.

What are evaluation devices then and why we need them? Karpik (2010, 3) argues, that evaluation of ‘incommensurable’ products, ‘singular goods’ is driven “by the search for a ‘good’ [NB!] or the ‘right’ ”. Evaluation of such goods, then, can not be reduced to utility calculation providing and requires something to base choices on. ‘Judgement devices’ then form their foundation. Karpik separates them into (Aspers and Beckert 2011, 20–21): (1) personal networks, serving as a source of advice, (2) critics and guides (‘cicerones’), (3) marketing approaches to guide buyers, including advertisement and locations (‘confluences’), and, finally, (4) rankings.

Based on this approach, Kornberger (Kornberger 2015, 110), defines brand as a ‘space’, where in “dynamic, decentralized, and messy process”, “[d]ifferent, locally produced claims, calculations, and categorizations about an object meet”, possibly competing and conflicting with each other.

Research Questions

Based on this framework, I will proceed to my analysis of eSports brand value on the case of Dota 2, trying to answer the following research questions:

1. How can a virtual autograph price on Steam Community Market be explained using statistics and factors, associated with the brand value?

2. How a brand value is formed and evaluated by the community

• What are evaluation devices, participating in this process? • What role do media-related devices play? Chapter 4

Market Pricing Model: Trading Autographs

Market Snapshot

The goal of the first chapter of empirical results is to analyse how price of a particular virtual good with no functional value but with a special symbolic meaning in Dota 2 universe, i.e. virtual autograph, could be deconstructed to different dimensions of value, associated with a notion of personal and team brand in eSports.

As a source of empirical material I use Steam Community Market — an official platform by Valve where players can purchase and sell game-related items for a number of games.

I argue that this setting provides a unique opportunity to capture what market participants, e.g. professional and non-professional players, spectators, game artists, consider valuable.

Firstly, market setting, i.e. existence of a large number of sellers and buyers for every particular good, allows to see what economists call “market equilibrium”, where the price is a result of market coordination, and not dictated by one side or one participant.

Secondly, I consider an action of buying a particular virtual good for real money a stronger signal of value preferences in comparison to signals elicited from surveys and interviews (cf. behavour vs. intent Hamari and Keronen 2016).

While these arguments might become less significant than a new type of market participants who would treat goods on the particular market as financial instruments become more widespread, data gathered on this stage are treated as a snapshot of a state of the market, and state of the value preferences. A snapshot here means that I use point-in-time batch of data from the market for the analysis. This does not allow me to analyse dynamics, and possibly makes prices gathered vulnurable to fluctations, that is why I mostly make relative compar-

19 CHAPTER 4. MARKET PRICING MODEL: TRADING AUTOGRAPHS 20 isons of prices in different groups, focusing on orders more than on particular numbers.

I must note that there are other ways of acquiring items in Dota 2, which are mostly out of scope of this analysis, although I will refer to them where necessary. For example, one subject of the study may be the influence of the introduction of Community Market on older mechanisms of buying and selling virtual items, including the items made by workshop artists. This new platform that allows us to see for the first time what is valuable in practice, at the same time, most likely, as a side effect causes money and power redistribution for freelance artists, who depended on monopolistic channels of sales. These effects are of great interest today, but they are out of the scope of my thesis.

Virtual Autographs as an Object

While I will discuss this in a greater detail in the next chapter, main buyers of autographs are non-professional players in Dota 2 who actively participate in eSports spectatorship on different levels — from weekly events to national and international tournaments.

As in the case of non-cyber sports, and non-virtual settings, autographs, are of very contextual value: outside of the community symbolic value of an autograph diminishes with the only exception of investment value and the emergence of markets for which in eSports is just the matter of time.

Virtual autographs in Dota 2, existing in the form of autograph runes, i.e. virtual items with an imprint of a professional player, an eSports media commentator or a Dota 2 artist, have even more contextually restricted value.

Firstly, they are only visible in game, being integrated into player’s character set of virtual clothes.

Secondly, you can trade the autographs, in general, only on a restricted set of platforms.

These two reasons require an additional analysis in the light of discussion “what it means ‘to own’ a virtual item”, but it is out of scope of the discussion, for now, though I will briefly return to this question in the next chapter.

In this analysis I also restrict the set of autographs to those of professional players, specifically, the participants of The International Series (TI) – the most prestigious tournament series in Dota 2. The first reason for that is that their value dimensions are in alignment with personal and team branding in Sports, and, as such could be encompassed in one model, without making it too restricted, or too complex. Inclusion of Dota 2 artists, for example, CHAPTER 4. MARKET PRICING MODEL: TRADING AUTOGRAPHS 21 would require to add extra variables, capturing their value dimensions specifics. The second reason is that in most of the sources of data I observe some form of Matthew effect (Merton and others 1968; 1988; Easley and Kleinberg 2010 ch. 18) – data on famous players are updated more frequently and contain more details than for less famous players and other kinds of signatories, in turn making them even more visible due to up-to-date and full character of available data. While this effect might be still in place for the TI players, however, it is less pronounced. The third reason is that TI events characterised by extensive media coverage using different media channels, and studying media influence was one of my main interests in this analysis.

Autographs as a Brand’s Imprints…

Konberger (2015, 108), treats brands as ‘semantic spaces’, resulting and performed by competing evaluation devices. In turn, in this work I look at the virtual autographs, traded on Community Market, as on brand’s market imprint of a kind, a good without any other value apart from incorporated in the personal, and, consequently, team brand of the person whose virtual signature is traded. What is more important here, that the case discussed is an extremely rare case where this kind of value directly traded in the market, not being ‘packed’ in a good, combining brand with functional, aesthetic and other kinds of value.

I start the analysis in a theory-guided exploration manner, using existing factors of sport brand value, based on the analysis of sports branding literature, and extract groups of factors, for which there is a consensus on their influence (Yang and Shi 2011, 352–53; Yang, Shi, and Goldfarb 2009, 1098–9).

… and Dimensions of Their Value

I will now describe groups of factors which influence brand value and their re- operationalisation for Dota 2 case. Then I briefly describe relevance and specifics of each group. These large groups of factors are personal performance of the player (often called in eSports and sports simply ‘statistics’ or ‘stats’), tournament performance, and, the last but not the least, media saliency.

Let us now discuss each of these groups in more details. CHAPTER 4. MARKET PRICING MODEL: TRADING AUTOGRAPHS 22

Personal playing performance

This group directly characterises skills of players. While skills, of course, contribute to the brand value only mediated by crowd perception, in this part of the thesis I will take into account direct indicators of personal performance, i.e. total number of games in professional matches, winrate (proportion of matches player won) and KDA (Kills, deaths, assists) ratio. KDA less than one means, that a player was “killed” more often than participated in “killing” and vice versa.

Tournament performance

This is arguably the most important for team brands. Since 2011, Valve has been organising an annual tournament named The International (TI). TI is an event where the most “famous” and “successful” teams compete for the title of the world champion. The prize pool has been increasing from tournament to tournament from 1.6 million USD to 15 million USD for the last TI. Final rounds of the last tournament were not only widely media covered online, but also broadcasted live on ESPN and a Swedish TV channel. TI series is, of course, not the only one. Other tournament series are stratified to Premier, Major, Minor, Monthly, Weekly. Premier and Major events can be characterized not only by a dramatically bigger size of the prize pool, but also by LAN-finals — a real-life part of the event, which draws increased media and community attention.

As I mentioned earlier, on this stage only player’s performance in TI series is used, as it is difficult to correctly aggregate all other types of tournaments in the quantitative model without making it too complex, and ability of such a construct to serve as a signal more effective than TI performance in questionable. On the variables level some indicators capturing different aspects of TI performance were constructed. First of all, I took the best result of a player in TI series, i.e. the highest place he and his team achieved on any of TI tournaments. Next, the number of times a player participated in tournaments of TI series is taken into account. Lastly, recency effect, which could be of influence on its own, is accounted for by inclusion of the results of each player at the last TI (2015).

Media saliency

This group is the last and arguably the most complex of taken under consideration. Based on the information of players’ profiles on Liquipedia, wiki-based encyclopaedia of Dota 2 world, where information on players’ interviews is collected, I constructed a number of simple CHAPTER 4. MARKET PRICING MODEL: TRADING AUTOGRAPHS 23 quantitative media indicators. The total number of interviews for a player is included as a first predictor in the group. While, as it was mentioned earlier, it could be influenced by Matthew effect, and popular players’ interviews could be covered in their profiles more completely, no additional steps to control for that are performed at this stage. Firstly, I assume that the group of TI participants is homogeneous enough not to have huge differences in the profile completeness, secondly, players and fans can participate in curation of their pages, which should partly compensate the bias if any.

While the number of interviews might be a good predictor on its own, influence of interviews may obviously vary for different media and interviewers. Again, while more complex coding schemas are possible, I categorize interviewers into the following categories: community sites, sites specific to Dota 2, sites covering games and eSports in general, official sites of Dota 2 teams, and platforms non-related to games. Improvement of this categorization is one of the possible ways of further development of this model.

Community sites category includes such sites as reddit/dota2, teamliquid.net, which are mostly maintained by and populated by community members. Personal blogs and fan sites are also in this category.

Dota2-specific sites are media platforms associated with the game, for example, Joindota.net, dotablast.com. They usually have professional reporters, news feeds and accounts in social media. These sites cover news related to Dota 2: eSports tournaments, teams, player transfers.

Sites about games and eSports in general, for example, 2p.com, GosuGamers, cover Dota 2 mostly in their news and publish eSports related information: tournament grids and sched- ules, bets, results.

Dota 2 official team sites usually contain information about rosters, team members, sponsors, news, tournament performance and official announcements.

Non-game related media is the least spread category for players interviews. It, however, could partly account for player’s level of support in their home country, as it mostly contains the interviews, taken by player’s home country media.

In addition, media coverage on major and premier tournaments, especially on offline events (LAN-finals) is included.

Team Roles

In sports branding literature, interrelationship between player’s and team’s brand is complex and highly dependent on the nature of sports. CHAPTER 4. MARKET PRICING MODEL: TRADING AUTOGRAPHS 24

In this paper results looking like “team A brand value is higher than team B” are of no use, so direct inclusion of team variable is not considered, and only two factors are partly account for team-player relationship. First, it is the player-team performance on tournaments, which I covered earlier in this chapter. Here I just do not engage in disentangling this duality, asking for an assessment of combined effect. The second factor I took into consideration here is more complex. It is a role of a player in a team.

The role of a player in Dota 2 characterises formalized position of a player in priority to earn in-game gold during the match, according to the possible impact on the game outcome. There are several roles, placed in the following priority: ‘Carry’, ‘Mid’, ‘Offline’, ‘Support’. This priority reveals an average “impact” of heroes on the game result, and it could be the case that players with ‘more important’ roles could be more well-known.

Data, Method, and Model

Using one-time snapshot of Steam Community Market data, I parse information about the item title, the number of lots in the market and the minimal price of an item. The dataset with autographs consists of 400 items of 113 players who participated in TI series. No longitudinal research of prices is performed due to the time frame of the research. While this could have posed a lot of problems if the goal was price prediction, I assume that resulting orders of prices are more stable to price fluctuations.

The information about players and teams is gathered from Dota 2 section of TeamLiquid.net, which is one of the most authoritative community-maintained sources on the subject. Players’ in-game performance statistics was gathered from Dotabuff.com. Grouped input variables of the model are described in Table 4.1.

The quantitative modelling approach is chosen to satisfy two requirements. First, I want a method or methods, which would support a theory-guided exploration: extraction of statistical dependencies and links without strict specification and restrictions of the model, based on data supplied. Second, I want a resulting model to operate more in ‘supervised segmentation’ sense (Provost and Fawcett 2013, ch. 3), extracting natural stratification based on the provided data and attributes. I focused my attention on supervised methods (James et al. 2013, 26), as I analyse factors, associated with depended variable – autograph’s price, thus I exclude cluster analysis and other unsupervised methods.

Traditional regression and structural equation approaches were discarded in relation to these two requirements. Traditional linear regression requires special treatment of highly connected independed variable groups (cf.“multicollinearity” James et al. 2013, 101–2; Jr et al. CHAPTER 4. MARKET PRICING MODEL: TRADING AUTOGRAPHS 25

Table 4.1: Groupped Model Variables Group of Variables Variable Variable in Tree

Tournament performance: Best result on The International series team_best_result_ti Recent year of participation (TI) team_year_of_last_ti_participation Result of participation in last tournament (TI) team_last_result_ti Number of participations in The International series team_number_ti_participation Fact of participation on last The International team_ti_2015_participation Fact of participation on The Shanghai Major team_sm_participation Number of top-3+ places at premier and major events team_sum_top3_places_main_events for team which player participated in Playing performance: Number of professional games played player_number_games Total winrate in professional games player_kda Total kda in professional games player_winrate Media saliency: Total number of interviews player_interviews_overall Number of interviews to community sites player_interviews_community Number of interviews to dota2 sites player_interviews_dota Number of interviews to sites about games player_interviews_game Number of interviews to team sites player_interviews_team Number of interviews to sites not related to games player_interviews_not_game Number of interviews to tournament media crew player_interviews_tournament Player’s profile: Country player_country Role in the team player_role

2009, 201, 219), essentially forcing to leave only one variable of each group, chosen based on overall performance of the model on the whole data. I assume that my data might have local effects and patterns of interplaying factors in some segments of the data, which we might miss with traditional regression. When it comes to structural equation models, the same agument stands only in part, but pre-specification of theory-suggested relationship between variables is required (Jr et al. 2009, 611–19). However, no claims on structural relations between the factors adapted from traditional sports branding are made, as such a claim can lead on my highly segmented and fine-granular data to the loss of interesting patterns, which can not be observed on the data from traditional sports.

That is why I use the quantitative modelling method called Unbiased Recursive Partitioning (Conditional Trees) (Hothorn, Hornik, and Zeileis 2006). ‘Unbiased’ here is not a marketing claim or a promise of correct results, it is just a reference to improvement, or, better say, alternative approach to older methods of building tree-based models (Strobl, Malley, and Tutz 2009). As the name suggests, this approach operates by recursively extracting smaller and smaller segments of the data set, with the goal of finding a split sequence (a tree), which ensures an optimal prediction of the outcome variable. It must be noted, that there are no randomness in choice of the split (cut-off) point. In this particular method to avoid some un- wanted statistical properties of the model, at each step the algorithm chooses among possible splits the one associated with maximal statistical criterion value, ensuring statistical signif- icance of each split, and reducing the bias towards the variables with many distinct values, CHAPTER 4. MARKET PRICING MODEL: TRADING AUTOGRAPHS 26 which was a characteristics of earlier methods. The splitting process in each node stops when no variable can give another statistically significant split. Thus, terminal nodes of the tree in the model contain prediction of the outcome (log(Price)) and show associated price distribution in each node, as each node has more than one observation with close (provided the model fit is good), but not similar price values.

In the next section I show the fitted model and discuss it in detail, which should help to understand the modelling logic.

To test a potential impact of the variables not included in the optimal tree model, I use more advanced machine learning approach called Random Forests, introduced by Breiman (2001), more accessibly discussed in general framework of recursive partitioning by Strobl (2009). This approach builds basic recursive partitioning algorithms by building and aggregating results of many (usually thousands) of tree-based models. Without going deeper into details, let me mention that from each tree it randomly selects a subset of predictors to choose from and a subset of data to fit the model, ensuring that all predictors have a chance to contribute into the resulting model. In this work it is used not to better predict the price, but for its side effect: to receive variable importance measures for all variables. Recent advances in this direction (Strobl, Torsten Hothorn, and Achim Zeileis 2009; Strobl et al. 2008) allow to account form multi-collinearity and get non-biased influence estimates, separating the effects of each particular variable from others.

Market in Action

This section starts with the fitted tree model (Fig. 4.1). In its context I discuss, first, how to follow it and what it tells us about the autographs’ pricing. Then I proceed to discussion of ‘competing explanations’ in th context of conditional variable importance. Last, technical notes on model fit and validity are provided.

Price-Predicting Factors …

As the reader can see, there are clear price orderings between the resulting segments, and the splits can help us to explain how pricing works. Before discussing the particular segments, let us look at the quantity variable. It is understandable from basic microeconomics that supply (i.e. quantity, in our case – the number of a particular player autographs being on the market at our snapshot point) is connected with the price: more autographs of the player are on the market, lower are their price. We, though, are interested on brand value associated variables, CHAPTER 4. MARKET PRICING MODEL: TRADING AUTOGRAPHS 27

Figure 4.1: Fitted conditional tree model for autographs so let us proceed to them.

The first segmentation is based on Team performance: the tree separates the players whose current teams took 1-3 places on major and premier tournaments more than 5 times. For this group, which in general has higher prices of autographs, a player’s role in a team is the next important factor, influencing the price. As I mentioned earlier, the first and the second roles are higher in the priority chain and receive more attention compared to other roles, so autographs pricing in these two groups works differently.

For players in the more visible first and second roles it is essential to demonstrate good game performance, which is illustrated by the next split, separating players in these roles with total KDA > 4.32, and predicting them higher price of an autograph, comprising ~$75. Nonetheless, players who have worse performance could achieve higher autograph prices, provided they possess the title of World Champion.

Next, media saliency comes to play: for players in less visible third, forth and fifth roles having less than 4 interviews is associated with a price around $10, which is less in comparison with their more media visible colleagues.

The same logic of explanation is applicable to less successful teams, and here the advantages of supervised segmentation approach is again visible: composition and order of the factors in the branch for less successful teams is quite different.

At this point, we see that all of the groups of factors, associated with brand value, i.e. personal player’s performance, tournaments performance and media salience work in interplay and play CHAPTER 4. MARKET PRICING MODEL: TRADING AUTOGRAPHS 28

Figure 4.2: Conditional Variable Importance a significant role in pricing.

Let me again reiterate, that prices which are used as the outcome are the result of voluntary market transactions between independent buyers and sellers, so our model characterise their preferences and valuation orders.

… and Alternative Explanations

As the reader probably noted, only a share of factors we discussed in details earlier was used. What about the others? Are they all irrelevant? As might be intuitively clear, most of the factors in each group are correlated (connected) with each other. Thus, in our model only the predictors, ensuring on each step that the most significant split among all the alternatives is chosen, are left.

However, we have a way to see how good the others are in predicting the autograph price, and which are likely to be not associated with the price at all.

In Fig. 4.2 we have a benchmark of different factors, associated with price. It allows us, for example, to understand better to what extent different media channels are different when it comes to connection with the price.

On the figure different colours represent different groups of factors. Here the reader can see CHAPTER 4. MARKET PRICING MODEL: TRADING AUTOGRAPHS 29 that interviews to different types of media do, indeed, have different effects. This result might form one of the departure points for future study: we know they have different effects, we might as well try to find out why exactly. Another interesting suggestion from the model is that the last tournament result is less important than overall team performance up to date. Personal player’s performance indicators are proven to have a close price predictive strength, occupying the middle of the graph. The most important, but the least interesting to the study, factor is the supply (quantity of the autographs of a prticular player on the market.

Model Quality and Validity

Our tree model (Fig. 4.1) can be, in part, assessed using traditional measures for linear regression. It has R2 = 0.62, i.e. the model explains approximately 62% of the variance, which is not perfect, but quite good result, considering ephemeral nature of the subject which the price model is trying to predict: I would argue that higher degree of uncertainty is associated with valuation on a status market, in comparison to standard markets. All splits are statistically significant with a correction, necessary to account for multiple comparisons (Holm 1979).

As we see from the results, the approach I choose indeed allows us to see some interesting local effect, for example, media influence for the players in 3rd-5th roles, and extract different orderings of the factors for different segments of the model (cf. left and right parts of the tree). Using variable importance measures I am also able to shed some light on benchmark of multi-collinear and competing factors, thus allowing to inform possible further directions of studies.

Chapter Discussion. Status Market, Brands, Rankings and Media

I consider autographs market an extremely rare example of pure status market (Aspers 2009), comparable, or, maybe even more pure than fashion models market studied by Mears (Beck- ert and Aspers 2011, ch. 7). In the discussed case, different dimensions of acquiring and sustaining the status, and associated with sports brand value, are captured, and their effects could be studied in an interplay. A clear role of media coverage in autograph pricing of high- performing players in less visible roles, and a value of the champion status for players in the first and the second roles are interesting examples of such an interplay.

It is worth noting, that the market pricing snapshot, while important in capturing value, measures it only indirectly. There is no reason why players with a particular KDA have the autographs with a particular price, other than this statistics serves as a measure of performance, CHAPTER 4. MARKET PRICING MODEL: TRADING AUTOGRAPHS 30 and is used as an argument in comparing ‘good’ and ‘bad’ players.

In the next chapter I try to extend the reflection about this process, focusing on particular media, resources, concepts, which serve as a part of socially constructed ‘devices’ of measuring value. Chapter 5

Capturing The Process: Market Construction on /r/dota2

In the previous chapter a quantitative model was demonstrated, showing how different factors, capturing different dimensions of brand value, in their interplay are connected with virtual autograph prices. While important step in understanding how valuation works in case of personal and team brands in Dota 2, the model can not tell us much about how different devices are used by the community to establish value. For example, it does not tell us how personal performance statistics are connected with pricing.

In this chapter I try to dive deeper in the process using qualitative data from /r/dota21 – ‘subreddit’ of Reddit.com. This is one of the most popular platforms of (mostly English-speaking) Dota 2 community, containing discussions of both non-professional and eSports aspects of the game, including discussions on trading virtual items.

The chapter is structured as following. In the next section I describe data and analytical procedure, which is in the spirit of netnography and qualitative media analysis. The third section covers topics, connected with community perception and reflection of virtual autograph: the object that connects two parts of this research, and its degree of equivalence to offline world ones. Then, I discuss how community constructs judgements and order via main instruments, which, as was shown in the previous chapter, are connected with autographs pricing. After- wards, discussions on what is good player and team are analysed. I conclude the chapter with an attempt to discuss how these elements contribute to understanding the overall picture of the process.

1reddit.com/r/dota2

31 CHAPTER 5. CAPTURING THE PROCESS: MARKET CONSTRUCTION ON /R/DOTA2 32

Data and Method

Empirical basis of this chapter is data from /r/dota2, a ‘subreddit’ of Reddit.com, dedicated to Dota 2 discussions. Reddit API access is used to perform search queries and gather posts and comments. While API access requires registration and authorisation, the same data can be accessed without registration using Reddit web site, so I treat it as public.

The first sample is constructed using the query “title:match AND title:discussion” and con- taines 733 threads with 82 372 comments, where a number of comments for each post is limited to 200 comments. This query maximises chances to acquire judgements and discussion, relevant to the research goals. On the next step, posts are filtered out, as they mostly contain information about matches, and less likely to contain discussions.

Additional samples are constructed with the queries based on players’ nicknames, which are gathered for the previous chapter quantitative model, team names, a word ‘autograph’ and combinations of aforementioned words.

Then instruments of R package for text analysis Quanteda (Benoit and Nulty 2016) are used to perform search queries on downloaded samples and look for search terms in concordance. To highlight potentially interesting posts and comments in a large sample, ‘arguing score’ is used, constructed using ‘Arguing Lexicon’ patterns for English language (Somasundaran, Ruppenhofer, and Wiebe 2007), in Quanteda and R.

While computational tools are used to gather the data, and computational lingustics approach to highlight potentially interesting documents, general logic of this part of the study is qualitative media analysis (Altheide and Schneider 2012) treatment of ethnographic content analysis, with an accent on constantly ‘checking’ and enriching ‘prior theoretical claims’ [26]. A theoretical focus of the analysis is on usage of constructs associated with the brand value in establishing what ‘good’ player and team are, in the process of the analysis themes are extracted and discussed.

Lastly, in quotations, provided in this chapter, names of the discussion participants are hidden. This is due to two reasons: (1) it reduces the cognitive load, required to make some sense of the mix of different concepts, names and terms in the quotations, (2) this, in part, preserves the author’s right to delete the post later as without a nickname the quotation is harder to attribute to the author from the text. CHAPTER 5. CAPTURING THE PROCESS: MARKET CONSTRUCTION ON /R/DOTA2 33

Autographs

In this section I will show how virtual autographs are discussed on /r/dota2. This question can not be directly solved by considering virtual autographs full equivalent of their offline world counterparts. While for motivation for consumption of virtual goods in general there is a solid body of research (Hamari and Keronen 2016; Lehdonvirta and Castronova 2014), autographs, as I mentioned in the literature review, have a special meaning. They are the way to show fan support for a player, and, as such, they are not only a collectable token, but also a symbol of a link between a player and a fan, often associated with a special circumstances and physical co-presence, which is lacking in case of virtual autographs.

Indeed, there is evidence of community reflections:

User A: I still don’t understand the point of the autographs. You haven’t met them, you just purchased an one of a possibly infinite amount of something they were fine with spreading. Ultimately it just feels like a odd way to give money to a personality you like, rather than just say, sub them on twitch or something. I don’t understand the reasoning behind it all tbh. Why are people buying these over other items? They probably get a bigger cut if you use the money on them somewhere else, I imagine twitch takes less of a % than valve does.

User B: For me it’s more-a-less to say I like what this guy does in Dota and showing support. Tobi deserves it.

User C: Other people then see your items with the persons name and find out more about him… it’s also a great thing from a publicity standpoint.[1]

Here a concern is even if it is a form of support, it might be not the most efficient, compared to others (e.g. streaming donations). Other participants stress possible important reasons to buy autographs: (1) acknowledgement of a player as a good player, (2) showing support, (3) helping a favourite player from a publicity standpoint.

Another concern is about the symbolic value of autograph as a token, because it is not associated with a meeting with a player. I should note here, that there are some mechanisms introduced to strengthen this link. Participants of major off-line events were introduced to an opportunity to get real and virtual autographs in a signature party with their favourite players, using their participant badges connected to Steam account.

Monetary dimension of support, though, is not considered as neglectable neither by signers, CHAPTER 5. CAPTURING THE PROCESS: MARKET CONSTRUCTION ON /R/DOTA2 34 nor by other users:

Caster A: Just want to say thank you to anyone who does buy an autographed chest, regardless of which personality/caster they choose. It’s awesome for Valve to allow us to directly benefit from the community support through ingame chests

User B: Can you choose the autograph you get? Do the autographers get money from it?

Caster A: Yes you can choose when purchasing, and we do get a cut of the revenue, when our autograph is chosen![2]

It is worth noting here, that the discussions are about the mechanism of buying autographs via Store, controlled by Valve, which is, possibly, why a big accent is made on the financial support.

However, introduction of the market drives the logic from signer’s support to price and value comparisons, as clear from the discussion below:

User N: Four fucking dollars?!?!

User M: yo, brother it’s R$12,79 in Brazil, imagine paying $12,79 for each. It hurts:/[3]

Market setting and detachment of a seller from ‘the autographer’ turns on the market logic. As it was mentioned in the theoretical framework, this calls for evaluations and comparisons. As market participants, we now need to establish what are we paying for, not only in attribu- tion to degree of our support, but also in comparison to other alternatives:

User A: Hello guys. I was wondering who casters/professional players with autograph plays a lot Lina? I know that [Caster’s name] plays a Crystal Maiden a lot. So I have customized already her. Now want the same for Lina’s items. Please suggest who would be good to my Lina arcana? Sincerely, noob

User B: SumaiL or Solo

User A: Thanks man, will get Solo, since Sumail one too expensive =)[4]

While here are no evident traces of the multidimensionality of a personal brand, as can be already seen, the participants separate players who are ‘good’ in different roles. I will return CHAPTER 5. CAPTURING THE PROCESS: MARKET CONSTRUCTION ON /R/DOTA2 35 later in this chapter to formation of shared understanding of players’ and teams’ evaluation, but before that I will also mention another important topics, which contribute to understand if and to what extent virtual autographs are analogues of their “physical” counterparts, namely the question of virtual goods ownership.

Ownership is one of the questions, extensively debated in relation to online games. F2P business model, microtransactions and monetary trade in games are quick to convince players that they can ‘own’ something. But a virtual good is just a number of bits of code on a corporate server. This discussion takes place in law studies literature (Fairfield 2005; Sheldon 2006) and game studies (Lehdonvirta and Castronova 2014, 162–64). It is also very similar to media and communication studies discussion on exploitation in Web 2.0 (Fuchs 2013, ch. 11.1, Rey (2012)). I here will mention only very narrow and practical aspect of the question, namely bugs.

The following critical reflection of a player is a good example of this logics’ clash. Dota 2, being a piece of software, is managed in the commercial software development logic, were bugs are a part of the process, and, in addition in mass consumer web service logic, where acquiring new users and introducing new features has much higher priority than fixing bugs. Customers, on the other hand, are stimulated to pay for virtual goods, and, in essence, call for a logic of treat them as goods:

Recently there’s been a terrible amount of unaddressed complaints about items being broken, lost animations, missing particles, non-working gems, lost items. […]

To put into perspective, an organisation that sends out flawed goods will usually have to recall the goods and make a huge loss in the process. While this virtual economy doesn’t behave like that due to the ability to almost instantly replace, repair, and replicate any items for the consumer, it doesn’t mean that you can dedicate any less resources into ensuring that your customers are getting what they paid for. […] Imagine if this wasn’t the virtual world. What valve is doing right now would be extremely irresponsible; once the items have “gone on sale”, even though there’s a manufacturer’s defect, all they care about is releasing more new products to earn greater profit and revenue, ignoring the issues with the old ones.[5]

In the case of autographs it means that your ownership or ability to demonstrate it is limited, conditional on some strange unpredictable logic, which is illustrated by this response to the question of a user whose autograph does not ‘work’ if anybody has the same problem: CHAPTER 5. CAPTURING THE PROCESS: MARKET CONSTRUCTION ON /R/DOTA2 36

Yep, I have the same problem. I have a golden Universe autograph rune that looked lovely in Source 1, but as soon as I switched to reborn the signature now is replaced with a purple checkerboard image. I submitted a Steam support request months ago, took them forever to respond and basically did nothing to help.[6]

In conclusion of the section, let me reiterate what we know about autographs so far. They are not full analogues of ones in offline world, namely they have looser symbolic link to the player (“you haven’t met them”). They acknowledge that buying the autograph could serve as a way of showing support and contribute to player’s publicity, and acknowledge that a player is a ‘good player’. We also see that a transition to a market logic and associated separation of player from seller prioritizes different way of thinking, which includes judgements on value in comparison with price.

Let’s now proceed further to see how this judgements work in the connection with main concepts, value dimensions and instruments we discussed earlier.

Player and Team in the Eyes of Spectators

In this section I discuss how the community members judge on what a ‘good’ player and team is (and what is not). This discussion is important as it contributes to our research on two levels. First, it provides us with another insight of personal and team brands as singularities. Second, it gives us, on a qualitative level, understanding of the aspects which are very important for sport brands, but are not captured with the quantitative model in the previous chapter, namely ‘style’ and characteristics of community-oriented performance.

As it was mentioned earlier, eSports encourage spectatorship in a form of streaming. It is worth noting, that streaming is connected with another direct form of fan support – donations to streamers.

Personal Performance Statistics

Here I look at how performance statistics of players serves as an evaluation device. In the previous chapter our quantitative model showed us that KDA (Kills-Deaths-Assists) ratio is associated with price differentiation. To what extent is it really used in logic of evaluation? Let us see: CHAPTER 5. CAPTURING THE PROCESS: MARKET CONSTRUCTION ON /R/DOTA2 37

User Q: Mu is back! He’s played incredible and happy to see him back in old form. His average kda in the china qualifiers have been an incredible 11.59.

User F: Mu has always been a Top 3 mid, and the best one in China. Possibly the deadliest, most aggressive laner and still manages to be consistent as fuck.[7]

In this case, link is mapped by users between the particular personal statistics value and ranking (‘being a Top 3 mid’).

However, KDA, with its simple interpretation, is not the only statistics in play. The following discussion includes MMR – Matchmaking Rating, which is an algorithmic device, introduced by Valve to algorithmically match players by their skill level in non-professional games, but, due to visibility in ranking boards, is used as evaluation of skill directly, despite the fact that its calculation is more opaque, compared to simpler statistics:

User A: He was 8962 last night and today has won two ranked games. First game (Puck), second game (Anti-Mage) Pre-game video The rest of OG cheering him on Moon tweet of 9k MMR

[…]

User N: Born too late to explore the earth. Born too early to explore the universe. However, born in the right period to witness miracle reach 9k MMR

User Z: I mean was there any doubts he would be the first to hit 9k? Dude is a beast, plays several heroes well (even Wisp for Christ sake) and has the right approach to the game. Dude is the Messi/Ronaldo of Dota.[8]

It is worth noting, that the impact of both personal statistics (KDA and MMR) is quite big – in 17.5% of the match threads in the sample one of them is mentioned, with MMR seemingly being much more widespread as a device of comparison among all categories of players, professional or not. The fact that personal or team statistics serves as an evaluation device is not new to eSports, and connects well to existing practices of traditional sports.

Levels of players’ and teams’ evaluation. Player-team matching

As it would be also true for traditional sport community, spectators discuss and critically re-evaluate players and teams, not only in direct terms of performance statistics: CHAPTER 5. CAPTURING THE PROCESS: MARKET CONSTRUCTION ON /R/DOTA2 38

Player B: […] nobody on diggity [team] was clicking with each other that last game. also doesn’t help that era [player] showed up with his inability to make crucial decisions under pressure–that fight at top where he ran past the dark seer wall at like 200 health was the beginning of the unraveling

Player A: I know, I was like, “wtf Era, why would you do that??”.. There was no possible way he could have killed the Spectre without getting killed and it didn’t seem like a worthwhile trade..[9]

Here are critical re-evaluations are about a particular game, but they also take form of judgements about player-team matching, and player’s game in general, taking, as it happens, of- fensive and flaming form:

User A: I hope Mushit fangays finally realize that Mushit has always played like shit post-TI4 and has always ruined his teams Ehome/TI5 fnatic (flashback to his super fail earthshakers at TI5 shudders). I liked DJ/Net/MidOne[players]. I hope they get a better team soon![10]

On this level the notion of a ‘good player’ is compared with the notion of a ‘good team’. Traditional sports practice shows that parity between them depends on the team size. In Dota 2 case we can see that commenter refers to particular players of the team, not the team as a whole.

The last level of comparison here is between the teams and in the relation to the concept of ‘good enough to participate in a major event’, for which the reader can easily find parallels in traditional team sports.

In this example commenters discuss a particular team’s poor performance and questionable, in their opinion, decision to invite team to a major tournament:

User A: such COL [team] deserves a direct invitation ?

User B: They got top 6 at Shanghai Major [important tournament], so yeah

User C: [about other teams] CDEC got 2nd, LGD got 3rd, VG got 4th, Ehome got 5th in TI5 [important tournament], so what? Has COL achieved anything other than top 6 in Shanghai Major? […] China won 3/6, and 2/6 of those events didn’t invite any Chinese teams, so Chinese teams won 3/4 events that they participated lately […] CHAPTER 5. CAPTURING THE PROCESS: MARKET CONSTRUCTION ON /R/DOTA2 39

User D: They worked hard before TI5 [important tournament] and that’s why they got good results at TI5. Surely CoL haven’t achieved anything big comparing with Chinese teams but it doesn’t mean what Swindle said was wrong.[…] They worked really hard after Shanghai Major [important tournament]. […] That’s why they regain their shape.*[11]

Community oriented performance and lifestyle

Such subtle concepts as ‘style’, both playing and personal, ‘personality’, revealed in community-oriented performances in streaming, and background also contribute to the brand:

User F: I really love Dendi interviews. What really impressed me is his loyalty to the organization. Whoever Dendi will get for his wife, she will probably be extremely happy. And he is really realistic in a lot of aspects.

User L: Still one of the best personalities out there. I love him and i will be a Na’vi fangay until their disband becouse they bringed my attenction to the dota competitive scene. I really don’t care if people think that they are tier 3 teams they are still one of the most entertaining team to watch […] [12]

So, entertainment skills on a personal and team level come to play. In addition to personal charisma, and a rewarded player-team connection (demonstrated ‘loyalty’), this combination appears to overcome poor performance.

Players and Teams Brand Singularity

Karpik (2010) concept of ‘singularity’ and Kornberger’s (2015) concept of brand as “semantic space [which] makes values visible” are, perhaps, the best illustrated by the following answer on “Who are the most well known DotA players” on Quora:

Xu ‘BurNing’ Zhilei comes from China. Famous for his carry [game role] skills, Burning is a legend among Dota players. Usually hailed as the top carry player, Burning enters the Dota hall of fame with his insane GPM [game statistics]. With his insane farm, he easily carries his team to victory. Displaying ex- CHAPTER 5. CAPTURING THE PROCESS: MARKET CONSTRUCTION ON /R/DOTA2 40

cellent map awareness and presence, his actions hide the numerous years of experience in Dota. Anyone can be a good farmer. But not everyone can be a good carry. His signature heroes are Morphling, Anti-Mage, Naga Siren and Lone Druid.

[…]

WehSing ‘SingSing’ Yuen is a Dutch Dota 2 player. Gaining a huge fanbase for his entertaining personality and in-game dominance, Singsing is famous for risky plays and his signature phrases such as ‘Shoot arrow, Hit arrow’. His signature heroes are POTM and Kunkka.[13]

Here all of the value dimensions and some devices I mentioned, and some which were not included in the model, are shining together, as described by Kornberger in relation to brands, including role-related skills, personality, ‘stats’, game-specific skills, and personality characteristics.

Chapter Discussion. Organising Devices and Algorithms

In this chaper I focus on deeper qualitative analysis, supporting and enriching the results from quantitative modeling.

I show that, while having some reservations about the nature of virtual autographs, related to their detachment from ‘real’ meeting (when bought online), community members treat them as a form of supporting their favourite players, which is in agreements with a brand-related motivations of sport fans in general (Filo, Lock, and Karg 2015). However, market logic of treating autographs becomes more visible in association with the Steam Community Market, and players discuss them as collectables, evaluating whose autograph will be a ‘right’ one for the particular game character.

This drives us to the devices around which evaluation of personal and team brands takes place in the community, ‘making values visible’ (Kornberger 2015, 105), and showing that all the dimensions, associated with brands of sportsmen (Arai, Ko, and Ross 2014) are taken into consideration in Dota 2 community as well, though taking into account game and sports specifics.

This specific, for example, comes to play as algorithmic comparison devices, such as KDA and MMR, which are widely used in the community to compare the performance both inside the professional and fan communities, and between them. As spectators and fans are mostly non- CHAPTER 5. CAPTURING THE PROCESS: MARKET CONSTRUCTION ON /R/DOTA2 41 professional Dota 2 players themselves, they are in the same algorithmic evaluation devices continuum as their idols.

This might be one of the reasons why algorithmic devices (e.g. MMR), being non-transparent, are not rejected as a measure of skill – while not completely understanding the mechanics, players have been assessed and reassessed themselves, which gives them some substitute of transparency: shared experience.

Being taken into account together with more traditional performance indicators and personal characteristics, these organising devices (Kornberger 2015) constantly work on performative construction of a notion ‘what is a good player/team’ and thus on informing brand value and autographs prices. Chapter 6

Concluding Notes

In this thesis I convey the first results of the study of pricing and valuation mechanisms of eSports brands in Dota 2.

Using Community Market for Dota 2 players’ autographs, /r/dota2 discussions, and netno- graphic analysis of some additional sources, I show that some prominent features of status market in Aspers’ (2009) sense come to play and further tried to dissect the valuation mechanisms, resulting from interaction of Valve platforms, teams and players, media, fan community and other participants.

I demonstrate that sportsmen brand dimensions, i.e. personal performance statistics, team tournament performance, amd media saliency, operationalized in Dota 2 specific terms, together in a complex interplay form brand value and influence autograph pricing (RQ 1). In particular, I highlight an heightened role of media visibility in some local segments, and different contributions to the autographs price by different groups of media.

The need of deeper understanding of the evaluation process (RQ 2) motivated the second part of the analysis, showing how different organising devices are performed and reflected on by the community, constituting (Kornberger 2015) in competition and collision personal and team brands.

In particular, I show that, while main motivation of buying virtual autographs is similar to real world ones, showing support, introduction of market trade turns on another motivation, dictated by market logic, i.e. motivation to get a ‘right’ autograph, associated with a ‘good’ player. Then the roles of various game statistics and concepts as evaluation devices are discussed. Here some earlier missed facts can be seen, for example, an important role of player’s personality and streaming performance in brand formation, and important role of algorithmic organising devices, such as MMR statistics, which tighten together professional players

42 CHAPTER 6. CONCLUDING NOTES 43 and fans in a simngle evaluation continuum.

Practical and Theoretical Implications

On the practical level, this study, bearing in mind its exploratory nature and early stage, can serve as a starting point to analyse and inform brand strategies, though much more domain area expertise needs to be involved to make it true.

On the theoretical level, this research contributes into economic sociology and valuation studies, providing new empirical example of some ideal-type concepts: status market in Aspers’ (2009) interpretation, and singular goods as in (Karpik and Scott 2010), and provoking discussion on what evaluation processes virtual markets can make more transparent to study, using virtual world properties as ‘Petri dishes’ for social scientists (Castronova and Falk 2009).

Another implication is a revealed need of discussion on algorithmic devices – black-box machine learning based algorithms, more and more used uncritically by people not only in special meaning spaces, like MMR in Dota 2, but in more general settings of social media and internet platforms – in the context of economic sociology and valuation studies. While rankings and reputation systems are analysed in some detail (Kornberger, Justesen, Mouritsen, et al. 2015) in this context, algorithmic devices are much more difficult to analyse, as their work with any possible biases and discriminatory decisions, are not transparent.

Possible study on the intersection of economic sociology and valuation studies, and emerging media studies field of algorithm audit (Barocas, Hood, and Ziewitz 2013) might be a good starting point of research in this direction with an exploration of possible applicability of some findings and the approach to studying new social constructs and conventions that arise from the emergence of two-sided market platforms (Rochet and Tirole 2003; Rochet and Tirole 2006; Choudary, Alstyne, and Parker 2016; Evans and Schmalensee 2016), such as Uber, AirBnB, recruting platforms, and crowdsourcing sites being the other further starting point. List of Source References

1. https://www.reddit.com/r/DotA2/comments/2axele/massive_update_to_all_autographs/ 2. https://www.reddit.com/r/DotA2/comments/3fbuny/dota_2_update_main_client_ july_31_3015/ctn690a 3. https://www.reddit.com/r/DotA2/comments/3fbuny/dota_2_update_main_client_ july_31_3015/ctn6fuf 4. https://www.reddit.com/r/DotA2/comments/4en8rf/question_about_autographs/ 5. https://www.reddit.com/r/DotA2/comments/2kyenq/valve_please_hire_or_hire_ more_dedicated/ 6. https://www.reddit.com/r/DotA2/comments/41svc5/level_10_ti5_autograph_rune_ not_working/ 7. https://www.reddit.com/r/DotA2/comments/4i37rc/mu_is_back/ 8. https://www.reddit.com/r/DotA2/comments/4ireom/miracle_hit_9k_mmr/ 9. https://www.reddit.com/r/DotA2/comments/4i4iyw/the_manila_major_europe_ qualifier_day_4_match/ 10. https://www.reddit.com/r/DotA2/comments/4ik6rz/epicenter_moscow_col_vs_fnatic_ postmatch/?ref=search_posts 11. https://www.reddit.com/r/DotA2/comments/4ii6gx/epicenter_moscow_day_1_match_ discussion/d2yda5b 12. https://www.reddit.com/r/DotA2/comments/2bcb5g/dendi_and_iceiceice_signed_ hundreds_of_autographs/ 13. https://www.quora.com/Who-are-the-most-well-known-DotA-players

44 List of Figures

4.1 Fitted conditional tree model for autographs ...... 27 4.2 Conditional Variable Importance ...... 28

45 List of Tables

4.1 Groupped Model Variables ...... 25

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