Disclaimer: The research that is the focus of this article was conducted by Chong-U Lim, who has previously contributed to the AI and Games site. This article is derived from analysis of published work and from discussions with Chong-U himself.
Video games like any other art form are a means of expression for their creators: translating ideas of story and character through function and play. In some instances, players themselves are afforded expression through the manner in which they interact with games. This can arise from making specific decisions on the use of specific mechanics to making specific decisions in narrative-driven games or even the implicit objectives we set ourselves. This latter case being rather evident in communities that rally around specific targets: such as speed running or score runners in games such as Spelunky and Dark Souls.
One increasing area of self-expression in more recent years is through cosmetic customisation: ranging from calling cards and emblems in Call of Duty to armour sets in World of Warcraft. In many instances, avatar customisation does have a tangible impact upon player performance, most notably in role-playing and adventure games given these items influence in-game attributes. However, in a vast number of cases, customisation options are purely cosmetic. What is consistent however, is the ability for players to make a statement of their ability, their attitude or their dedication to a given game based on attributes of their profile and avatar. Games in the Free to Play (F2P) market exploit this phenomena by providing exotic or extravagant cosmetic items at a price to players, often through real-world currency. These micro-transactions are immensely profitable for games such as Dota2, League of Legends and Team Fortress 2, to the point it is now adopted in full-price games such as Rainbow Six: Siege and Overwatch.
What is of interest to us, is whether any relationships exist between these cosmetic elements and a players performance and interactions within the game itself. We've previously explored how in-game performance can correlate with aspects of a players personality and age in Battlefield 3. However, in this case, the focus is on a player's in-game performance and whether this may have an impact on subsequent decision made for self-expression, through customisation of avatars in Team Fortress 2.
Team Fortress 2
Team Fortress 2 (TF2) is a first-person shooter online multi-player game developed by Valve and a sequel to both Team Fortress - a mod for the original Quake - and Team Fortress Classic - a conversion of the Team Fortress mod to the Goldsource engine used for Valve's inaugural title Half Life. The Team Fortress series is focussed upon team-based and objective-driven play that is heavily reliant on cooperation. Each team is composed of a selection of mercenaries complete with their own strengths, weaknesses and weaponry. Players are tasked to complete a variety of different objectives depending on the game mode selected: ranging from capturing control points on the map, to stealing enemy flags, escorting bombs and even play sports in the more recent updates in 2016.
A collection of hats in TF2
The Sniper vs Spy update of 2009 introduced the use of hats to Team Fortress 2: in which each of the nine in-character classes could now wear a hat during the play. While starting with just nine hats (one for each character), the series has since expanded to over 1200 items that can be worn on hands, torso and head. In addition, a series of quality and rarity classifications has arisen over time, due to the challenge and difficulty in attaining specific items.
Starting in 2011, Team Fortress 2 migrated from a paid-for product to free-to-play (FTP), in which the game itself would be free to download and play, but new unique equipment such as weapons and outfits would be available through use of microtransactions via the Steam distribution platform. While a handful of items carry features that can have a minor (to the point of negligible) affect on gameplay, the vast majority are purely cosmetic in nature. While many items can be obtained through normal play or crafting, many are only available to players upon attaining specific ranks, achievements and statistics or through promotions for other games on the Steam platform.
Research into Team Fortress 2 hat-usage was conducted by MIT grad student Chong-U Lim as part of a masters thesis. The focus of the work was to see whether the range of hats available and the rarity of certain hats, combined with the diversity of hats worn by players can tell us something about the players themselves. The rationale was driven by the fact that cosmetic items in online games often act as an expression of achievement that is not necessarily tied to a player's skill. This melding of the players persona with their in-game representation is denoted in the research as 'phantasmal identities': a blend identity between the avatar in a video game with the real-world perceptions and beliefs of the human player. This actually leads to a form of projection of the players identity in an often ridiculous and exaggerated fashion.
Lim gathered profile data from 200 players on Steam as part of the research process: gathering information on how they interact in Steam forums, communicate on each others profiles as well as gather a stack of information on item acquisition for Team Fortress 2. With this data in-hand, Lim conducted a number of processing techniques and statistical methods in order to garner useful data from it. Players are categorised by two distinct metrics that are used in greater detail throughout the research later on: status performance and tie strength.
Status performance is a relationship between of player performance and avatar customisation. This is largely reliant on establishing the value of items players own and the way in which they perform their self-expression. To do this, Lim gathered data from a third-party pricing site for hats to ascertain two key elements:
- The collected value: the total monetary value of hats in a players TF2 inventory.
- The used value: monetary value of all hats equipped across all of their TF2 characters.
This collection of hats was cross-referenced against a third-party website for price listings both for regular and 'unusual' hat types. Players are then grouped based on their status performance using what are known as clustering techniques: a form of analysis that allows for grouping of data based on the features of the dataset, resulting in tight-knit groups or clusters. Lim adopted the k-means clustering algorithm to group players more accurately based upon their status performance.
Meanwhile tie strength is related to the number of friends a person establishes on a social network with respect to their activity. Naturally, this varies depending on the social network itself and the nature with which connections arise. For example, the tie-strength between Facebook and Twitter will vary significantly given the nature of the relationship between friends on each network and the manner in which user activity is disseminated; with Facebook reliant largely on posting and commenting one anothers wall while Twitter is a broadcast medium. This is ascertained in Steam by calculating a number of key variables:
- User's number of friends and the length of their relationship on Steam.
- Posts on their own walls and the walls of their friends.
- The amount of words exchanged between wall posts.
- The number of virtual items the player has on Steam and specifically, the number of items they have accrued through trading on the platform.
- The number of common applications: games that are shared among all of your friends (typically those that you would play together).
- The number of positive and negative emotional words used in social interactions (measured through sentiment analysis).
- Measuring the number of mutual friends and common groups among players. Allowing for an understanding of social clusters that exist on the platform.
This data is naturally rather coarse, so it's then tidied up by running a statistical method known as Principal Component Analysis to streamline it for future calculations by reducing the data into key features it expresses. This allows for the data to be discussed on a 'social status' level, rather than a level of specific data ranges and constraints, which is pretty difficult to do and even harder when trying to explain it all in 10 minute YouTube videos.
With all of this data acquired and reduced, the last part was to adopt a method of merging them together: to be able to . To bring this together, Lim adopts Support Vector Machines: and ultimately
With the status performance and social status identfied, the next and critical component was how to understand how these two elements interact with one another: can we predict a status performance based on a players social status? This led to an analysis of how the clustered status performance groups (and the labels that are associated with them) relate to the social status markers. This required both sets of data to be appropriately clustered such that any relationships can be properly mapped. By clustering the social status data by also using k-means, two tests are made: first, to understand whether *any* relationship exists between the two clusters, followed by a second test to establish whether a specific social status could imply status performance. This last part is achieved through use of Support Vector Machines (SVM): a form of supervised machine learning that can be used for classification puroposes. In this instance it is used to train an appropriate classification model for prediction of player behaviour.
With the data acquired from Steam, Lim was able to identify a broad range of monetary value in the hats worn, ranging from hats worth nothing in a monetary sense, to some worth over $1100. With subsequent analysis, the monetary value of hats can be attributed to three status performance clusters shown below with players of lower status performance on-average wore hats worth less than 25 cents.
Meanwhile high status performance shows players on-average wore hats valued at almost $200. Interestingly, medium and high status-performance players had higher valued items on average equipped versus in their inventory, meanwhile low status-performance players on-average had more valuable items in their inventory. So what does that mean? Well it suggests that players interests and intentions have an impact on the hat usage: with low status-performance players not really caring whether or not they're wearing cool or interesting hats (even if they have them lying around) while high status-performance place a strong emphasis on wearing high-value cosmetic items and showing off their bling.
Further principal component analysis against the dataset of player profiles was able to establish four characteristics or social behaviours that occur on the Steam platform:
- Players can have high social interaction despite the games they have in common being single-player games.
- Players can have both close relationships with specific players as well as - in the context of the Steam - many friends.
- Players who typically trade with one another have a higher chance of engaging in social discourse via Steam, both before and after transactions are completed.
- Players fluctuate between those that typically post on their own wall versus those who will focus on the use of the walls to maintain contact with friends.
Now this all sounds rather common sense and is largely to be expected but the beauty of it is that these conclusions are derived from the PCA analysis of the data. In other words, these characteristics, which sound rather nominal, are being reached from statistical analysis of the data provided, which is pretty freaking cool. With this completed, a final clustering effort took place for player profiles that match those specific behavioural traits. The final results identify 5 specific clusters of players which relate their social status to status performance and is summarised in the table shown below.
It appears that the three status performance groups can be broken down into five player profile clusters. With group 1 focussed on the low status performance players - meaning you don't really care about how prominent your performance is presented online, through to group 5 which is exclusively medium to high status performance where the most valuable and flashiest of hats are worn. In short, you could actually build a model for the relationship between how much people chat with friends on Steam and the hats they wore in TF2. A final classification analysis using support vector machines was capable of predicting what groups you as a player could fit into with an accuracy of over 60% just by modelling hats and Steam performance.
What's interesting about this type of work is the implications it presents: most notably that there are strong relationships that can be found between a player’s real-world identity and social behaviour with their virtual identity in Team Fortress 2. This can be of real interest to designers of both games and social media platforms, ensuring they provide adequate technologies to not only enable users but also to be wary of the effects of any coupling between real-world and virtual identities that might occur. It leaves scope for application of real world cosmetic marketing to be applied and build parallels to real world phenomena: such as limited edition or design apparel or building promotions with developers for cosmetic items. But as noted in Lim's papers publishing this research, there are issues of social impact and this type of behaviour could result in sociological issues such as perceived privilege or margnialisation. We've since seen evidence of promotions, exclusive content and the community frustration occur in the likes of not just TF2 but also PayDay 2, Killer Instinct, Overwatch, Call of Duty: Black Ops 3 and many other online games. If anything, Lim's research foretold many of the current trends in cosmetic items in online gaming, because the research shows that this can have a tangible effect on players interactions and that ultimately, some players will place a great amount of attention on their aesthetics.
- Chong-U Lim, and D. Fox Harrell. (2013). "Modeling Player Preferences in Avatar Customization using Social Network Data: A Case-Study using Virtual Items in Team Fortress 2." In Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG2013), Niagara Falls, Canada. Aug-11 - Aug-13, 2013. pp. 153 - 160.
- Chong-U Lim (2013). "Modeling Player Self-Representation in Multiplayer Online Games using Social Network Data", S.M. Thesis, 2013, Massachusetts Institute of Technology, USA.
Further information can be found on the project can be found at its dedicated website: the 'Steam-Player-Preference Analyzer and the AIR Status Performance Classifier'.