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Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2
Abstract
Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in arena environments, and the gameplay is focused on tactical combat. Mastering MOBAs requires extensive practice, as is exemplified in the popular MOBA Defence of the Ancients 2 (DotA 2). In this paper, we present three data-driven measures of spatio-temporal behavior in DotA 2: 1) Zone changes; 2) Distribution of team members and: 3) Time series clustering via a fuzzy approach. We present a method for obtaining accurate positional data from DotA 2. We investigate how behavior varies across these measures as a function of the skill level of teams, using four tiers from novice to professional players. Results indicate that spatio-temporal behavior of MOBA teams is related to team skill, with professional teams having smaller within-team distances and conducting more zone changes than amateur teams. The temporal distribution of the within-team distances of professional and high-skilled teams also generally follows patterns distinct from lower skill ranks.
... However, the first relevant research gaps are identified in Figure 2. None of the selected studies is focused specifically on or includes professional players (defining professional players as players that play in professional eSports teams). In that respect, it's true that Drachen et al. (2014) attempt to relate player skill to spatio-temporal behaviour in order to understand the differences in behaviour between amateur and professional players during a match, but doesn't involve these players personally to understand their perspective on these differences. ...
... In Rioult et al. (2014), an exercise of prediction using topological measures on the team-area of the polygon described by the players, inertia, diameter or distance to the base-is conducted, highlighting its potential for a strategic analysis of team play. Work by Drachen et al. (2014) points in the same direction but making use of spatio-temporal behaviours of the team. It also brings an additional and relevant variable to the mix: skill level. ...
... On the other hand, Batsford (2014) leaves players aside and aims to calculate an optimal jungling route in DOTA2 using various algorithms and looking at the experience obtained over time. It is a different approach that fits into the same idea as the previous two examined papers ( Rioult et al., 2014;Drachen et al., 2014): what should players do to play optimally in a match? ...
This article aims to perform a literature review of the available research that focuses strictly on MOBA (multiplayer online battle arena) games. First, a review identifying the relevant papers published since 2011 is conducted, exploring them systematically to extract similarities, gaps and main findings. Results show how League of Legends is the most explored game, with player experience and toxic behaviour as popular topics for research. Second, as MOBA games remain underexplored by researchers despite their vast, enthusiast community, as well as projection and influence on contemporary game designers, a proposal on future lines for research is provided.
... But they use the standard measure of kills (and deaths), rather than the win-probability-filtered ones we introduce in this paper. Drachen et al. (2014) describe a way of extracting positional data for Dota2, but the challenges of data extraction differ substantially between Dota2 and LoL. Schubert, Drachen, and Mahlmann (2016) describe a process for determining encounters for Dota2 that has some similarities to one of the ways in which we define team fights. ...
Vast data on eSports should be easily accessible but often is not. League of Legends (LoL) only has rudimentary statistics such as levels, items, gold, and deaths. We present a new way to capture more useful data. We track every champion’s location multiple times every second. We track every ability cast and attack made, all damages caused and avoided, vision, health, mana, and cooldowns. We track continuously, invisibly, remotely, and live. Using a combination of computer vision, dynamic client hooks, machine learning, visualization, logistic regression, large-scale cloud computing, and fast and frugal trees, we generate this new high-frequency data on millions of ranked LoL games, calibrate an in-game win probability model, develop enhanced definitions for standard metrics, introduce dozens more advanced metrics, automate player improvement analysis, and apply a new player-evaluation framework on the basic and advanced stats. How much does an individual contribute to a team’s performance? We find that individual actions conditioned on changes to estimated win probability correlate almost perfectly to team performance: regular kills and deaths do not nearly explain as much as smart kills and worthless deaths. Our approach offers applications for other eSports and traditional sports. All the code is open-sourced.
... eSports is a rapidly growing phenomenon characterized by its own culture and community, comprising not only players and spectators, but also other stakeholders such as game development companies, media, and investors. Taylor and Witkowski argued in 2010 that eSports would become mainstream culture as computer games continue to spread into everyday life, and the latest statistics support them; eSports holds a considerable market value ($898 million in 2016, estimated to $1,238 billion by 2019) and attracts massive amounts of spectators (213.8 million spectators world-wide in 2016). 1 eSports is an emerging area of research within the wider field of computer games research (e.g.,Drachen et al., 2014;Faust, Meyer, & Griffiths, 2013;Seo & Jung, 2016;Smith, Obrist, & Wright, 2013;Taylor, 2016). Still, research on eSports is limited as previous research has mainly focused on theoretical discussions, such as aesthetics and comparisons between eSports and sports (e.g.,Ferrari, 2013;Jenny, Manning, Keiper, & Olrich, 2016), and discussed eSports from a player perspective (e.g.,Bowman, Weber, Tamborini, & Sherry, 2013;Carter & Gibbs, 2013;Johnson, Nacke, & Wyeth, 2015). ...
A pre-study of spectators' perspectives on eSports was conducted in collaboration with two Swedish game development companies. The main goal was to identify factors that contribute to qualitative spectator experiences and how they can influence game design. A qualitative approach was chosen to explore spectators' perspectives on eSports through observations and focus-group interviews of 28 participants in total. Results indicate that spectatorship is a complex issue that goes beyond the mere watching of a game. We identified four themes that are important for qualitative spectator experiences: the need for an overview of game events; highlighting and exposing hidden objects and events; viewer- and commentator-friendly game pacing; the importance of professional commentators and casters. Based on the results, we present design guidelines and recommendations for the development of games in eSports.
... The overall aim was to contribute to the problem of predicting network trac in MMOGs accurately. In esports analytics (as dened by Drachen et al. (2014c), a small number of publications have focused on clustering or proling player behavior. Gao et al. (2013) targeted the identication of the heroes that players are controlling and the role they take. ...
The availability of large scale game behavioral data has led to a tremendous amount of attention to business intelligence in game development and-research. The analysis of player behavior has rapidly emerged to become an integrated component of game development, and is used to address a range of challenges such as improving game design, manage monetization, predict behavior, recommend games, detect fraud or to build an understanding of human behavior in online environments in general. One of the key challenges in contemporary game analytics is pattern nding in behavioral telemetry, and the construction of actionable models of behavior based on patterns. When applied to players, this process can be broadly referred to as proling. There are a number of challenges in behavioral proling, including the constant growth in data volume, the variety (dimensionality) and volatility of big data in game development. In this chapter the current work in behavioral proling in digital games is described and analyzed, and the state-of-the-art established. The techniques used for behavioral proling in games are described across descriptive statistics and machine learning. Furthermore, a number of examples are provided that covers a range of techniques from machine learning: clustering, archetypal analysis, decomposition into directional components and decision trees. Each have specic advantages in overcoming the challenge of extracting interpretable behavioral proles from behavioral game telemetry data.
This paper examines the evolving landscape of modern digital games, emphasizing their nature as live services that continually evolve and adapt. In addition to engaging with the core gameplay, players and other stakeholders actively participate in various game-related experiences, such as tournaments and streaming. This interplay forms a vibrant and intricate ecosystem, facilitating the construction and dissemination of knowledge about the game. Such knowledge flow, accompanied by resulting behavioral changes, gives rise to the concept of a video game meta . Within the competitive gaming context, the meta represents the strategic and tactical knowledge that goes beyond the fundamental mechanics of the game, enabling players to gain a competitive advantage. We present a review of the state-of-the-art of knowledge for game metas and propose a novel model for the meta knowledge structure and propagation that accounts for this ecosystem, based on a review of the academic literature and practical examples. By exploring the dynamics of knowledge exchange and its influence on gameplay, the review presented here sheds light on the intricate relationship between game evolution, player engagement, and the associated emergence of game meta.
Many important indicators of the inputs, mediators and outcomes of team performance have remained stubbornly resistant to sustained and detailed measurement. Team-based video games, now played by untold millions of people worldwide, offer the potential to provide data to support the analysis of these and other team-centered phenomena; however, theoretically grounded guidance is needed to guide game selection and modification to support such analysis. Accordingly, this paper explores prospects and challenges for using currently available open video games (that is, ones which are free, open-source, and open-architecture) to improve understanding of team performance. The approach combines systematic classification, structured critique and close analysis to identify games with strong potential for explorations of teamwork by human or mixed human-agent teams. The paper concludes with a discussion of promising trajectories for future research.
In Multiplayer Online Battle Arena (MOBA) games, teams of players compete in combat to complete an objective and defeat the opposing team. To stay alive, players must closely monitor their character's status, especially remaining health. Understanding how health may change in the near future can be vital in determining what tactics a player may use. We analyzed replay logs of the game Defense of the Ancients 2 (DotA 2) to discover methods to predict how players' health evolves over time. For DotA 2, our results suggest that forecasting changes in a player's health can be done by viewing gameplay as two separate processes: normal gameplay flow in which changes in health are smaller and more regular, and less frequent but higher-impact events in which players experience larger changes in their health, such as team battles. We accomplished this by considering health data as two separate, but interleaved, time series in which separate processes govern low magnitude changes in health from high magnitude changes. In this paper, we present a value-split approach to predicting changes in health and describe the results of our approach using autoregressive moving-average models for low magnitude health changes and a combination of statistical models for the larger changes.
Although movement is essential in location-based games to get from one point of interest to the next, it is seldom taken into account for the game design and the selection of locations. Instead, player movement is usually analyzed after the fact, i.e. when the game is ready to play. In this paper we compare this post-hoc movement analysis with an approach that utilizes the methods for movement analysis to inform the game design itself. We show that both approaches have their merits and solve different tasks, but that there is a benefit of taking movement more serious in designing location-based educational games.
Smart decision making at the tactical level is important for Artificial Intelligence (AI) agents to perform well in the domain of real-time strategy (RTS) games. This paper presents a Bayesian model that can be used to predict the outcomes of isolated battles, as well as predict what units are needed to defeat a given army. Model parameters are learned from simulated battles, in order to minimize the dependency on player skill. We apply our model to the game of Starcraft, with the end-goal of using the predictor as a module for making high-level combat decisions, and show that the model is capable
of making accurate predictions.
The relationship between leadership development and multiplayer online battle arena games (MOBA) are examined using two popular games of this genre: Defense of The Ancients (DotA) and Heroes of Newerth (HoN). Similar existing research notably includes IBM’s “Leadership in Games and at Work: Implication for the Enterprise of Massively Multiplayer Online Role-Playing Games” published in 2007, positively concluding the contribution online games have towards leadership development. Close-ended surveys created based on Fred Fiedler’s contingency model (1967) and Kurt Lewin’s leadership model (1939) were distributed in Thailand both in written and online formats. The targeted respondent demographic is unemployed Thai game players 13 years old or above. Survey content determined the respondent’s leadership style (authoritarian, democratic or laissez-faire) and game role (carry, support or ganker). Multinomial logistic regression and factor analyses determined that the 3197 eligible surveys collected shows a relation between MOBA games and leadership development. Based on the findings, it is concluded that those who are characterized as the game role “carry” are more likely to have the authoritarian leadership style and less likely to have laissez-faire leadership style. Those who are characterized as the game roles “support” and “ganker” are more likely to have the democratic leadership style and less likely to have authoritarian and laissez-faire leadership styles. Other demographic data such as age, income and education level are found to be statistically insignificant in influencing leadership development. The conclusions presented impacts the current available literature, which tends to present a comparatively negative view relevant to online games and individual behavior. Potential game development based on research findings may aid in the improvement of individuals’ leadership capabilities.
R package, available on CRAN
The development and professionalization of a video game requires tools for analyzing the practice of the players and teams, their tactics and strategies. These games are very popular and by nature numerical, they provide many tracks that we analyzed in terms of team play. We studied Defense of the Ancients (DotA), a Multiplayer Online Battle Arena (MOBA), where two teams battle in a game very similar to rugby or American football. Through topological measures – area of polygon described by the players, inertia, diameter, distance to the base – that are independent of the exact nature of the game, we show that the outcome of the match can be relevantly predicted. Mining e-sport's tracks is opening interest in further application of these tools for analyzing real time sport.
Evaluating the spatial behavior of players allows for comparing design intent with emergent behavior. However, spatial analytics for game development is still in its infancy and current analysis mostly relies on aggregate visualizations such as heatmaps. In this paper, we propose the use of advanced spatial clustering techniques to evaluate player behavior. In particular, we consider the use of DEDICOM and DESICOM, two techniques that operate on asymmetric spatial similarity matrices and can simultaneously uncover preferred locations and likely transitions between them. Our results highlight the ability of asymmetric techniques to partition game maps into meaningful areas and to retain information about player movements between these areas.
As an increasing number of human activities are moving to the Web, more and more teams are predominantly virtual. Therefore, formation and success of virtual teams is an important issue in a wide range of fields. In this paper we model social behavior patterns of team work using data from virtual communities. In particular, we use data about the Web community of the multiplayer online game Dota 2 to study cooperation within teams. By applying statistical analysis we investigate how and to which extent different factors of the team in the game, such as role distribution, experience, number of friends and national diversity, have an influence on the team's success. In order to complete the picture we also rank the factors according to their influence. The results of our study imply that cooperation within the team is better than competition.
Player satisfaction is particularly difficult to ensure in online games, due to interactions with other players. In adversarial multiplayer games, matchmaking typically consists in trying to match together players of similar skill level. However, this is usually based on a single-skill value, and assumes the only factor of “fun” is the game balance. We present a more advanced matchmaking strategy developed for Ghost Recon Online, an upcoming team-focused first-person shooter (FPS) from Ubisoft (Montreal, QC, Canada). We first show how incorporating more information about players than their raw skill can lead to more balanced matches. We also argue that balance is not the only factor that matters, and present a strategy to explicitly maximize the players' fun, taking advantage of a rich player profile that includes information about player behavior and personal preferences. Ultimately, our goal is to ask players to provide direct feedback on match quality through an in-game survey. However, because such data were not available for this study, we rely here on heuristics tailored to this specific game. Experiments on data collected during Ghost Recon Online's beta tests show that neural networks can effectively be used to predict both balance and player enjoyment.
The primary goal of this thesis is to present novel methodologies for the exploratory analysis of psychological data sets that support researchers in informed theory development. Psychological data analysis bears a long tradition of confirming hypotheses generated prior to data collection. However, in practical research, the following two situations are commonly observed: In the first instance, there are no initial hypotheses about the data. In that case, there is no model available and one has to resort to uninformed methods to reveal structure in the data. In the second instance, existing models that reflect prior hypotheses need to be extended and improved, thereby altering and renewing hypotheses about the data and refining descriptions of the observed phenomena. This dissertation introduces a novel method for the exploratory analysis of psychological data sets for each of the two situations. Both methods focus on time series analysis, which is particularly interesting for the analysis of psychophysiological data and longitudinal data typically collected by developmental psychologists. Nonetheless, the methods are generally applicable and useful for other fields that analyze time series data, e.g., sociology, economics, neuroscience, and genetics. The first part of the dissertation proposes a clustering method for time series. A dissimilarity measure of time series based on the permutation distribution is developed. Employing this measure in a hierarchical scheme allows for a novel clustering method for time series based on their relative complexity: Permutation Distribution Clustering (PDC). Two methods for the determination of the number of distinct clusters are discussed based on a statistical and an information-theoretic criterion. Structural Equation Models (SEMs) constitute a versatile modeling technique, which is frequently employed in psychological research. The second part of the dissertation introduces an extension of SEMs to Structural Equation Modeling Trees (SEM Trees). SEM Trees describe partitions of a covariate-space which explain differences in the model parameters. They can provide solutions in situations in which hypotheses in the form of a model exist but may potentially be refined by integrating other variables. By harnessing the full power of SEM, they represent a general data analysis technique that can be used for both time series and non-time series data. SEM Trees algorithmically refine initial models of the sample and thus support researchers in theory development. This thesis includes demonstrations of the methods on simulated as well as on real data sets, including applications of SEM Trees to longitudinal models of cognitive development and cross-sectional cognitive factor models, and applications of PDC on psychophysiological data, including electroencephalographic, electrocardiographic, and genetic data.
Most computer games that are set in simulated 3D worlds require the player to act according to his or her current location. For the programming of convincing artificial game agents this poses the problem of providing them with a sense of where they are. However, due to the growing size and complexity of games, manual annotations that capture all essential areas and aspects may soon become infeasible. In this paper, we therefore propose a data-driven approach to the semantic clustering of in-game locations. Following the paradigm of gamebot programming by human demonstration, we apply the technique of spectral clus- tering to automatically derived waypoint maps. First results underline that this approach indeed provides spatial partitions of game maps that make sense from a gamers point of view.
User-behaviour analysis has only recently been adapted to the context of the virtual world domain and remains limited in its application. Behaviour analysis is based on instrumentation data, automated, detailed, quantitative information about user behaviour within the virtual environment (VE) of digital games. A key advantage of the method in comparison with existing user-research methods, such as usability-and playability-testing is that it permits very large sample sizes. Furthermore, games are in the vast majority of cases based on spatial, VEs within which the players operate and through which they experience the games. Therefore, spatial behaviour analyses are useful to game research and design. In this paper, spatial analysis methods are introduced and arguments posed for their use in user-behaviour analysis. Case studies involving data from thousands of players are used to exemplify the application of instrumentation data to the analysis of spatial patterns of user behaviour. Reference to this paper should be made as follows: Drachen, A. and Canossa, A. (xxxx) 'Evaluating motion: spatial user behaviour in virtual environments', Int. J. Arts and Technology, Vol. x, No. x, pp.xx–xx. Biographical notes: Anders Drachen is a Visiting Researcher with the Institute for Informatics, Copenhagen Business School and a Developer at Game Analytics Technologies, Denmark. His work focuses on user research in game development and game research, including game testing, user testing, user experience analysis, psychophysiological measurements, heuristic analysis, behavioural analysis and data visualisation. He collaborates with a range of game development companies to improve user-oriented testing in game development.
We present a data mining approach to opponent modeling in strategy games. Expert gameplay is learned by applying machine learning techniques to large collections of game logs. This approach enables domain independent algorithms to acquire domain knowledge and perform opponent modeling. Machine learning algorithms are applied to the task of detecting an opponent's strategy before it is executed and predicting when an opponent will perform strategic actions. Our approach involves encoding game logs as a feature vector representation, where each feature describes when a unit or building type is first produced. We compare our representation to a state lattice representation in perfect and imperfect information environments and the results show that our representation has higher predictive capabilities and is more tolerant of noise. We also discuss how to incorporate our data mining approach into a full game playing agent.
A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation. This silhouette shows which objects lie well within their cluster, and which ones are merely somewhere in between clusters. The entire clustering is displayed by combining the silhouettes into a single plot, allowing an appreciation of the relative quality of the clusters and an overview of the data configuration. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters.
Over the last decade, Massively Multi-player On-line Role Playing Games (MMORPGs), have become big business. In typical MMORPGs, players pay a monthly subscription to the game publisher who hosts the game and provides pe- riodic content updates. To be successful, game publishers must characterize their player population so that they can provision sucient resources to support the game and so that they can update the game in a timely manner. To this end, this paper provides the first, long-term study of a pop- ular MMORPG from its launch. Our dataset encompasses the entire lifetime of "EVE Online" (1, 2), a science-fiction based MMORPG that has supported nearly 1 million unique players, 67 million player sessions, and 17,000 player years of gameplay since its launch in May 2003.
Recording player gameplay data has become a prevalent feature in many games and platform systems. Players are now able to track their achievements, analyze their past gameplay behaviour and share their data with their gaming friends. A common system that gives players these abilities is known as a player dossier, a data-driven reporting tool comprised of a player's gameplay data. Player dossiers presents a player's past gameplay by using statistical and visualization methods while offering ways for players to connect to one another using online social networking features. This paper presents a framework for understanding how player dossiers function and fit into the process of playing games. While a common feature of player dossier systems is to merely list the rewards a player has received during play these systems also validate other gameplay motivations that may interest players besides gathering achievements. Player dossiers contextualize gameplay allowing players to analyze what they find important and share gaming information with a wider community. This turns the process of exploring past gameplay into its own reward beyond any awarded to a player in game.
The collection and analysis of behavioral telemetry in digital games has in the past five years become an integral part of game development. One of the key challenges in game analytics is the development of methods for characterizing and predicting player behavior as it evolves over time. Characterizing behavior
is necessary for monitoring player populations and gradually improve game design and the playing experience. Predicting behavior is necessary to describe player engagement and prevent future player churn. In this paper, methods and theory from kernel archetype analysis and random process models are utilized
to evaluate the playtime behavior, i.e. time spent playing specific games as a function of time, of over 6 million players, across more than 3000 PC and console games from the Steam platform, covering a combined playtime of more than 5 billion hours. A number of conclusions can be derived from this large-scale analysis, notably that playtime as a function of time, across the thousands of games in the dataset, and irrespective of local differences in the playtime frequency distribution, can be modeled using the same model: the Weibull distribution. This suggests that there are fundamental properties governing player engagement as it evolves over time, which we here refer to as the Playtime Principle. Additionally, the analysis shows that there are distinct clusters, or archetypes, in the playtime frequency distributions of the investigated games. These archetypal groups correspond to specific playtime distributions. Finally, the analysis reveals information about player behavior across a very large dataset, showing for example that the vast majority of games are players
for less than 10 hours, and very few players spend more than 30-35 hours on any specific game.
The recently emerged field of game analytics and the development and adaptation of business intelligence techniques to support game design and development has given data-driven techniques a direct role in game development. Given that all digital games contain some sort of spatial operation, techniques for spatial analysis had their share in these developments. However, the methods for analyzing and visualizing spatial and spatio-temporal patterns in player behavior being used by the game industry are not as diverse as the range of techniques utilized in game research, leaving room for a continuing development. This paper presents a review of current work on spatial and spatio-temporal game analytics across industry and research, describing and defining the key terminology, outlining current techniques and their application. We summarize the current problems and challenges in the field, and present four key areas of spatial and spatio-temporal analytics: Spatial Outlier Detection, Spatial Clustering, Spatial Predictive Models, Spatial Pattern and Rule Mining. All key areas are well-established outside the context of games and hold the potential to reshape the research roadmap in game analytics.
Modern massive-multiplayer-online games (MMOs) allow thousands of users to simultaneously interact within a virtual spatial
environment. Developing an MMO servers yields a lot of new challenges in managing and mining spatial temporal data. In this
paper, we describe problems of handling spatial data in an MMO and outline why analyzing spatial patterns is an important
task for keeping an MMO successful over a long period of time. Though many of these problems share a close connection to well-known
tasks in temporal and spatial databases, there are many significant differences as well. Thus, we will describe the similarities
to established problems and outline new challenges for managing and mining spatial temporal data in MMOs.
This paper reports on the preliminary findings of a study examining the nature of rules in the online multiplayer game modification Defense of the Ancients (DotA). It was found that players use numerous truce calls (categorised broadly as fainties, parlay, pax and cheap) to negotiate rules or the maintenance of 'fair play' in a game. The possibility of providing feedback on the use of truce calls to developers as part of the design process is also considered.
Distributed Virtual Environment (DVE) topology management and message propagation schemes have been proposed for many years. Evaluating DVE message propagation schemes requires a variety of assumptions whose verity significantly affects results, such as details about avatar movement characteristics. We implemented two schemes for waypoint and hotspot detection, and examined their applicability for characterising avatar movement. We confirmed that waypoint detection does not yield good results for characterising human avatar movement, and gained new insight into why by rendering avatar movement as point clouds. We implemented an existing hotspot detection model, and proposed an enhancement to help overcome one limitation of cell-based hotspot detection. We were able to immediately apply this hotspot detection technique to help analyse group movement. We discovered that although a third of movement time in the battlegrounds is spent in inter-node journeys, less than a quarter of these journeys are made in groups.
We present a system for enhancing observation of user interactions in virtual environments. In particular, we focus on analyzing behavior patterns in the popular team-based first-person perspective game Return to Castle Wolfenstein: Enemy Territory. This game belongs to a genre characterized by two moderate-sized teams (usually 6 to 12 players each) competing over a set of objectives. Our system allows spectators to visualize global features such as large-scale behaviors and team strategies, as opposed to the limited, local view that traditional spectating modes provide. We also add overlay visualizations of semantic information related to the action that might be important to a spectator in order to reduce the information overload that plagues traditional overview visualizations. These overlays can visualize information about abstract concepts such as player distribution over time and areas of intense combat activity, and also highlight important features like player paths, fire coverage, etc. This added information allows spectators to identify important game events more easily and reveals large-scale player behaviors that might otherwise be overlooked.
Riot Games' League Of Legends Officially Becomes Most Played PC Game In The World
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Mining Tracks of Competitive Video Games
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