DOCSLIB.ORG

Metagame Autobalancing for Competitive Multiplayer Games

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Metagame Autobalancing for Competitive Multiplayer Games Metagame Autobalancing for Competitive Multiplayer Games Daniel Hernandez∗, Charles Takashi Toyin Gbadamosiy, James Goodmanz and James Alfred Walker∗, Senior Member, IEEE ∗Department of Computer Science, University of York, UK. fdh1135, [email protected] yDepartment of Computer Science, Queen Mary University of London, UK. fc.t.t.gbadamosi, [email protected] Abstract—Automated game balancing has often focused on make judgements about the state of the meta-game and provide single-agent scenarios. In this paper we present a tool for designers with insight into future adjustments, such as [2]. balancing multi-player games during game design. Our approach There are, however, several problems with this approach. requires a designer to construct an intuitive graphical represen- tation of their meta-game target, representing the relative scores Analytics can only discover balance issues in content that that high-level strategies (or decks, or character types) should is live, and by that point balance issues may have already experience. This permits more sophisticated balance targets to negatively impacted the player experience: this is a reactive be defined beyond a simple requirement of equal win chances. We approach and not a preventive one. Worse, games which do then find a parameterization of the game that meets this target not have access to large volumes of player data - less popular using simulation-based optimization to minimize the distance to the target graph. We show the capabilities of this tool on examples games - cannot use this technique at all. inheriting from Rock-Paper-Scissors, and on a more complex Furthermore, the process of data analytics itself is not asymmetric fighting game. typically within the skill-set of game designers. It is common for studios that run multiplayer games to hire data scientists I. INTRODUCTION to fill this need. This, in combination with the trial and Achieving game balance is a primary concern of the game error nature of the balance process, results in increased costs, designer, but balancing games is a largely manual process of becoming as a bottleneck for the development of new content. trial and error. This is especially problematic in asymmetric The importance of meta-game balance and the aforemen- multiplayer games where perceived fairness has a drastic tioned issues motivate alternate approaches to game balance. impact on the player experience. Changes to individual game This paper presents one such alternative - an automated elements or rules can have an impact on the balance between simulation-based approach to meta-game balance of mul- high-level strategies that depend on these, but this impact is tiplayer games. Our approach allows designers to directly unknown before changes are made and can only be guessed specify a meta-game balance state and have the game param- at by designers through experience and intuition. We term eters that would create the desired meta-game be discovered this balance between emergent high-level strategies the ‘Meta- automatically by a group of agents. game balance’. While in-house tools can be built for the adjustment and authoring of individual game elements. There are no tools for balancing and adjusting -game elements. II. PRELIMINARY NOTATION Game balancing takes a lot of time and resources, with Cursive lowercase letters represent scalars (n). Bold low- current trends indicating a systematic increase in the cost n ercase, vectors (π 2 R ). Bold uppercase, matrices (A 2 of game development [1]. It is reliant on human intuition n×n R ). and expert knowledge to estimate how changes in the game mechanics affect emergent gameplay. Human play testing as A. Game parameterization part of this process is time consuming, requiring many human Every video game presents a (potentially very large) number testers for long play-sessions, which grow longer with more of values that characterize the game experience, which we complex games. In short, human play testing does not scale. shall refer to as game parameters. These values can be An alternative approach to the discovery of meta-game numerical (such as gravitational strength, movement speed, changes that arise from game changes is through data ana- health) or categorical (whether friendly fire is activated, to lytics. Large scale multiplayer titles that have access to large which team a character belongs). As a designer, choosing quantities of player data can use a variety of techniques to a good set of parameters can be the difference between an excellent game and an unplayable one. We let E denote Thank you, Ozan Vardal, Nick Ballou and Sebastian Berns for your θ generous help in coding Workshop Warfare. This work was funded by a game environment, parameterized by an n-sized parameter the EPSRC Centre for Doctoral Training in Intelligent Games and Game vector θ 2 fΠi≤nΘig, where fΠi≤nΘig represents the joint Intelligence (IGGI) EP/L015846/1 and Digital Creativity Labs. parameter space, and Θi the individual space of possible values for the ith parameter in θ. 978-1-7281-4533-4/20/$31.00 c 2020 Crown B. Meta-games D. Empirical Response Graphs What a meta-game is can mean different things to dif- A directed weighted graph of v 2 N+ nodes can be ferent players. For example in deck-building games such as denoted by an adjacency matrix G 2 Rv×v. Each row i in Hearthstone, the ‘meta’ is usually interpreted to indicate which G signifies the weight of all of the directed edges stemming + decks are currently popular or especially strong; while in EVE from node i. Thus, gi;j 2 R corresponds to the weight of Online an important part of the ‘meta’ is player diplomatic the edge connecting node i to node j, where 1 ≤ i; j ≤ v. alliances, as well as which ship types are good against which Given an evaluation matrix Aπ computed from a set of others. See [3] for a good discussion of this notation. strategies (or agents) π, let its response graph [6] represent In this work we define a meta-game as a set of high-level the dynamics [4] between agents in π. That is, a representation strategies that are abstracted from the atomic game actions. of which strategies (or agents) perform favourably against Reasoning about a game involves thinking about how each which other strategies in π. In a competitive scenario, a individual action will affect the outcome of the game. In response graph shows which strategies win against which contrast, a meta-game considers more general terms, such as others. As a graph, each strategy i is represented by a node. An how an aggressive strategy will fare against a defensive one. edge connecting node i to node j indicates that i dominates In meta-games, high level strategies are considered instead j. The weight of the edge is a quantitative metric of how of primitive game actions. Take a card game like Poker. favourably strategy i performs against j. Figure 1a shows a Reasoning about a Poker meta-game can mean reasoning about response graph for the game of Rock-Paper-Scissors (RPS). how bluff oriented strategies will deal against risk adverse A response graph can be readily computed from an evalua- strategies. tion matrix. Each row i in an evaluation matrix A denotes The level of abstraction represented in a meta-game is de- which strategies i both wins and loses against, the former fined by the meta-game designer, and the same game can allow being indicated by positive entries and the latter by negative for a multitude of different levels of abstraction. For instance, ones. Therefore, generating a response graph G from an in the digital card game of Hearthstone, meta-strategies may evaluation matrix A is as simple as setting all negative entries 1 −2 correspond to playing different deck types, or whether to play of A to 0 such that, for instance, A = 2 −1 , becomes 1 0 more offensively or defensively within the same deck. A game G = ( 2 0 ). designer may want to ensure that no one deck type dominates, but be happy that a particular deck can only win if played E. Graph distance offensively. There is a rich literature on measuring distance between C. Empirical win-rate matrix meta-games graphs [7]. We concern ourselves here with a basic case. We are interested in measuring the distance between two graphs An interesting meta-game definition that has recently re- v×v which share the same number of nodes, G1; G2 2 R , ceived attention in multiagent system analysis [4] defines a and differ only in the weight of the edges connecting nodes. normal form game over a population of agents π, such that Because graphs can be represented as matrices, we look at the action set of each player corresponds to choosing an differences between matrices. We denote the distance between agent π 2 π from the population to play the game for 1 2 1 2 i two graphs G and G by d(G ; G ) 2 R. Equation them. How these agents were created is not relevant to us; (1) represents the average absolute edge difference and (2) these agents could use hand-crafted heuristics, be trained with represents the mean squared difference (MSE). reinforcement learning, evolutionary algorithms or any other P 1 2 P 1 2 2 method. i;j jgij − gijj i;j(gij − gij) n×n (1) (2) Let Wπ 2 R denote an empirical win-rate matrix. n n The entry wi;j for i; j 2 fng represents the win-rate of many head-to-head matches of policy πi when playing against policy Preliminary results showed no empirical difference between πj for a given game.
Load more

Recommended publications
  • Flexible Games by Which I Mean Digital Game Systems That Can Accommodate Rule-Changing and Rule-Bending
    Let’s Play Our Way: Designing Flexibility into Card Game Systems Gifford Cheung A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2013 Reading Committee: David Hendry, Chair David McDonald Nicolas Ducheneaut Jennifer Turns Program Authorized to Offer Degree: Information School ©Copyright 2013 Gifford Cheung 2 University of Washington Abstract Let’s Play Our Way: Designing Flexibility into Card Game Systems Gifford Cheung Chair of the Supervisory Committee: Associate Professor David Hendry Information School In this dissertation, I explore the idea of designing “flexible game systems”. A flexible game system allows players (not software designers) to decide on what rules to enforce, who enforces them, and when. I explore this in the context of digital card games and introduce two design strategies for promoting flexibility. The first strategy is “robustness”. When players want to change the rules of a game, a robust system is able to resist extreme breakdowns that the new rule would provoke. The second is “versatility”. A versatile system can accommodate multiple use-scenarios and can support them very well. To investigate these concepts, first, I engage in reflective design inquiry through the design and implementation of Card Board, a highly flexible digital card game system. Second, via a user study of Card Board, I analyze how players negotiate the rules of play, take ownership of the game experience, and communicate in the course of play. Through a thematic and grounded qualitative analysis, I derive rich descriptions of negotiation, play, and communication. I offer contributions that include criteria for flexibility with sub-principles of robustness and versatility, design recommendations for flexible systems, 3 novel dimensions of design for gameplay and communications, and rich description of game play and rule-negotiation over flexible systems.
    [Show full text]
  • Game Design 2 Game Balance
    CE810 - Game Design 2 Game Balance Joseph Walton-Rivers & Piers Williams Friday, 18 May 2018 University of Essex 1 What is Balance? Game Balance Question What is balance? 2 Game Balance “All players have an equal chance of winning” – Richard Bartle Richard covered a combat example in the first part of the module. 3 On Strategies Game Balance • What about higher level strategies? • Zerg rush? • Dominant strategies • Metagaming 4 Metagaming - Rock Paper Scissors • A beats B, B beats C, C beats A • If there are lots of A players, people will play C • Then there are a lot of C players, so people play B • and so on... 5 Metagaming - Dominant Strategies • What if A is significantly stronger? • No one will use the other two strategies • We want to encourage variety in play 6 Can we detect this? • Can we detect strategies which are overpowered? • Try to punish strategies we don’t want to see • We did this earlier in the week with rotate and shoot! • Can we measure this? 7 Automated Game Tuning • Academics seem to think so... • Ryan Leigh et al (2008) - Co-evolution for game balancing • Alexander Jaffe et al (2012) - Restricted-Play balance framework • Mihail Morosan - GAs for tuning parameters 8 Game Curves First Move Advantage First Move Advantage • Typically affects turn based games • Going first in tac tac toe means either a win or adraw • White has > 50% win rate over all games • Worse effects if you have resources • We need a way of dealing with this 9 First Move Advantage Magic Second player gets an extra card Go Second player gets 7.5 bonus
    [Show full text]
  • Cgcopyright 2013 Alexander Jaffe
    c Copyright 2013 Alexander Jaffe Understanding Game Balance with Quantitative Methods Alexander Jaffe A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2013 Reading Committee: James R. Lee, Chair Zoran Popovi´c,Chair Anna Karlin Program Authorized to Offer Degree: UW Computer Science & Engineering University of Washington Abstract Understanding Game Balance with Quantitative Methods Alexander Jaffe Co-Chairs of the Supervisory Committee: Professor James R. Lee CSE Professor Zoran Popovi´c CSE Game balancing is the fine-tuning phase in which a functioning game is adjusted to be deep, fair, and interesting. Balancing is difficult and time-consuming, as designers must repeatedly tweak parameters and run lengthy playtests to evaluate the effects of these changes. Only recently has computer science played a role in balancing, through quantitative balance analysis. Such methods take two forms: analytics for repositories of real gameplay, and the study of simulated players. In this work I rectify a deficiency of prior work: largely ignoring the players themselves. I argue that variety among players is the main source of depth in many games, and that analysis should be contextualized by the behavioral properties of players. Concretely, I present a formalization of diverse forms of game balance. This formulation, called `restricted play', reveals the connection between balancing concerns, by effectively reducing them to the fairness of games with restricted players. Using restricted play as a foundation, I contribute four novel methods of quantitative balance analysis. I first show how game balance be estimated without players, using sim- ulated agents under algorithmic restrictions.
    [Show full text]
  • Game Balancing
    Game Balancing CS 2501 – Intro to Game Programming and Design Credit: Some slide material courtesy Walker White (Cornell) CS 2501 Dungeons and Dragons • D&D is a fantasy roll playing system • Dungeon Masters run (and someCmes create) campaigns for players to experience • These campaigns have several aspects – Roll playing – Skill challenges – Encounters • We will look at some simple balancing of these aspects 2 CS 2501 ProbabiliCes of D&D • Skill Challenge • Example: The player characters (PCs) have come upon a long wall that encompasses a compound they are trying to enter • The wall is 20 feet tall and 1 foot thick • What are some ways to overcome the wall? • How hard should it be for the PCs to overcome the wall? 3 CS 2501 ProbabiliCes of D&D • How hard should it be for the PCs to overcome the wall? • We approximate this in the game world using a Difficulty Class (DC) Level Easy Moderate Hard 7 11 16 23 8 12 16 23 9 12 17 25 10 13 18 26 11 13 19 27 4 CS 2501 ProbabiliCes of D&D • Easy – not trivial, but simple; reasonable challenge for untrained character • Medium – requires training, ability, or luck • Hard – designed to test characters focused on a skill Level Easy Moderate Hard 7 11 16 23 8 12 16 23 9 12 17 25 10 13 18 26 11 13 19 27 5 CS 2501 Balancing • Balancing a game is can be quite the black art • A typical player playing a game involves intuiCon, fantasy, and luck – it’s qualitave • A game designer playing a game… it’s quanCtave – They see the systems behind the game and this can actually “ruin” the game a bit 6 CS 2501 Building Balance • General advice – Build a game for creavity’s sake first – Build a game for parCcular mechanics – Build a game for parCcular aestheCcs • Then, aer all that… – Then balance – Complexity can be added and removed if needed – Other levers can be pulled • Complexity vs.
    [Show full text]
  • The Making of a Conceptual Design for a Balancing Tool
    Södertörns högskola | Institutionen för Institutionen för naturvetenskap, miljö och teknik Kandidatuppsats 15 hp | Medieteknik | höstterminen 2014 The Making of a Conceptual Design for a Balancing Tool Av: Jonas Eriksson Handledare: Inger Ekman Abstract Balancing is usually done in the later phases of creating a game to make sure everything comes together to an enjoyable experience. Most of the time balancing is done with a series of playthroughs by the designers or by outsourced play testers and the imbalances found are corrected followed by more playthroughs. This method occupies a lot of time and might therefore not find everything. In this study I use information gathered from interviews with experienced designers and designer texts along with features from methods frequently used for aiding the designers to make a conceptual design of a tool that is aimed towards simplifying the process of balancing and reducing the amount of work hours having to be spent on this phase. Keywords Balancing, Interviews, Conceptual Design, Game Development, External Tool 2 Sammanfattning Balansering görs framförallt i de senare faserna när man skapar ett spel för att se till att alla delar tillsammans skapar en bra upplevelse. För det mesta utförs balanseringen i form av upprepade speltester genomförda av antingen utvecklaren eller inhyrda testpersoner. Obalanserade saker som upptäcks korrigeras och följs sedan av ytterligare tester. Denna metod tar väldigt lång tid att utföra och på grund av detta är det inte säkert att alla fel upptäcks innan spelet lanseras. I den här studien använder jag information som insamlats från intervjuer med erfarna designers och designtexter sida vid sida med funktioner från metoder som ofta används för att underlätta för utvecklarna.
    [Show full text]
  • Metagame Balance
    Metagame Balance Alex Jaffe @blinkity Data Scientist, Designer, Programmer Spry Fox 1 Today I’ll be doing a deep dive into one aspect of game balance. It’s the 25-minute lightning talk version of the 800-minute ring cycle talk. I’ll be developing one big idea, so hold on tight! Balance In the fall of 2012, I worked as a technical designer, using data to help balance PlayStation All-Stars Battle Royale, Superbot and Sony Santa Monica’s four- player brawler featuring PS characters from throughout the years. Btw, the game is great, and surprisingly unique, despite its well-known resemblance to that other four-player mascot brawler, Shrek Super Slam. My job was to make sense of the telemetry we gathered and use it to help the designers balance every aspect of the game. What were the impacts of skill, stage, move selection, etc. and what could that tell us about what the game feels like to play? Balance In the fall of 2012, I worked as a technical designer, using data to help balance PlayStation All-Stars Battle Royale, Superbot and Sony Santa Monica’s four- player brawler featuring PS characters from throughout the years. Btw, the game is great, and surprisingly unique, despite its well-known resemblance to that other four-player mascot brawler, Shrek Super Slam. My job was to make sense of the telemetry we gathered and use it to help the designers balance every aspect of the game. What were the impacts of skill, stage, move selection, etc. and what could that tell us about what the game feels like to play? Character Balance There were a lot of big questions about what it’s like to play the game, but at least one pretty quantitative question was paramount: are these characters balanced with respect to one another? I.e.
    [Show full text]
  • Time Balancing of Computer Games Using Adaptive Time
    TIME BALANCING OF COMPUTER GAMES USING ADAPTIVE TIME-VARIANT MINIGAMES A Thesis Submitted to the College of Graduate Studies and Research In Partial Fulfillment of the Requirements For the Degree of Master of Science In the Department of Computer Science University of Saskatchewan Saskatoon By AMIN TAVASSOLIAN © Copyright Amin Tavassolian, March 2014. All rights reserved. Permission to Use In presenting this thesis in partial fulfilment of the requirements for a Postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis. Requests for permission to copy or to make other use of material in this thesis in whole or part should be addressed to: Head of the Department of Computer Science University of Saskatchewan 176 Thorvaldson Bldg., University of Saskatchewan 110 Science Place Saskatoon, Saskatchewan (S7N 5C9) Canada i ABSTRACT Game designers spend a great deal of time developing balanced game experiences.
    [Show full text]
  • Whose Game Is This Anyway?”: Negotiating Corporate Ownership in a Virtual World
    “Whose Game Is This Anyway?”: Negotiating Corporate Ownership in a Virtual World T.L. Taylor Department of Communication North Carolina State University POB 8104, 201 Winston Raleigh, NC 27605 USA +1 919 515 9738 [email protected] Abstract This paper explores the ways the commercialization of multiuser environments is posing particular challenges to user autonomy and authorship. With ever broadening defi nitions of intellectual property rights the status of cultural and symbolic artifacts as products of collaborative efforts becomes increasingly problematized. In the case of virtual environments – such as massive multiplayer online role-play games – where users develop identities, bodies (avatars) and communities the stakes are quite high. This analysis draws on several case studies to raise questions about the status of culture and authorship in these games. Keywords Avatars, Internet, virtual environments, games INTRODUCTION While the history of virtual environments has so far been primarily written with an eye toward either the text-based worlds of MUDs or social graphical spaces like Active Worlds and VZones/WorldsAway, massive multiplayer online 227 Proceedings of Computer Games and Digital Cultures Conference,ed. Frans Mäyrä. Tampere: Tampere University Press, 2002. Copyright: authors and Tampere University Press. role playing games (MMORPG) have dramatically popularized virtual worlds [1]. The MMORPG genre now boasts hundreds of thousands of users and accounts for millions of dollars in revenue each year [2]. While multiplayer games are at their most basic level simply that, a game, they should be more richly seen as spaces in which users come together online and invest enormous amounts of time inhabiting a virtual space, creating characters, cultures, and communities, gaming together, making dynamic economies, and exploring elaborate geographical terrain.
    [Show full text]
  • Game Design Concepts
    Game Design Concepts: An experiment in game design and teaching Ian Schreiber Syllabus and Schedule 3 Level 1: Overview / What is a Game? 6 Level 2: Game Design / Iteration and Rapid Prototyping 15 Level 3: Formal Elements of Games 21 Level 4: The Early Stages of the Design Process 33 Level 5: Mechanics and Dynamics 47 Level 6: Games and Art 61 Level 7: Decision-Making and Flow Theory 73 Level 8: Kinds of Fun, Kinds of Players 86 Level 9: Stories and Games 97 Level 10: Nonlinear Storytelling 109 Level 11: Design Project Overview 121 Level 12: Solo Testing 127 Level 13: Playing With Designers 134 Level 14: Playing with Non-Designers 139 Level 15: Blindtesting 144 Level 16: Game Balance 149 Level 17: User Interfaces 160 Level 18: The Final Iteration 168 Level 19: Game Criticism and Analysis 174 Level 20: Course Summary and Next Steps 177 Syllabus and Schedule By ai864 Schedule: This class runs from Monday, June 29 through Sunday, September 6. Posts appear on the blog Mondays and Thursdays each week at noon GMT. Discussions and sharing of ideas happen on a continual basis. Textbooks: This course has one required text, and two recommended texts that will be referenced in several places and provide good “next steps” after the summer course ends. Required Text: Challenges for Game Designers, by Brathwaite & Schreiber. This book covers a lot of basic information on both practical and theoretical game design, and we will be using it heavily, supplemented with some readings from other online sources. Yes, I am one of the authors.
    [Show full text]
  • Demonstrating the Feasibility of Automatic Game Balancing
    Demonstrating the Feasibility of Automatic Game Balancing Vanessa Volz Günter Rudolph Boris Naujoks Abstract Game balancing is an important part of the (computer) game design process, in which designers adapt a game prototype so that the resulting gameplay is as entertaining as possible. In industry, the evaluation of a game is often based on costly playtests with human players. It suggests itself to automate this process using surrogate models for the prediction of gameplay and outcome. In this paper, the feasibility of automatic balancing using simulation- and deck-based objectives is investigated for the card game top trumps. Additionally, the necessity of a multi-objective approach is asserted by a comparison with the only known (single-objective) method. We apply a multi-objective evolutionary algorithm to obtain decks that optimise objectives, e.g. win rate and average number of tricks, developed to express the fairness and the excitement of a game of top trumps. The results are compared with decks from published top trumps decks using simulation-based objectives. The possibility to generate decks better or at least as good as decks from published top trumps decks in terms of these objectives is demonstrated. Our results indicate that automatic balancing with the presented approach is feasible even for more complex games such as real-time strategy games. I. Introduction balancing process with tools that can automati- cally evaluate and suggest different game pa- The increasing complexity and popularity of rameter configurations, which fulfil a set of (computer) games result in numerous chal- predefined goals (cf. [14]). However, since the lenges for game designers.
    [Show full text]
  • Detecting Cheaters in a Distributed Multiplayer Game
    Detecting Cheaters in a Distributed Multiplayer Game Justin D. Weisz School of Computer Science Carnegie Mellon University [email protected] Abstract ing market predicted to be worth $2.3 billion by 2005 [17]. Services such as Microsoft’s XBox Live [22] have Cheating is currently a major problem in today’s mul- introduced multiplayer gaming to the video game tiplayer games. One of the most popular types of console, lowering the cost of participating in online cheating involves having the client software render games, and increasing the number people playing on- information which is not in the player’s current field line games. Even movie theatres are being converted of view. This type of cheating may allow a player into large gaming centers, increasing the availability to see their opponents through walls, or to see their and exposure of online multiplayer games [15]. opponents on a radar, or at extreme distances, when Current multiplayer games are divided into two they would normally not be able to. Currently, much types — first person shooter (FPS) games, which can research is being done to learn how cheating can be have up to around 64 players in a game world [19], detected and prevented in the context of client-server and massively multiplayer online role playing games multiplayer games, but little research is being done (MMORPG), which support around 6,000 players in studying how cheating behaviors can be detected or one world [17]. Both of these types of games are prevented in a distributed context. immensely popular. For example, the GameSpy net- In this research, we study what kinds of cheating work [7] is a service used by game players to find behaviors are possible in a distributed game environ- other game players to play games with.
    [Show full text]
  • Character Balance in MOBA Games
    Character Balance in MOBA Games Faculty of Arts Department of Game Design Authors: Emanuel Palm, Teodor Norén Bachelor’s Thesis in Game Design, 15 hp Program: Game Design and Programming Supervisors: Jakob Berglund Rogert, Masaki Hayashi Examiner: Mikael Fridenfalk June, 2015 Abstract As live streaming of video games has become easier, electronic sports have grown quickly and they are still increasing as tournaments grow in viewers and prizes. The purpose of this paper is to examine the theory Metagame Bounds by applying it to League of Legends and Dota 2, to see if it is a valid way of looking at character balance in the Multiplayer Online Battle Arena game genre. The main mode of both games consist of matches played on a map where a team of five players is up against another team of five players. Characters in the games generally have four abilities and a number of attributes, making them complex to compare without context. We gathered character data from websites and entered the data into a file that we used with the Metagame Bounds application. We compared the graphs that the data yielded with how often a character wins and how often it is played. To examine whether the characters were balanced we also played the games and analysed the characters in depth. All statistical data gathered was retrieved over the span of a few hours on the 21st of April 2015 from the websites Champion.gg for League of Legends and Dotabuff for Dota 2. Sirlin’s (2001) definition of multiplayer game balance is “A multiplayer game is balanced if a reasonably large number of options available to the player are viable – especially, but not limited to, during high-level play by expert players.” and with the data we see that these games are balanced in terms of characters according to that definition.
    [Show full text]