UNIVERSITY OF FRIBOURG BACHELOR THESIS Data Preparation and Analysis in Support to Cheating Detection: The Case for Economic Momentum in CS:GO Author: Supervisors: David Bucher Prof. Dr. Philippe Cudré-Mauroux Dr. Giuseppe Cuccu August 18, 2019 eXascale Infolab Department of Informatics Boulevard de Pérolles 90 • 1700 Fribourg • Switzerland phone +41 (26) 300 84 65 fax +41 (26) 300 97 31 [email protected] www3.unifr.ch/inf iii UNIVERSITY OF FRIBOURG Abstract Faculty of Science and Medicine Department of Informatics Bachelor Data Preparation and Analysis in Support to Cheating Detection: The Case for Economic Momentum in CS:GO by David Bucher In recent years, there has been an increasing amount of competition in the video game industry. More and more people play video games, more people watch stream- ers playing famous titles. This has evolved into competition - so called esports. With money to earn from video games, the temptation for players to cheat becomes bigger. The traditional tools to fight against cheaters in video game competitions are anti- cheat that can recognize cheat server or client side. But in the same time, cheating methods have also evolved in order to make their detection almost impossible. In this work we try to see how the understanding of a game mechanic can help to iden- tify potential cheaters. We focus on the first-person shooter Counter-Strike:Global Offensive and its "economy cycle". We collected a large amount of data from official league competitions and exhibit a standard for how the game looks from the specific point of view of the economy system. We computed the distribution of the different scenarii in a match (sequence of rounds won or lost by a team). This could in princi- ple serve as a basis to identify outliers as potential cheating suspects. The approach could be generalized to other mechanics and apply to other games. Keywords: CS:GO, cheating detection, esports, game mechanics, data collecting, demo parser, crawler, pattern distribution v Contents Abstract iii 1 Introduction1 1.1 Game Overview................................1 1.2 Actors in the CS:GO Competition......................3 1.3 Professional Scene...............................3 1.4 Thesis Structure................................4 1.5 Contributions.................................4 2 Cheating5 2.1 Overview....................................5 2.2 Cheating Techniques.............................5 2.3 Current State..................................6 3 Economy9 3.1 Description...................................9 3.2 Earnings.................................... 10 3.3 Spendings................................... 10 3.4 Strategical Implication............................ 10 3.5 Visualization of the Economical Situation................. 11 3.6 Cheater and the Economy.......................... 11 4 Collecting the Data 13 4.1 Data Selection................................. 13 4.2 Demo Parser.................................. 13 4.3 ESEA Crawler................................. 14 4.4 Final Crawler Version............................. 17 5 Analysis 19 5.1 Preface..................................... 19 5.1.1 Time Line............................... 19 5.1.2 Overtime................................ 19 5.2 Methods.................................... 20 5.2.1 Database Size............................. 20 5.3 Experiences.................................. 20 5.3.1 Entropy................................ 20 5.3.2 Pattern Distribution......................... 20 5.3.3 Round and Game Win Rate..................... 21 5.3.4 The Path of a Game.......................... 21 5.4 Analysis Conclusion............................. 21 vi 6 Results 23 6.1 Experiences Results.............................. 23 6.1.1 Entropy................................ 23 6.1.2 Pattern distribution.......................... 23 6.1.3 Round and game win rate...................... 24 6.1.4 The path of a game.......................... 25 6.2 Results Conclusion.............................. 27 7 Conclusion 29 7.1 Conclusion................................... 29 7.2 Future Work.................................. 29 vii List of Figures 1.1 CT and T rounds win rate per maps: last 6 months, between top20 teams in offline events............................1 1.2 "CS is way more than being good at shooting people" - Maniac, a swiss professional player..............................2 2.1 Vac and Game bans[17]............................7 2.2 Personal experience data...........................7 4.1 Parser output example............................ 14 4.2 Parser time running on a folder of 16 demos............... 14 4.3 ESEA making it hard to crawl........................ 15 4.4 Traffic through the mitmproxy....................... 17 4.5 Headless test result: missing plugins.................... 18 4.6 Rate limited, but crawler loading a page with proxy........... 18 6.1 ESEA EU MDL 29............................... 24 6.2 ESEA EU MDL 30............................... 24 6.3 ESEA EU ADVANCED 29.......................... 25 6.4 ESEA EU ADVANCED 30.......................... 25 6.5 ESEA EU MAIN 29.............................. 25 6.6 Comparing game and round win rates................... 26 6.7 All five season considered at the same time, X axis is the number of round played, Y axis is the number of round won by the team that started as CT.................................. 26 1 Chapter 1 Introduction 1.1 Game Overview Counter-Strike:Global Offensive, commonly designated as CS:GO, is a first person shooter video game in which two teams of five players face each other. One team acts as a Terrorist team (T), the other as the Counter Terrorist team (CT). The goal for the T is to plant a bomb (2 minutes for it), and then make sure it explodes (additional 40 seconds). The goal for the CT is to prevent it, by first defending the two bombsites and - when the bomb is planted - to defuse it. The match is played as a succession of short rounds scoring each 1 point. After the first 15 rounds, players switch their roles as CT and T. The first team to score 16 points wins the match. The players compete on a "map". A "map pool" consists of seven "official" map. On those square-shaped maps the T start on the top of the map, CT on the bottom. Those two area are called "spawn". The bombsites (A and B) are located on the left and right. When departing from their spawn, CTs always arrive first on the bombsite - we call this the defender’s advantage. For this reason most map favor the CT side. The team playing as CT have higher chances to win a round than the T as shown in Figure 1.1[2]. Outside of this four elements, maps have very different structures leading to various play styles on each of them. It is not rare to see a team win easly on one team against their opponent and then losing even more clearly on another map against the exact same opponent. FIGURE 1.1: CT and T rounds win rate per maps: last 6 months, be- tween top20 teams in offline events To decide whether a team starts on the CT or T side, they first play a "knife round" a fast played round without any weapon that does not count on the overall score, 2 Chapter 1. Introduction but allows the winner to decide his starting role. While Figure 1.1 may suggest that the team starting the game on the CT side have more chances to win the game, ithat is not the case. Competitive players and teams do indeed have a prefered starting side, but it is not always the statistically best one. Consider the extreme case where one side has a 100% to win, after 15 rounds, it is 15-0, teams change their role, and at the end of the second half the score is 15-15. In other words, to win a game you need to beat the odds. In competition we distinguish two types of match. Best of one (BO1) and best of three (BO3), refering to the amount of maps played bewteen the two teams to decide a winner. In a BO1, each team alternatively vetoes three maps, the remaining one is the one played for the match. In a typical BO3, each team vetoes first one map, then each "picks" one - meaning these two maps are being played - and then again ban each one map. The remaining map is used as a "decider" in case both team won on a map. FIGURE 1.2: "CS is way more than being good at shooting people" - Maniac, a swiss professional player Here we are just scratching the surface of the game, Figure 1.2 illustrate how complex the game is - which aspects enter into the equation when playing the game on a competitive level[3]. There are many different types of player with their own kind of impact, mastering specific aspect of the game. We will talk later about the "economy" aspect of the game, how it works, and its consequences, in a dedicated Section. We propose that this mechanics has a strong influence on how a match is played. 1.2. Actors in the CS:GO Competition 3 1.2 Actors in the CS:GO Competition The game itself provides a matchmaking system where players can start a match with similar rules to the ones used in competition. It offers a ranking based on grad1 from “silver1” to “the global elite”. This is most regarded as the fun way to play the game. Faceit[4] is an independent platform offering its own matchmaking sys- tem - favored by competitors. It provide better server quality, an ELO2 based rank- ing allowing direct comparison between player, and comes with its own anti-cheat client. While it is regarded as the best ranking ladder, it is not suited for competition because it is “player-" and not "team-oriented". ESEA[5] is an other independent platform offering a service similar to Faceit, but is additionally involved in league organization all around the world. Every player can join the competition by register- ing as a team of at least 5 players in the “Open league” corresponding to his region (Europe, North America, etc). The promotion system offer the possibility to climb one’s way to the absolute top teams in the world in about a year. Division from bottom to top: Open, Intermediate, Main, Advanced, MDL, Pro League.