Classification of Player Roles in the Team-Based Multi-player Game Dota 2

Eggert, Christoph H.; Herrlich, Marc; Smeddinck, Jan David; Malaka, Rainer

doi:10.1007/978-3-319-24589-8_9

Cited by 70 publications

(69 citation statements)

References 14 publications

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“…They looked at the temporal distribution of the distances between players in the same team and how this distribution varies between high/low skill teams. Eggert et al [23] were interested in classifying player's behaviors via machine learning. These studies are focused on quantifying collective behaviors of teams in Dota 2 and relate these behaviors to player's roles that are not the official hero types we used in our analysis.…”

Section: Related Workmentioning

confidence: 99%

“…This type of game provides indeed key insights on how users behave and improve their skills over time to reach success [19]- [22]. By studying individual matches over time, we not only explore how players improve while playing consecutive matches, but also monitor how their performance changes based on the role they are impersonating [23]. In particular, we are interested in deepening our understanding of the way players either enhance or worsen their skills in the game.…”

Section: Introductionmentioning

confidence: 99%

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Performance Dynamics and Success in Online Games

Sapienza

Peng

Ferrara

2017

2017 IEEE International Conference on Data Mining Workshops (ICDMW)

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Abstract-Online data provide a way to monitor how users behave in social systems like social networks and online games, and understand which features turn an ordinary individual into a successful one. Here, we propose to study individual performance and success in Multiplayer Online Battle Arena (MOBA) games. Our purpose is to identify those behaviors and playing styles that are characteristic of players with high skill level and that distinguish them from other players. To this aim, we study Defense of the ancient 2 (Dota 2), a popular MOBA game. Our findings highlight three main aspects to be successful in the game: (i) players need to have a warm-up period to enhance their performance in the game; (ii) having a long in-game experience does not necessarily translate in achieving better skills; but rather, (iii) players that reach high skill levels differentiate from others because of their aggressive playing strategy, which implies to kill opponents more often than cooperating with teammates, and trying to give an early end to the match.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Dynamics and Success in Online Games

Sapienza

Peng

Ferrara

2017

2017 IEEE International Conference on Data Mining Workshops (ICDMW)

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“…In summary, while much previous work focuses on macropredictions (predicting the winning team), there has been a small amount of work on micro-level events like encounters and hero health changes [3], [17], [18], [20], [21] but only one previous work has focused on in-game forecasting with mixed success. None of the existing work has focused on professional/semi-professional levels or predicted hero deaths.…”

Section: Related Workmentioning

confidence: 99%

Time to Die: Death Prediction in Dota 2 using Deep Learning

Katona

Spick

Hodge

et al. 2019

2019 IEEE Conference on Games (CoG)

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Esports have become major international sports with hundreds of millions of spectators. Esports games generate massive amounts of telemetry data. Using these to predict the outcome of esports matches has received considerable attention, but micro-predictions, which seek to predict events inside a match, is as yet unknown territory. Micro-predictions are however of perennial interest across esports commentators and audience, because they provide the ability to observe events that might otherwise be missed: esports games are highly complex with fast-moving action where the balance of a game can change in the span of seconds, and where events can happen in multiple areas of the playing field at the same time. Such events can happen rapidly, and it is easy for commentators and viewers alike to miss an event and only observe the following impact of events. In Dota 2, a player hero being killed by the opposing team is a key event of interest to commentators and audience. We present a deep learning network with shared weights which provides accurate death predictions within a five-second window. The network is trained on a vast selection of Dota 2 gameplay features and professional/semi-professional level match dataset. Even though death events are rare within a game (1% of the data), the model achieves 0.377 precision with 0.725 recall on test data when prompted to predict which of any of the 10 players of either team will die within 5 seconds. An example of the system applied to a Dota 2 match is presented. This model enables realtime micro-predictions of kills in Dota 2, one of the most played esports titles in the world, giving commentators and viewers time to move their attention to these key events.

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“…al. [57] exploit replay files of a real time strategy game to classify player types, using behavioral lowlevel data during game play. Even if replay files may not be stored by default, it demonstrates the ability of the game software to log each small game interaction, which is sufficient to reproduce a complex game play.…”

Section: Serious Games and Data Privacymentioning

confidence: 99%

“…Another kind of data available for use are player stats -data that defines the current status of the player in the game. The massively multiplayer online game (MMOG) EVE Online [57] serves as an example. EVE Online contains a comprehensive data model -a great amount of game related (player status) data has to be managed.…”

Section: Serious Games and Data Privacymentioning

confidence: 99%

Ethical Stewardship: Designing Serious Games Seriously

Sandovar

Braad

Streicher

et al. 2016

Entertainment Computing and Serious Games

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Abstract. In this chapter, we propose an ethical framework for serious game design, which we term the Ecosystem for Designing Games Ethically (EDGE). EDGE expands on Zagal's categorization of ethical areas in game design by incorporating the different contexts of design and their use. In addition, we leverage these contexts to suggest four guidelines that support Ethical Stewardship in serious game design. We conclude by discussing a number of specific areas in which ethics plays a role in serious game design. These include games in (a) a military context, (b) the consideration of privacy issues, and (c) the evaluation of game design choices.

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Classification of Player Roles in the Team-Based Multi-player Game Dota 2

Cited by 70 publications

References 14 publications

Performance Dynamics and Success in Online Games

Performance Dynamics and Success in Online Games

Time to Die: Death Prediction in Dota 2 using Deep Learning

Ethical Stewardship: Designing Serious Games Seriously

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