Mining Tracks of Competitive Video Games

Rioult, François; Métivier, Jean-Philippe; Helleu, Boris; Scelles, Nicolas; Durand, Christophe

doi:10.1016/j.aasri.2014.08.014

Cited by 42 publications

(42 citation statements)

References 2 publications

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“…Additional contributions are made in terms of a method for obtaining and visualizing accurate spatio-temporal data from DotA 2 [32]. The work presented extend previous work on MOBAs, and highlight the use of spatio-temporal patterns to analyze gameplay [4][5][6]. As noted in the introduction, the basic goal of the work presented here is to explore MOBAs, and work towards results and tools that will aid DotA 2 players in visualizing, analyzing and improving their performance.…”

Section: Conclusion and Discussionmentioning

confidence: 97%

“…Recent related work has shown there are a variety of ways to approach the problem of strategy description, evaluation and prediction in MOBAs [e.g. 5,6], and current work has only begun to tap into the rich and varied behavioral data available from MOBAs. Future work will include investigating match properties not tied to time series and investigate if they are also captured by the team distribution clusters.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…The combat emphasis means that players and teams must have a strong tactical knowledge and invest substantial time to master the game, similar to comparable off-line sports. In summary, MOBAs are of interest from the perspective of sports analysis [5] and game analytics [7] in general as a major e-sport, backed by a growing industry. Given the relative dearth of analytical work on e-sports, with notably exceptions e.g.…”

Section: Introductionmentioning

confidence: 99%

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Skill-based differences in spatio-temporal team behaviour in defence of the Ancients 2 (DotA 2)

Drachen

Yancey

Maguire

et al. 2014

2014 IEEE Games Media Entertainment

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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 focussed on tactical combat. In this paper, we present three data-driven measures of spatiotemporal behaviour in Defence of the Ancients 2 (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 behaviour varies across these measures as a function of the skill level of teams, using four tiers from novice to professional players. Results from three analyses indicate that spatio-temporal behaviour of MOBA teams is highly related to team skill.

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Section: Conclusion and Discussionmentioning

confidence: 97%

Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Skill-based differences in spatio-temporal team behaviour in defence of the Ancients 2 (DotA 2)

Drachen

Yancey

Maguire

et al. 2014

2014 IEEE Games Media Entertainment

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“…Summerville et al [16] used machine learning to predict draft picks in Dota 2. Rioult [17] discussed some of the general applications of mining player tracks from esports games. Drachen et al [18] used classification for studying the movement patterns of Dota 2 teams across skill levels.…”

Section: Related Workmentioning

confidence: 99%

“…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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