Player Skill Modeling in Starcraft II

Avontuur, Tetske; Spronck, Pieter; Zaanen, Menno van

doi:10.1609/aiide.v9i1.12682

Cited by 12 publications

(9 citation statements)

References 23 publications

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“…As we saw before, ranks 0, 1, and 5 are predicted with high accuracy, while ranks 2, 3, and 4 are predicted with lower accuracy. Avontuur, Spronck, and Van Zaanen (Avontuur, Spronck, and Van Zaanen 2013) predicted league levels of StarCraft players with 44% accuracy on average, but with close to 90% accuracy for the best players. Similar to our ranking model, their model has higher performance in prediction of top and low levels, but it has lower performance in prediction of mid levels.…”

Section: Combined Modelsmentioning

confidence: 92%

Predicting Victory in a Hybrid Online Competitive Game: The Case of Destiny

Ravari

Spronck

Sifa

et al. 2021

AIIDE

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Competitive multi-player game play is a common feature in major commercial titles, and has formed the foundation for esports. In this paper, the question whether it is possible to predict match outcomes in First Person Shooter-type multi-player competitive games with mixed genres is addressed.The case employed is Destiny, which forms a hybrid title combining Massively Multi-player Online Role-Playing game features and First-Person Shooter games. Destiny provides the opportunity to investigate prediction of the match outcome, as well as the influence of performance metrics on the match results in a hybrid multi-player major commercial title. Two groups of models are presented for predicting match results: One group predicts match results for each individual game mode and the other group predicts match results in general, without considering specific game modes. Models achieve a performance between 63% and 99%in terms of average precision, with a higher performance recorded for the models trained on specific multi-player game modes, of which Destiny has several. We also analyzed performance metrics and their influence for each model. The results show that many key shooter performance metrics such as Kill/Death ratio are relevant across game modes, but also that some performance metrics are mainly important for specific competitive game modes. The results indicate that reliable match prediction is possible in FPS-type esports games.

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Section: Combined Modelsmentioning

confidence: 92%

Predicting Victory in a Hybrid Online Competitive Game: The Case of Destiny

Ravari

Spronck

Sifa

et al. 2021

AIIDE

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show abstract

“…Multiple researchers have investigated detection of player skills in RTS games. Avontuur et al (Avontuur, Spronck, and Van Zaanen 2013) built a model to determine a player's StarCraft league based on observations of player features during the early game stages. Thompson et al (Thompson et al 2013) examined the differences between player skills across the leagues.…”

Section: Related Workmentioning

confidence: 99%

StarCraft Winner Prediction

Ravari

Bakkes

Spronck

2021

AIIDE

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In game-playing, a challenging topic is to investigate an evaluation function that accurately predicts which player will be the winner of a two-player match. Our work investigates to what extent it is possible to predict the winner of a StarCraft match, regardless of the races that are involved. We developed models for individual match types, and also general models for predicting the winner of non-symmetric matches, symmetric matches, and general matches. The contribution of this paper is (1) a generic and relatively accurate model for winner prediction in StarCraft, and (2) a detailed analysis of which features are the principal component in accurately predicting the winner in this complex game. Specially, our results show that we can predict the winner of a match with an accuracy of more than 63% in average over all time slices, regardless of the time slice and the combination of the match types. A study of which features are most important for the prediction of the match results, shows that the economic aspects of StarCraft matches are the strongest predictors for winning, followed by the use micro commands.

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“…Siming Liu et al [16] recognized players through extracted features and Random Forest. T. Avontuur et al [6] developed a model that predicts skills based on data collected during the early portions of the game. This work is the most similar study to ours, showing an accuracy of 47.3%.…”

Section: Related Workmentioning

confidence: 99%

“…The weakness of this system is that even if a professional game player wins all of the placement games, he/she would not still be placed at the highest league right away. Beginners also suffer from being placed at the leagues that require higher adeptness than they currently have [6]. Therefore, as a result, each player would need at least five rounds to be assigned to appropriate leagues.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction for StarCraft II League Prediction

Lee

Ahn

2021

Electronics

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In a player-versus-player game such as StarCraft II, it is important to match players with others with similar skills. Studies modeling player skills were conducted, with 47.3% and 61.3% performance. In order to improve the performance, we collected 46,398 replays and compared features extracted from six sections of replays. Through the comparison of the six datasets we created, we propose a method for extracting features from a single replay. Two algorithms, k-Nearest Neighbors and Random Forest, which are most commonly used in related studies, are compared. Our research showed a outperforming accuracy of 75.3% compared to previous works. Although no direct comparison has been made with the current system, we conclude that our research can replace the placement games of five rounds.

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Player Skill Modeling in Starcraft II

Cited by 12 publications

References 23 publications

Predicting Victory in a Hybrid Online Competitive Game: The Case of Destiny

Predicting Victory in a Hybrid Online Competitive Game: The Case of Destiny

StarCraft Winner Prediction

Feature Extraction for StarCraft II League Prediction

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