publication . Preprint . 2016

Real-time eSports Match Result Prediction

Yang, Yifan; Qin, Tian; Lei, Yu-Heng;
Open Access English
  • Published: 10 Dec 2016
Abstract
In this paper, we try to predict the winning team of a match in the multiplayer eSports game Dota 2. To address the weaknesses of previous work, we consider more aspects of prior (pre-match) features from individual players' match history, as well as real-time (during-match) features at each minute as the match progresses. We use logistic regression, the proposed Attribute Sequence Model, and their combinations as the prediction models. In a dataset of 78362 matches where 20631 matches contain replay data, our experiments show that adding more aspects of prior features improves accuracy from 58.69% to 71.49%, and introducing real-time features achieves up to 93....
Subjects
free text keywords: Statistics - Applications, Computer Science - Artificial Intelligence, I.2.1, Computer Science - Learning
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[1] Thomas E. Batsford. Calculating optimal jungling routes in dota2 using neural networks and genetic algorithms. Project, University of Derby, 2014.

[2] Joseph C. Bonneau. Beyond “Playing the Percentages”: Application of collaborative filtering for predicting baseball matchups, 2006.

[3] Shuo Chen and Thorsten Joachims. Predicting matchups and preferences in context. KDD, 2016.

[4] Kevin Conley and Daniel Perry. How does he saw me? a recommendation engine for picking heroes in dota 2. Course project, Stanford University, 2013.

[5] Anthony C. Constantinou, Norman E. Fenton, and Martin Neil. pi-football: A bayesian network model for forecasting association football match outcomes. Knowledge-Based Systems, 36, 2012.

[6] Anders Drachen, Matthew Yancey, John Maguire, Derrek Chu, Iris Yuhui Wang, Tobias Mahlmann, Matthias Schubert, and Diego Klabajan. Skill-based differences in spatio-temporal team behaviour in defence of the ancients 2 (dota 2). IEEE-GEM, 2014.

[7] Lars Magnus Hvattuma and Halvard Arntzen. Using elo ratings for match result prediction in association football. International Journal of Forecasting, 26, 2010.

[8] Nicholas Kinkade and Kyung yul Kevin Lim. Dota 2 win prediction. Course project, University of California, San Diego, 2015.

[9] Jamie Lowder, Dave Wong, Lynn Gao, and James Judd. Classifying dota 2 hero characters based on play style and performance. Course project, The University of Utah, 2015.

[10] Hao Yi Ong, Sunil Deolalikar, and Mark Peng. Player behavior and optimal team composition for online multiplayer games. 2015. [OpenAIRE]

[11] François Rioult, Jean-Philippe Metivier, Boris Helleu, Nicolas Scelles, and Christophe Durand. Mining tracks of competitive video games. AASRI, 2014. [OpenAIRE]

[12] Kuangyan Song, Tianyi Zhang, and Chao Ma. Predicting the winning side of dota2. Course project, Stanford University, 2015.

[13] Pu Yang, Brent Harrison, and David L. Roberts. Identifying patterns in combat that are predictive of success in moba games. Proceedings of Foundations of Digital Games, 2014.

Abstract
In this paper, we try to predict the winning team of a match in the multiplayer eSports game Dota 2. To address the weaknesses of previous work, we consider more aspects of prior (pre-match) features from individual players' match history, as well as real-time (during-match) features at each minute as the match progresses. We use logistic regression, the proposed Attribute Sequence Model, and their combinations as the prediction models. In a dataset of 78362 matches where 20631 matches contain replay data, our experiments show that adding more aspects of prior features improves accuracy from 58.69% to 71.49%, and introducing real-time features achieves up to 93....
Subjects
free text keywords: Statistics - Applications, Computer Science - Artificial Intelligence, I.2.1, Computer Science - Learning
Download from

[1] Thomas E. Batsford. Calculating optimal jungling routes in dota2 using neural networks and genetic algorithms. Project, University of Derby, 2014.

[2] Joseph C. Bonneau. Beyond “Playing the Percentages”: Application of collaborative filtering for predicting baseball matchups, 2006.

[3] Shuo Chen and Thorsten Joachims. Predicting matchups and preferences in context. KDD, 2016.

[4] Kevin Conley and Daniel Perry. How does he saw me? a recommendation engine for picking heroes in dota 2. Course project, Stanford University, 2013.

[5] Anthony C. Constantinou, Norman E. Fenton, and Martin Neil. pi-football: A bayesian network model for forecasting association football match outcomes. Knowledge-Based Systems, 36, 2012.

[6] Anders Drachen, Matthew Yancey, John Maguire, Derrek Chu, Iris Yuhui Wang, Tobias Mahlmann, Matthias Schubert, and Diego Klabajan. Skill-based differences in spatio-temporal team behaviour in defence of the ancients 2 (dota 2). IEEE-GEM, 2014.

[7] Lars Magnus Hvattuma and Halvard Arntzen. Using elo ratings for match result prediction in association football. International Journal of Forecasting, 26, 2010.

[8] Nicholas Kinkade and Kyung yul Kevin Lim. Dota 2 win prediction. Course project, University of California, San Diego, 2015.

[9] Jamie Lowder, Dave Wong, Lynn Gao, and James Judd. Classifying dota 2 hero characters based on play style and performance. Course project, The University of Utah, 2015.

[10] Hao Yi Ong, Sunil Deolalikar, and Mark Peng. Player behavior and optimal team composition for online multiplayer games. 2015. [OpenAIRE]

[11] François Rioult, Jean-Philippe Metivier, Boris Helleu, Nicolas Scelles, and Christophe Durand. Mining tracks of competitive video games. AASRI, 2014. [OpenAIRE]

[12] Kuangyan Song, Tianyi Zhang, and Chao Ma. Predicting the winning side of dota2. Course project, Stanford University, 2015.

[13] Pu Yang, Brent Harrison, and David L. Roberts. Identifying patterns in combat that are predictive of success in moba games. Proceedings of Foundations of Digital Games, 2014.

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