Predicting Winning Team and Probabilistic Ratings in “Dota 2” and “Counter-Strike: Global Offensive” Video Games

Makarov, Ilya; Savostyanov, Dmitry; Litvyakov, Boris; Ignatov, Dmitry I.

doi:10.1007/978-3-319-73013-4_17

Cited by 39 publications

(48 citation statements)

References 30 publications

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“…3) Within a DotA 2 match, each hero plays a particular role in the game, for example, supporting other heroes. Yang et al [19] analyzed 5 roles to label the nodes in their combat graphs and Makarov et al [2] also used 5 roles and built separate models for each role using in-game features as the training data for each model. Makarov et al [2] predict winners by combining the individual models and weighting them to factor in the various roles' contribution to a win.…”

Section: A Data Used For Predictionmentioning

confidence: 99%

“…Yang et al [19] analyzed 5 roles to label the nodes in their combat graphs and Makarov et al [2] also used 5 roles and built separate models for each role using in-game features as the training data for each model. Makarov et al [2] predict winners by combining the individual models and weighting them to factor in the various roles' contribution to a win. Semenov et al [14] generate a vector from the number of heroes playing each of 4 roles while [20] compared using 9 roles with just 3 roles as features and found 3 roles outperformed with respect to prediction accuracy with logistic regression.…”

Section: A Data Used For Predictionmentioning

confidence: 99%

“…Most authors use a sliding window approach to generate fixed-width vectors [21], [10] or a timeseries generated from the beginning of the game to the current time [17] for input to the various machinelearning algorithms. The features can be attributed to individual heroes, individual roles [2] or collectively to teams. Schubert et al [5] evaluated a broad range of features.…”

Section: ) Many Aspects Of Dota 2 Games Can Be Extracted Asmentioning

confidence: 99%

“…Post-game features: Other features are generated postmatch and summarize the game, notably the game end state. 7) Kinkadze et al [13] and Makarov et al [2] use postmatch statistics as features to train a prediction algorithm, such as team rewards, team kills and match duration [13] or gold and experience earned by each player [2]. Wang [16] notes that game duration is important as the win-rate of particular heroes and particular hero combinations varies according to game duration so Wang [16] subdivided games into 15 minute phases when predicting matches.…”

Section: ) Many Aspects Of Dota 2 Games Can Be Extracted Asmentioning

confidence: 99%

“…Esports is the term used to describe video games that are played competitively and watched by, normally large, audiences [1]. Esports is an important research field across academia and industry just in terms of size [1], [2], [3], [4], [5]. Goldman Sachs [6] predicted a compound annual growth rate of 22% with the market worth $1.1 billion by 2019 and Superdata [7] estimated there will be 330 million spectators by 2019.…”

Section: Introductionmentioning

confidence: 99%

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Win Prediction in Multiplayer Esports: Live Professional Match Prediction

et al. 2021

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Esports are competitive videogames watched by audiences. Most esports generate detailed data for each match that are publicly available. Esports analytics research is focused on predicting match outcomes. Previous research has emphasised pre-match prediction and used data from amateur games, which are more easily available than professional level. However, the commercial value of win prediction exists at the professional level. Furthermore, predicting real-time data is unexplored, as is its potential for informing audiences. Here we present the first comprehensive case study on live win prediction in a professional esport. We provide a literature review for win prediction in a multi-player online battle arena (MOBA) esport. The paper evaluates the first professional-level prediction models for live DotA 2 matches, one of the most popular MOBA games and trials it at a major international esports tournament. Using standard machine learning models, feature engineering and optimization, our model is up to 85% accurate after 5 minutes of gameplay. Our analyses highlight the need for algorithm evaluation and optimization. Finally, we present implications for the esports/game analytics domains, describe commercial opportunities, practical challenges, and propose a set of evaluation criteria for research on esports win prediction.

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Section: A Data Used For Predictionmentioning

confidence: 99%

Section: A Data Used For Predictionmentioning

confidence: 99%

Section: ) Many Aspects Of Dota 2 Games Can Be Extracted Asmentioning

confidence: 99%

Section: ) Many Aspects Of Dota 2 Games Can Be Extracted Asmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Win Prediction in Multiplayer Esports: Live Professional Match Prediction

et al. 2021

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The Evaluation of Rating Systems in Online Free-for-All Games

Dehpanah

Ghori

Gemmell

et al. 2021

Advances in Data Science and Information Engineering

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Online competitive games have become increasingly popular. To ensure an exciting and competitive environment, these games routinely attempt to match players with similar skill levels. Matching players is often accomplished through a rating system. There has been an increasing amount of research on developing such rating systems. However, less attention has been given to the evaluation metrics of these systems. In this paper, we present an exhaustive analysis of six metrics for evaluating rating systems in online competitive games. We compare traditional metrics such as accuracy. We then introduce other metrics adapted from the field of information retrieval. We evaluate these metrics against several well-known rating systems on a large real-world dataset of over 100,000 free-for-all matches. Our results show stark differences in their utility. Some metrics do not consider deviations between two ranks. Others are inordinately impacted by new players. Many do not capture the importance of distinguishing between errors in higher ranks and lower ranks. Among all metrics studied, we recommend Normalized Discounted Cumulative Gain (NDCG) because not only does it resolve the issues faced by other metrics, but it also offers flexibility to adjust the evaluations based on the goals of the system.

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Transformer-Based Deep Reinforcement Learning in VizDoom

Sopov

Makarov

2022

Communications in Computer and Information Science

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Predicting Winning Team and Probabilistic Ratings in “Dota 2” and “Counter-Strike: Global Offensive” Video Games

Cited by 39 publications

References 30 publications

Win Prediction in Multiplayer Esports: Live Professional Match Prediction

Win Prediction in Multiplayer Esports: Live Professional Match Prediction

The Evaluation of Rating Systems in Online Free-for-All Games

Transformer-Based Deep Reinforcement Learning in VizDoom

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