Ranking factors of team success

Pobiedina, Nataliia; Neidhardt, Julia; Moreno, María del Carmen Calatrava; Werthner, Hannes

doi:10.1145/2487788.2488147

Cited by 41 publications

(21 citation statements)

References 15 publications

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“…In particular, we analyze a well-known MOBA game: Defense of the ancient 2 (Dota 2). 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].…”

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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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: 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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“…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. 8) Hero win rate can be calculated either: as a coefficient using logistic regression of previous game statistics [22], using pairwise win rates (5 Radiant heroes x 5 Dire heroes = 25 hero combinations) [17] or as a team synergy calculated by summing the win rates of hero pairs in each team [13]. Kalyanaraman [12] uses a genetic algorithm to calculate success sets of heroes which contribute the most to victory from the co-occurrence network in their graph-based approach (see item 5).…”

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

confidence: 99%

“…9) The human player's skill is very important in determining match winners. It can be represented by their final score and current skill (skill rating percentile) [17] or their performance calculated using logistic regression on 17 different features [22]. 10) The Player-Hero skill combines the player's skill with the hero success by calculating 8 features to describe the players previous play records using this hero [17].…”

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

confidence: 99%

“…10) The Player-Hero skill combines the player's skill with the hero success by calculating 8 features to describe the players previous play records using this hero [17]. 11) Social ties inside the team (the degree of social friendship between team members represented by max # f riends) [22] are important factors in prediction.…”

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

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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“…The dataset was crawled using Steam API (friendship information) and Dota2 API (team membership information) from Valve. A detailed description of the dataset is available in our previous work [15]. Nodes correspond to users of Steam in both cases.…”

Section: Datasetsmentioning

confidence: 99%

Exploring Supervised Methods for Temporal Link Prediction in Heterogeneous Social Networks

Rümmele

Ichise

Werthner

2015

Proceedings of the 24th International Conference on World Wide Web

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In the link prediction problem, formulated as a binary classification problem, we want to classify each pair of disconnected nodes in the network whether they will be connected by a link in the future. We study link formation in social networks with two types of links over several time periods. To solve the link prediction problem, we follow the approach of counting 3-node graphlets and suggest three extensions to the original method. By performing experiments on two real-world social networks we show that the new methods have a predictive power, however, network evolution cannot be explained by one specific feature at all time points. We also observe that some network properties can point at features which are more effective for temporal link prediction.

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Ranking factors of team success

Cited by 41 publications

References 15 publications

Performance Dynamics and Success in Online Games

Performance Dynamics and Success in Online Games

Win Prediction in Multiplayer Esports: Live Professional Match Prediction

Exploring Supervised Methods for Temporal Link Prediction in Heterogeneous Social Networks

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