Performance Dynamics and Success in Online Games

Sapienza, Anna; Peng, Hao; Ferrara, Emilio

doi:10.1109/icdmw.2017.124

Cited by 14 publications

(13 citation statements)

References 22 publications

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“…Then we verified our findings via randomization to exclude the possibility that the performance deterioration phenomenon was created by chance (random effect). The right plot of Figure 3 shows the reshuffled data, where the effect of match position is disrupted: as we would expect, the lines flatten out suggesting that indeed the position of a match in a session has an effect on performance, corroborating the performance deterioration hypothesis in line with recent research results [22], [23], [24].…”

Section: Rq3: Influence Of Social Ties On Individuals Over Sessionssupporting

confidence: 86%

“…Other than individual online behaviors, short-term deterioration effects has also been found in virtual teams in MOBA games. [22], [23], [24]. These results pose the basis for RQ3 and RQ4: our analysis revealed that social ties can play a role in mitigating performance deterioration over the course of an activity session, however such a mitigation is not homogeneous across all individuals, but tend to benefit more the low-skilled or low-experienced ones, while high-skill or high-experience individuals may not be affected by social ties when it comes to mitigating performance deterioration.…”

Section: Performance Deterioration Dynamicsmentioning

confidence: 99%

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The Influence of Social Ties on Performance in Team-Based Online Games

2021

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Social ties are the invisible glue that keeps together human ecosystems. Despite the massive amount of research studying the role of social ties in communities (groups, teams, etc.) and society at large, little attention has been devoted to study their interplay with other human behavioral dynamics. Of particular interest is the influence that social ties have on human performance in collaborative team-based settings. Our research aims to elucidate the influence of social ties on individual and team performance dynamics. We will focus on a popular Multiplayer Online Battle Arena (MOBA) collaborative teambased game, Defense of the Ancients 2 (Dota 2), a rich dataset with millions of players and matches. Our research reveals that, when playing with their friends, individuals are systematically more active in the game as opposed to taking part in a team of strangers. However, we find that increased activity does not homogeneously lead to an improvement in players' performance. Despite being beneficial to low skill players, playing with friends negatively affects performance of high skill players. Our findings shed light on the mixed influence of social ties on performance, and can inform new perspectives on virtual team management and on behavioral incentives.

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Section: Rq3: Influence Of Social Ties On Individuals Over Sessionssupporting

confidence: 86%

Section: Performance Deterioration Dynamicsmentioning

confidence: 99%

The Influence of Social Ties on Performance in Team-Based Online Games

2021

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“…This genre of games, including Heroes of the Storm, Dota 2, and League of Legends, has attracted researchers from different fields, especially because they provide a unique way to study the influence of role-playing in competitive games [1,2], the impact of cooperation [3,4] versus individual player attitudes [5,6], social behaviors [7][8][9], user commitment [10], etc. The analysis of MOBA games also allows for the discovery of useful information to study the social dynamics of player communities.…”

Section: Introductionmentioning

confidence: 99%

Non-Negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games

Sapienza¹,

Bessi²,

Ferrara³

2018

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Abstract:Multiplayer online battle arena is a genre of online games that has become extremely popular. Due to their success, these games also drew the attention of our research community, because they provide a wealth of information about human online interactions and behaviors. A crucial problem is the extraction of activity patterns that characterize this type of data, in an interpretable way. Here, we leverage the Non-negative Tensor Factorization to detect hidden correlated behaviors of playing in a well-known game: League of Legends. To this aim, we collect the entire gaming history of a group of about 1000 players, which accounts for roughly 100K matches. By applying our framework we are able to separate players into different groups. We show that each group exhibits similar features and playing strategies, as well as similar temporal trajectories, i.e., behavioral progressions over the course of their gaming history. We surprisingly discover that playing strategies are stable over time and we provide an explanation for this observation.

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“…Previous research investigated factors influencing human performance in MOBA games. On the one hand, studies focus on identifying player's choices of role, strategies as well as spatio-temporal behaviors (Drachen et al, 2014 ; Yang et al, 2014 ; Eggert et al, 2015 ; Sapienza et al, 2018b ) that drive players to success (Sapienza et al, 2017 ; Fox et al, 2018 ). On the other hand, performance may be affected by player's social interactions: the presence of friends (Pobiedina et al, 2013a ; Park and Kim, 2014 ; Sapienza et al, 2018b ), the frequency of playing with or against certain players (Losup et al, 2014 ), etc.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Optimal Team Composition

Sapienza¹,

Goyal²,

Ferrara³

2019

Front. Big Data

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Cooperation is a fundamental social mechanism, whose effects on human performance have been investigated in several environments. Online games are modern-days natural settings in which cooperation strongly affects human behavior. Every day, millions of players connect and play together in team-based games: the patterns of cooperation can either foster or hinder individual skill learning and performance. This work has three goals: (i) identifying teammates' influence on players' performance in the short and long term, (ii) designing a computational framework to recommend teammates to improve players' performance, and (iii) setting to demonstrate that such improvements can be predicted via deep learning. We leverage a large dataset from Dota 2, a popular Multiplayer Online Battle Arena game. We generate a directed co-play network, whose links' weights depict the effect of teammates on players' performance. Specifically, we propose a measure of network influence that captures skill transfer from player to player over time. We then use such framing to design a recommendation system to suggest new teammates based on a modified deep neural autoencoder and we demonstrate its state-of-the-art recommendation performance. We finally provide insights into skill transfer effects: our experimental results demonstrate that such dynamics can be predicted using deep neural networks.

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Performance Dynamics and Success in Online Games

Cited by 14 publications

References 22 publications

The Influence of Social Ties on Performance in Team-Based Online Games

The Influence of Social Ties on Performance in Team-Based Online Games

Non-Negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games

Deep Neural Networks for Optimal Team Composition

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