Identifying and evaluating successful non-meta strategies in league of legends

Lee, Choong-Soo; Ramler, Ivan

doi:10.1145/3102071.3102081

Cited by 14 publications

(7 citation statements)

References 5 publications

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“…Further, MOBA games boast metagaming strategies, which are collectively decided by players (the crowd) as the most optimal strategy for the team or for each champion. For example, [16] finds that the mostly widely successful team composition in LoL consists of one player in each of the five positions, although some non-meta teams have significant advantages.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game

He,

Tran,

Jiang

et al. 2021

Preprint

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The popularity of online gaming has grown dramatically, driven in part by streaming and the billion-dollar e-sports industry. Online games regularly update their software to fix bugs, add functionality that improve the game's look and feel, and change the game mechanics to keep the games fun and challenging. An open question, however, is the impact of these changes on player performance and game balance, as well as how players adapt to these sudden changes. To address these questions, we use causal inference to measure the impact of software patches to League of Legends, a popular team-based multiplayer online game. We show that game patches have substantially different impacts on players depending on their skill level and whether they take breaks between games. We find that the gap between good and bad players increases after a patch, despite efforts to make gameplay more equal. Moreover, longer between-game breaks tend to improve player performance after patches. Overall, our results highlight the utility of causal inference, and specifically heterogeneous treatment effect estimation, as a tool to quantify the complex mechanisms of game balance and its interplay with players' performance.

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Section: Related Workmentioning

confidence: 99%

Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game

He,

Tran,

Jiang

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Other papers use the public Riot API data for team style analysis or other aspects not directly related to our purpose here. For example, Lee and Ramler (2017) analyze whether typical team composition choices of players are in fact optimal, and do Nascimento et al (2017) use machine learning and cluster analysis to identify seven profiles of varying team success.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart kills and worthless deaths: eSports analytics for League of Legends

Maymin

2020

Journal of Quantitative Analysis in Sports

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Vast data on eSports should be easily accessible but often is not. League of Legends (LoL) only has rudimentary statistics such as levels, items, gold, and deaths. We present a new way to capture more useful data. We track every champion’s location multiple times every second. We track every ability cast and attack made, all damages caused and avoided, vision, health, mana, and cooldowns. We track continuously, invisibly, remotely, and live. Using a combination of computer vision, dynamic client hooks, machine learning, visualization, logistic regression, large-scale cloud computing, and fast and frugal trees, we generate this new high-frequency data on millions of ranked LoL games, calibrate an in-game win probability model, develop enhanced definitions for standard metrics, introduce dozens more advanced metrics, automate player improvement analysis, and apply a new player-evaluation framework on the basic and advanced stats. How much does an individual contribute to a team’s performance? We find that individual actions conditioned on changes to estimated win probability correlate almost perfectly to team performance: regular kills and deaths do not nearly explain as much as smart kills and worthless deaths. Our approach offers applications for other eSports and traditional sports. All the code is open-sourced.

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“…Lee and Ramler used a support vector machine to identify player roles in League of Legends (LoL) [48] based on item and spell choices. They used these to identify unconventional team composition strategies and evaluate their success rates compared to conventional teams [30]. While clustering is an effective way of quickly identifying and differentiating player types based on in-game behaviors [43], the data used tends to focus entirely on aggregated numbers [14,43,46].…”

Section: Playermentioning

confidence: 99%

"And then they died": Using Action Sequences for Data Driven,Context Aware Gameplay Analysis

Kleinman¹,

Ahmad²,

Teng³

et al. 2020

Preprint

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Many successful games rely heavily on data analytics to understand players and inform design. Popular methodologies focus on machine learning and statistical analysis of aggregated data. While effective in extracting information regarding player action, much of the context regarding when and how those actions occurred is lost. Qualitative methods allow researchers to examine context and derive meaningful explanations about the goals and motivations behind player behavior, but are difficult to scale. In this paper, we build on previous work by combining two existing methodologies: Interactive Behavior Analytics (IBA) [2] and sequence analysis (SA), in order to create a novel, mixed methods, human-in-the-loop data analysis methodology that uses behavioral labels and visualizations to allow analysts to examine player behavior in a way that is context sensitive, scalable, and generalizable. We present the methodology along with a case study demonstrating how it can be used to analyze behavioral patterns of teamwork in the popular multiplayer game Defense of the Ancients 2 (DotA 2). CCS CONCEPTS• Human-centered computing → Visualization.

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Identifying and evaluating successful non-meta strategies in league of legends

Cited by 14 publications

References 5 publications

Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game

Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game

Smart kills and worthless deaths: eSports analytics for League of Legends

"And then they died": Using Action Sequences for Data Driven,Context Aware Gameplay Analysis

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