Search Results for author: Anders Drachen

Found 9 papers, 5 papers with code

Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics

2 code implementations29 May 2023 Alan Pedrassoli Chitayat, Florian Block, James Walker, Anders Drachen

Therefore, the proposed methodology for representing characters can increase the life-spam of machine learning models as well as contribute to a higher performance when compared to traditional techniques typically employed within the literature.

Dota 2

From Theory to Behaviour: Towards a General Model of Engagement

1 code implementation27 Apr 2020 Valerio Bonometti, Charles Ringer, Mathieu Ruiz, Alex Wade, Anders Drachen

In the present work we operationalize engagement mechanistically by linking it directly to human behaviour and show that the construct of engagement can be used for shaping and interpreting data-driven methods.

Modelling Early User-Game Interactions for Joint Estimation of Survival Time and Churn Probability

1 code implementation27 May 2019 Valerio Bonometti, Charles Ringer, Mark Hall, Alex R. Wade, Anders Drachen

The model proposed is very suitable for industry applications since it relies on a minimal set of metrics and observations, scales well with the number of users and is explicitly designed to work across a diverse range of titles.

Time to Die: Death Prediction in Dota 2 using Deep Learning

1 code implementation21 May 2019 Adam Katona, Ryan Spick, Victoria Hodge, Simon Demediuk, Florian Block, Anders Drachen, James Alfred Walker

Even though death events are rare within a game (1\% of the data), the model achieves 0. 377 precision with 0. 725 recall on test data when prompted to predict which of any of the 10 players of either team will die within 5 seconds.

Dota 2

Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

no code implementations17 Nov 2017 Victoria Hodge, Sam Devlin, Nick Sephton, Florian Block, Anders Drachen, Peter Cowling

Given the comparatively limited supply of professional data, a key question is thus whether mixed-rank match datasets can be used to create data-driven models which predict winners in professional matches and provide a simple in-game statistic for viewers and broadcasters.

Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2

no code implementations24 Mar 2016 Anders Drachen, Matthew Yancey, John Maguire, Derrek Chu, Iris Yuhui Wang, Tobias Mahlmann, Matthias Schubert, Diego Klabjan

Results indicate that spatio-temporal behavior of MOBA teams is related to team skill, with professional teams having smaller within-team distances and conducting more zone changes than amateur teams.

Clustering Dota 2 +2

Going Out of Business: Auction House Behavior in the Massively Multi-Player Online Game

no code implementations24 Mar 2016 Anders Drachen, Joseph Riley, Shawna Baskin, Diego Klabjan

The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed.

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