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anders drachen's Blog   Expert Blogs

 

I am a data scientist working with games. 

My work centers on the analysis of user behavior in – and around – games. The reason I work with games is simple: They provide unprecedented access to incredibly detailed measures of behavior. Using logging technology, it is possible to capture the second-by-second interaction between user and game. This kind of precision is not possible in the real world, and provides incredible opportunities to study human behavior and how it can be affected – whether for the purposes of improving the user experience, optimizing design, facilitating learning, or monetization. One of the central challenges in this endeavour is the massive scale these kinds of behavioral data can reach, and the high degree of complexity. Imagine working with data describing thousands of actions across hundreds of variables from millions of players. Now imagine adding contextual data – demographics, video capture, physiological data, personality information, geographic information and much more … deriving actionable insights from such datasets requires not only analytical skills but also a deep understanding of an passion for games. I fundamentally form part of an international community of analysts in the private and academic sectors who try to derive meaning from user behavior in games, in order to inform game development.

I blog about all of this at www.andersdrachen.wordpress.com and blog.gameanalytics.com

That was my informal bio. Here is the formal one:

Anders Drachen, Ph.D. is a veteran Data Scientist, currently operating as Lead Game Analyst for Game Analytics (www.gameanalytics.com). He is also affiliated with the PLAIT Lab at Northeastern University (USA) and Aalborg University (Denmark) as an Associate Professor, and sometimes takes on independent consulting jobs. His work in the game industry as well as in data and game science is focused on game analytics, business intelligence for games, game data mining, game user experience, industry economics, business development and game user research. His research and professional work is carried out in collaboration with companies spanning the industry, from big publishers to indies (e.g. Square Enix, Crystal Dynamics, IO Interactive). He writes about analytics for game development on blog.gameanalytics.com, and about game- and data science in general on http://www.andersdrachen.wordpress.com. His writings can also be found on the pages of trade publications such as Game Developer Magazine and Gamasutra. His research has been covered by international media, including Wired and Forbes.

He is one of the most published scientists worldwide on the topic of game analytics, user research, game data mining, and user profiling, having authored more than 60 research publications on game analytics, user testing, and business intelligence in game development. He is also one of the editors of the book “Game Analytics – Maximizing the Value of Player Data”http://www.springer.com/computer/hci/book/978-1-4471-4768-8), a compendium of insights from more than 50 top experts in industry and research. He is a former member of the board of the International Game Developers Association Special Interest Group on Game User Research. His research work was recognized with a best paper award at the largest media conference in Northern Europe, MindTrek, in 2009. He has organized several international conferences and workshops and served on dozens of conference committees. 

He also really likes ice cream.

 

Expert Blogs

5 billion hours of play. 6 million players. 3000 games on Steam. 1 distribution model that explains how time spent playing games on Steam rises and falls as a function of time.


In this fourth and last post in the cluster analysis series, we show how cluster analysis operates in practice on player data, as well as how to present the results of cluster analysis. Grab a cup of coffee and sit down in a comfy chair for this one!


In the previous post of this series we introduced the theoretical foundations of cluster analysis and the various categories of algorithms. In this post we take a specific look at the challenges associated with running a cluster analysis on behavioral tel


Clustering is imminently useful for finding patterns in gameplay data. In this second post in the clustering series, we briefly outline several classes of algorithms and discuss the types of contexts they are useful in.


In this post, we introduce the foundations of cluster analysis, and discuss its application in finding patterns in the behaviors of players, and developing profiles of how people play your game. First in a series of four.


Posted by anders drachen on Fri, 09 May 2014 02:37:00 EDT in Business/Marketing, Design, Console/PC, Indie, Social/Online
In the past few years researchers have published a substantial amount of work on MMORPG economies the most complex economic system that exists in games. In this post we introduce some of the interesting papers from academia and beyond.



anders drachen's Comments

Comment In: [Blog - 07/07/2014 - 08:48]

Hi everyone r n r ...

Hi everyone r n r nThanks for commenting on the post. I hope I can answer your questions. r n r n@Ben Weber: All agreed re. lifetime fitting being interesting. In this case we used bins of 1 hour duration per player, aggregating these across all players for that game. ...

Comment In: [Blog - 09/11/2013 - 08:37]

Hi All r n r ...

Hi All r n r nApologies for responding to your comments late. r n r nArnaud: I take your point about rotating the graphs, I will remember this in the future. r n r nJohan/Isaac: yep, in the olden days some special characters were possible in WOW hyphens etc. . ...

Comment In: [Blog - 08/27/2013 - 10:00]

Great article Ben, thanks for ...

Great article Ben, thanks for collating all this information.