Spoilers, Spoilers, Spoilers. If you have not finished the single player portion of Portal 2 do not read any further. If you have, you may pass. Also check out Giantbomb’s Portal 2 spoiler interview with Valve’s Erik Wolpaw, Jay Pinkerton, and Chet Faliszek. It’s great.
I ran into a problem with Portal 2 around chapter 7 of the game. I didn’t want to solve the puzzles anymore.
Like many others I jumped on the Magicka bandwagon this week. Something about not having to level up 85 times to get awesome spells appealed to me. But seriously, it’s great to have games that offer “crafting spell casting” in contrast to typical, one-button per spell or gesture-based casting like in Black and White.
Before I purchased the game I was roaming around online in search of Magicka information and found Magickapedia, a standard game wiki. However, I found this page entitled “Possible Spell Combinations” and stopped, reviewing the long list of possible element combinations available in the game.
At first I thought, what a great visualization. The length of the page is long, which helps express the large number of element combinations. It’s also setup in a hierarchy format – Arcane element first, Water element last – and follows that format to present each of the 1123 possible combinations (each element cancels another element out, hence the possible spells is not [10 - the number of elements] to the power of [5 - the number of element slots]).
Since the spells are setup in a hierarchy they can also be represented using an Icicle graph, which is a space saving visualization for presented hierarchically categorized data. And with that it lead me to create an entire infographic around the element combinations in Magicka.
Big thanks to the contributors at Magickapedia, I used the site as my reference source. Also, thumbs up to Arrowhead Games for putting up high res concept art, and for producing a great game. Finally, protovis was used to produce the icicle graph, which is the visualization package I used to develop the Dead Space 2 analytic tool, Data Cracker.
“Play with Data” is a series of articles looking at game analytics and how game data plays a major role in games. A role that is less about the gamepocalypse and more about play, with data.
I’m sick of my family saying “you’re not working, you’re playing” every time I mention I’m studying a game. Mainly because it is true. Well, it is a little of both. When I play I analyze. There should be nothing wrong with that.
Why do we believe that analyzing something, approaching it scientifically and methodically, has to be serious work. In fact, that is exactly the opposite of what most serious data analysts and scientists believe.
Take a few of Stephen Few’s … *intentional … personality traits a good data analyst should exhibit: Interested, Curious, Self-motivated, Open-minded, Flexible and Imaginative. Those sound very playful to me. These traits are lumped in right next to the Analytical, Skeptical and Methodical traits as if there were no differences between them.
If play is a part of analysis then why shouldn’t we study how play can combine with, oh let’s say, information visualization (infovis), a heavy hitter in the realm of data analysis.
In a new article written for the Parsons Journal for Information Mapping I tackle that very issue. Using games as my medium of choice, as if I would use another, I examine how play and infovis fuse together to form playful visualizations to support and promote play.
While it would be easy to argue that gameplay itself is a form of infovis, I take a literal route providing game-related examples which mimic typical infovis systems and exist just outside of normal gameplay. Examples like Darkfall’s political map, players using cartology to represent the political turmoil of Darkfall’s continents, or Need For Speed’s Autolog recommendation system, which proceduralizes the phrase “neener neener” as a driver of competition.
The article ends with a post-mortem of sorts: given the examples explored, how do data analysis interactions promote different types of play and/or players. It will be interesting to see how these interactions continue to crop up in my other articles about game analytics, even when discussing analytics meant for serious analysis of games.
I will be presenting Data Cracker at GDC 2011.
Over the summer I had the pleasure to work at EA Redwood Shores as an intern. Usually being an EA intern means joining a development team for a few months, working on one of EA’s games. However, for my internship I was placed in the CCO, the Chief Creative Office, a research oriented group at EA. The CCO basically houses some of the heavy hitters at EA, like Rich Hilleman (EA’s CCO) and Scott Cronce (EA’s CTO), along with a number of team members working on various projects. A much smaller operation compared to the size of a typical EA development team but none the less a very powerful one.
During the summer my fellow intern, Jeff, and I were charged with the task to research, design and build a game analytic system for an EA team. Game analytics basically refers to the practice of capturing data related to gameplay, for example recording a player’s behavior while they play, and analyzing the data for insightful information that can help improve the game’s design. Seeing as my dissertation work surrounds the topic of game analytics I could not have pitched a better project to EA than the one we were given. Plus, since we worked at the CCO, we had a lot of freedom to meet with many development teams and discuss their game analytic needs.
The team we ended up working with was the Dead Space 2 Multiplayer team. As of this writing, and throughout the entire summer, the Dead Space 2 multiplayer gameplay is still shrouded in mystery. This made the project even more relevant because (a) the multiplayer feature is new to the Dead Space 2 franchise and (b) it was early enough in the dev cycle that any game analytic tool that was built could have a huge impact on the design. The experience was flat out one of the best projects I have ever been a part of, to say the least.
The tool we ended up building is called Data Cracker, which is a play on the concept of ‘Planet Cracking’ that exists within the lore of Dead Space. Data Cracker taps into data depicting player events that occur multiplayer matches. Without giving anything away, an obvious example of a player event is which player(s) won the match. Those events are sent to EA’s servers and the tool grabs the data, aggregates it into different values for analysis and visualizes those values within a web interface.
My main task on the project was working on the visualizations. We ended up choosing Protovis for our visualization library (built in javascript), mostly due to the fact that the group wanted to focus on HTML5 (however I’m still a diehard Flash developer and think Flare is an awesome visualization package). It turned out to be a fairly good library to work with and my only complaint is that it is much harder to create complex interactions compared with Flash. I was able to combine Protovis with other javascript libraries like jQuery however, which fixed some of the interaction/animation limitations of Protovis.
I can’t say much more than that at this time but I am working on papers that dive into the design and development of the tool. Those will appear on my site soon as will a number of off-shoot game analytic projects that I am working on for the rest of the year.
This video is from CHI 2010 where I presented “The Implications of Improvisational Acting and Role-Playing on Design Methodologies.” The talk describes how improvisational theatre and role-playing performance techniques work, how they have been used by designers and where the techniques differentiate from one another.
