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Posts Tagged ‘games’

Aggregate player preference for the first 20 building created in Illyriad

June 7th, 2015 2 comments

I was at the Microsoft Gaming data hackathon today. Gaming is very big business and companies rarely publish detailed game data. Through contacts one of the organizers was able to obtain two gaming datasets, both containing just under 300M of compressed of data.

Illyriad supplied a random snapshot of anonymised data on 50,000 users and Mediatonic supplied three months of player data.

Being a Microsoft event there were lots of C# developers, with data analysis people being thin on the ground. While there were plenty of gamers present I could not find any that knew the games for which we had data (domain experts are always in short supply at hackathons).

I happened to pick the Illyriad data to investigate first and stayed with it. The team sitting next to us worked on the Mediatonic data and while I got to hear about this data and kicked a few ideas around with them, I did not look at it.

The first thing to do with any dataset is to become familiar with what data it actually contains and the relationships between different items. I was working with two people new to data science who wanted to make the common beginner mistake of talking about interesting things we could do; it took a while for my message of “no point of talking about what we could do with the data until we know what data we have” to have any effect. Of course it is always worth listening to what a domain expert is interested in before looking at the data, as a source of ideas to keep in mind; it is not worth keeping in mind ideas from non-domain experts.

Quick Illyriad game overview: Players start with a settlement and construct/upgrade buildings until they have a legendary city. These buildings can generate resources such as food and iron; towns/cities can be conquered and colonized… you get the picture.

My initial investigation of the data did not uncover any of the obvious simple patterns, but did manage to find a way of connecting some pairs of players in a transaction relationship (the data for each player included a transaction list which gave one of 255 numeric locations and the transaction amount; I reasoned that the location/amount pair was likely to be unique).

The data is a snapshot in time, which appeared to rule out questions involving changes over time. Finally, I realized that time data was present in the form of the order in which each player created buildings in their village/town/city.

Buildings are the mechanism through which players create resources. What does the data have to say about gamers preferential building construction order? Do different players with different playing strategies use different building construction orders?

A search of the Illyriad website located various beginners’ guides containing various strategy suggestions, depending on player references for action.

Combining the order of the first 20 different buildings, created by all 50,000 players, into an aggregate preference building order we get:

Library
Storehouse
Lumberjack
Clay Pit
Farmyard
Marketplace
Quarry
Iron Mine
Barracks
Consulate
Mage Tower
Paddock
Common Ground
Brewery
Tavern
Spearmaker
Tannery
Book Binder
Flourmill
Architects` Office

A couple of technical points: its impractical to get an exact preference order for more than about 10 players and a Monti Carlo approach is used by RankAggreg and building multiple instance of the same kind of building were treated as a single instance (some form of weighting might be used to handle this behavior):

The order of the top three ranked buildings is very stable, but some of the buildings in lower ranks could switch places with adjacent buildings with little impact on ranking error.

Do better players use different building orders than poor players? The data does not include player ability data as such; it included game ranking (a high ranking might be achieved quickly by a strong player or slowly over a longer period by a weaker player) and various other rankings (some of which could be called sociability).

Does the preference for buildings change as a players’ village becomes a town becomes a city? At over 200 minutes of cpu time per run I have not yet had the time to find out. Here is the R code for you to try out some ideas:

library("plyr")
library("RankAggreg")
 
get_build_order=function(df)
{
# Remove duplicates for now
dup=duplicated(df$building_id)
 
# Ensure there are at least 20
build_order=c(df$building_id[!dup], -1:-20)
return(build_order[1:20])
}
 
# town_id,building_id,build_order_for_town
#1826159E-976D-4743-8AEB-0001281794C2,7,1
build=read.csv("~/illyriad/town_buildings.csv", as.is=TRUE)
 
build_order=daply(build, .(town_id), get_build_order)
 
build_rank=RankAggreg(build_order, 20)

What did other teams discover in the data? My informal walk around on Saturday evening found everybody struggling to find anything interesting to talk about (I missed the presentation on Sunday afternoon, perhaps a nights sleep turned things around for people, we will have to check other blogs for news).

If I was to make one suggestion to the organizers of the next gaming data hackathon (I hope there is one), it would be to arrange to have some domain experts (i.e., people very familiar with playing the games) present.

ps. Thanks to Richard for organizing chicken for the attendee who only eats pizza when truly starving.

Update

Usage statistics for the game DDNET.

Free range software developers: Are they cost effective?

July 14th, 2013 No comments

I have just been reading an eye-opening article by Ramin Shokrizade about the techniques that online game designers use to extract money from players. Playing computer games and writing software have a great deal in common, two important characteristics they both share are being immersive and very enjoyable.

Reward Removal

From the article: “The technique involves giving the player some really huge reward, that makes them really happy, and then threatening to take it away if they do not spend.” Hmm, this sounds familiar. Beginner programmers are very resistant to deleting any code they have written, whereas more experienced developers are much less resistant to deleting code but they often put up a fight if an attempt is made to remove a feature they are responsible for creating.

“The longer you allow the player to have the reward before you take it away, the more powerful is the effect.” Wot! Remove this feature? What if somebody somewhere is using it?

“… uses the same technique at the end of each dungeon again in the form of an inventory cap. The player is given a number of “eggs” as rewards, the contents of which have to be held in inventory. If your small inventory space is exceeded, again those eggs are taken from you unless you spend to increase your inventory space.” Why are there no researchers with this kind of penetrating insight investigating how to make software engineering more cost effective? We continue to suffer from the programming is logic by other means world view, promulgated by the failed mathematicians that populate so many computing departments.

Premium Currencies

“To maximize the efficacy of a coercive monetization model, you must use a premium currency, …” [a premium currency is in-game money that is disconnected from real wold money]. The lesson here is that if you want software developers to make decisions relating to real world events you need to provide a direct and transparent connection to the real world. Hide the connection under layers of abstraction or vague metrics and developers can be easily fooled into making poor decisions.

Skill Games vs. Money Games

“A game of skill … ability to make sound decisions primarily determines … success. A money game … ability to spend money is the primary determinant of … success. Consumers far prefer skill games to money games, …. A key skill in deploying a coercive monetization model is to disguise your money game as a skill game.”

I think most developers consider their job to be one of making skillful decisions rather than one of making money for their employer, rationalizing that these skillful decisions result in their employer making money. Hmm, how much time do developers spend in skillful activity for what appear to outsiders as obscure coding issues; skillful activity is enjoyable while doing what makes most money for one’s employer can result in having to do lots of really dull and boring tasks. I cannot help but think that skill here is playing the role of a premium currency.

The big difference between playing a game and writing software is that in most cases a game has a well defined ending, a path exists to get there and players know when they get there. One of the reasons that managing software developers is like herding cats is that the ‘end’ is often very fuzzy and ill-defined. This does not mean that factory farming techniques are not applicable to software development, just that we have not yet figured out which techniques work.