Mining the gaming data #mariokart #eaglelake #games #data


Anyone who knows me well will know I’m not the best computer game player in the world. Not since the days of Manic Miner have I actually had any real drive to complete or compete in a computer game. Until Mario Kart came along I wasn’t a great fan of multiplayer games either, just not competitive.

Fast forward to December 2012 and I was playing Eagle Lake by Conquest Dynamics for the iPad.  While the gameplay may appear a bit retro and, dare I say it, simple it does make you think on your fingers and as I’ve found to my cost it’s pretty addictive. For multiplayer games though I don’t want to play someone in the room or a friend.  Here’s why (and potentially the how).

To your end and extended.
It’s no fun playing a game against someone you know you’re going to beat. Neither is it fun being on the other side of the coin continually getting thrashed as your not up to scratch yet. The person I want to play against is just slightly better than I am, a better chance of either beating or coming very close to. And with that thought you can actually beat that person then there’s a higher probability of me returning to the game.


Mario Kart is an interesting game as when you run it through the Nintendo WFC then you can race against others from all over the planet. The one thing you don’t really know is their ability.  Unconfirmed while it is, I’d bet that the selection is basically random with no real logic behind. Find out who’s online and see if they’re waiting for a game.

With Eagle Lake the selection could be a simple as “find someone who’s on the same level as me”. But how about finding players who run the same tactics as me? As this gameplay works on a few levels, where you move to and how much you shoot things at the other tanks and finally do you go hunting for the bonusy bits.

From this information (assuming that it’s going back to a server somewhere for processing) then the game could make intelligent opposition decisions for you by clustering the data in three dimensions.  

Player Fred: moves little but fires a lot waiting for the enemy to come to him.
Player John: reserves firepower but always makes the first move 
Player Shelly: she collects bonuses as a priority than firing

Game players are grouped in to personality types: Achievers, Explorers, Socialisers and Killers. While these terms were horribly assigned to gamification marketing campaigns as different types of loyalty customer, I prefer them for this use only.

Once you align the clusters of user data against their previous gameplay then you have a good idea of what they are, making it easier to group with like minded (or polar opposites) players.

As we move on with tablet computing/gaming the notion of alike players will become more important. It’s a case of good data capture (if we allow it) and good mining techniques. It’s not just a case of creating a game in my opinion, it’s an issue of creating the most enjoyable playing experience based on my previous games, finding me players that will enhance my game and make me come back more and more to extend my gaming abilities.

Now this I find interesting. 

One response to “Mining the gaming data #mariokart #eaglelake #games #data”

  1. Analysis of how the user plays is an interesting field – a good game would look how the player is playing in a one-person game, and adjust the gameplay accordingly – making it just difficult enough to be challenging, without being frustrating.I’m not happy with the idea of gather statistics of how people play and use apps over the internet. I know one developer on Twitter who regularly boasts about how many people are playing his game "right now". This makes me very uneasy. What other information is he gathering? What is he doing with it? Do people know? Do they even care? I know I don’t use his apps directly as a result of his boasts (and it’s nothing to do with the fact his software is better and more popular than mine :-)Back to the game though, and I programmed it using GameKit, the API provided by Apple to find other players. It’s rather dumb, and doesn’t allow the use of skill levels and so on.. Obviously it would be possible to write a replacement that did, but this game was free and took a month to code, and adding intelligent matchmaking could add weeks or months of development time by itself. I’m not sure GameKit can be extended to be smarter, which would be the obvious and easiest solution – maybe that’s coming in iOS 7.Once you have worked out how to analyze a player’s gaming style, you’ve worked out how to make good AI enemies. That’s not easy, but lifts a game from the ordinary to the special.

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