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Hot Failure: Tuning Gameplay With Simple Player Metrics
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Hot Failure: Tuning Gameplay With Simple Player Metrics


December 16, 2010 Article Start Previous Page 4 of 4
 

The Future

For my next game, I'll definitely employ automatic metrics reporting again. In addition to death positions, I may add events based on different forms of death; it'd probably be useful to know how exactly a player died, not just where. And, depending on the game, it might be useful to report a history of positions before the death so that an individual player's path through a level can be traced.

However, the key to this kind of system is simplicity; collecting data isn't useful unless I also have reliable tools to process it later. For the next title, I'll probably leave the basic reporting and storing mechanism alone and focus most of my time on writing better tools for crunching the numbers.

I'm also wondering whether aggregated output from this form of player metric can be used to inform runtime dynamic difficulty systems.

If the game were capable of reading aggregated output back from a server, it could change itself based not only on the play of a single player, but on the average habits of millions of players. The availability of this data opens up all sorts of interesting possibilities.

Player metrics are not a perfect replacement for user testing, but they are a pretty useful approximation. And because they allow you to test a much larger group of users than would be possible with individual testers, metrics can tell you more about your game in the long run.

The cost to benefit ratio was extremely positive for Replica Island; by keeping the runtime and server dead simple, I learned much about my level designs and the habits of my players, and my game got a lot better as a result. My only regret is that I did not employ this kind of system on earlier games -- it seems applicable to pretty much any genre on pretty much any platform.

How To Make Heat Maps

Generating heat maps isn't hard, but information on the exact procedure can be hard to find. I used a method similar to the one described here.

The basic procedure is as follows:

Prepare a grayscale image of a circle that goes from black in the center to transparent on the edges in a radial gradient. This is your event spot image.

Prepare a color gradient image. The bottom should be white, or red, or whatever color you choose to indicate "most intense" on the heat map. The top of the image should be black, and with several other colors in between. This image will be used as a lookup to colorize your output later.

Generate a list of event positions.

Calculate the maximum number of overlapping data points (i.e. the number of events that occurred at the most common xy position). This is the value of maximum heat.

For each unique location on the event list, draw the spot image to a canvas at the location of the event. Draw the image at ((number of events at this location) / (maximum heat) * 100%)) opacity. Use the multiply transfer mode (src * dest) to blend each spot to the canvas.

When finished, you should have an image with a bunch of black spots on it of varying shades of darkness. This is the intermediate output image.

Take the output image and remap its color table using the gradient image. Take the alpha value of each pixel and use it to look up a Y offset in the gradient image to find the color value for that pixel.

Take the resulting image and blend it over your level art. The event hotspots in the level will be shown as colored areas, with the intensity of color increasing in areas where more events occurred.

When doing this work, make sure that you keep your color space (particularly the opacity calculation in step 5) within regular 8-bits-per-channel ranges (or consider using a format that supports floating point pixels). It is easy to introduce precision bugs that will only manifest when there are so many data points that the contribution of a single event falls below 1 percent. Tools like ImageMagick can help you do this.


Article Start Previous Page 4 of 4

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