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Posts Tagged ‘Data Visualization’

What Ludum Dare Rating Categories Matter Most?

Posted by
Wednesday, April 13th, 2016 11:23 am

When we reach the end of a Ludum Dare event and evaluate each others’ games, we grade each game with a series of categories: Overall, Fun, Coolness, Graphics, Audio, Theme, Innovation, Humor, and Mood. When the results are announced, the top 100 games in each category are displayed, and of course it is every competitor’s dream to land in at least one of those top-100 lists.

The Overall category holds pride-of-place. Arguably, the winner of the Overall category is the winner of the event. It’s satisfying to do well in the other categories, but until you’re ranked #1 Overall, there is still room for growth in your Ludum Dare performance.

Which brings me to this question: Have you ever wondered which other categories make the biggest difference in how you rank overall? For example, do people who do well Overall also tend to do especially well in Graphics, or Theme, or Innovation? Coolness is a measure of how much you played other peoples’ games; does a great Coolness ranking help you achieve a good Overall ranking? If you want to succeed Overall, does it pay to focus on Fun, or Graphics, or Audio, or Mood?

If you’re anything like me, you’re just itching to know.

And now you can.

 

For the last several Ludum Dare events I’ve analyzed the relationships between category rankings by looking at the scores of the top 100 Compo entries. In each event I’ve analyzed the correlation between how games did in each of the nine categories. What I’ve found is that there are strong correlations, and they’re not necessarily what you would expect.

Take a look at this.

Correlations per Event

Correlations per Event

What you see here is a chart of how well each category did relative to the Overall category in each of the last eleven LD events. The blue line represents Fun, for example. This shows that more than any other category, Fun correlates strongly with Overall. If you do well in Fun, you tend to do well overall; if you do poorly in Fun, you tend to do poorly overall.

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LD34 Visualisation and Analysis

Posted by (twitter: @jezzamonn)
Saturday, January 23rd, 2016 7:01 am

TD;LR? Just look at the pretty pictures.

Hi all!

Before I begin, lets just remember Correlation != Causation

So, using the data that “” scraped from this Ludum Dare, I created some plots showing how each of the different categories correlate with the overall category. Here’s the lot of them (It’s a big image, so click to see it full size). Compo games are blue dots, and Jam games are red.

ld34 correlations

There’s a quite few interesting things there, but here’s a few little points.

As we’ve seen from previous analyses (Google told me that’s the plural of analysis) of Ludum Dares, the fun category has the highest correlation with the overall category, and humour correlates the least.

Another interesting thing is that the audio category is split for Jam and Compo games. You’re more likely to get a better overall score with the same audio score if you entered a Compo game, perhaps suggesting people are more forgiving for average audio in Compo games.

The ID plot may seem meaningless, except that IDs are given sequentially, and so it roughly shows what score people got in relation to how long they’ve been around Ludum Dare. It’s slightly skewed in favour of veterans, but not much, showing that newbies have just as good of a chance of making a great game.

 

But what I wanted to focus on is how the number of votes you got relates to the overall rating you get.

ld34 votes vs score

Now, this plot is a little hard to read because there’s so many people clustered up in the left, which hides the significance a little bit. You can see a slight upward trend as you get more votes, but it’s that clear. If you compare it to the plot of Votes Given vs Overall, you can understand it a bit better.

ld34 votes given vs score
Because there are so many people that cast/received between 20 and 50 votes, you would expect to see more extreme results in that area, just because there are more games. This is what we see with the Votes Given vs Overall plot — as the votes get larger there are less scores near the top and the bottom, mostly because there are less games there and it’s unlikely to get a really good or really bad rating. (That being said, there are some relationships here, but they’re not as significant as in the votes received plot)

In the Votes Received vs Overall plot, we have games that got high ratings with a large number of votes. This would be pretty unlikely if they were unrelated, just because less games got that many votes, indicating that there is a correlation.

But please remember CORRELATION IS NOT CAUSATION!!! It’s totally wrong to say that this means that if you want to do better you should try to get more ratings, because more ratings = higher overall. Instead, we have to say: Ok, there’s a relationship, what theories can we come up with that might explain it.

When you think about it, it would make sense that games that are really good would tend to get more votes, because people share them more.

Even though the general trend is upward, we can also see that there are games that get a lot of votes in a way that’s unrelated to how good the game is, such as people who are hugely popular or do a lot of publicity.

 

Finally, an issue that often comes up is the concerns that games that didn’t get many votes could sneak a high score just by being lucky with the ratings they got. If you look at the plot, there aren’t that many games that didn’t do well that didn’t also get quite a few votes. There are a few, but as there are a lot of games that got a relatively small number of votes, there would also be a lot more if it was entirely up to chance.

This doesn’t mean it’s perfect, just not all that bad.

That’s all for now! Thanks again to Liam for the useful data!

[Experimental] Ludum Dare Data Visualization

Posted by (twitter: @cboissie)
Monday, July 16th, 2012 6:05 pm

About:

Hi there!

A few months ago, I proposed a quite vague idea about a new “cartography” module for the upcoming LD23. Web Cartography is more and more used because of its curiously innovative and interesting aspect.

europeanPoliticalWeb

EuropeanPoliticalWeb (Linkfluence)

The picture above was made by a French startup and is representing the European Political seen through the web. (Based on semantic web crawlers) .

Now you may ask: “What’s the damn connection with Ludum Dare” ?

Just a few games…

With the increasing popularity of the event, we see more and more game proposed for each LD session. Also, the initial idea was to realize a cartography of the submitted games.

 

Why? 

To have a better visualization of the whole game submissions. Take a look to statistics in an original and interactive way.

  •  Which games are  available for a specific platform? Multi-platform?
  •  Which games have more votes, coolness? (main nodes) => Imagine a visual helping tool for voting.

…and numerous other possibilities.  (Why not something more realtime-oriented based on database snapshots?)

 

Proof of concept:

Using available public data and python scripts, I extracted and classified data concerning each game entries of a given Ludum Dare composition (platforms, ratings, creators,votes…). I’ve written a small web application displaying  large directed graphs, generated from these data sets.

You can find my work over here: http://cboissiere.com/projects/ldviz/

Don’t be afraid by the messy aspect of those graphs, it’s mainly because of the huge size of the data sets. And don’t forget it’s still experimental =)

And of course, the source code is over here: https://github.com/cboissie/LD_Viz

 

Tell me more:

It’s basically two kind of graphs:

  • WordCloud: We extract each words from all game titles. The words used together in a same title are linked to each other. For instance, if you click on the “TINY” node, you will see all the words that were used conjointly (like “WORLD”, or “PLANET”). The size of the node is proportional to the word occurrence.
  • MultiPlatform: In this graph, games and their respective platforms are linked (Windows, OSX etc…). The size of a platform node is proportional to the number of game ported on this platform.

 

Instructions:

  • You can change anytime the dataSet (between LD21,22 and 23) and the graph type.
  • Zoom with the mouse wheel.
  • Click on a node to see its immediate neighbors.
  • The “Start algorithm” button apply a “Force Atlas 2” algorithm to the current graph (see http://en.wikipedia.org/wiki/Force-based_algorithms_(graph_drawing)). You can stop the execution of the algorithm by clicking again on the same button. This algorithm will place the nodes in a more convenient way, give it a try!

 

TL;DR :

 

Here’s a quick web app prototype for visualizing interactive graphs of game entries from old Ludum Dare compos. You can see two kind of graphs: Word cloud (most used words for a specific theme) and Multi-Platform (Game names associated with their respective platform(s)). http://cboissiere.com/projects/ldviz/

 

Feel free to contact me at  clemzbox[at]gmail[dot]com or via Twitter.

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