Tag Archives: keyboard

Click Clack I was Taken Aback

I’ve been trying to resist.

I’ve been trying to convince myself that there is no practical need for this.


But then we went to Best Buy last week and I got it anyway.

It lights up and has colors, yo. How could I resist?

It’s also super clicky-clicky, which badass.


(The intense color of the light is not captured on this video; the colors are SUPER BRIGHT.)



The only thing I really know in ASL, apart from about 5 signs, is the alphabet. This app is a cool way to practice recognizing the letter signs in a QWERTY layout, even if you don’t need to send any sign language messages. It’s also got a few word signs as well.

I’d really like to learn ASL to the point where I could have a conversation with someone using it (at least, a conversation in which I wouldn’t have to look ridiculous spelling out every single word). I CAN spell relatively quickly, though; I think that comes from the fact that when I’m walking and listening to music, I like to try and sign the first letter of every word in the song as it plays.

Because what else am I supposed to be doing with my hands when I’m walking?

Scrabble Letter Values and the QWERTY Keyboard

Hello everyone and welcome to another edition of “Claudia analyzes crap for no good reason.”

Today’s topic is the relationship between the values of the letters in Scrabble and the frequency of use of the keys on the QWERTY layout keyboard.

This analysis took three main stages:

    1. Plot the letters of the keyboard by their values in Scrabble.
    2. Plot the letters of the keyboard by their frequency of use in a semi-long document (~50 pages).
    3. Compute the correlation between the two and see how strongly they’re related.


Step 1
There are 7 categories of letter scores in Scrabble: 1-, 2-, 3-, 4-, 5-, 8-, and 10-point letters. The first thing I decided to do was create  gradient to overlay atop a QWERTY and see what the general pattern is. Here is said overlay:

 Makes sense. K and J are a little wonky, but that might just be because the fingers on the right hand are meant to be skipping around to all the other more commonly-used letters placed around them. This was the easiest part of the analysis (except for making that stupid gradient; it took a few tries to get the colors at just the right differences for it to be readable but not too varying).

Step 2
I found a 50 or so page Word document of mine that wasn’t on anything specific and broke it down by letter. I put it into Wordle and got this lovely size-based comparison of use:

I then used Wordle’s “word frequency” counter to get the number of times each letter was used. I then ranked the letters by frequency of use.

I took this ranking and compared it to the category breakdown used in Scrabble—that is, since there are 10 letters that are given 1 point each, I took the 10 most frequently used letters in my document and assigned them to group 1, the group that gets a point each. There are 2 letters in Scrabble that get two points each; I took the 11th and 12th most frequently used letters from the document and put them into group 2. I did this for all the letters, down until group 7, the letters that get 10 points each.

So at this point I had a ranking of the frequency of use of letters in an average word document in the same metric as the Scrabble letter breakdown. I made a similar graph overlaying a QWERTY with this data:

Pretty similar to the Scrabble categories, eh? You still get that wonky J thing, too.

Side-by-side comparison:

Step 3
Now comes the fun part! I had two different ways of calculating a correlation.

The first way was the category to category comparison, which would require the use of the Spearman correlation coefficient (used for rank data). Essentially, this correlation would measure how often a letter was placed in the same group (e.g., group 1, group 4) for both the Scrabble ranking and the real data ranking. The Spearman correlation returned was 0.89. Pretty freaking high.

I could also compare the Scrabble categories against the raw frequency data, which would require the use of the polyserial correlation. Since the frequency decreases as the category number increases (group 1 has the highest frequencies, group 10 has the lowest), we would expect some sort of negative correlation. The polyserial correlation returned was -.92. Even higher than the Spearman.

So what can we conclude from this insanity? Basically that there’s a pretty strong correlation between how Scrabble decided to value the letters and the actual frequency of letter use in a regular document. Which is kind of a “duh,” but I like to show it using pretty pictures and stats.




Today’s song: Sprawl II (Mountains Beyond Mountains) by Arcade Fire