Catch Framing Predictability

There has been more and more talk lately about pitch framing, which is the ability for catchers to impact the runs scored in a game by garnering strikes from pitches outside the strike zone. My initial skepticism around it has largely been due to the huge impact it can seem to have. For instance, last year the difference between the best pitch framer (Matt LuCroy) and the worst (John Buck) is estimated at 50 runs. That's a five win difference. That's hard to believe.

But today I wanted to look at a different aspect: its predictability. That is, if a catcher is good at framing in one year, can we reasonably assume he'll be good at it the next year? One way to look at this is to look at all catchers and how they did from year to year. If they do well one year, will the do well the next and vice versa?

(I wondered this because I was looking up Kurt Suzuki's framing numbers. They're usually been negative, but there have been some positive ones sprinkled in. I wondered how common that is.)

There is a neat little statistical gizmo to do this called a correlation coefficient. A correlation coefficient examines two sets of numbers and gives back a number between -1 and 1.

• 1 means there is a perfect correlation, like between the temperature in Celsius and the temperature in Fahrenheit.
  
• -1 means there is a perfectly negative correlation, like the amount you spend in a month and your checking balance.
  
• And if it's 0, that means the numbers have no correlation, like Joe Mauer's batting average and the migratory penguin population.
  

You can find the results of my study here.

The bottom line: there is a lot of predictability. The runs per season had a correlation of .76, which is high. But the correlation on pitches per game is even higher .82.

So catchers who have had a large positive effect end up continuing to do so. Unfortunately, most of the Twins who will play catcher this year didn't have a large positive effect last year. In fact, none of them did:

Kurt Suzuki: -9.1

Josmil Pinto: -4.3

Chris Herrmann:-4.6

Eric Fryer: -0.8

I didn't choose that order to emphasize the negative. I chose listed them in my predicted order of innings caught. It's almost as if the worse they are at pitch framing, the more likely they are to play catcher. And this is where John starts rubbing his temples.

And yet, that still might be better than last year. Because last year Joe Mauer was average (0.4) and Ryan Doumit was horrendous (-15.9). Still, it appears that new catching corps may not be doing the Twins revamped pitching staff any favors this year.

Since I'm sure you might want to do something like this yourself (and really, why wouldn't you - YEAY MATH), I thought I'd spell out the steps.

1. I pulled all the data I could from this great site and pasted it into a spreadsheet. It has all the catcher framing data from 2013 through 2007.

2. I added one column to that data: "Prev Yr." You'll see why in a minute.

3. I imported that spreadsheet into an Access DB twice, once as a table called "Following" and another as "Previous".

4. I created a query joining those two tables, joining fields First Name, Last Name and "Prev Yr" from following to "Year" from the Previous field. I pulled the Names, Years, Per Game and RAA fields from each table.

5. Copy and paste the results from the query back into an Excel spreadsheet.

6. Use the "Correl" function to compare the values in the two "Per Game" and two RAA" fields.

Ta Da!