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A Deeper Dive Into Defensive Catching Statistics

A Deeper Dive Into Defensive Catching Statistics

The catcher: A pitcher’s batterymate, defensive stalwart, and tough guy. He is asked to do things such as pitch blocking, framing, fielding, and throwing runners out. For starters, this usually means 120 games a season in a crouch, diving in the dirt, and springing up to fire a howitzer. This is probably the furthest thing from easy; it’s hard enough to try and track the ball with a mask on while playing in your youth…

When you think of the best to do it, you think of Johnny Bench, Yogi Berra, maybe even Salvador Perez. These three are universally regarded as some of the best catchers of all time for their work behind the plate. But, what actually is measured when talking about a catcher’s defense?

It has come to my attention that a significant portion of Yankee fans, as well much of the entire league, is under the impression that Gary Sanchez is not good—excuse me—horrendous at defense because of his passed ball problem. Despite being a Yankee fan, I too can realize that he struggles with pitch blocking, but I also knew that he does a few other defensive tasks really, really well. I thought and thought and finally came up with a net defensive formula, not just for Sanchez, but for every catcher in the league, with the intention of tracking, weighting, and recording who is really getting it done behind the dish.

So, did I vindicate Sanchez? Let’s find out…


The Factors

This is going to be the part where I break down all of the numbers that went into my formula, so if you don’t want to read all of this math, then scroll down to the spreadsheet embedded below. I’ll also add that this formula is far from perfect, but it’s as good as I will get it.

I took numbers from the following 5 stat categories:

  • Blocking
  • Fielding
  • Caught Stealing %
  • Pop Time
  • Catcher ERA

I chose blocking stats because this project started because of Sanchez, so that was a given. Fielding percentage is also a given; making a putout is an important part of any baseball player’s repertoire. Stealing numbers are also equally as important, as it’s a catcher’s job to throw out runners trying to swipe an extra bag. I included both caught stealing percentage and pop time because sometimes you can get bailed out by a slower runner or an amazing tag, but pop time takes care of the absences here and it’s about how quickly a catcher can get a ball to the bag. Some of my friends who are very familiar with baseball also suggested catcher ERA, as another huge part of what a catcher has to do is call all 100+ pitches of a game, and to do that well.

I left out some statistics; for example, I did some digging and decided to leave out frame rate due to its marginal impact. The ones that I chose were the most important ones I could find.

I used the 40 catchers in baseball that, for the most part, had seen the field by the greatest amount of blocking chances in 2018 relative to August (this started as a blocking project that I decided to take one step further, so I based it off of that). Some guys like Travis d’Arnaud are also on here, despite having barely played in 2018. Having 40 catchers to select from is about all it takes here, though; any more or less from different guys wouldn’t affect the data much.

The big ones are in here, so I think it’s pretty reliable. It takes some past players as well (i.e. guys like Evan Gattis who haven’t caught much in 2018). I tried to get everything based off of averages and percentages, which makes it easier to track data over the course of multiple seasons (2016-2018). Everything is also weighted, and some have more/less weight than the others. I’ll get into that shortly.

For blocking, I took the # of blocking chances-passed balls, then divided that number by the # of blocking chances to get a stop rate. From there, I figured out a league average stop rate, then took the difference from the league average and multiplied it by a factor of 8000 to get a more visible number. So, a good blocking catcher has a positive figure, and a bad one has a negative figure.

For fielding percentage, I calculated a net fielding percentage from 2016 and on, so (chances-errors)/chances = fielding percentage, and I did the same thing with the averages and multiplication, only this time by 2750.

Caught stealing percentage is the same drill; figure out each catcher’s percentage since 2016. I didn’t have to multiply the difference from the mean by anything, because it all nicely came out to numbers in the range I was looking for, but again, I will explain this in a bit.

When I had to deal with pop time, I just took the average pop time of the catcher over the course of the past three seasons, figured out the difference, and multiplied it by a factor of 150. For anyone who doesn’t know what pop time is, it’s the amount of time from the instant the ball hits the catcher’s glove until it reaches second or third base.

Catcher ERA was a bit different. If you look at the ERA of a guy like Gary Sanchez and compare it to that of Sal Perez, the difference is going to be very, very stark. It’s not fair to Perez, who is trying to catch and call a poor pitching staff, so his numbers are going to be higher than the likes of a catcher on a playoff team. Instead, I took the catcher’s relative cERA, so how he compares to the other catchers on his current team. This is a much better way to judge cERA, as the circumstances for calling the games are the same for every member of the team. I only did this with data from the current team, so for example, Jonathan Lucroy only has his 2018 data in here.

As far as weighting the numbers goes, I wanted to create it so that whatever factors were chosen, the best scores would be roughly 10 greater than average, and the lowest 10 less than average. I weighted cERA more than everything else, because I consulted some people who said that they thought it was the most important statistic for a catcher. I took a little bit of weight off of fielding % and stop % because if you look at the percentages, you’re dealing with a 99.6% versus a 99.8% success rate—splitting hairs, if you will. It still has a significant weight, just one or two units lower than something like pop time.

I then take all of these scores and add them together, to create a net catching score (NCS). A league average catcher would score zero, greater than average is greater than zero, and less than average is less than zero.

All of these numbers are as of mid-August 2018. Again, I didn’t make this flawlessly, but it gets the job done.

Drumroll please…


The Scores


[embeddoc url=”” download=”all” viewer=”microsoft”]


I grouped each subcategory together; each catcher that is above average in said category has their numbers in green, and the sub-average ones are in red. The final score is all the way to the right and bolded. The findings might stun you. I’ve highlighted some of the catchers that may interest you below.


SBC – Stopped Ball Constant

RSC – Runners Stopped Constant

cERAC – Catcher’s ERA Constant (relative)

PTC – Pop Time Constant

FC – Fielding Constant

NCS – Net Catching Score


Salvador Perez – 55.239

Holy smokes is he good…

Perez killed it in every aspect of the formula, unsurprisingly. His fielding, ability to throw runners out, and game calling are particularly outstanding. He has a positive score in every category as well, which helps to ensure his obscenely high mark here. In terms of a catcher to model your defensive game after, this is the guy you want.

Buster Posey – 34.985

Coming in at second place is Buster Posey—another “surprise”. He is also near the top and gives Sal a run in some categories, especially with his outstanding blocking and cERA. What causes him to fall is his slow pop time, as well as his caught stealing rate which is not negative, but not elite. That’s really the only “weak” spot we can try to create for an amazing defensive catcher.

Sandy Leon – 23.243

The fourth-highest scorer on this list is the first real shocker, as well as a platoon player: Hurricane Sandy. Leon’s blocking actually hurts him, as his numbers there are among the worst in the league. However, he more than makes up for it with a great cERA, fielding percentage, and the ability to throw runners out.

Yadier Molina – 14.016

Another surprise—well, not really a surprise but I bet a bunch of people thought Molina would score a lot higher. He is good at blocking balls and fielding, but his cERA is merely average, and he gains only 1 point with the two stealing categories highlighted. By no means a problem behind the plate—definitely a guy you want in the lineup every day. But, my findings surprised me a bit, as I expected them to be better.

J.T. Realmuto – 13.684

Right behind Molina is J.T. Realmuto. A not-so-stellar cERA and fielding percentage bring down Realmuto’s score. However, he makes up for it with a stellar pop time and positive marks in another area. I was a bit taken aback here too, given how highly people tout him for his all-around game. Makes you wonder about how important someone’s bat really is.

Gary Sanchez – 0.437

Yes, I will answer your immediate question: Gary Sanchez is slightly above average at defense. His blocking is horrendous, that’s no secret, and his fielding numbers are pretty bad as well. But Sanchez makes up for these deficiencies with a great ability to throw runners out, as well as an outstanding cERA, which credits his game-calling abilities.

Yasmani Grandal – -8.264

The first big name with a negative score. Grandal isn’t exactly regarded as a defensive monster, but his numbers aren’t as bad as they look. He isn’t good at blocking, but he receives good marks for cERA, and fielding. You know you won’t get a ton of errors and a good game caller, coupled with a nice bat.

Christian Vazquez – -20.129

Vazquez probably surprised me the most here—many people that I have talked to rave about his defense, but it seems to be lacking a bit here. His blocking numbers, fielding numbers, and cERA are not good at all. But, one thing he does do really well despite a slow pop time, is throw runners out—his caught stealing percentage is among the highest in the league.

Willson Contreras – -23.934

Here’s another guy that is regarded as one of the best catchers in the game, with the third-worst defensive score out of the 40 surveyed. Contreras’s score would be a lot better if his cERA and fielding percentage didn’t drag him down. However, both marks there are pretty bad, so overall, he’s subpar. However, his bat is electric, so you definitely want him in your lineup.


So, there you have it. Actually being able to see how a team’s backstops perform defensively shines light on platoon teams. For example, both Yan Gomes and Roberto Perez are above-average defensively, but Perez is better behind the plate. However, Gomes has a better bat, so depending on the matchup, one may be more important than the other, so they usually balance each other out. The usual suspects for the most part are up pretty high – Posey, Molina, Realmuto. Perez is on another planet. Some guys who are touted as good catchers struggled a bit here – Grandal, Contreras. Guys who we perceive as defensive stalwarts like Christian Vazquez are deceptively not so.

And then there’s Gary. The often-criticized Yankee backstop. Seemingly can’t do anything right behind the plate. Some say he is the worst defensive catcher in the league. I say that is blasphemy. It’s no secret that his blocking is subpar; he is the worst in the league by a very wide margin. However, he does other things like throw runners out really, really well, and these up his score to the point where he is marginally above average. Couple that with his bat (that needs to heat up), and you are talking about a special player overall.

So, what can you learn from this? Looks can be deceiving, there is more to catching than just blocking, and platoon catchers make more sense than they did before, now that you can see a bit better of a visual of the metrics behind the science.

And no more slander of my now-proven-to-be-average catcher. That will not be tolerated.




Special thanks to my friends TJ, Jonny, Tim  and Ben, for providing insight on how to weight the numbers, and whose support of the Red Sox and bashing of Gary Sanchez led me down this rabbit hole to prove that theory wrong.

Featured image was taken by Wendell Cruz of USA Today Sports.


Follow me on Twitter: @svdecaps


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