Examining Two Years of Catch Probabilities

Earlier today, my first post about catch probability was published. In that post, I mostly looked at the two years of information, together. In here, I’d like to keep the two years separate. I mean, this is a whole new data source, with a number of potential applications. Why would I ever write just the one article and stop there? I have a weekly quota to hit, and this is better than whatever else there is to analyze.

Catch probability is a new Statcast metric, which you can read about here. As complicated as it might seem, it’s actually quite simple to understand, and it can give us better answers to questions people have been asking for decades. This is where you can find all the data, so you can poke around on your own. This is all brand new, and it’s kind of a first draft. The data will improve as adjustments are made for batted-ball direction and for outfield dimensions. Already, though, we can learn from what’s been provided.

Let’s start here by looking at the five distinct buckets for 2015 and 2016. So-called 5 Star plays are the toughest plays to make, while 1 Star plays are borderline routine. I should note that the most routine plays — and there are so, so many of them — appear to be excluded. Looking at the automatic outs doesn’t tell anybody anything. In this table, you can see year-to-year frequencies, and also year-to-year catch rates.

Catch Probability Distributions
Play Type 2015 Frequency 2016 Frequency 2015 Catch% 2016 Catch%
5 Star 23% 24% 9% 8%
4 Star 14% 13% 43% 40%
3 Star 15% 15% 69% 67%
2 Star 16% 16% 85% 82%
1 Star 32% 32% 93% 93%
SOURCE: Baseball Savant

Probably nothing in here to dwell on. In the last two years, the plays have been distributed almost exactly the same. The denominator is total plays in the five buckets, not all possible opportunities. So, of the bucketed batted balls, about a third are 1 Star plays, and about a quarter are 5 Star plays. Meanwhile, this last season, the catch rates experienced little dips. I can’t interpret that for you; I don’t know what it means. It’s just something to be aware of.

But I doubt that’s what people are interested in. The players themselves are what people are interested in. Moving on to that, using all the bucket averages, I calculated estimated +/- figures for all the outfielders. I then converted those into +/- figures per 150 opportunities. This is expressed as plays, not runs. There are 75 outfielders who had at least 50 opportunities in both 2015 and 2016. Here’s how their rates have been related year-to-year:

You Aren't a FanGraphs Member
It looks like you aren't yet a FanGraphs Member (or aren't logged in). We aren't mad, just disappointed.
We get it. You want to read this article. But before we let you get back to it, we'd like to point out a few of the good reasons why you should become a Member.
1. Ad Free viewing! We won't bug you with this ad, or any other.
2. Unlimited articles! Non-Members only get to read 10 free articles a month. Members never get cut off.
3. Dark mode and Classic mode!
4. Custom player page dashboards! Choose the player cards you want, in the order you want them.
5. One-click data exports! Export our projections and leaderboards for your personal projects.
6. Remove the photos on the home page! (Honestly, this doesn't sound so great to us, but some people wanted it, and we like to give our Members what they want.)
7. Even more Steamer projections! We have handedness, percentile, and context neutral projections available for Members only.
8. Get FanGraphs Walk-Off, a customized year end review! Find out exactly how you used FanGraphs this year, and how that compares to other Members. Don't be a victim of FOMO.
9. A weekly mailbag column, exclusively for Members.
10. Help support FanGraphs and our entire staff! Our Members provide us with critical resources to improve the site and deliver new features!
We hope you'll consider a Membership today, for yourself or as a gift! And we realize this has been an awfully long sales pitch, so we've also removed all the other ads in this article. We didn't want to overdo it.

This is important! And a crucial check, before moving forward. It’s easy to observe a reasonably strong and linear relationship in the data, which is basically what you’d expect if the data were telling you something about player ability. You might hold correlations to higher standards than other people, but here, I think the story checks out. Even given the assumptions being made, even given the adjustments that haven’t been included yet, the data has achieved an R^2 of 0.62. One negative interpretation would be that the data is supplying consistent noise. That it’s somehow biased in consistent ways. A more positive interpretation would be that, even in this simple form, the data does a fair job of informing you about talent.

So let’s think about talent! I’ve got those 75 outfielders who’ve played fairly often in each of the last two years. Here are the outfielders who made the biggest improvements in +/- per 150 opportunities:

Catch Probability +/- Improvements
Player 2015 +/- per 150 2016 +/- per 150 Change
Steven Souza Jr. -10 5 15
Giancarlo Stanton -7 5 12
Melvin Upton Jr. -3 9 12
Avisail Garcia -6 5 11
Nick Markakis -14 -6 8
Christian Yelich 0 7 8
Angel Pagan -5 2 7
Jason Heyward 11 18 7
Marcell Ozuna -8 -1 7
Ender Inciarte 13 20 6
SOURCE: Baseball Savant

All the data comes with uncertain error bars. I’ve avoided decimal points in the presentation because I don’t want to convey a greater degree of precision than I should. I also don’t know exactly how a defensive player would get better at making catches, given that this measure takes positioning into account. But, way to go, Steven Souza Jr. The more familiar advanced numbers reflect the same general thing: By DRS and UZR, Souza was most recently an above-average defender. There’s the potential for a heck of a player in there if Souza ever becomes better at tapping into his strength.

You’ll notice that, within this top 10, there are three Marlins. I’ll add a fourth. Two years ago, Giancarlo Stanton, Marcell Ozuna, Christian Yelich, and Ichiro Suzuki made 241 plays, against 250 expected plays. This past year, the same players made 262 plays, against 250 expected plays. That would be an improvement of 21 plays, worth something in the vicinity of 20 or 25 runs. Now, maybe, this is randomness, or something else. Maybe something changed about how data is recorded in Marlins Park. But the Marlins also just had a new outfield coach, in Lorenzo Bundy. Could have something to do with Lorenzo Bundy. I can’t pretend to know more about this than I do.

Every sorted leaderboard has two ends:

Catch Probability +/- Declines
Player 2015 +/- per 150 2016 +/- per 150 Change
Gregor Blanco 2 -18 -20
Billy Burns 16 -3 -19
Mark Trumbo -25 -43 -18
Yoenis Cespedes 4 -10 -15
Joc Pederson 11 -2 -13
Lorenzo Cain 23 12 -11
Jacoby Ellsbury 16 4 -11
Norichika Aoki 4 -6 -10
Mike Trout 5 -4 -9
Starling Marte 6 -2 -9
SOURCE: Baseball Savant

In the earlier scatterplot, I’m sure you noticed the data point in the lower left-hand corner. It was sort of out there by itself. That’s Mark Trumbo, in scatterplot form. Trumbo, two years ago, looked like a bad outfield defender. Trumbo, last year, looked like a catastrophic outfield defender. I don’t think it’s news to anyone that Mark Trumbo, in the outfield, isn’t very good, but, remember how 1 Star plays are converted 93% of the time on average? This past year, Trumbo had 19 1 Star play opportunities, and he converted 11 of them. I don’t even know how that happens. Looks like a bad job by Mark Trumbo. The Orioles re-signed Pedro Alvarez, to play outfield.

Gregor Blanco shows up here with the largest drop, and it might not be coincidental that he was bothered by some knee discomfort. Lower-body injuries might also help explain, say, Lorenzo Cain making an appearance. As far as I can tell, Billy Burns stayed healthy, but DRS and UZR think he was worse in 2016, too. That’s bad for a player whose year-to-year wRC+ dropped from 103 to 52. Offensive performance and defensive performance can move around. For Burns, both moved very far in the wrong direction.

There’s still so much to explore. So many new avenues to go down. I want to caution, again, that this is new and therefore unproven. There are adjustments that will be made, and not every player’s opportunities are created the same. But we are most definitely entering a new era of defensive analysis, and we’ve been waiting for this for years. One can’t help but dig into what we have. Among players last year with at least 50 opportunities, Mark Trumbo was the worst defensive outfielder by far. The best, by a small margin, was rather surprisingly Desmond Jennings, followed less surprisingly by Billy Hamilton and Kevin Kiermaier. I can’t say yet how much we should make of that fact. I just love that we get to think about it.





Jeff made Lookout Landing a thing, but he does not still write there about the Mariners. He does write here, sometimes about the Mariners, but usually not.

17 Comments
Oldest
Newest Most Voted
C Dial
9 years ago

I posted on the other article, and run value of plays to CF (on average, not by Star position)
CF 0.842
LF 0.831
RF 0.843

5 star plays are 2Bs, with a few triples in the gaps and in the RF corner. the 4 stars are probably between a single and a double (about half?) and really, those have to be averaged off of the league average on the other 92% of those plays, or you introduce “back up fielder” influence

Joe WilkeyMember since 2025
9 years ago

Maybe something changed about how data is recorded in Marlins Park.

Didn’t they change the fences between 2015 and 2016? Lowered them and moved them in, as I recall. Since this doesn’t take into account catch location yet, I would imagine that has something to do with it.

OTMHeartBBCMember since 2025
9 years ago
Reply to  Joe Wilkey

bingo

Oblarg
9 years ago
Reply to  Joe Wilkey

Which raises the question, do they calculate the probabilities separately for every year, or not? If not, there are going to be some pretty huge artifacts when ballparks make significant changes.

Like I said in the first post about these stats – we need to know the details of how they’re being calculated. I won’t go so far as to say they’re *useless* without the details, but they’re certainly much harder to meaningfully interpret. It is totally remiss to leave them out.

C Dial
9 years ago
Reply to  Oblarg

Right now there are only two years; I suspect the numbers won’t change much – humans can only cover so much distance in such amount of time.

BipMember since 2016
9 years ago

Interesting that Joc is on the decliners list because his UZR actually got better last year. His range component improved too.

Aaron
9 years ago
Reply to  Bip

I believe his positioning improved significantly, which is reflected in UZR, but his actual ability to get to balls may have decreased, reflected in catch probability.

Keet
9 years ago

It’s amazing to me how many strides advanced analytics have taken in the past few years in regards to baseball. I’ll never forget being a kid and arguing with anyone who would listen that Jeter wasn’t a bad defensive shortstop. Of course, I was totally incorrect, but no one had the ability to prove me wrong!

It’ll be fascinating to see how the game develops as we begin to see player’s hit the professional leagues who have only ever lived in the post-sabermetrics area. I expect to see overall improved defense and pitch selection as scouting reports become more and more detailed, but the wonderful thing about innvoation and advancement is that you never know where it will come from and what it will change. All I know is that I’m happy we’re finally finding better and better ways to quantify the often mysterious “defensive skill.”

Zachary Schuricht-Hackett

jamesbrooksMember since 2021
9 years ago

Jeff, this is huge. Do you have access to an Arm Strength database as well, that we can poke around in?

johansantana17Member since 2026
9 years ago

“Maybe something changed about how data is recorded in Marlins Park. ”

The fences were moved in at Marlins Park in the offseason between 2015 and 2016. Source: http://m.mlb.com/news/article/153568328/fences-to-be-moved-in-lowered-at-marlins-park/

MGL
9 years ago

Very soon UZR and DRS, at least the range component, will be obsolete, which is exactly the way it is supposed to be.

Moving fences in should not necessarily change catch rates! Remember these are based on distance and hang time. Should have nothing much to do with the size of the park. The only thing that changes is this:

Lower fences allow some catches over the wall. Higher fences have balls bounce off fence. Not exactly sure how they account for this in this metric. I assume that a ball off the wall is considered a non-catch like any other ball even if it couldn’t be caught. But is it considered “0% chance of catch” or does it have some non-zero number just based on distance and hang time? I think the latter.

Someone could check LF at Fenway to see what the numbers look like.

Now, moving in fences changes positioning which could change catch rates for two reasons: One, players may have to come in or move back a different % of the time and this “catch metric” makes no distinction (right now) even though clearly a catch going back is harder especially hit right at a fielder.

Also the closer to a wall a ball the less fast a fielder can run (he has to worry about hitting the wall) so moving IN fences could actually cause catch rates to go DOWN. Again this metric is not accounting for that right now either.

So the assumption that because they moved fences in the catch rates should go UP with the same skill is not supported by any evidence or any logical thinking that I know of. It’s tricky.

C Dial
9 years ago
Reply to  MGL

The logical thinking is “less ground to cover”. Which would make sense if fields were at “max ground coverage”, or something. There is not much of a chance that moving fences in would make catch rates go down. Players would adjust positioning.

Joseph MeyerMember since 2016
9 years ago

Why are we creating buckets of arbitrary size for these catch probabilities? Doesn’t this simply add discritization noise to the data? I feel like we should be able to perform a weighted sum for catch +/- based on the actual catch probability and not on the probability of each bucket.

Lee TrocinskiMember since 2016
9 years ago
Reply to  Joseph Meyer

One would assume that is how any metric would be computed on the Statcast side, but we don’t have access to each individual catch, so this is the best we can do right now.

bbibb
9 years ago
Reply to  Joseph Meyer

They address this toward the end of this week’s Statcast podcast (a live recording of a lecture those dudes gave at a stats conference). Basically, they said that they group these catches into buckets for “broadcast,” so that it is easier for the masses to understand. The weighted sum for each catch is behind the scenes.

Joseph MeyerMember since 2016
9 years ago
Reply to  bbibb

Thanks for the response. Mass understanding is a noble cause that I didn’t really think about.

Aaron Judge's Gavel
9 years ago

Data was presented that shows Mike Trout is not good at something. Thanks for ruining my day.