Early Insights From Statcast’s Outfield Catch Probability Metrics

Amazingly, in an Opening Day game where Shohei Ohtani struck out double-digit batters in six shutout innings, the most memorable highlight of the night didn’t come from him, or even Mike Trout. In the bottom of the fifth, Oakland third baseman Jace Peterson sent a fly ball to right field. Hunter Renfroe gave chase, but it appeared to be going over his head — until he leapt up, stuck out his glove to the left while facing right, and somehow made an incredible no-look catch to the delight of Ohtani and the Angels. Even Peterson had to smile.
WHAT A CATCH BY HUNTER RENFROE ?
— MLB Network (@MLBNetwork) March 31, 2023
Baseball Savant has recently released outfield catch probabilities for individual plays, and we can learn a lot from analyzing the differences between the perceived difficulty of a play from watching it on a broadcast compared to its actual catch probability. Renfroe’s circus catch in Oakland offers a perfect example: While his acrobatics were necessary to make the catch, that was only because of a poor jump. He backpedaled for the first few steps, then ran at less-than-full speed while having to crane his head around to keep track of the ball. Renfroe ended up making the catch 39 feet from his initial position in an opportunity time of 4.2 seconds — a play that has a catch probability of 99%, and that’s even when accounting for the difficulty of running backwards (which is included in calculating the odds).
For comparison, here’s a play with a near-identical distance and opportunity time made by Renfroe’s backup, Brett Phillips.
Phillips didn’t need luck or heroics to make the out here; in fact, he was able to camp out for a bit before the ball fell into his glove. A good chunk of his route was completed before the broadcast had switched to the outfield camera.
In other words, Renfroe’s play is made with little fanfare almost every time. That includes him: He was perfect on fly balls with 99% catch probability in 2022, though he did let a few in the 90–95% range drop for hits. Much of the focus that observers put on the quality of a outfielder’s defense naturally comes from what can be seen on TV – but the data indicates that what we can’t see is what truly separates the great fielders from the poor ones.
Statcast’s jump metric breaks down the first three seconds after a ball is struck into three parts: the reaction, burst, and route. We’ll work backwards from the time the ball is caught, starting with route efficiency, which measures how far a player runs compared to the straight-line distance between his initial position and the ball’s landing spot. A bad route can often be seen on a broadcast camera: players struggling to change direction while trying to make up lost ground. In this example, Jarren Duran (who finished second-to-last in route efficiency last year) fails to make a play with an 80% catch probability, and it’s clear that he thought the ball was going deeper than it actually did.
But many players can make up for poor route efficiency with elite reactions and speed. Trent Grisham, tied with Duran in route futility, had 14 OAA in 2022 thanks to his top-tier instincts and sprint speed. In fact, there appears to be a mildly negative correlation between route efficiency and overall distance covered for outfielders. That isn’t to say that taking good routes makes you worse at hunting down fly balls. Having a good feel for the end location of a fly ball is a byproduct of experience, and with experience comes age that can slow a player down. For example, 2021’s best route runner was Andrew McCutchen, whose 15,000 innings of experience in the grass certainly aided him. But because of poor reactions and slower legs, he was worth -7 OAA that season. And only one of last season’s top 14 players by route distance above average finished with a positive OAA. So while routes might be the easiest aspect of outfield range to evaluate outside of dropped catches and errors, route efficiency tells us surprisingly little about a fielder’s overall skill.

While many players are adept at taking direct paths to the ball, top speed is often the biggest differentiator on difficult plays. The burst component of Statcast’s jump tracking tests a player’s quickness closing in on a ball, measuring feet covered 1.5–3 seconds after a ball has been struck. This component most directly correlates with sprint speed; seeing Phillips, Jose Siri, and Michael Harris II near the top of the leaderboard confirms this. On the other end of the spectrum is Andrew Vaughn, who covered three feet less than the average outfielder. He had 25th-percentile sprint speed on the basepaths but often didn’t reach his top speed in the outfield; in 27 play opportunities with a catch probability below 75% last season, he converted just one of those balls into outs.
But while watching video, I found a play that I thought looked rather impressive. On a ball slicing toward the corner, Vaughn got a quick beat on the ball, tracking it into his glove on the run. Try to guess the catch probability as you watch:
I polled a few people and got estimates in the 50–80% range, which is reasonable for a seemingly difficult play that required a good jump and route but not necessarily elite athleticism (like this play from Luis Robert Jr.). So I was surprised to find the actual probability was 99% — a routine play. Vaughn’s clean route and running catch masked the fact that he never reached full speed. And while a quick burst isn’t strictly necessary for a routine play, he was significantly slower than other outfielders when he had to run a longer distance. His routes were actually a bit above average last year, but his subpar speed puts a hard ceiling on the radius of his range.
But while the lightning-quick speed of Robert Jr. and sluggishness of Vaughn is easily discernible to viewers, more subtle differences in burst speed can have substantial effects on OAA and be harder to compare. For example, a one-foot gap in ground covered in the burst stage was the difference between Max Kepler’s 8 OAA and Aaron Hicks‘ -1, despite exactly equal marks in the other jump components and the same number of fielding opportunities.
Finally, there’s the reaction phase — the initial read that a fielder gets on a batted ball. Specifically, Statcast measures the distance covered in the correct direction in the first 1.5 seconds after contact. Unfortunately, broadcast cameras usually don’t switch to the outfield until after this window has elapsed, making it often impossible to evaluate reaction time on the screen. But jumps are extremely important when it comes to making catches; even a player with top-tier sprint speed and a perfect route will find it difficult to make up for a poor read, like Bryan Reynolds on this play. Again, make a prediction on the catch probability here:
Reynolds comes in charging hard, getting near his top speed (he’s a 75th-percentile runner) and taking a clean path to the ball. But his sliding catch fails, and the ball drops right in front of him. It seems like there’s not much more he could have done, right? But his catch probability on this play was 90%. Consider what he’s doing right as the broadcast switches to the outfield camera: He’s coming forward but hasn’t turned on the jets and is looking up as if he hasn’t found the ball yet. His deep positioning as the camera switches also suggests that he took a step back at impact rather than immediately crashing in.
Contrast Reynolds’ attempt with this catch by Enrique Hernández on a ball with near-identical opportunity time, distance covered, and direction:
In the world of reaction time on fly balls, Hernández is a god amongst men: His 4.3 feet above average in the reaction stage was a full foot ahead of anyone else, and in 2021, his 4.7 feet cleared the field by a full two feet. That’s why despite being in the bottom ten in route efficiency among 104 qualified defenders, he still covered more ground in the first three seconds of a play than any of his competitors. In that clip above, he slows down mid-route from full speed, knowing that he’ll get to the ball easily. Reynolds, on the other hand, had far more ground to make up after the first second or two of the play, and that was a constant issue for him: Last year, he finished tied for fifth-worst in reaction, losing 1.4 feet relative to average.
Let’s wrap up by computing the importance of each individual jump component on overall defensive efficacy as measured by OAA. As expected, reaction and burst have very strong correlations, though it’s a little surprising to see that the latter has a slight edge. On the jump leaderboard, the spread of measurements is a bit wider for reaction; Hernández’ 4.3 feet above average beats out Kyle Isbel’s league-leading 2.9 feet above average on bursts. But it makes sense that great acceleration in the burst phase lets players reach a higher top speed, which is often a difference maker on long runs. Route efficiency does end up with a slight negative correlation, both due to older players being better route runners and because players with elite reactions might take suboptimal routes but still come out ahead in the end.
| Component | Correlation to OAA |
|---|---|
| Reaction | .740 |
| Burst | .916 |
| Route | -.319 |
Statcast’s catch probability data gives us insights on which fielders give themselves a huge advantage before the TV cameras are on them. Catches like Renfroe’s are immediately recognizable as excellent, but some of the best fielders in the game can cover absurd distances without the need to slide or stick the glove out without looking. These metrics teach us a lot about what we can and can’t evaluate from traditional broadcast angles, and allow us to put shine on players who make their incredibly difficult jobs look like just another day in the office.
Kyle is a FanGraphs contributor who likes to write about unique players who aren't superstars. He likes multipositional catchers, dislikes fastballs, and wants to see the return of the 100-inning reliever. He's currently a college student studying math education, and wants to apply that experience to his writing by making sabermetrics more accessible to learn about. Previously, he's written for PitcherList using pitch data to bring analytical insight to pitcher GIFs and on his personal blog about the Angels.
Where can we find this on Savant?
https://baseballsavant.mlb.com/leaderboard/outfield_jump
I mean where to search by individual plays.
Yeah curious on the individual play data too
Any help @KyleKishimoto?
if you go to an individual outfielder’s page in the “fielding” tab and scroll down, you can see plays plotted on a plane with the axes being opportunity time and distance, if you click on those it shows the video
Thank you! I didn’t realize that was a feature. Cool stuff.
Terrific article.
I remember watching the Braves on TBS and when they’d pan out to Andrew Jones on a fly ball, he’d often be standing in a spot and not move and make the catch.
I’m not surprised the Renfroe catch was high percentage, but it was fun to watch.
Just wanted to say I had the same thought about Andruw Jones reading this article. By the time the camera turned to the field, he always seemed to be already waiting where the ball had been hit.
Kyle, great stuff! A couple of questions about Baseball Savant.
Thanks.
i’m not sure how the OAA formula for infielders works (it has to take in far more variables than the outfield one) – but my guess would be that it doesn’t control for hops and assumes the percentage of bad hops everyone gets will balance out in the long run
Thanks Kyle
Outfield jump comprises three factors:
Reaction, the feet covered in the first 1.5 seconds,
Burst, the feet covered in the next 1.5 seconds, and
Route, the feet covered versus the most direct route to the ball.
So in your example, if you lose the ball in the sun, your Reaction feet will probably be lower on a particular catch. Then your Burst distance will make up the distance. Your route may be less efficient if you have to course correct on the way to the ball.
So yes, the Statcast Outfield Jump stats take all that into account.
Thanks Hugh. If I understand correctly, while it does take into account reaction, burst and route, it doesn’t appear to take into account the fact that a reaction time might be slowed and possibly route altered due to the ball being lost in the sun. In my example, any OF’er standing in that spot would have been obscured and slowed. Hence, the savant probability of the catch would not be accurate.
I understand your point, but there’s not really a need to try to account for that. Light is an issue on every play. Does a ball get lost in the sun? Does it get lost in the lights when playing during the evening? Or does the player adjust the park and deals with the light issues to make the catch?
The way statcast deals with catch probability is simple:
1. How far did the fielder have to go?
2. How much time did he have to get there?
In general, all of the issues that you’re talking about are going to even out in the sample size. Statcast has lots of data on outfield catches already, so it can establish an initial probability of a catch being made given how long the ball is in the air and how far away the player was.
The other thing is that not every catch counts for Outfield Jump statistics. Outfield Jump is only measured for catches that have a 90% Catch Probability or lower. So if there was a seemingly easy play that isn’t made because it got lost in the lights, it doesn’t count for Outfield Jump. If it was a difficult play in the first place, it just becomes part of the statistical picture.
How players deal with the light is an accurate part of their reaction to balls hit to the outfield. If that is constantly an issue for a player, that should be baked into the statistic.
Is it just me or is there a 30-second ad (the same ad each time) running prior to each highlight? Makes it very unappealing to view the examples while reading the article.
uBlock. Add to your browser.
Thanks for the tip!
This is fascinating stuff. One thing:
Is this actually true? It strikes me as a skill that some players may just have, while others can work to acquire it on the job and others still never get it. You cite McCutchen as an example, but what reason do we have to think he picked this up by experience, rather than just being good at it (or, equally, by pre-major-league experience)? He certainly looked pretty good at this when he was young, too.
watching vaughn ‘play’ outfield is a bit like water drip torture.
Amazing article! I wrote about outfielder jump when it was brand new in 2019, and found very similar results. Interesting to see that these still hold!
Omg, I’ve been waiting for a series of articles like this for awhile! This community (and baseball at large) really needs practice bridging “the eye test” with new defensive metrics by way of just reviewing plays. Using gifs and metrics together is incredibly powerful.
I would strongly recommend turning this article into a series. Specifically, I would love to see…
1) Interactive piece where you ask readers to make assessments about catch probability, route efficiency, or burst speed. Teach us to be scouts and help scouts convert their eyes to these metrics!
2) Converting plays into defensive metrics. How does Reynolds missing that catch impact his defensive metrics? Like… how many misses lead to a specific OAA? Maybe it will be math heavy but it’s easy for me to know a HR is 4.0 impact on slugging and a double is 2.0. Likewise, giving up 2 runs over 6 IP easily maps to 3 ERA.
3) How do these pieces change over time for players? You alluded to mccutchen’s age possibly being a factor… what if we followed a few players over their career (eg Brett Gardner) to understand common patterns?
4) You could segment each of the above by OFs, IFs, and then maybe 1B.
I’m not your editor, so you can ignore me but I’ve really wanted to see someone tackle this area for years.
those are all interesting ideas – i will say #2 on your list has already been written about – the runs per play formula depends on position but you can read about it here: http://tangotiger.com/index.php/site/comments/statcast-lab-is-there-a-different-run-value-needed-based-on-the-infield-slice
thank you sir for the additional reading! look forward to more of your content.
I don’t have a lot to add, but I am a huge fan of these leaderboards at baseball savant and I was glad to see this article. Would love to read more about them.