Another Way to Tell a Hitter’s Having a Good Year

Near the end of the game between the Blue Jays and Rays yesterday, the camera panned to center field. Evan Longoria was at the plate, and the Jays broadcast team was talking about the third baseman’s power. “He’s got some power to right field, too, now, and I think that’s why you’ll see the outfielders, especially the center fielder and right fielder playing a couple steps back,” said Dan Shulman. “Look at how deep Kevin Pillar is in center field. That’s only a couple of steps, it seems like, for Pillar, from the warning track!” he continued. “We have not seen Kevin Pillar play that deep,” concurred Buck Martinez.

It was impressive. That little dot in center is Pillar. Looks like a wallflower at a middle-school dance.


He was 361 feet from the plate at that moment. It makes sense, given Longoria’s spray chart this year. You’ll notice that Pillar is shaded a little bit to right, which is where Longoria hits many of his deep outs.

Source: FanGraphs

But the Blue Jays were pushing the envelope a bit. Call it situational defense, maybe, because Pillar was playing more than 30 feet further back than the average center fielder against Longoria this year. Given that there were two outs in the eighth inning of a tie game and Brad Miller and Nick Franklin were scheduled to hit behind Evan Longoria, there’s a certain amount of making sure to stop the big hit doesn’t sink the team. In a league where it probably pays to play deep, this was playing just a bit deeper on a guy who hits them deep.

The Rays’ third baseman is actually in the top tier of the league when it comes to how deep the center fielder plays, even if this was further than usual. Here are the top-15 average depths among hitters that have seen 1000 pitches or more.

Sluggers Seeing the Deepest Centerfielders
Name Count Average CF Distance
Nolan Arenado 2411 329.0
Carlos Gonzalez 2041 327.9
Edwin Encarnacion 2544 327.0
Mark Reynolds 1733 326.8
Trevor Story 1753 326.4
Jose Bautista 1797 326.2
Josh Donaldson 2620 326.0
Giancarlo Stanton 1752 325.5
Evan Longoria 2199 325.4
Steven Souza Jr. 1674 325.1
Steve Pearce 1172 324.9
Chris Davis 2381 324.7
Troy Tulowitzki 1796 324.6
Miguel Cabrera 2199 324.6
Ryan Raburn 1009 324.6
SOURCE: Statcast

Maybe that’s the best takeaway from this iconic moment: Longoria is being played deeper, because, you know, he’s hitting the ball deeper. Like most of the guys on this list, he’s slugging. He’s outperformed his projected slugging percentage by almost 90 points.

We’ve used stats like these to reverse-engineer an understanding of the type of data that teams have. We know they have better data, and they move quicker when it comes to adjustment, so we’ve looked at things like fastball distance from the center of the zone to identify slugging breakouts. Could this one also tell us which power breakouts teams deem sustainable?

Here’s a new way of looking at the center-field depth stat. I’ve taken the hitters with the biggest difference between their actual slugging percentage and their projected one, and converted the average distance into a rank stat. Look how quickly the “believable” slugging outbreaks come into focus.

Slugging Breakouts Against Average Center-Field Depth
Name Count Average CF Distance Distance Rank Proj SLG Actual SLG Diff
Sandy Leon 1016 316.6 146 0.318 0.553 0.235
Tyler Naquin 1337 313.1 216 0.374 0.548 0.174
Ryan Schimpf 1231 317.2 135 0.398 0.567 0.169
Brian Dozier 2531 316.4 151 0.405 0.574 0.169
Daniel Murphy 1997 315.2 182 0.433 0.598 0.165
Trevor Story 1753 326.4 5 0.422 0.567 0.145
Aledmys Diaz 1556 317.2 136 0.382 0.517 0.135
David Ortiz 2146 324.2 16 0.498 0.629 0.131
Jake Lamb 2129 320.8 53 0.413 0.538 0.125
Matt Joyce 1062 316.2 158 0.375 0.497 0.122
Corey Seager 2228 316.5 150 0.421 0.536 0.115
Jung Ho Kang 1226 318.4 105 0.420 0.534 0.114
Jose Altuve 2190 317.9 117 0.431 0.543 0.112
Yoenis Cespedes 1842 320.1 69 0.456 0.566 0.110
Hernan Perez 1332 311.3 243 0.347 0.453 0.106
DJ LeMahieu 2402 321.3 48 0.384 0.490 0.106
Charlie Blackmon 2194 321.7 41 0.432 0.537 0.105
Cameron Rupp 1371 318.1 113 0.356 0.460 0.104
Yasmany Tomas 1716 321.5 44 0.409 0.510 0.101
Matt Carpenter 2060 317.6 122 0.425 0.525 0.100
SOURCE: Statcast
Rank is out of 272 hitters. Proj SLG is pre-season slugging projected by Steamer.

It’s easy to be glib, point to Leon’s age and declining exit velocity, and call that a win for our center-field defense distance stat. Look at the players that have been hitting for power all year and have explanations behind them, like Trevor Story, Jake Lamb, and Yasmany Tomas, and how they’ve been defended, and you’ll really like this stat.

But what about Daniel Murphy? Teams must know he’s standing closer to the plate and attacking fastballs and that his power breakout is sustainable on some level. Maybe they do, and this stat isn’t that awesome at capturing breakouts. Especially if the hitter is a pull hitter. Because look at Murphy’s spray chart. His deepest hits are all down the line.

Source: FanGraphs

That pull bias might also explain Tyler Naquin’s center-field depth, since his furthest hit balls are either pushed or pulled. So maybe Ryan Schimpf is the most interesting case here: teams are lukewarm on the old rookie’s power to center. But he has hit homers and long outs in that direction, and has power to all fields. Are teams missing on him when it comes to where they play their center fielder? Is it a function of the parks in which he’s played? Or will he come to earth quickly, as his projections suggest?

Source: FanGraphs

Perhaps we could wrestle our way backwards to a predictive stat here. If we included the average depth of all the outfielders instead of just the center fielder, we would overcome this pull/push bias. We could correct for home parks, since all the Coors’ sluggers see deep center fielders. If we did these things, we could maybe use the teams’ defenses to help us understand which breakouts they believe in. But, even then, it would probably be better to start with the player’s batted-ball profile itself, because that’s what informs the defense. It’s what comes first. And we have a lot more information there.

Still, it’s fun to look up, see a center fielder playing deeper than you’ve ever seen before late in a close game, and wonder what that means.

With a phone full of pictures of pitchers' fingers, strange beers, and his two toddler sons, Eno Sarris can be found at the ballpark or a brewery most days. Read him here, writing about the A's or Giants at The Athletic, or about beer at October. Follow him on Twitter @enosarris if you can handle the sandwiches and inanity.

Newest Most Voted
Inline Feedbacks
View all comments
7 years ago

The first thing I noticed about the leaderboard was the amount of Rockies on there. Do you think that has something to do with their park being so big? In other words, do you think it’d be beneficial to do the calculation as how close they are, on average, relative to the centerfield dimension at their home park? Nolan Arenado, for example, would have the average centerfielder playing 79.3% (329/415) of the way between home plate and center

7 years ago
Reply to  bensnider94

it probably does have something to do with the distance. it’s a huge outfield out there.

Jacob Phillips
7 years ago
Reply to  bensnider94

I had the exact same thought. Arenado is at least a believable #1, but Ryan Raburn at #15? Add in the fact that they have 4 of the top 5, plus Blackmon and Lemahieu hanging out in the 40’s, and there is definitely something else going on here.

It’s not just the large outfield either – the thin air probably scares defenders back a couple more feet, too. I thinks this article is really cool and goes a LONG way for a “Proof of Concept,” but there would have to be some control along the lines of general Park Factors before it could be implemented into some real leaderboard or projection system.

7 years ago
Reply to  bensnider94

I was going to mention something similar, but with the Jays bats (EE, JB, JD and Tulo).

Joe Wilkeymember
7 years ago
Reply to  bensnider94

This is absolutely the first thing I noticed. I think this needs to be a Dist+ kind of stat. It would take a bit of work, but I envision something like (PlayerCFDist/ParkCFDist)*100, weighted to the number of PA in each park. I would think this would not only normalize the effect of the park, but reduce the effect of the CF (if a particular CF likes to play deeper or shallower).