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2019 Atlanta Braves player projections: position players

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A less fluid group that’s probably going to be the team’s strength once again

Miami Marlins v Atlanta Braves Photo by Dylan Buell/Getty Images

Another year, another set of projections. By now, with this being something like the fifth year of this exercise, you’re likely pretty familiar with it. If you aren’t, there’s a brief refresher (as well as some info about what’s changed) below.

Why do we care about projections, anyway?

Well, mostly, because they’re fun. It’s one thing to see how something happens in a vacuum — what you get is the knowledge of what happened. But when you combine that with what was expected to happen, you add contours to everything. You open up avenues of discussion about what was and wasn’t as expected, how it differed, why it differed, and so on. Numbers and outcomes need context, and there’s no better context than a set of well-formed expectations.

I can go on at length about the use of projections as a planning tool, but instead I’ll just offer a few links:

(1) https://blogs.fangraphs.com/you-should-trust-the-projections/

(2) https://www.talkingchop.com/2018/11/6/18065670/atlanta-braves-projection-retrospective-2018-position-players

(3) https://blogs.fangraphs.com/lets-make-sure-were-honest-about-projections/

If you read those, I think you get a sense of what this exercise is doing and trying to say, but also not doing nor trying to say. Projections aren’t destiny, they’re just a way of thinking about the future. And by presenting probability distributions rather than only point estimates, I’m hoping to make that more clear.

What projection systems are being looked at?

The same three: Steamer, ZiPS, and IWAG. The former two are available on the Fangraphs player pages, the third one is a kooky system I cooked up and have continued to update and adorn with bells and whistles over the last half-decade. Why no other systems? Mostly, it dovetails with the next question — i.e., I only really care about projecting a few outputs and most other systems either don’t do this directly or are based on a completely different framework that doesn’t interface with those outputs. So yes, theoretically, one could take PECOTA and translate all the counting stats/etc. projected therein into wRC+ and Def and so on, but that’s a lot of work for potentially not much gain. (Also worth noting is that PECOTA uses a different replacement level.) And while there are lots of fantasy baseball-oriented projection systems, I have little interest in those because they don’t consider defense, park adjustments, and so on.

Why should I care about IWAG?

You shouldn’t. Only I should care, because I made it, and my goal is to keep making it better until maybe one day other people should care. We’re not anywhere near there right now, though.

Okay, but assuming I do care, what’s different in IWAG this year?

On the position player side, I made two big adjustments. One (and this also applies to the pitching side) is something called the Matt Wisler adjustment, intended to ratchet down the expected performance of players who are able to thrive in the high minors but can’t really do the same in the majors. This actually has very minor effects on any Braves position player projections this year, as the Braves aren’t carrying any Four-A types, but the adjustment does affect a few calculations here and there. IWAG has always tended to be more optimistic than Steamer and ZiPS with respect to certain players, and while this adjustment doesn’t change this tendency for those players, it does narrow the gap somewhat.

Second, the defensive component for minor leaguers has been rebuilt from scratch. IWAG actually did pretty well in terms of defensive value relative to Steamer and ZiPS last year, but I felt the old system was really complicated and tried to make too many assumptions with uncertain data, which ran against one of my basic modeling principles. I’m personally pretty curious to see what this more streamlined/simplified approach does; it’ll be annoying to me if it’s consistently worse.

Can you refresh me on the stats being analyzed?

For position players, we’re really only looking at three things, and they’re all covered in the TC Baseball Analysis Primer. wRC+ is being used as the catch-all hitting statistic. Def is being used as the catch-all defensive statistic, though keep in mind that it includes the positional adjustment, so a +5 left fielder is the same in value as a +5 shortstop as far as Def goes. And, of course, we have the main thing to care about — WAR as a catch-all for total player production. wRC+ is already on a rate basis, but both Def and WAR are presented per 600 PAs for position players.

Something interesting about the Braves (and not uncommon, just more relevant for 2019 than for past years) is that a lot of their outlook really relies on health. While every team’s outlook really relies on health in the end, the Braves have some known potentially-really-good performers with less-than-stellar health track records or expectations. One interesting analysis could be comparing just how the projection systems see playing time, separate from performance across said playing time. However, Steamer and ZiPS don’t actually vary very much in this respect, and I don’t have access to their underlying probability distributions anyway. So, while I present a non-rate basis WAR probability distribution below, one thought worth keeping in the back of your head is that in addition to player performance variability, the Braves are also especially subject to health variability this year.

Any other questions — leave ‘em in the comments!


Before I get to the charts, I’m going to copy-paste last year’s discussion on probability distributions below. That way, no one needs to go elsewhere to read it, and I have the peace of mind that I put the relevant disclaimers into the post.

Player projection is, in some ways, really an odds-making exercise, yet many projections that get published are just the estimates. I get why that is: saying “Player X has a 30% chance of being replacement level, a 10% chance of being a 1 WAR player, a 30% chance of being a 2 WAR player, and then a bunch of outcomes of 3 WAR or above with low probabilities” is a mouthful, and can be really hard to wrap one’s head around. The expected value of any distribution can be thought of as its point estimate, and point estimates are easy to digest: “this system says Player X is a 2.7 WAR player who will hit for a 125 wRC+!” However, I think a lot of nuance is lost while clarity (perhaps spurious clarity?) is gained. So, I’m presenting all of the IWAG distribution curves for wRC+ and WAR below.

In addition to modeling player performance, IWAG also models health (likelihood of injury and missing time), as well as playing time related to performance. Therefore, on the WAR distribution, you will also see a golden line, which traces a player’s likely production based on playing time. Note that the relationship between these, as modeled, can be complex. IWAG figures that if a player is being awful, he may lose his lineup spot, and thus accumulate fewer PAs. And, if a player is rocking it and gets even more playing time, he may regress further with that extra playing time. As a result, the tails are shortened on the WAR distributions as opposed to the WAR/600 distributions.

For each player, IWAG also provides a “confidence” measure, which I’ve summarized into four buckets (very low, low, moderate, and solid). The lower the confidence measure, the more splayed out the distribution tends to be, as IWAG has less certainty that the player will have an outcome in any particular range. However, the charts are drawn to avoid showing the distribution across very fringe probabilities. Therefore, the lower the confidence, the more you should keep in the back of your mind that there’s a greater relative chance that the outcome could occur somewhere to the left or the right of the pictured distribution. Where the confidence is solid, the chance of this happening is consequently fairly low.

Also, as a final note, which I cannot emphasize enough: the dots for the Steamer/ZiPS projections on all charts below are placed there simply for illustrative convenience. I am not making any representation whatsoever that I have any knowledge of the Steamer/ZiPS outcome distributions, or that they look anything like the IWAG distributions being presented. Their placement is solely so you can compare all projections for a player on each chart. To save space, I’m placing the Steamer-related dots in blue and the ZiPS-related dots in orange; the IWAG dots will stay green, except for the playing-time-adjusted WAR projections, which will stay gold.

Because projections are provided below for each player as a set of three images (IWAG point estimate table, IWAG wRC+ distribution with point estimates, IWAG WAR and WAR/600 distribution with point estimates), I’m only adding a tiny bit of commentary for each of the 13 players examined. You can mostly use the distributions presented to see where IWAG is coming from and how that compares to Steamer/ZiPS, but of course I would be overjoyed to discuss any of this in the comments or via another medium.

Summary Table

(Note: This table is being ordered by modeled central estimates of playing time. The position field doesn’t reflect any specific modeling assumption, and just exists to flag the likely usage of the player.)

Note that this table is only rate estimates. No playing time adjustments are included. Do not sum the WAR/600 column for an estimate of team production. All Steamer/600 and ZiPS/600 estimates done by basic math via the statistics provided on the Fangraphs player pages (Steamer|ZiPS/600 = Steamer|ZiPS/#ofPAs * 600).

Freddie Freeman

In aggregate, there’s not much to say about Freddie Freeman that hasn’t already been said. He’s been around a four- or five-win player in four of his last six seasons, with the other two being split between above and below that threshold by a win each. Aside from getting hit on the wrist, he’s been reasonably durable and that helps him accumulate more value. IWAG sees more upside than downside (fatter tail going right relative to a steeper slope on the left-hand side of the WAR hump), though Steamer/ZiPS may disagree.

Hitting-wise, there’s a bit of variability about whether Freeman is going to be the more-single-spraying .200 ISO guy with a 130s wRC+ that we saw before he went beast mode as well as last year, or the .270 ISO guy with a 150s wRC+ that he was when he rampaged across the league. IWAG thinks somewhere in the middle of these (though that’s an average, and again, this in and of itself is less likely than the variant on either side), Steamer and ZiPS are closer to the “2018 is more likely to repeat” camp.

Ozzie Albies

A lot of Ozzie Albies-centered discussion has focused on his poor showing after his hot start, as well as concern about not being able to hit righties well. (These two things aren’t unrelated...) But, while there’s nothing to be gained from ignoring warning signs, it’s also a bad idea to write off hot starts as though they didn’t happen. Albies finished 2018 with a 100 wRC+; his career wRC+ is 103. Unsurprisingly, then, the hitting expectations for him are more of the same, with a pretty constrained, normal distribution-looking spread. He might struggle against righties, sure, but he’ll make up for it enough against lefties to look average-ish, is the general idea.

Production-wise, like Freeman, Albies benefits from an expectation of durability. The overall shape of his probability distribution isn’t particularly novel, with the general idea that his defense and baserunning basically gave him around a win over the top of a league-average batting line. The downside from a Braves fan perspective is that there’s not much of an expectation of Albies meeting or beating his 2018 season (IWAG sees about a one-in-four chance of this); the upside is that he’s flooring at around a league-average performance rate. In terms of small bits without further commentary: IWAG is the low system here (and that’s unusual); ZiPS has Albies being incredibly good defensively this year.

Ronald Acuña Jr.

Out of all the projections here, I imagine this one might throw people for a loop the most. After all, the kid just put up a 3.7 fWAR season (4.6/600) in which he already showcased an ability to make adjustments and tenderize league pitching for multiple months. Yet, the three systems are in agreement that, at least as far as central estimates go, Acuña will hit notably worse than he did in 2018, with aggregate production similar to best bud Albies, rather than pushing a five-win mark. So, what gives?

Well, I can’t speak to Steamer and ZiPS other than reporting outputs, but for IWAG, there are two things that I can speak to. First, Acuña was a top-third xwOBA outperformer last year. His .388 wOBA is indeed commensurate with a 140s wRC+; but his .370 wOBA is really more consistent with a 130(ish) mark. Second, there are far more “great start, worse performance later” players than “great start, even more ridiculous performance later” players in baseball history. (This is another way of describing one form of regression to the mean.) The probability, though slight, that the phenom is, well, not a phenom, drags down the expected performance to a greater extent than the possibility of improvement increases it. This is pretty easily visible on the charts — while five-win rate performance is still possible, there’s more weight placed on the opposite end of the spectrum, closer to a two-win rate. Balance those against one another and you end up with something in the mid-3.0s, pushing 4.0, than something above. Acuña’s Def is projected to be negative across the board (and was very quite negative last year), so that also drags down the overall expected production to some extent.

Anyway, bet the over if you’d like. See what happens. But, before (or after) you do that, take a stroll through the Fangraphs player pages of: Aaron Judge, Carlos Correa, Jose Abreu, Wil Myers, Mike Trout, Cody Bellinger, Corey Seager, Kris Bryant, Bryce Harper, and Buster Posey. Then maybe the projections will make more sense in context.

Ender Inciarte

The Ender Inciarte projection exercise always amuses me. Mainly, it’s because Steamer has had him closer to 2 WAR than 3 WAR for as long as I can remember, and he always checks in at 3 WAR anyway. At this point, projecting Inciarte isn’t too complicated: slightly below-average bat, with a win or so in defense and baserunning to make up for it. In 2015, Inciarte out-hit his xwOBA by .046. The next year, it was .029. After that, .037. Last year, though, it fell to “just” .018. Inciarte’s xwOBA has actually been the same (more or less) all three years as a Brave: .290, .291, .291. Only the wOBA itself has varied. IWAG was perfectly happy to buy in to “xwOBA-beating voodoo magic” while it kept happening (this is an aspect of how it works), but with the extent to which Inciarte defied the baseball gods narrowing last year, the projection for his offense scales down accordingly. It’s not a big change, but that’s why you see a mid-90s projection than a “bounceback” to something closer to 100.

Also, for those curious, the reason why both ZiPS and IWAG have 2.7/600 projections despite ZiPS having a two-run edge on defense and a two-point edge in wRC+ is baserunning, as ZiPS projects him to be one run below average on the bases next year.

Nick Markakis

For way more on Nick Markakis and his projections, see here: https://www.talkingchop.com/2019/1/23/18194127/projecting-2019-nick-markakis-atlanta-braves-steamer-iwag-projections.

At this point, Nick Markakis is what he is. We’ve seen some better batting lines through somewhat-incidental launch angle increases here and there. Maybe he’ll do that again for part of the year. Maybe he won’t. If he doesn’t, he’ll end up perhaps slightly below average with the bat; if he does it for a couple of months, he may end up somewhat above average in that respect. His defense and baserunning aren’t too likely to evolve in some kind of beneficial way at this point, so his WAR projections are similar: the below-average batting line makes him something around, or slightly below, a 1-win player; the above-average batting line can push him to an average, 2-win performance or so if it occurs. In the end, the main thing is that the three projection systems are in relatively close agreement about the outlook for Markakis’ 2019 (the half-win spread in WAR/600 is among the lowest for any player in this post), and yet those outlooks are below average. Once again, it appears that the Braves will be using a bench-caliber player as a full-time starter.

Josh Donaldson

For more on Josh Donaldson and his projections, see: https://www.talkingchop.com/2018/11/27/18113824/projecting-2019-josh-donaldson-and-brian-mccann-atlanta-braves-iwag-steamer-projections.

There are really two Donaldsons that comprise the projections. One is the returning former MVP — that’s the right hump of both distributions. The other is a guy suffering a lot of decline, whether due to injury, aging, or both. That’s the smaller hump on the distributions, and responsible for the relatively flat golden actual-WAR-projection line. Is the reality going to be somewhere in the middle? That seems like a good bet.

The big question with Donaldson may be more about how many PAs he’ll get than about how good those PAs will be. The good news is that assuming that the player-page listings on Fangraphs for PAs reflect actual projections for playing time, both Steamer and ZiPS have Donaldson getting upwards of 450 PAs in 2019. IWAG is less sanguine and counts on him for only 400 or so, but an expected value of 3.5 WAR is nothing to sneer at — especially to the extent the Braves have above-replacement level substitutes for him, which they do. One minor point of curiosity: how will Donaldson’s defense show out in 2019? IWAG thinks he’s now slid into net below-average territory at third base; Steamer and ZiPS don’t, though aren’t projecting him for wild positives defensively either.

Dansby Swanson

The Dansby Swanson projection exercise is interesting, and somewhat frustrating, like the player himself. If the played-a-season-with-a-bad-wrist happenstance had happened to a different player, one with a more established track record, it’d be easier to write off the resulting offensive struggles. But what to do when a guy puts up an 80 wRC+ with a potentially-bad wrist, but had a 73 wRC+ in his career before then?

The result is a fairly wide spread in terms of the probability distribution. The offensive side could range from a 60 to a 110 wRC+, which is massive. (Compare this to the probability distribution without a flat line at the tails for anyone else, e.g., Ender Inciarte’s is like 80 to 105, Freeman is 130 to 160, etc.) The WAR side spans a substantially below-replacement rate to one for a four-win player. There’s a lot of variability in Swanson’s defense, too — even though the point estimates for all three systems have him with positive Def above the baseline +7.5 run adjustment for shortstops, Swanson’s actually had more innings in seasons with non-positive UZR/DRS values than he has with positive ones (just 2018). The mixture of these two vials of uncertainty is what produces his overall WAR probability distribution: he could be replacement level or he could be a solid regular. As with all these bimodal distributions, the projection systems end up somewhere in between.

Swanson is one of the only two players (Albies) of the 13 presented here where IWAG is the most negative of the three projection systems. As none of the projection systems see him as quite an average contributor, shortstop could have been another place to upgrade this offseason, and is the Braves’ other weak link in their lineup, in addition to right field.

Johan Camargo

Johan Camargo is either really weird or really interesting. Perhaps both. He’s been an xwOBA overperformer, but he hasn’t done it consistently in the same way. He’s been quite bad (close to the bottom tenth) on an xwOBA basis when facing right-handed pitching, but has mashed lefties in a manner around the upper third of the league by the same metric. (Not quite, he’s around the 36th percentile, along with... yes, truly: Adam Duvall.) Defensively, he’s been really good at third base, without much of a healthy sample anywhere else. Put those things together and... who knows, really?

Camargo’s probability distributions therefore show a ton of variability. His potential wRC+ ranges from an unplayable 50ish to a heady 120, with a lot depending on his usage as well as which Camargo iteration he decides to be at any given point. Correspondingly, we also see that with his overall WAR chart: he’s got one local maximum where he’s replacement level, and a more likely one where the combination of good hitting and good defense makes him, well, really good, which is what we saw from him last year. I’m not quite sure why Steamer has him penalized defensively so much — maybe it’s assuming he’s going to play a ton in the outfield where his lack of footspeed may harm him? In any case, it’s not really a disagreement about his bat driving the 1.3-win-per-600 gap between Steamer and IWAG, as all three systems see him as somewhere around average in that regard.

Camargo’s career 780 major league PAs have failed to resolve his identity crisis to date — maybe the ones he gets in 2019 will. Or maybe not, and he’ll continue to be an enigma. Either way, the Braves could do way, way worse as a bench-type fill in, and they may be doing worse with some positional starters, too.

Tyler Flowers

As an older catcher, the general idea is that Tyler Flowers may well be fairly average, but he’s only going to be able to get about half the playing time needed to put up 2 WAR, on average. What stands out to me here is that ZiPS and Steamer are less positive about him sticking with any xwOBA-based offensive gains, putting him more in the 85-105 wRC+ probability range than the big “hey Tyler Flowers transforms some hard grounders into hard liners/flies” right hump. Intuitively, that’s pretty true of the WAR projections as well. In any case, an average-to-above catcher is nothing to sneer at, whether he hits at an above-average rate or a slightly below average one.

Of course, though, none of this has anything to do with Flowers’ framing, and that’s where he really shines. I don’t know enough yet about framing distributions and age-related shenanigans, but my general expectation is that Flowers will add around 3.5 wins with his framing over 600 PAs, which means somewhat below two wins at his projected playing time. That takes him from a pedestrian 1 WAR option in a timeshare to something more like 3 WAR, which, woo. (Just gotta figure out that whole who-loses-WAR-if-catchers-gain-framing-WAR thing first.)

There’s probably something to be said here about the potential for Flowers to maximize his performance by largely facing left-handed pitching. But, with a catching partner in Brian McCann, it’s unclear whether this will really come to pass. Flowers should certainly be starting against all lefties, but he’s going to see a lot of right-handed pitching too, and that drives down his expected offensive contribution.

Brian McCann

For more on Brian McCann and his projections, see: https://www.talkingchop.com/2018/11/27/18113824/projecting-2019-josh-donaldson-and-brian-mccann-atlanta-braves-iwag-steamer-projections.

The good news for Braves fans regarding Brian McCann is that, for whatever it’s worth, IWAG thinks there’s some upside in his bat (and Steamer isn’t that cool on his glove, either). The bad news is that there’s also some pretty substantial downside. (Also, Steamer doesn’t see him as too different from Flowers on a rate basis.) If McCann bounces back from an injury-marred 2018, he could give the Braves something around 1.5 wins in a timeshare; if he doesn’t, outcomes may be closer to a win instead. It’s weird to talk about upside with a 35-year-old, but there’s not too much else to say — either McCann will show some glimpses of his much more successful pre-2018 days, or he’ll just be a pretty meh option as a secondary catcher.

Framing-wise, McCann’s skills have either cratered (per Baseball Prospectus) or maintained at a below-average rate over the last two years (per Statcorner). Neither is encouraging, and both suggest that he has more downside to his value than upside when framing is considered. Best case guess: he doesn’t actively hurt his team with framing; worst case: he hurts them a bit, but not too much. Stranger things have happened, but I’m not sure the expectation should be that he’ll move his personal value needle too much in either direction based on framing alone.

Adam Duvall

If you’re one of the few people in the universe that hasn’t taken a side in the internecine conflict known as “Adam Duvall, History’s Greatest Monster or Tragically-Misunderstood Platoon Bat” and were looking for projections to help you lean one way or the other, I’m sorry to report that you’re out of luck. Even the projections can’t agree. Steamer (weirdly, I think) has Duvall as a terrible fielder, which is something that seems pretty counter to his career thus far, and therefore, below replacement level. If you believe Steamer, the Braves should cut Duvall right now. On the other hand, ZiPS and IWAG see a bench player in him, which makes perfect sense. The key thing here: there’s no real disagreement about the central expectation of a mid-80s wRC+ for Duvall, so the variance is really in expectations about his defense.

Of course, perhaps the bigger question is where Duvall is done as a major league contributor or not. Distributionally, IWAG doesn’t quite think so, giving him a bigger chance of bouncing back than being awful, though the awfulness is not particularly unlikely either. That means that Duvall is a bit of an odd duck for a bench role — it makes sense for bench players to be targeted for lower variance so that you don’t bleed crazy wins when a starter gets hurt (hey, Braves, this describes Nick Markakis perfectly!). But, Duvall is the opposite, in that he might be quite productive as a backup... or terrible. There’s a decent chance of either, and it still remains to be seen whether the Braves will even take the gamble in the end, what with his contract being non-guaranteed and all.

Charlie Culberson

I could use this blurb to talk about a lot of things. Those things might be how Steamer just absolutely loathes Culberson, how ZiPS thinks he’s more or less replacement level but with good defense, and how IWAG has him more as a mediocre bench option. Those things might be about how, yes, Charlie Culberson may have been the luckiest baseball player in existence last year, but the bimodal shape of his hitting curve is influenced by some launch angle (vertical and radial) he may have made last season, though still dragged down by his terrible plate discipline stats. They might be about how he continues to be an enigma in the sense that his defensive reputation is entirely unsupported by (an admittedly still small sample of) defensive metrics.

But, instead, this blurb is going to note just one thing; Dansby Swanson has an 80 wRC+ point estimate from IWAG. Charlie Culberson has an 80 wRC+ point estimate from IWAG. There’s no need to read between the lines; the truth has been laid bare. Charlie is Dansby. Dansby is Charlie. The computers (well, this one computer) confirm it.

(Also, Braves, in the most optimistic case here, the Bulbasaur is a below-average hitter that doesn’t provide defensive value... and Jose Iglesias settled for a minor league deal this offseason. Just sayin’.)

Austin Riley (with caveat)

Why “with caveat?” Well, it mostly has to do with potential usage. Austin Riley, to date, has only appeared at third base as a professional. But, his best opportunity may be in the outfield. (As of the time of writing, though, I believe Riley has not had a single outfield inning in Spring Training, however.) I can never speak for Steamer or ZiPS, but IWAG is limited in that it currently doesn’t have a way to handle “guy will play vastly different position in the majors than one he played in the minors” where there is literally zero data on him at the new position. So, as far as IWAG goes, this projection is for Riley at third base. I’m kind of wondering if the Steamer one doesn’t have Riley in the outfield, since -8.0 Def suggests some really, really bad third base work.

In any case, IWAG is very high on Riley’s bat, and therefore his overall profile. The error bounds are massive (of the 13 players here, Riley is in his own tier of very low confidence) relative to the other players, but IWAG’s calculus generally suggests that players like Riley through their minor league tools tend to at least hit okay (mid-90s wRC+) with a lot of upside potential. The “hit okay” part appears to be the only part that Steamer/ZiPS concur with, as their point estimates look like what they would be if the entire right hump were gone.

Without any extra knowledge, I have no reason to assume that Riley would do anything other than play an average corner outfield spot if placed there. If so, his production would tank a full win relative to what’s shown. Still, a corner outfielder with an above-average hitting line clocking at near 2 WAR for league minimum is pretty awesome if you can get it. The upside in IWAG is potentially exciting too.


Putting it together - position players

Below are a couple of tables that sum up the above to get a total position player WAR number.

This first one are the PAs as they appear on the Fangraphs Depth Charts, with the minor exception that I replaced Raffy Lopez and Alex Jackson with 0 WAR generic backup catcher ghost guy, just to keep the focus on the same 13 players discussed above. Under this scenario, the Braves are projected between around 23 WAR (which would’ve put them 14th in MLB last year) and 28 WAR (fifth). However, that high estimate from IWAG uses PA distributions that IWAG doesn’t think are realistic, so for a comparison, I made the below:

The spread of PAs is a little less concentrated on the best players here (mostly Donaldson), hence the decrease. Also, pay no attention to where certain players appear, as the PA totals are really just meant to build up to “how many PAs will this guy get” rather than specific usage, so Johan Camargo’s PAs got filled up before anyone else’s, hence why he’s listed as the backup at first base. This spread, between around 22 WAR and 27 WAR, may be somewhat more reasonable when talking health into account, though I can’t actually be sure of that because it’s not fully self-evident to me the extent to which health is really taken into account up and down the Depth Charts PA totals. In any case, that range is between seventh and 14th in MLB based on last year’s teams, and IWAG’s 26.8 here is very close to the actual 26.6 team position player fWAR the Braves managed last year.

In short, they’ll be an average-to-good crew of position players. Looking at it probabilistically...

This is actually a really constrained range of outcomes — the 35 WAR top-end estimate is better than any position player group in 2018, but not really anything historic, as it wouldn’t even top the 1998 or 2003 Braves squads, or finish in the top 5 in position player WAR in the 2010s. The pretty cool thing, though, is the fairly high ceiling projected — 17 WAR or so is something akin to a “most performance is awful” for the Braves, and that’s still higher than 14 teams managed last year alone. So, it’s a pretty small range where the central expectation is pretty good, not the best, but very far from the worst. (In case you’re curious, one of the reasons why so many teams rack up low WAR values is that they continue to play below-replacement-level players. The Diamondbacks, who finished last year with 16.4 fWAR for their position players, gave five players that finished below 0.0 WAR 200 or more PAs, including 309 to -0.8 fWAR Chris Owings and 320 to -0.3 fWAR Jon Jay.)

Anyway, so, that’s the position players. Starting pitchers will be next (time TBD, when it’s done), followed by a brief detour into relievers before we wrap it up with some overall thoughts on the season.