Rays Extend Rookie Brandon Lowe

Late Tuesday night, Ken Rosenthal reported that the Tampa Bay Rays had agreed to a six-year, $24 million contract extension with 24-year-old second baseman and outfielder Brandon Lowe. Lowe is our 46th overall prospect, the top one in the 50 FV tier, and the No. 5 prospect in a loaded Rays system.

According to the Tampa Bay Times’ Marc Topkin, the deal also includes two club option years, which, along with incentives, could bring the total value to $49 million; if those options are exercised, Lowe will be 32 when the deal ends. Lowe will now obviously be making much more during his pre-arb seasons than he would have with standard contract renewals, but the possibility of overarching changes to baseball’s compensation structure in the next CBA currently make it impossible to evaluate the latter parts of the deal on Lowe’s end.

If he becomes the type of player I expect him to be — Lowe has power, walks at an above-average clip, and plays several positions including a passable second base, all of which makes me think he’s a two to three win player — a $4 million average annual value would make Lowe a bargain for the Rays. Based on Craig Edwards’ work at our site (and Driveline Baseball’s recent attempt to refine that research), 50 FV position player prospects like Lowe should be valued at $28 million, quite close to the value of his deal, excluding of the team option years. The AAV of the two option years, which would encompass Lowe’s age-31 and 32 seasons, is $12.5 million, almost exactly what D.J. LeMahieu received this offseason (age 30, two years, $24 million), and LeMahieu has been what we’d call a 50 in prospect parlance, as he was on average about a two win player during his tenure with Colorado. Read the rest of this entry »

Astros Pay Alex Bregman Now To Avoid Paying Him Later

Coming on the heels of Mike Trout’s humongous contract extension, news broke that Alex Bregman and the Astros had agreed to an extension of their own worth $100 million, with Mark Berman first to report the deal. While the Trout contract is the biggest of all time, the Bregman deal is not without intrigue. Bregman, who was still a full year away from arbitration, is the first star-level player to sign a pre-arb contract extension in nearly five years. The last player at or above Bregman’s level of production to sign a contract like this was Trout, who signed his six-year, $144.5 million contract back in 2014.

Since 2014, the number of contract extensions buying out free agent years has decreased. When Luis Severino signed his deal earlier this offseason, Jeff Sullivan ran the numbers on the quantity of extensions by offseason, providing this graph.

In the five years leading up to the 2014 season, there were about 25 or so extensions per season, and in the five years since, the numbers have dropped in half. Since Severino signed, we have had Jose Leclerc, and now Alex Bregman, but those extension figures aren’t going up a ton this year. It isn’t just that the number of extensions have gone down; the quality of players signing those extensions has declined as well. We saw Trout’s big deal ahead of the 2014 season; the year before, Buster Posey, who was Super-2 arbitration eligible, signed an even bigger contract covering more seasons. It was Andrew McCutchen the year before Posey. Matt Carpenter and Jason Kipnis, who were several years older than Bregman but also coming off very good years, signed six-year deals with options guaranteeing themselves around $50 million each. Read the rest of this entry »

Houston Rewards Pressly’s Liftoff with Two-Year Deal

It wasn’t the biggest extension announced yesterday — it wasn’t even the biggest Astros extension announced yesterday — but Ryan Pressly’s two-year, $17.5 million deal with Houston, which was first reported by Chandler Rome, was a big deal for Pressly, a big deal for Houston, and a big deal for relievers. The deal will pay Pressly $2.9 million in 2019, his final arbitration year, then $8.75 million in each of 2020 and 2021. There’s a vesting club option for 2021, as well. It’s believed to be the biggest extension ever signed by a reliever not expected to close games for his team (that’s still Roberto Osuna’s job, at least for the time being) and is a tremendous accomplishment for a player who had a 4.70 ERA (with a 4.36 FIP) as recently as 2017.

But of course that 2017 performance isn’t what the Astros are paying for. They’re paying for what he did in Houston last August and September (which is strike out 32 men and walk just three in 23.1 innings pitched) and what they think he can do for them going forward (which is presumably more of the same). Héctor Rondón, Joe Smith, Collin McHugh, and Will Harris are all expected to become free agents at the conclusion of the 2019 season, and locking Pressly up now means the Astros will have one less thing to worry about next winter. For Pressly, this deal gives him the job security that has absolutely never been a guarantee in the years since he signed with the Red Sox as an 11th-round pick back in 2007.

The conventional wisdom is that relievers are inherently volatile — with a few, Mariano Rivera-shaped exceptions — and so giving them multi-year contracts is the kind of thing you only do when you’re competing for their services on the open market. You certainly wouldn’t expect to see a forward-thinking team like the Astros locking up a reliever with such a short track record of success — during his time in Minnesota at the beginning of 2018, Pressly had a 3.40 ERA and a 2.95 FIP — for two additional years when they’re competing against nobody but themselves. Read the rest of this entry »

Kiley McDaniel Chat – 3/20/19


Kiley McDaniel: Coming to you live from ATL a little later than usual because I’m moving onto my third contractor now. Scout has chased all the squirrels and chipmunks and is taking a nap next to me. On to your questions:


Ben M: It feels like to date we aren’t getting the same type of negative reports on the high schoolers that caused players like gorman to slide last year. Is that accurate?


Kiley McDaniel: Not a question I get very often. I think Gorman may have stood out more because he was a top 10 overall prospect for us wire to wire but had some clear deficiencies that got a little worse during the spring


Kiley McDaniel: That said, we kept him in the top 10 (we settled on him at 7th, he went 19th overall) because we thought those things were fixable and the strengths were too good to pass up


Kiley McDaniel: So I wouldn’t say that was a unique amount of negative info on a top prospect. We have said Abrams probably can’t play SS longterm, Witt has real hit tool questions, Espino has a really long arm stroke and may throw too hard too early, etc. which is on par with the Gorman stuff


shf9: What’s going on with Carter Stewart?  He’s falling fast down your draft rankings.

Read the rest of this entry »

2019 Positional Power Rankings: Left Field

This morning, we considered the catcher position. This afternoon, the positional power rankings take us out to left field.

Has batted ball data and modern defensive positioning altered the defensive spectrum? It likely won’t surprise readers to learn that the average wRC+ by position starts with first base and right field, but it may be revelatory to learn that the gap between right and left field has been pretty wide. The last four years, the average right fielder has produced an average wRC+ 4.75 ticks higher than his counterpart in left. The offensive bar at third base has also been higher on average than in left field during the last four years.

Why? Perhaps improved defensive positioning on the infield has enabled more bat-centric players to play third base when, in years past, they’d be at first. Most hitters are right-handed, and increased focus on pulling the ball in the air could have quickly made defensive range in left field more important than it has been in the past. The average sprint speed among left fielders is now on par with that at shortstop. Is it a long term, tectonic shift that should impact things like prospect evaluation? It’s hard to say definitively at this point because so much about the game is changing and still has the potential to change. But it’s worth discussing — eventually. For now, here are our current left fielders. Read the rest of this entry »

Picking the Perfect Baseball-Themed March Madness Bracket

One of my favorite sporting events of the year is just around the corner. And no, I’m not referring to Opening Day, though it indeed fits both conditions. I am, of course, talking about March Madness.

For a stat nut like me, March Madness is the perfect time of year. It combines sports with both probability and unpredictability. It’s also quite fun to see fans from all over the country supporting their local universities and alma maters in the biggest basketball tournament (and workplace distraction) in the United States.

The only thing missing from March Madness is a baseball spin. But for those like me who enjoy both the Madness and Opening Day, I have a solution: the perfect baseball-themed March Madness bracket. When I say “perfect,” I don’t mean literally perfect. Unfortunately, there is just a 1 in 9.2 quintillion chance that this bracket (or any other bracket) will achieve perfection.

It is perfect, though, in another sense. The second qualifier, “baseball-themed,” is important. This bracket can indeed call itself the perfect baseball-themed bracket. Let me show you how.

The process behind this is rather simple. I compiled all 68 teams in the tournament, and using Baseball-Reference’s Draft Index, was able to easily search every major league player to come from one of these schools. I then ranked each school by total WAR produced by those players.

I should note that this is only in the MLB Draft era (1965-present), and that this list only includes players who were drafted from said school. For example, if Devan Fink played baseball at Michigan but then transferred to the University of Florida and was subsequently drafted out of Florida, the Gators would get all of the credit for having harbored Devan Fink.

Without further ado, the rankings: Read the rest of this entry »

The Yankees Sign Gio Gonzalez for Minimal Risk, Money

Back in 2011, the Yankees had a lot of fun after signing two aging starters to minor league contracts, when they brought on Bartolo Colon and Freddy Garcia. Colon was deemed to be done by many. He went so far as to get a stem cell injection in his shoulder to give it another go. Garcia had a so-so season in 2010 and was working with significantly diminished stuff from his prime, but the two combined for 4.8 WAR, helping the Yankees on the way to a division title. Eight years later, New York has signed Gio Gonzalez to a minor league deal. He will earn $3 million if he reaches the big leagues, with incentives based on games started. He has the ability to opt-out on April 20 should he not receive a major league assignment. The circumstances that led to signing Gonzalez now, and Colon and Garcia then, are different, but the best-case scenario might be the same: big bang for the buck from a veteran arm.

Normally, there are five starting pitchers in a set rotation; two of the Yankees’ are injured this spring. Luis Severino, the staff ace, has an inflamed rotator cuff and is expected to be out until May. CC Sabathia is recovering from angioplasty and right knee surgery, and is projected to return sometime in April. That leaves two spots open beyond the cast of James Paxton, Masahiro Tanaka, and J.A. Happ.

The Yankees already have depth arms with major league starting experience in Domingo German, Jonathan Loaisiga, and Luis Cessa. German and Cessa in particular are having pretty good springs, but we all know that spring training can be a bit of a mirage. Those three combined for 1.8 WAR last year, which isn’t inspiring. But they are young and have shown flashes of promise, so it is possible that they could break out this year. For now, they are talented question marks. And even if the Yankees decide to carry two of the three, they would still face issues when it comes to rotation depth. As we’ve seen from the 2016 Dodgers, anything could happen to human beings who throw baseballs for living. Read the rest of this entry »

2019 Positional Power Rankings: Catcher

After Craig Edwards and Rian Watt surveyed the current state of second and third basemen yesterday, our positional power rankings continue today with catcher.

The catching position hit an 11-year low in wRC+ (84) and also dipped below 50 WAR for the first time in that same span (49.9). Just two guys topped 4.0 WAR, and neither Yasmani Grandal (4.9) nor J.T. Realmuto (4.8) reached a full 5.0 wins. Compare that to just five years ago, when five guys had at least 5.0 WAR and two of them rounded to eight wins: Yan Gomes (5.3), Rene Rivera (5.3), Russell Martin (6.5), Buster Posey (7.8), and Jonathan Lucroy (8.1). Part of the issue is that teams are much more open to splitting the duties among multiple players, which makes it tougher for even the top end to generate big numbers. In 2014, there were 23 catchers with at least 400 plate appearances, but that figure dipped to just 15 a year ago.

The 2019 pool has already lost a stalwart with Salvador Perez needing Tommy John surgery, although he hasn’t topped 1.0 WAR since 2014 and has surpassed an 89 wRC+ just once in that same time frame (103 in 2017). The position is aging and hasn’t backfilled with prospects quickly enough to replace the old guard. Looking at those top performers from 2014, we see that they are all still playing but managed just a combined 6.9 (nice) WAR despite four of the five logging at least 350 plate appearances.

Do not fret, though, the backfill is on the way! Danny Jansen debuted last year but will play out his rookie season in 2019. Francisco Mejia has essentially had sips of coffee each of the last two years and still holds rookie eligibility. Meanwhile, 10 other catchers made it into our Top 100 Prospects list, including three in the Top 50: Keibert Ruiz for the Dodgers (15), Sean Murphy for the Athletics (35), and Joey Bart for the Giants (41). Read the rest of this entry »

Effectively Wild Episode 1350: Season Preview Series: Astros and Tigers

Ben Lindbergh and Sam Miller banter at length about Mike Trout’s extension, why he may have wanted to stay with the Angels, what makes him different from other players, and why he’s underpaid despite his record contract, then preview the 2019 Houston Astros (44:38) with The Athletic’s Astros beat writer Jake Kaplan, and the 2019 Detroit Tigers (1:15:05) with Detroit Free Press Tigers beat writer Anthony Fenech.

Audio intro: Heatmiser, "Why Did I Decide to Stay?"
Audio interstitial 1: Neil Finn, "Astro"
Audio interstitial 2: John Doe and The Sadies, "Country Club"
Audio outro: Flamin’ Groovies, "The First One’s Free"

Link to Ben’s Trout extension article
Link to Sam’s Trout extension article
Link to Sam’s article about Trout on every team
Link to story about MLB considering raising minor-league salaries
Link to Banished To The Pen’s team preview posts
Link to preorder The MVP Machine

 iTunes Feed (Please rate and review us!)
 Sponsor Us on Patreon
 Facebook Group
 Effectively Wild Wiki
 Twitter Account
 Get Our Merch!
 Email Us: podcast@fangraphs.com

FanGraphs Pitch Framing

In 2008, when Dan Turkenkopf was the first to quantify the value of pitch framing, he noted that it appeared to be alarmingly important. Bill Letson was similarly astounded when he calculated the size of the effect in 2010. Max Marchi and Mike Fast each took a turn in 2011, finding large and highly correlated catcher values despite using different methods. Other sabermetric luminaries have contributed sophisticated methods and sanity checks, some of which I’ll touch on below. And yet, this terrifically important, seemingly well-established, and impressively repeatable defensive skill has been left out when calculating FanGraphs player WAR and ignored when Steamer forecasts pitchers and catchers. . . until now.

In what follows, I’ll briefly lay out a series of steps for calculating how many framing runs each catcher contributed as well as and how many extra strikes each pitcher was granted (or, in some cases, earned). This much has all been done and clearly described before thanks to Dan Brooks and Harry Pavlidis; that research was updated and expanded upon by Pavlidis and Jonathan Judge. I’ll then compare the values I’ve obtained to the ones created by Baseball Prospectus, StatCorner, and Sports Info Solutions and demonstrate (I hope) that those extra strikes really do result in extra strikeouts and fewer walks. Lastly, I’ll discuss what this means for Steamer forecasts.

Modeling the Strike Zone

It all starts with the strike zone and I started by using generalized additive models to estimate the probability of a strike in any count, to either left-handed or right-handed batters at each location in and around the plate. On the first pass (shown below), I created strike zones averaged across seasons and, on the second pass, looked for changes in the strike zone by season. The blue contour lines in the images below show where strike calls are a coin flip and the red dashed lines show where we’d expect a 25% or 75% chance of a strike. If you’re read Matt Carruth or Jon Roegele, you’ll be unsurprised to see a small 0-2 strike zones (shown in the upper right facets) and large 3-0 strike zones (in the lower left).

Distributing Extra Strikes and the Case of Ryan Doumit

We can now compare actual strike calls to predicted strike probabilities. I used a logistic mixed effects model (with batters and pitchers as random effects) to split up credit for extra strikes between pitchers, catchers and dumb luck (or, if you prefer, unattributed variance). For now, I left umpires out of the model reasoning that pitchers and catchers each see roughly their share of pitcher-and-hitter-friendly umpires. Also, I’m looking for a model that we can update daily throughout the season and adding random effects quickly adds computational time.

These extra strikes can be roughly translated into saved runs at a rate of 0.135 saved runs per additional called strike. When we do that we find the following highlights and low-lights of (PITCHf/x era) framing history:

Best Catcher Framing Seasons, 2008-2018
Year Catcher FramingRuns
2011 Jonathan Lucroy 42.4
2008 Brian McCann 37.5
2011 Brian McCann 34.1
2010 Jonathan Lucroy 32.4
2008 Jose Molina 32.1
2017 Tyler Flowers 31.9
2013 Jonathan Lucroy 31.8
2009 Brian McCann 31.6
2008 Russell Martin 28.1
2010 Yadier Molina 27.2
Worst Catcher Framing Seasons, 2008-2018
Year Catcher FramingRuns
2008 Ryan Doumit -57.8
2009 Gerald Laird -32.3
2014 Jarrod Saltalamacchia -31.8
2011 Carlos Santana -30.3
2012 Carlos Santana -27.6
2008 Chris Iannetta -26.6
2009 Ryan Doumit -24.6
2010 Jorge Posada -24.2
2008 Gerald Laird -23.9
2014 Kurt Suzuki -22.8

“Negative 57.8 runs?” I hear you cry! Well, Jeff Sullivan asked this same question when confronted with Baseball Prospectus’ estimate of -63 runs for this same Doumit season in an article entitled “How Bad Could a Pitch Framer Possibly Be?” Sullivan noted that Doumit was estimated to have lost 200 runs due to poor framing over the course of his career. For a sanity check on this number, he compared how pitchers fared with Doumit behind the plate relative to other catchers on the same team and found that pitchers had in fact allowed 213 more runs with Doumit.

Comparisons to Baseball Prospectus, StatCorner, and Sports Info Solutions

The following charts compare our new Framing Runs to the ones provided by Baseball Prospectus, StatCorner and Sports Info Solutions. Each data point is one catcher season. I’ve kept the axes the same across all three graphs so that you can gauge the relative spreads in runs. One thing that jumps out is that Sports Info Solutions is decidedly more cautious in their numbers than the other three systems.

The following tables shows the correlations between the systems from 2010 (the first year of SIS numbers) through 2018 and the year-to-year correlations for catchers from one season to the next. Our model appears to be most similar to the Baseball Prospectus model but all four models are highly correlated. Our model and BP’s model have the highest year-to-year correlations for catchers but, again, all four systems are in the same ballpark.

Catcher Framing System Correlations
Fangraphs BP StatCorner SIS Y-to-Y
Fangraphs 1.00 0.96 0.92 0.93 0.742
BP 0.96 1.00 0.90 0.91 0.732
StatCorner 0.92 0.90 1.00 0.87 0.698
SIS 0.93 0.91 0.87 1.00 0.695

xxFIP really works (and so does pitch framing)!

In 2013 Chris Carruthers asked “How can we take a good ERA Estimator like xFIP, and eliminate framing and bias?” To answer this question he created metrics with inputs that are not affected by the umpire call: swinging strike rates, ball in play rates, zone and out-of-zone looking rates (with zones defined by the rulebook rather than by the umpires) and foul rates. Carruthers created expected strikeout (xK%), walk (xBB%) and xFIP (xxFIP) metrics using only these bias-free inputs. I updated these metrics so that their means for each season match the means of pitchers’ actual rates and then asked whether the differences between pitchers’ actual strikeout rates, walk rates and xFIPs and Carruther’s metrics could be predicted by pitch framing.

More specifically, I built regression models to predict the differences between a pitcher’s actual rates and Carruthers’ bias-free metrics based on the pitcher’s framing runs per game (including runs attributed to the pitcher himself, to his catchers and to luck).

If all these saved strikes our model thinks it sees, really do cause a divergence between xFIP and Carruther’s xxFIP in just the way we might imagine, we’d expect every run per game of framing to correspond to 0.92 runs of difference between the biased and un-biased ERA estimators (since 92% of runs are earned).

Fitting linear model: I(xFIP – adj_xxFIP) ~ FramingRuns_ per9
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.01602 0.004262 -3.76 0.000172
FramingRuns_per9 -0.8884 0.02031 -43.74 0

According to our regression model, each framing run predicts a 0.89 difference (with a standard error of 0.02). Not bad! We might also be interested in how much framing affects strikeout and walk rates.

Fitting linear model: I(BB% – adj_xBB) ~ FramingRuns_ per9
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0005538 0.0001919 -2.886 0.003915
FramingRuns_per9 -0.03881 0.0009144 -42.45 0
Fitting linear model: I(K% – adj_xK) ~ FramingRuns_per9
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.002668 0.0002269 11.76 1.603e-31
FramingRuns_per9 0.03932 0.001081 36.36 2.599e-258

Our models show that each framing run (per game) adds 3.9% to a pitchers strikeout rate and subtracts 3.9% from his walk rate. Or put another way, it adds 7.8% to the difference. What does this amount to in terms of runs? For that, we can use Tango’s kwERA formula:

kwERA = 5.40 − 12 · (SO% − BB%)

Adding 7.8% to the difference between SO% and BB% should add 12 · 7.8% = 0.94 to a pitcher’s ERA. Again, this is consistent with the 0.92 we’d expect.

These regression models gives me confidence that, although the magnitude of our pitch framing numbers seems astounding, it’s not far wrong. Or, in truth, it gives me further confidence since I was already a believer. If you’re still on the fence, I’d recommend reading Mitchel Lichtman’s research which shows that catcher projected to be good/bad framers really do save/cost runs in the way we’d expect.

Player Projections

With framing numbers now in hand, it’s time to start forecasting. The following table shows a projection for Yasmani Grandal who is expected (by Steamer) to catch 120 games and see 7,599 called pitches (within a 3.5’ x 3.5’ box around the strike zone) in the upcoming season. The “Framing Rates” are Framing Runs per 6000 called pitches and the seasonal weights are how much weight we give to each called strike from that season. The projection is the weighted average of Grandal’s past framing rates with 1000 league average called pitches added to the mix (the “regression towards the mean”). The upshot is that Grandal is expected to save 17.5 runs by virtue of his framing in 2019. His teammates, Manny Pina and Erik Kratz, contribute an additional 1.3 runs of framing to bring the Brewers team total to 18.8 runs.

Yasmani Grandal 2019 Framing Projection
season CalledPitches FramingRuns FramingRate season_weight proj_weight
2018 6654 13.0 11.7 1.00 6654
2017 6571 20.1 18.3 0.41 2714
2016 6550 25.7 23.5 0.17 1117
2015 5624 23.0 24.6 0.07 396
2014 4156 14.7 21.2 0.03 121
league average 1000 0.0 0.0 1.00 1000
2019 7599 17.5 13.8 NA NA
Projected Framing Runs Leaders (with Framing Runs per 120 Games)
Catcher Team Games FramingRuns FramingRuns120
Yasmani Grandal MIL 120 17.5 17.5
Tyler Flowers ATL 74 15.4 24.8
Jeff Mathis TEX 70 12.8 21.8
Austin Barnes LAN 70 11.6 19.8
Austin Hedges SDN 73 11.0 18.0
Martin Maldonado KCA 102 10.3 12.1
Buster Posey SFN 114 8.5 8.9
Max Stassi HOU 50 8.5 20.2
Roberto Perez CLE 55 7.0 15.4
Jorge Alfaro MIA 101 6.5 7.7
Projected Framing Runs Trailers (with Framing Runs per 120 Games)
Catcher Team Games FramingRuns FramingRuns120
Tucker Barnhart CIN 116 -11.6 -12.1
Willson Contreras CHN 113 -11.6 -12.2
Omar Narvaez SEA 97 -9.8 -12.1
Robinson Chirinos HOU 70 -8.4 -14.3
Isiah Kiner-Falefa TEX 25 -5.2 -24.5
Mitch Garver MIN 46 -4.9 -12.8
Kurt Suzuki WAS 58 -4.6 -9.5
Jonathan Lucroy LAA 70 -4.5 -7.6
Welington Castillo CHA 76 -3.7 -5.9
Michael Perez TBA 35 -3.4 -11.8

We use the following three steps to adjust pitcher projections based on framing:

1. Adjust past strikeout and walk rates to what they would have been with typical ball and strike calls. This includes removing any benefit pitchers received due to extra strikes regardless of whether these strikes were attributed to the catcher, the pitcher or plain old luck.

2. Forecast 2019 strikeout and walk rates (in the usual way) using these adjusted strikeout and walk rates.

3. Adjust these forecasted rates based on each pitcher’s projected ability to get extra strikes as well as the abilities of his team’s catchers.

The affects on individual pitcher’s projections are generally quite small. Pitchers who were good at getting extra strikes are projected to continue to get extra strikes going forward so that much is mostly a wash. Pitchers expected to benefit from catchers with soft hands often benefited from these same skilled framers before (meaning that good framing was baked into their numbers already) and, even where their battery mates have changed, they’re only feeling a fraction of that catcher’s contribution to the team.

Nonetheless, here are the pitchers who benefited the most from adding framing to Steamer.

Pitchers with better Steamer Projections
Pitcher team IP oldERA newERA diff
Mike Minor TEX 175.4 4.60 4.44 -0.16
Brian Flynn KCA 40.0 4.41 4.28 -0.12
Chris Martin TEX 50.0 4.07 3.94 -0.12
Danny Duffy KCA 185.2 4.36 4.25 -0.11
Jose Leclerc TEX 65.0 3.64 3.53 -0.11

. . . and the pitchers who took a hit.

Pitchers with worse Steamer Projections
Pitcher team IP oldERA newERA diff
Marco Estrada OAK 129.7 5.24 5.41 0.17
Alex Wood CIN 137.4 3.99 4.15 0.16
Yusmeiro Petit OAK 60.0 4.39 4.53 0.14
T.J. McFarland ARI 35.0 4.07 4.19 0.13
Jerry Blevins OAK 25.0 4.47 4.60 0.12

We can add this all up and see how this affects the number of runs each team is expected to allow.

Teams with better Steamer Projections
team ChangeInRunsAllowed
Royals -13.2
Brewers -10.2
Rangers -9.3
Braves -7.3
Giants -6.6
Teams with worse Steamer Projections
team ChangeInRunsAllowed
Mariners 8.8
Diamondbacks 6.3
Athletics 5.7
Angels 4.5
Astros 4.4

The Royals, Brewers and Rangers feel the benefit of acquiring Martin Maldonado, Yasmani Grandal, and Jeff Mathis, respectively, while the Mariners get dinged for adding Omar Narvaez and the Diamondbacks feel the loss of Mathis.

WAR Adjustments

Once we’ve estimated how many runs each catcher saved (or, in the case of projections, might be expected to save) we translate those runs into wins and adjust WAR accordingly. We can adjust pitcher WAR by doing essentially the inverse and subtracting out the value of catcher framing. Essentially, we’re shifting runs (and thus wins) between pitchers and catchers since catchers deserve some share of the credit (or blame) for what transpired.

Please see David Appelman’s WAR post for all the details.