Archive for March, 2017

The 2017 Atlanta Braves: A .500 Team?

The 2016 Atlanta Braves were built to suck.  After all, starting a season 0-9 basically kills any hope left in the fan base, and gets them prepared for the contagious losing.  For the few fans who paid to go see their beloved Braves play in the now retired Turner Field, losing 93 games is heartbreaking.  A large volume of articles exists detailing the extent at which the Atlanta Braves, under both John Hart and John Coppolella, are remodeling their organization.  This article serves the purpose of examining one thing:

2016 Atlanta Braves



Runs Scored Runs Against

Run Differential

First Half

31-58 307 414 -107

Second Half

37-35 342 265


That’s right! The 2016 second-half Atlanta Braves won more games than they lost!  If you did not already know this you either (a) are not a Braves fan, or (b) could not manage to care less.  However, this could have some real value behind it.  While the Braves managed to be outscored by 20 runs in the second half, they still managed to win two more games than they lost.  They scored 35 more runs in 17 fewer games.  Their runs/game increased 3.45 to 4.75, which would have placed them in between the Mariners and Cardinals in that regard had it been 4.75 the entire 2016 season.  The most important takeaway is how much better the second-half Braves were at preventing runs — 149 fewer runs allowed than in the first half.  Shaving off that many runs in only 17 fewer games is huge.

But let’s not get ahead of ourselves.  A winning record is unsustainable at a deficit of 20 runs in 72 games.  But I am not asking whether the 2017 Atlanta Braves can win even 82 games.  Can they win 81?  Could the great finish down the stretch of the 2016 season carry over into 2017?  While going .500 is technically meaningless because a .500 team will not make the playoffs, not losing more than they win in the new SunTrust Park will energize the organization and the fan base, and prepare the team for future success.

When the 2016-2017 offseason kicked off, the Braves signed two popular starting pitchers, and acquired one via trade, to eat innings so their crop of young pitching could ripen on the farm.

Braves 2017 Offseason Acquisitions (2016 Statistics)





Bartolo Colon

15-8 3.43 3.99 1.50 6.01 1.21


R.A. Dickey

10-15 4.46 5.03 3.34 6.68 1.37


Jaime Garcia 10-13 4.67 4.49 2.99 7.86 1.37


Two of the three had subpar years in 2016.  The other one became an internet sensation for his antics in the batter’s box and even hit a homer against the San Diego Padres.  But let’s assess what each pitcher brings to Atlanta’s rotation.

Bartolo Colon ages like a fine wine.  His ERA was better last year than any Atlanta starter except Julio Teheran.  While pitching record is not a statistic to measure performance, it is worth noting he won more games last year than any Atlanta starter.  He was better pretty much across the board than anyone not named Julio Teheran.  But can he keep this level of production up?  I would like to think so.  His two-seam velocity has stayed relatively consistent over the past three years.  All the Braves should ask Colon to do is turn in around 20 quality starts (he turned in 19 last year).  Consistency was a hallmark of his time with the Mets, and should continue in Atlanta for at least the 2017 season.

The other old guy the Atlanta Braves picked up this offseason happens to be knuckleballer — R.A. Dickey, 2012 National League Cy Young Award winner.  While Dickey will more than likely not be in the running for any hardware as he nears his 43rd birthday, he can still meet the immediate needs of his new team.  From 2011 to 2015, Dickey’s lowest inning count was 208.2, and peaked in his legendary 2012 with 233.2 innings pitched.  This is what the Braves need.  They need Dickey to turn in a mountain of good, quality innings.  If he could get over 200 innings again, and remain viable at the big-league level, then it is mission accomplished.

The third major addition to the Atlanta rotation is southpaw pitcher Jaime Garcia.  On December 1 of last year, the St. Louis Cardinals accepted minor-league infielder Luke Dykstra, right-handed pitcher John Gant, and righty Chris Ellis for Garcia’s services.  First, let’s look at the positives of this — Garcia is a definite mid-rotation talent, who posted a 3.73 ERA in 31.1 IP and a 3.18 ERA in 28.1 IP in April and May of last year, respectively.  He gives Atlanta a lefty in a rotation filled with righties.  The downside?  His low ERAs early in the season turned into a 5.40 ERA in June and a 5.60 ERA in the second half of the season.  So much for success in the second half driving this article, right?  Let’s remain optimistic.  After all, that is the whole purpose of this.  Garcia’s HR/FB rate was up from 7.1% in 2015 to a ghastly 20.2% in 2016.  He got consumed by the league-wide power surge.  I do not think such a high rate is sustainable or will happen again.

Let’s make a prediction.  Bartolo Colon makes us all fall back in love with “The Great Bart-Bino” all over again and he turns in around 16-20 quality starts for the upstart Braves.  Dickey, the workhorse of the staff, follows suit and dizzies batters with his knuckler for over 200 innings.  Garcia returns to early-2016 form, and posts something in the ballpark of 1.5 WAR.  Of course, the likelihood of all three scenarios playing out is small, but what I am trying to get across is it is possible.

Now, time to switch gears. The Braves lineup has changed its look dramatically since this time last year, sticking with a solid mixture of recognizable names and some guy named Dansby Swanson.  Here is a look at their projected Opening Day lineup:

2017 Atlanta Braves


Name Bats 2016 WAR

Projected WAR


Ender Inciarte L 3.8



Dansby Swanson R 0.9



Freddie Freeman L 6.5



Matt Kemp R 0.0



Nick Markakis L 1.7



Brandon Phillips R 0.8



Adonis Garcia R 0.2



Tyler Flowers

R 0.3


The projected WAR was retrieved from ZiPS projection

Look at the first half of their lineup.  To me, those three guys, Inciarte, Swanson, and Freeman, look like the core of a team poised to wreak havoc on the NL East before the end of this decade.  It is hard to project exactly what we are going to get out of Dansby Swanson, but most Braves fans and analysts expect him to take reign as the face of the franchise.

Starting in the leadoff spot is Ender Inciarte, who was brought over as icing on the cake in the Shelby Miller trade that landed Swanson and pitching prospect Aaron Blair.  In his first year in Atlanta, Inciarte posted a .732 OPS and won a Gold Glove for his outstanding play in center field.  I really could not think of a better leadoff guy for the Braves.  He is signed through 2021 at a team-friendly cost of $30.5 million, with a $9-million team option in 2022.  In his first years in the bigs, Inciarte has played in at least 118 games, posted a WAR above 3.7 (produced a figure of 5.3 in 2015), and shows no sign of slowing down as his prime years lay ahead. What if he crosses the 3.0 WAR plateau for the fourth time in four seasons, and maybe even adds another Gold Glove?  That is all his organization needs out of him.

Inciarte is a vital part of the Braves defense, which, according to 2017 PECOTA projections, leads the NL East in Fielding Runs Above Average (they are projected to attain an average figure of 3.6, while the other four teams are either at 0.0 or negative). explains FRAA as an “individual defensive metric created using play-by-play data with adjustments made based on plays made, the expected numbers of plays per position, the handedness of the batter, the park, and base-out states.”  In short, the higher the number, the better the fielder, and vice versa.  The higher the team average, the better the team is overall in the field.  In his Gold Glove campaign, Inciarte registered a FRAA of 23.0, according to BP.  The graduation of Dansby Swanson and the addition of web-gem-prone second baseman Brandon Phillips will certainly strengthen the middle cone of the field.  Just how good is this team going to be at preventing runs?  Many projection systems think they will be around the top of their division, and many fans are excited to see the double-play tandem of Swanson and Phillips at work.

Freddie Freeman is the undisputed anchor of the lineup, and has finally seen the Braves ADD instead of SUBTRACT from the lineup around him.  The addition of Matt Kemp has helped tremendously.  With a recognizable slugger swinging behind Freeman, managers and pitchers had to pitch to him in the latter months of the year. With Kemp slotted behind him, Freeman hit to the tune of a .340/.456/.665 slash with 16 home runs and 18 doubles.  Kemp also matched the theme of this article with a strong second half — hitting .280/.336/.519 with 12 bombs in 241 plate appearances as a Brave.  The duo should have Braves fans excited for a full season of similar production from Freeman if Kemp is behind him.  Kemp, on the other hand, has a lower bar to pass, and could re-tool his value as an offensive player in his first full year off the West Coast.

So why is it unreasonable for the 2017 Atlanta Braves to win 81 games?  I do not think it is that far-fetched.  This article has not mentioned their incredibly deep farm system, which includes guys such as Ozzie Albies, Sean Newcomb, and Lucas Sims, but instead focuses on the immediate roster — a roster which has the potential to do unexpected things in 2017.  The dominoes would have to fall in all the right places, but this is baseball.  Anything is possible.

Theodore Hooper’s Official 2017 Atlanta Braves Prediction: 81-81


The statistics used in this study were found on, and, and the rosters on were a great help in referencing players and transactions. 

Let’s Build Our Own Catch Probability Metric

By now you’ve seen the Statcast Catch Probabilities. They’re great! Or, at the very least, they’re a shiny new toy to play with until the regular season rolls around. But, as you may have noticed, there are a few frustrating details about it — namely, the actual math behind the statistic is completely opaque, and the details about when an individual catch happened are hard to find. So let’s fix those two problems! We’ll create a catch probability metric that anyone can compute in Excel, using data that anyone can download easily.

You may have noticed a problem with this plan, though — the data that is used for the official Statcast catch probability isn’t easily accessible. We’ll have to make do with what we can get from the Statcast search at Baseball Savant. Specifically, instead of using hang time and distance traveled, we’ll use exit velocity and launch angle. Note that this completely disregards defensive positioning and it even disregards the horizontal angle off the bat*! It’s going to make for a less perfect metric, of course, but (spoiler alert) it will turn out okay.

*This really makes more sense if you think about it in terms of probability of the hitter making an out. The old saying goes “hit ’em where they ain’t” but in recent years we’ve come to understand that it’s really “hit it hard and in the air.”

I’m not going to go into the details of how I computed this metric; it’s standard machine learning stuff. If you want to follow along with the computation, I’ve put my code up on GitHub. Instead of going through all that here, I’ll just jump to the finish line: the formula for catch probability ends up being

1/(1+exp(-(-10.152 + 0.057 * hit_speed + 0.218 * hit_angle)))

Now you might be worried that such a simple formula, excluding tons of information, might be totally worthless. I was worried about that too! But applying this formula to a test set revealed this formula to be surprisingly accurate:

Catch Probability Assessment
Statistic Value
Accuracy 0.8385
Precision 0.8338
Recall 0.8671
F1 0.8501

(if you’ve never seen those numbers before — closer to 1 is better. Trust me, it’s pretty good.)

Well, that’s all well and good, but how can you get this for yourself and play around with it? Start by downloading the data you’re interested in from Baseball Savant. For instance, you can get all the data from, say, May 1 of last year by going here. Download the CSV with the link at the bottom and then you can simply add the above formula in a new column in Excel. If you need a concrete example of how this looks in Google Sheets, I’ve put one here.

Okay, now you’ve got this, but what are you going to do with it? One possibility is to use this to try to figure out which plays the official metric estimated as being difficult. For instance, let’s say you’ve noticed that Miguel Sano made two highlight-quality plays but you don’t know Mike Petriello well enough to ask him which ones those are. Just compute your own probabilities and you’re off! Although, as expected, the numbers differ. Our numbers do have Sano making two plays in the 0-25% range, but they’re not the same ones that Statcast flagged (sorry about the quality of the GIFs).

Catch #1: estimated catch probability 18.3%

Catch #2: estimated catch probability 21.3%

The Twins announcers praised his first step in the former video, while in the second they talked about how the ball “hung up” for Sano to be able to catch it. Not spectacular plays by any means, but neither were the other two, of course.

Finally, because I’m sure you’re curious, here’s the top catch of 2016 according to this metric (estimated catch probability: 8.6%).

Of course it’s a Kevin Kiermaier catch. Hey, at least we know we’re doing something right.

Carl Edwards Had a Bad Day in a Great Year

I’ve been particularly intrigued recently by Carl Edwards Jr., a Cubs reliever who got called up last season. He had always seemed to be surprisingly good, but I wasn’t aware quite how good he was until I calculated wOBA allowed by pitchers in 2016 and found that he had the third-lowest in the league, behind only Kenley Jansen and Zach Britton, and in front of Clayton Kershaw, Aroldis Chapman, and Andrew Miller. Ranking Edwards among four of the game’s top closers and the game’s best starter seemed strange. Here are the six pitchers with the lowest wOBA against last season:

Zach Britton 67.0 0.54 0.836 0.231 1.80 0.188
Kenley Jansen 68.2 1.83 0.670 0.244 1.34 0.188
Carl Edwards 36.0 3.75 0.806 0.162 2.79 0.201
Clayton Kershaw 149.2 1.68 0.722 0.256 1.78 0.202
Aroldis Chapman 58.0 1.55 0.862 0.268 1.42 0.206
Andrew Miller 74.1 1.45 0.686 0.258 1.68 0.209

Edwards stands out negatively in several respects here. He pitched the fewest innings out of that group by far, and was almost certainly put in the least-stressful situations. His ERA is almost two points higher than the next-highest, and his FIP is nearly a point higher than Britton’s, the second-highest mark. His BABIP is also remarkably low, due in part to luck and in part to the Cubs’ historically good defense. So why is his wOBA so remarkable?

Looking through Edwards’ game log, two bad appearances stand out:

  1. August 13th, where he allowed five runs on one hit and four walks while recording just two outs.
  2. September 17th, where he allowed three runs on three hits (two of them home runs) in an inning’s work.

If we remove the August 13th outing, Edwards’ ERA drops to 2.55, almost an entire point. If we remove the September 17th outing as well, it drops to 1.83. Removing the first performance, his FIP drops to 2.56. Removing the other brings it down to 1.96, which is still higher than the other five pitchers, but much closer. Bad pitching performances are part of a pitcher’s year, and shouldn’t be entirely disregarded. However, it seems likely that something was off (mechanically, physically, or mentally) on August 13th.

We’ll get back to these games later.

As the Cubs consistently carried three catchers last season, I thought it would be interesting to compare Edwards’ performance across all three:

The baseballr package also allows us to look at Statcast data from Baseball Savant:

type catcher count mph hit_dist hit_spd pct
CU Ross 27 81.16 119 73.6 16.67%
CU Montero 53 81.54 258.8 94.86 32.72%
CU Federowicz 3 81.25 1.85%
CU Contreras 79 81.02 208.8 87.74 48.77%
FF Ross 99 95.72 186.8 84.31 21.57%
FF Montero 147 95.44 211.1 83.77 32.03%
FF Federowicz 9 95.77 183.0 95.50 1.96%
FF Contreras 204 95.44 199.4 85.94 44.44%

From the table, we can see that Edwards throws two main pitches — a four-seam fastball and a curveball. He pitched most often to Willson Contreras, then Miguel Montero, then David Ross (and once to Tim Federowicz). Edwards threw his fastball a notch faster to Ross than other catchers, which could be due to the small sample size. He also threw his fastball more to Ross than other catchers; despite throwing 16.67% of his curveballs to Ross, he threw 21.57% of his fastballs to him. There are several reasons this might be the case:

1. Edwards focused on his fastball earlier in the season before gaining more confidence in his curve.
2. Ross saw that Edwards’ fastball was producing better results and called it more often.
3. Contreras was more confident in his agility and therefore ability to block a curveball than Ross was.
4. Random sampling and a small sample size.

Let’s take a look at the results these pitches got. By plotting hit velocity and hit distance, we can compare results across catchers:

When Ross was catching, Edwards tended to generate softer contact that went shorter distances. We can use the Statcast data to see why that is.

This chart, plotting spin against pitch velocity, shows something interesting: Edwards’ pitches had the highest spin when throwing to Ross. Curveballs with higher spin tend to induce more ground balls[^2], which is advantageous for Edwards thanks in part to the defense behind him. High spin on his four-seamer is essential to Edwards’ style, and that was maximized when Ross was catching. Of course, this isn’t necessarily related to the catcher. It could be the case that Ross just happened to catch Edwards on his better days. For greater parity, we can look to see what happens to Contreras’ numbers if we take away the two worst games in Edwards’ season.

Already we see that removing the two bad outings brings Contreras much closer to Ross and Montero in terms of average hit speed and hit distance. Now we can look at how removing the bad outing affects velocity and spin.

There isn’t any effect on the spin of Edwards’ curveball while Contreras is catching, but the spin on his fastball gets much closer to Ross than Montero. Let’s compare Edwards’ Statcast data between the August 13th outing and his overall averages (the bolded rows are from the 13th):

Type MPH Spin Extension
FF 95.55 1876 6.85
FF 94.60 1602 6.73
CU 81.20 1605 6.31
CU 81.49 1480 6.19

If we add in the other poor outing:

Type MPH Spin Extension
FF 95.54 1873 6.85
FF 95.08 1749 6.74
CU 81.23 1610 6.31
CU 81.12 1502 6.26

Adding in the second bad outing makes the numbers more similar, lending credence to the idea that the first outing was an outlier. On August 13th the spin on his fastball, his extension, and his velocity were all down a tick.

Carl Edwards had a quietly great year out of the bullpen for the Cubs. He was among the league leaders in wOBA against, which is initially surprising based on his peripheral numbers. Upon removing an outing where the spin rate and velocity on his signature pitch took a steep downturn, Edwards’ peripheral numbers match up more closely with the type of performance you’d expect from someone in that elite group of pitchers. Carl Edwards had a bad day — here’s to more good ones.

Desert Optimism

I recently had the opportunity to tour Chase Field, home of the Arizona Diamondbacks.  While there, I saw a lot of banners for Zack Greinke.  After all, he is the face of the franchise (if you’re not considering Paul Goldschmidt).  After signing a six-year/$206.5-million contract before the 2016 season, Greinke changed the focus and the philosophy of the Diamondbacks.  Suddenly, they were contenders.

After signing Greinke, the D-Backs traded for Shelby Miller, who was coming off what many considered one of the best years in baseball.  However, his price was laughable.  It cost Arizona top prospect Dansby Swanson, who has emerged as a candidate for a franchise player in Atlanta.  They also coughed up Ender Inciarte, a very capable center fielder who posted a .732 OPS and won a Gold Glove in 2016.  But wait, there’s more! The Braves also received pitching prospect Aaron Blair.

The purpose of this study is not to criticize former General Manager Dave Stewart’s transactions.  After all, he truly believed, after signing ace Zack Greinke, the Diamondbacks were in a position to win — and rightly so.  Stewart felt, as did many people inside the Arizona organization, their core was established.  Below is their lineup in 2016, with the players being who played the most at their position:

POSITION Name 2016 WAR Total
C Welington Castillo 2.4
1B Paul Goldschmidt 4.8
2B Jean Segura 5.7
3B Jake Lamb 2.6
SS Nick Ahmed 0.2
LF Brandon Drury 0.0
CF Michael Bourn 0.3
RF Yasmany Tomas -0.4
Total 15.6


AJ Pollock, who was coming off an All-Star season in which he produced 7.4 WAR and posted an .865 OPS, played in 12 games.  Inciarte was traded to the Braves after providing 5.3 WAR playing right field in 2015.  David Peralta, who started in left field in 2015, played in 48 games last year.  Nick Ahmed also had an injury-plagued season following a strong 2015 in which he put up 2.5 WAR in his first full year in the MLB.

The injuries to Pollock, Peralta, and Ahmed were unfortunate.  The Diamondbacks got near or around replacement-level production from their positions in 2016.  In a hypothetical situation, let’s say the three guys stay healthy, and, after subtracting their counterparts’ production, up the total runs scored by the Diamondbacks from 752 to 790 runs.  After some number crunching, the Diamondbacks’ Pythagorean expectation comes out to around 71 wins.  Give or take a few, a healthy trio of Pollock, Peralta, and Ahmed would have helped Arizona’s win expectation increase by between two and five games.

But let’s be optimistic — the hypothetical healthy trio helps Arizona to an expected 74-88 record, far better than their 69-93 actual record.  That would have moved them up in the standings from fourth in the NL West to…drum roll please…fourth in the NL West.  The problem Arizona experienced in 2016 was run prevention, not run support.  As a matter of fact, total runs increased to 752 from 720 in 2015, when they went 82-80.  However, the real increase was in runs allowed — up to 890 (!!!) in 2016, as opposed to 713 in 2015.

So why does a pitching staff that added Zack Greinke, a bonafide ace and top-tier talent, and Shelby Miller, who would fit well in the center of any rotation, give up such a whopping number of runs?  Catching.  Below is a chart of how many runs these two respective pitchers had prevented or added by their respective catchers in 2015:

Pitcher Team Catcher Framing Runs Rank
Zack Greinke LAD Yasmani Grandal +23.3 1st
Shelby Miller ATL AJ Pierzynski -8.7 103rd


As you can see, any pitcher would love to pitch to Yasmani Grandal.  In 2015, he ranked as the best in framing runs.  Essentially, what the statistic does is quantify the catcher’s ability to get strikes called, which is incredibly valuable to a staff.  Positive is good and negative is bad.  While there is not as direct a correlation between Shelby Miller’s success and AJ Pierzynski’s lack of pitch-framing ability, it is apparent there is a direct link between Greinke’s 2015 performance and Yasmani Grandal.

In 2016, Greinke and Miller both joined a staff caught by Welington Castillo.  The best way to describe Welington is he’s an offense-first, defense-second catcher.  The theme of this study is to advocate for the use of defense-first, offense-second catchers.  Look at this chart of past World Series champion catchers:

Year Team Name Framing Runs Rank
2012 SFG Buster Posey +20.0 4th
2013 BOS Jarrod Saltalamacchia -4.6 93rd
2014 SFG Buster Posey +21.5 2nd
2015 KCR Salvador Perez -7.5 99th
2016 CHC Miguel Montero +14.6 4th

After looking at that chart, there are a couple of observations to make.  One, three out of the five previous World Series teams have had top-four catchers in terms of pitch framing and pitch presentation.  Second, Jarrod Saltalamacchia was replaced by AJ Pierzynski who was replaced by Blake Swihart who is now competing with Sandy Leon and Christian Vazquez, both of whom are defense-first catchers lauded for their ability to frame pitches.  Third, Salvador Perez is the heart and soul of the Kansas City Royals, and I guarantee Dayton Moore could not care less about his pitch-framing abilities.

Essentially, what you should take away from this is teams that win have skilled catchers.  Luckily for the Giants, Buster Posey can also hit the baseball.  To bring this full circle back to the Diamondbacks — Wellington Castillo is the wrong type of catcher.  He does not frame like Posey or Montero, and the bat is nothing too special.

But alas! Castillo is no longer part of the Arizona organization! This offseason, freshly-appointed general manager Mike Hazen has added four new catchers to the picture: Chris Iannetta, Jeff Mathis, Hank Conger, and Josh Thole.  Let’s look at their pitch-framing stats from last year:

Name Team Framing Chances Framing Runs Rank
Chris Iannetta SEA 5,495 -13.8 102nd
Jeff Mathis MIA 2,248 +7.2 15th
Hank Conger TBR 2,366 +3.6 25th
Josh Thole TOR 2,410 +4.6 21st


As you can see, the Diamondbacks have added a starting catcher who is not very good at framing pitches and three back-ups who do or might fit the desirable profile of this study.  Chris Iannetta signed a $1.5 million, one-year deal; Mathis signed a $4 million, one-year deal; the other two are minor-league contracts. Hazen, who came over to the Diamondbacks from the Boston Red Sox (who are leaning towards more defense-first options at catcher), made some efforts to boost his catching corps’ defensive ability, but was it enough?

In a perfect world, I think a guy like Jason Castro fits the bill perfectly in Arizona.  While the financial situations in Arizona may have made the price for Castro too high, he fits the type of catcher this study calls for, and the type of catcher Zack Greinke and Shelby Miller deserve.  He tallied +16.3 framing runs in 6,623 chances in 2016, good for third in MLB behind Buster Posey and Yasmani Grandal.  He signed for $24.5 million over three years with the Minnesota Twins, and will surely help their young staff develop.

Let’s not dwell on the hypotheticals.  The Diamondbacks have five and a half million dollars invested in two guys: Chris Iannetta and Jeff Mathis.  While Iannetta had an abysmal year in 2016 in terms of framing runs, his track record is mixed.  In 2013, for example, he recorded a framing-runs figure of -16.6, which is comparable to his 2016 number.  In 2015, however, he recorded a figure of +13.1, good for fifth in all of baseball.  What caused such a dramatic, roller-coaster shift?  I do not know — that question could be the subject of an entire different study.

Should Iannetta get most of the starts, I would say Mike Hazen would not care if he hits below the Mendoza line if his defensive statistics match his 2015 numbers.  Should he not get most of the starts at catcher, they will more than likely go to veteran backstop Jeff Mathis.  Mathis, who is lauded for his skills behind the plate, is essentially a cheap Jason Castro.  If you divide the number of framing runs Mathis achieved in 2,248 chances last year, and multiply the decimal by Castro’s number of chances, you get around a number of +21.2 framing runs.  That would have ranked him third behind Grandal and Posey.  Of course, this method is unreliable because every chance is another chance for his framing runs to drop as well as increase.  With that being said, the efficiency of Mathis behind the plate makes giving him a chance to handle the Diamondbacks’ staff worthwhile.

The addition of Taijuan Walker, who was the return on shipping Jean Segura to Seattle, is a healthy investment in the pitching staff.  With him slotting in along with Zack Greinke, Shelby Miller, Robbie Ray, and Patrick Corbin, the Diamondbacks have the makeup of a sleeper-type rotation — one that could surprise a lot of people in 2017.  If the front office has embraced the importance of defense at the catcher position like their offseason moves suggest, their staff could cut down on runs allowed dramatically, putting their lineup in position to do some damage in the NL West this year.

One team who should be noted in this study is the Houston Astros.  Whether Jeff Luhnow’s front office emphasized framing runs and having defensively-elite catchers or not, two of the catchers mentioned in this study were teammates in Houston — Jason Castro and Hank Conger.  Castro and Conger were the only two backstops on the 2015 Houston Astros, the year Dallas Keuchel won the American League Cy Young award.  This serves the purpose of further validating the benefits a defense-first catcher can have on a pitching staff.

In conclusion, baseball is trending toward sacrificing offense for defense at a premium position.  One club that can change the face of their organization by embracing the principles outlined in this study is the Arizona Diamondbacks.  While the Diamondbacks may face public scrutiny for far after Shelby Miller and Zack Greinke are gone, fans should be optimistic about 2017.  An elite defensive catcher can make a world of difference in the performance of a pitching staff.


The statistics used in this study were found on, the historical rosters and statistics were found on and, and was a great help in referencing players and transactions.

When Do Managers Use the Hook?

For the uninitiated, this piece heavily relies on my previous work around refining the inning/score matrix to quantify bullpen usage, and more recently, using RE24 to adjust the score differential for the base/out state in cases where the pitcher is not entering into a “clean” inning.

In that most recent piece, I concluded by alluding to a sort of “leaderboard” for base/out state adjustments. One hypothesis that you might have – certainly, one that this author had – was that we might see elite non-closers at the top of the list, implying that those pitchers are being brought in with runners on base more often than usual. Although closers are generally among the most highly-regarded relief pitchers in the game, the managerial status-quo has been to use closers almost exclusively in the “clean inning” state entering the 9th. Thus, while closers might not lead in terms of score adjustments due to inherited runners, an elite setup man certainly might.

Without further ado, here’s what that leaderboard looked like in 2016.

Largest Average Negative Score Adjustments
Player Team # Apps Mean Adj. Score Mean Adj. Inn Score Diff Inn Diff
Colton Murray PHI 24 -2.30 6.90 -0.22 0.15
Chaz Roe ATL 21 -0.73 7.57 -0.21 0.11
Gavin Floyd TOR 28 0.54 8.04 -0.21 0.11
Dean Kiekhefer STL 26 -1.78 7.59 -0.21 0.13
Alex Wilson DET 62 0.18 6.97 -0.19 0.13
Carl Edwards CHC 36 1.31 7.84 -0.19 0.15
James Hoyt HOU 22 -1.77 7.26 -0.18 0.26
Jordan Lyles COL 35 0.68 7.34 -0.18 0.09
Tommy Layne NYY 29 0.83 7.49 -0.17 0.25
Matt Bowman STL 59 1.08 7.28 -0.17 0.06

So… this isn’t exactly what I thought I’d find. There aren’t any closers in this group, but there really aren’t many top-flight middle relievers, either. If anything, this group came in when the team was tied or trailing more often than not. What’s going on here?

What we can’t discern is whether mid-inning appearances tend to be high-leverage affairs. There are most certainly cases where long men are used in the middle of the 4th inning to relieve an ineffective starter. That situation isn’t interesting in a vacuum; but it may be interesting to know what portion of those mid-inning appearances are of this low-leverage variety, and which are of the high-leverage variety.

One way that we can answer this question is to stratify qualifying relief pitchers by their average inning when entering the game. To accomplish this, let’s define a “closer” as a pitcher with an average inning of 8.5 or higher, and a “middle reliever” as a pitcher with an average inning between 7 and 8.5. Then we can look at the percentage of appearances for each group which were not “clean” innings.

(Click the graph for an interactive version)

As you might expect – even if you vehemently disagree with the practice – closers very rarely enter the game mid-inning. 85-90% of their appearances come in clean innings. Middle relievers, on the other hand, come into the game at the start of an inning closer to 60-65% of the time. That number has been on the rise recently, which seems a bit odd, or at least, at odds with what we’ve seen in the postseason recently (more on that in a bit).

Some small percentage of the time – the area between the lines of the same color – pitching changes are made with 1 or 2 outs in the inning but with no one on base. This is probably not optimal: The pitcher coming into that situation has an easier-than-average job, as they’re essentially getting a shortened inning to work through. If a guy like Dellin Betances can face 300 batters in a season, why waste 20 of them on situations that are easier than average?

The orange lines represent a subset of the overall middle relief group where the team in question is either tied or has no greater than a 3-run lead, in either the 7th or 8th inning. These are situations of high importance and leverage. An effective manager might be employing mid-inning pitching changes more often in these situations in order to limit damage and preserve leads.

Yet, this subset isn’t very different than the overall middle relief group. Whatever difference exited in 2012 and 2013 has been eroded in the last few years, as part of a general trend: Mid-inning appearances in the regular season are becoming less common.

As a final step, let’s contrast this picture of usage with an analogous graph on postseason appearances. We’ll maintain the same definitions of “closer” and “middle reliever” for consistency.

(Click the graph for an interactive version)

Chaos! This graph looks more disorganized than the regular-season version, but then again, the postseason is more chaotic in general. We’re dealing with smaller samples and we can’t put too much faith into these trends. That said, two things stand out when comparing postseason usage to regular-season usage:

  • Closers are no longer treated as a special species. Even through 2014, closers were entering postseason games in clean innings about 80% of the time. In the postseason! When the managers are paying attention! When there are high-leverage situations at every turn! But in the past two seasons, closers have been used increasingly with runners on base – in fact, even more so than middle relievers have in close/lead situations during that time. Again, small samples, but this screams efficiency. If your closer is your most effective weapon, you should be using him with runners on base and a late lead, instead of using your second-most effective weapon instead.
  • Middle relievers have been used more often in “matchup” situations. 2014 and 2016 stand out in this regard, and it probably has something to do with guys named Bochy and Maddon representing large shares of the sample in those years. Recall that the gap between the dotted and solid lines of the same color represents the frequency of “1+ out, 0 on” appearances. Those gaps are huge in 2014 and 2016! While mid-inning appearances among all classes of pitchers were highest in 2016, that’s not the case at all for “men on base” appearances, which were more or less in line with historical norms. This represents an increase in match-up-based thinking, not leverage-based thinking.

These graphs look different, and they probably always will. Teams have relatively fewer resource constraints in the bullpen come October. They have more days off between games, and fewer games to budget resources for in the future.

That said, there’s been no carryover at all from the wild, and relatively new, bullpen management seen in the postseasons of 2015 and 2016. Constraints will limit the extent to which managers can call upon their best arms with runners on base late in games, but it would be hard to imagine that a status quo which holds the closer for the 9th inning almost 90% of the time can’t be improved upon in some way. Teams have spent more on bullpens, but they haven’t figured out how to use them any more efficiently in the regular season, and the differences we’ve witnessed in the postseason show that they’re only getting it about half right, even when it matters most.

Hitters Who Reached Base When They Shouldn’t Have

Some of my favorite moments in baseball are provided by players who, for one reason or another, absolutely should not have done what they just did. Ben Revere hitting a dinger. Vlad Guerrero swinging in the middle of an intentional walk. Willie Mays Hayes sliding short of the bag. Those moments are magical and hilarious.

And perhaps less amusing, but more exciting, there are guys who don’t give a flip if they’re behind in the count and don’t have any leverage. They’re still reaching base at a strong clip, providing small thrills by getting there when it looks like they’re all but back in the dugout. Below are the guys who did it most in 2016.

Read the rest of this entry »

Defensive Pillar or Offensive Killer?

Kevin Pillar recently announced that he played through a torn thumb for the majority of last season’s second half. I was curious to see how much this injury impacted his offensive production, so I decided to delve into his pre- and post-injury numbers. His elite defense in CF makes him a staple in John Gibbons’ lineup card on a daily basis — but how much would this injury negatively impact his ability to help his team at the plate?

Pillar is best known for his glove, and across the league he is often recognized for his diving catches in the outfield, like this gem.

However, despite him not being one of the Blue Jays’ major offensive contributors, the fact that his defense keeps him in the lineup every day, even when hurt, begs the question: At what point do you start sacrificing offense for stellar defense? Among the CFs who ranked in the top 15 in WAR last season with at least 400 PA, he was the only one with a negative offensive WAR component, at -11.9, which was the seventh-worst offensive WAR component last season for any qualified player. His 2016 wOBA (.295) and wRC+ (80) were identical to Billy Hamilton’s — not exactly the type of player you want to model your bat skills after.

But of course, his defense was incredible, as he led all OFs in defensive WAR (23.6), UZR/150 (26.3) and RngR (21.8). He was the third-best defender measured by WAR, only trailing Brandon Crawford and Francisco Lindor. His total WAR was 3.2 in 2016, and this is despite playing hurt from August 6 onward.

Pillar is an elite defender and a fan favorite — but what can he do offensively to make himself a positive offensive WAR player? First, let’s look at what he did pre-injury. Before August 6, Pillar slashed .261/.292/.385 with 7 HR, a 2.9 BB% and a 15.8 K%. When he returned from his DL stint, still feeling the effects of his torn thumb, he slashed .283/.338/.346 with no homers, but his walk rate was 7.8% and his strikeout rate dropped slightly to 14.2%. This is unfortunately based off a small sample size of 141 PA, and his batting average was inflated by a .333 BABIP, vs. his career mark of .305.

The major change to his output is what you would expect from someone battling a hand injury, and that was a major drop-off in power, as his ISO was cut in half after sustaining the injury. Pillar likes the ball up and in and struggles with pitches down and away. You can see how playing through a thumb injury could really hurt his ability to drive and pull the ball. Below is his pre- and post-injury ISO/P.


To further illustrate this point, below are his pull and hard-hit rates for the 2016 season. The line in the graph indicates when he returned from the DL.


He pulled the ball more, but was not able to barrel it up and make hard contact due to his thumb injury. He attempted to pull the ball on pitches not in on his hands, as shown by the two heat maps below for his pre- and post-injury swing percentage.

Despite having his setbacks, Pillar was able to post a slightly higher wOBA after his injury (.303 vs. .292), proving that he could still be a productive player, but in a different way. The Blue Jays quite frankly do not need Pillar to hit 15-20 HR and I don’t even think they care if he hits more than 10. Offensively, they need him to get on base more and provide speed on the basepaths. He needs to focus more on hitting line drives and ground balls rather than trying to hit fly balls, considering that last season Pillar had the third-worst batting average on fly balls (min 100 PA) at .139. He has already shown an improvement in changing his approach by decreasing his FB and IFFB rates by around 2.5% from 2015 to 2016.

All current projections on FanGraphs predict that Pillar will have another negative offensive season; however, in a healthy 2015 season he posted an offensive WAR component of 3.2. I believe that it is possible for him to do that again in a healthy 2017, but he needs to make a few adjustments at the plate and needs to stay healthy. This is something that he’s struggled a bit with in his career, as he tends to go all-out in the field.

He should have a better offensive season in 2017 than he did in 2016, and he appears to be healthy in spring training, so far hitting 10-for-20 with 5 doubles, which is promising but not that meaningful. I hope he can continue to make offensive strides this season without any injury setbacks, but expectations should probably be set that the Blue Jays organization and their fans may just need to accept the fact that they have a stud defender, yet not much more than a mediocre hitter.

The Opportunity Baseball Organizations Are Missing

I realize the title of the article is a very bold statement. If you are looking for conclusive proof through overwhelming data, I would suggest checking back several years from now, well after what I discuss will have largely played out. What I will offer, however, are signs and anecdotes that a significant opportunity does exist. That opportunity: A systematic process for both identifying and fixing hitters performing below potential.

Coming from an investment research background, I was able to discover several specific things where consensus views are either misplaced or do not exist. While I can’t get into specifics in terms of the “what” (yet), the “how”, and “why”, I was able to find these things are interesting to consider. This article (and possibly series of articles) could be considered a “ride along” if you will, where I will share some key parts that I believe are interesting to an analytically-focused baseball audience. Further, there is an upcoming fork ahead where a decision will be made as to strategic direction – attempting to influence wins or selling products. If the latter, I will detail everything either here or on a to-be-established blog.

There are different paths to research success. The keys that I’ve observed are: 1) Determine the primary drivers – i.e. pick a narrow lane, 2) Go deep to discover where consensus views are misplaced or do not exist, and 3) Constantly ask yourself where you might be wrong or what could you be missing. When I started research into hitting, it was this last item – the lack of self-questioning — that really stuck out. The coaching side of baseball at all levels seemed cemented in its views, clearly unwilling to consistently ask itself these very important questions. After almost getting punched by a coach several years ago, I was convinced that the emotion, ego and attachment to opinions that befall many smart investors were likely creating a large opportunity.

One more investing parallel and then I’ll get to some data. In the 2008 financial and housing crisis, one of the primary reasons that a tremendous opportunity to bet against the housing market arose was that the models, based on historical data, assumed housing prices would not decline on a nation-wide basis. However, a small number of investors, focusing on fewer, yet more significant signs were able to make billions by betting against the models and strongly-held consensus views. Similar to this example, baseball organizations don’t believe an opportunity exists because the historical data indicates that it doesn’t. Let’s take a look.

In the past nine years, there have been 92 cumulative changes to the hitting-coach position across major-league baseball. The pitching-coach position, on the other hand, has turned over only 45 times in the same period. The average age of the position is 52.6, and the coaches have an average 19.7 years removed from active play (read – all have significant legacy views). It doesn’t appear that any are adding significantly more value than the group and no individual or organization is consistently fixing broken hitters with recurring success. I believe the real signs are in the anecdotal evidence, which tell a completely different story.

Anecdotal Evidence an Opportunity Exists

J.D. Martinez – In early 2012, I sent a letter and video to his prior organization discussing the opportunity in fixing his mechanics, as well as the opportunity through a systematic process of identifying and fixing underperforming hitters (much the same as you are reading here). In December 2013,  after seeing the specific changes I was looking for, I made the following comment to Dan Farnsworth’s article – Rule 5 Darkhorse J.D. Martinez:

“…..These changes are some of the most significant (and in the right direction!) that I have seen for a major league player….. if he keeps moving his swing in this direction, he will be a major offensive producer in the next few seasons.”

He was released just a few months later. You likely know the rest of the story. Credit and thanks to Dan Farnsworth for writing the article.

Alex Bregman – Upon his major-league debut, I noticed a significant flaw that would likely prevent him from succeeding at the major-league level, and made the following comment in Eric Longenhagen’s post “Scouting Astros Call up Alex Bregman”:

“….only the power and HRs won’t be there consistently because he is cutting his swing so short. With his current approach, I think he’s going to have a far tougher road than what most are projecting.”

The swing shortness was of a particular type that I had come across with several other players who had used a particular swing-training device. I had a very high degree of conviction as to the likely results.

On August 7th, I noticed he had changed his swing and he and also said “It’s just a mechanical issue that we’re working out to get back to how it was.”  I made the following comment on the same post.

 “….. since his terrible start and now likely subsequent improvement may be cast as randomness, better luck, or just needing more major league ABs, I think the real story here is relatively clear – the changes in his mechanics and approach were the primary driving factors both on the way down and the way back up (hopefully?) and would have occurred regardless of the playing level (AAA or MLB).”

Subsequent to his statement of “getting back to my old swing,” he changed his public comments — stating that he really didn’t make a swing change. I’m guessing so that no one gets thrown under the bus. Since the media bought into the revised, post-spin version of events, that seems to be the current consensus view, even though it is clearly inaccurate.

Looking at these cases and other turnarounds, the key takeaways are:

1) The solutions are not coming from within the organization

In the vast majority of cases, players are finding their own solutions. Players seek out advice from other players as well as outside sources. There are numerous quotes from hitting coaches with comments along the lines of “I don’t mess with the mechanics. When they get here, they already know how to hit.” Many hitting coaches appear to have taken the Hippocratic oath approach of “do no harm.”

2) The examples of significant and sustained turnarounds are extremely limited

I screened for players with below-average wRC+ for at least two seasons and also a wRC+ of 120 or more for the past two years. J.D. Martinez was the only return. There have been other notable improvement stories – Jose Altuve, Josh Donaldson, Manny Machado, Nelson Cruz and Anthony Rizzo; however, all were generally at least average or better before the improvement.

Using the same methods that identified the players above (as well as other players commented on this site), I find approximately 50 players at the MLB level who are performing well below their potential and could realize transformational improvement – if given the correct prescription. I won’t bore you with the complete list, but here are the top seven.

  • Mike Zunino
  • Travis d’Arnaud
  • Ryan Flaherty
  • Kevin Kiermaier
  • Yasiel Puig
  • Jason Castro
  • Jake Marisnick


Depending on how things transpire, as noted in the first section above, I may go into detail on both the video and data analysis that leads to the conclusions above in future posts.

The Gap in the Middle

With baseball’s data/analytics side not going deep into mechanics and the coaching/player development side not doing significant research challenging current views, it is not too difficult to consider that there might be an opportunity gap in the middle, relative to new thoughts on mechanics. When I examine how these organizations with vast budgets and resources are missing key things, this “gap in the middle” seems to make the most sense. In hindsight, it was definitely a source underpinning my findings.

I believe it is fairly safe to say that baseball organizations are definitely missing something – it’s just a matter of the size of the opportunity. The recent fly-ball emphasis is a case in point. It’s somewhat ironic that this is being cast as something “new” when Ted Williams wrote and talked about it (i.e. the swing should not be down but up in the general plane of the pitch) 47 years ago. I am confident the “fly-ball movement” is not the magic bullet many seem to believe. Pursuing this path will only divert focus away from a more valid, comprehensive, and systematic solution.

Arguably, there is no other sport where mechanics play such a significant role in a player performing to potential. Without question, teams and coaches have struggled with this issue, given the high turnover of the hitting-coach position and the lack of consistent value-added input in regard to mechanics. Given the connection of mechanics to performance and performance to value, the possibility of an effective solution should not be considered lightly.

In weighing the evidence, on one side, there is significant historical precedent indicating systematically fixing players has not been possible. Clearly, even the best hitters in the game have not been able to transfer what largely exists in their muscle memory to other players. On the other side, there are a few anecdotes that may not seem significant in isolation; however, taken together, there is a logical story line that warrants consideration. The probability that the signs above are purely random and that they also have no connection to the bigger picture as discussed is extremely low. Given the stakes, shouldn’t organizations be asking themselves “What could we be missing?”

Brandon Finnegan’s Changeup Adjustment

Brandon Finnegan tweaked his changeup late last year, and the result was a significant boost to his overall game. Eno Sarris detailed how he threw it more while taking some zip off, widening the velocity gap between it and his fastball to about 10 mph. That led to an ERA well below three and a strikeout percentage of 30%, which are very baller numbers.

I don’t think it was just the velo difference that led to Finnegan’s outstanding results, though (I don’t think that’s what Sarris was saying, either). The pitch seems to have totally changed. Like, new address, new clothes, new cologne. New everything.



It didn’t just get slower. It became less erratic, less a noodle and more a frozen pea. It reminds me of when Cole Hamels wrangled his own changeup, where it went from seducing hitters with its movement to stifling them with its precision. I’m not saying Finnegan’s change now mimics that of Hamels, but it certainly became more wily at the end of last season.

The trajectory of it makes it look like it’s straight and narrow, right? And by itself, it is. It stands to reason that if Finnegan threw the changeup with more consistent trajectory, that it was located more consistently as well. That’s what appears to have happened. In the scope of sequencing his pitches, that’s extremely important, because it means he could rely on it more while hitters could accordingly rely on it less.

Source: FanGraphs


Finnegan turning down the velo and fine-tuning the location of his changeup are good things in a vacuum. But what those adjustments really did was make the pitch more closely resemble a four-seam fastball, and one left up in the zone, at that. He took his changeup and made hitters think it was a mistake. They accepted it graciously until it was too late. The results were essentially the same as when Lucy pulls the football out from under poor old Charlie Brown.

It’s important to acknowledge sample size here — six games isn’t much at all. But the adjustments produced results that should certainly encourage Finnegan to keep the altered approach with his changeup from the end of last year. What I’m curious about is how he builds off of this.

Only three other pitchers in the majors last year threw at least 2500 pitches between their four-seamer, sinker, slider, and changeup: Ervin Santana, CC Sabathia, and Chris Archer. Finnegan used his changeup more than them all year long, but that’s especially true from the end of August through season’s end. It was also the best of the group — by nearly three runs per 100 thrown!

Archer and Santana don’t throw a sinker, which leaves only Sabathia as a comparison for Finnegan through this context. There isn’t necessarily a lot that makes the two comparable aside from their arsenal, but there might be something Finnegan can learn here from his elder statesman.


He threw a fastball at a nearly identical rate as Sabathia did at his age. (Sabathia also wasn’t throwing a sinker yet, which was another step in his evolution.) This doesn’t speak to any grand finding, but it does acknowledge a pitcher’s youth. As time moved on, Sabathia learned to rely on his fastball less and less — 13 years later, he was throwing his four-seamer nearly 35% less often. In the case of Finnegan, he might take an additional step by relying on his sinker less and less, and, given the way his changeup fools hitters, he might benefit by throwing more four-seamers.

Maybe it’s intuitive that a pitcher should better balance his offerings to make himself less predictable. That doesn’t mean he’s going to figure it out, though. In terms of adjustments, baseball is paradoxically a game of “dance with who brought you” and “tinker ‘til you’re at the top.” Brandon Finnegan already seems to be getting more confident with the idea of tossing his pitches more equally. But it could also indicate advancing beyond what got him to the majors, to where he’s finding what can keep him there for a long time. The work he’s put into his changeup is just the first step.

Greg Bird: What Can We Actually Expect?

Stop me if you’ve heard this already. First base is a thin position right now. Sound familiar?  I thought so. For that reason, many of us will be bargain shopping this draft and auction season, and one name that comes up as a down-the-board option is Greg Bird.

Personally, I’ve had two major issues with ranking and projecting Greg Bird. The first is that he didn’t log any meaningful time last year due to injury. The second is in his 46-game, small-sample-size debut for the Yankees in 2015, he hit what I believed to be an exaggerated number of fly balls (51%). Further confounding the issue is that the percentage of those that turned into home runs (20.4%) seemed high compared to his output in the minors.

In my quest for a more perfect valuation of Greg Bird, I decided to grab all the game logs from Trenton and Scranton/Wilkes-Barre from 2015 and create my own larger sample size data set for his batted-ball outcomes. In the table below, I’ve listed his batted-ball outcomes from his minor-league games in 2015.

Greg Bird MiLB Batted Ball Outcomes 2015
Type # %
Grounders 82 34.6%
Liners 56 23.6%
Flies 120 46.5%
HR | HR/FB 11 9.1%
AA & AAA Games

The following are his batted-ball outcomes for 2015 at all levels including his call-up with the Yankees later that summer.

Greg Bird Batted Ball Outcomes 2015
Type # %
Grounders 110 30.3%
Liners 79 21.8%
Flies 174 47.9%
HR | HR/FB 22 12.6%
AA, AAA & MLB Games

The fly-ball rate (47.9%) is accompanied by a 16% infield fly ball proportion that was markedly better in his short stint with the Yankees (11%) than in his larger sample in the minors (18%). Through the solely statistical lens, I’d say he squared up a greater percentage of his small sample size fly balls with the Yankees. I did manage to confirm for myself that Bird does come with a very fly-ball-heavy batted-ball profile.

A large part of the reason fantasy league owners are excited about Bird is the park he plays in and the side of the plate he hits from. Yankee Stadium is a bomb-dropping paradise for lefties, and some of the success Bird had in his limited trial should be attributed to the more hitter-friendly parks he played in, versus what he saw in Trenton and Scranton/Wilkes-Barre. Courtesy of we can see Yankee Stadium plays with a 1.53 park factor for home runs in right field.

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Though I couldn’t locate hand-specific park factors for Trenton and Scranton/Wilkes-Barre, both play at around 0.75 for homers, which are very pitching-friendly. Playing half his games in these two parks certainly could have been the limiting factor for Bird’s somewhat lackluster 9.2% HR/FB mark in the minor leagues in 2015.

While it’s fair to say we don’t know Bird’s true-talent level on HR/FB just from his statistics, I did perform some very simple math and calculate the difference in the two sets of park factors on home runs (~1.5 / ~0.75 = ~2x). It’s plausible that Yankee Stadium could offer a 2x boost on his HR/FB. While Bird might not be at a true-talent level of converting 20% of his flies into homers, he might be in the 18% neighborhood. Armed with this larger set of data, I began looking for comps for Bird’s fly-ball and HR/FB rates. My goal was to pull players from either the 2015 or 2016 seasons that had fly-ball rates over 45% and a home run to fly ball ratio at or above 18%.

Greg Bird Comps On FB% & HR/FB%
Player Year FB% HR/FB%
Tommy Joseph 2016 45.1% 18.9%
Trevor Story 2016 47.1% 23.7%
Kris Bryant 2016 45.8% 18.8%
Miguel Sano 2016 45.8% 20.8%
Chris Carter 2016 48.7% 23.8%
Brandon Moss 2016 52.6% 19.4%
Mike Napoli 2016 45.1% 20.5%
David Ortiz 2016 45.1% 18.4%
Brian Dozier 2016 47.7% 18.4%
Todd Frazier 2016 48.7% 19.0%
Chris Carter 2015 51.8% 18.9%
Jose Bautista 2015 48.8% 18.4%

This does turn up an interesting list of sluggers with a wide variety of outcomes. If I relax the requirements a little further, you’ll start to get into the Joc Pederson, Lucas Duda, Luis Valbuena and Colby Rasmus group. Obviously this is a mixed bag of player outcomes because we haven’t tackled their BB% or K%, which impact the HR/SLG/TB categories in roto leagues or the bottom line in points leagues.

Pederson, Duda, Rasmus, Carter, Sano, Moss and Napoli all have a much higher K% than Bird has shown in Double-A and Triple-A. In total, across all his MILB at-bats in 2015, Bird struck out only 17.5% of the time. Though you might speculate the pitcher-friendly confines of his home parks would dictate letting him put the ball in play was a more favorable outcome. In his limited stint with the Yankees in 2015, he posted a K% right around that 30% neighborhood, which brings him back to my favorite comp for his current skills — Mike Napoli.

Bird also has other issues to contend with for fantasy baseball value which include: lineup slot, platooning, and, most recently — Chris Carter. For the sake of imagining the range of outcomes for Bird, let’s assume he got full time at-bats in the sixth slot in the Yankees lineup. We know that the sixth spot in the AL lineups averages around 675 plate appearances. If we use an 11% walk rate for Bird, that will leave him with ~600 at-bats to do HR/SLG/TB damage. My guess is Bird isn’t good enough to avoid a platoon, so for the sake of a range of predictions on his output I’m going to use the FanGraphs fans-predicted number of plate appearances (553) to give what I feel is a best-case set of scenarios for Bird’s home-run totals.

Bird HR Outcomes Given FB% and HR/FB
HR/FB 44% FB 45% FB 46% FB 47% FB 48% FB 49% FB 50% FB
14% HR/FB 24 25 25 26 26 27 27
15% HR/FB 26 26 27 27 28 29 29
16% HR/FB 27 28 29 29 30 31 31
17% HR/FB 29 30 30 31 32 32 33
18% HR/FB 31 32 32 33 34 34 35
19% HR/FB 33 33 34 35 36 36 37
20% HR/FB 34 35 36 37 37 38 39
* Assumes 390 balls put in play (11% BB; 20% K) on 553 PA

Bird may already be the left-handed version of Chris Carter. I’m even more bullish on Greg Bird than I was before I started the investigation, and easily the high man on his HR output when considering Steamer, Fans, ZIPS and Depth Charts. His batting average will ultimately depend on where he settles in on his K% and his ability to blast liners and grounders through for hits. I think he’ll be an interesting Statcast case to monitor early this year.