All Star Break: Getting Technical
This blog is going to be team independent. It will be a
little more statistically technical in nature and also longer than other blogs.
If this isn’t your thing, move on and we’ll see you next week.
First things first, let’s define a few terms and explain
what they mean.
FIP: Fielding Independent Pitching. FIP is a measure of a
pitchers Earned Run Average (ERA) that is intended to take the talent of the
defense behind the pitcher out of the equation. It weights strikeouts and
homeruns which pitchers can control. For example, it takes Batting Average
Balls In Play (BABIP) into account. Think about when your player hits a line
drive, you get excited because he crushed the ball and is going to get a hit,
well what if he gets unlucky and the ball is hit right at a fielder. FIP takes
this into account. If a pitcher is getting unlucky, for example if a lot of
soft contact by hitters keeps finding holes in the defense, his FIP will be
lower than his ERA…because his ERA is negatively influenced by bad luck or poor
fielding.
xFIP: this statistic is the same idea as FIP except that
home runs are standardized as part of the equation. The idea behind xFIP is
that home runs are slightly fluky. Pitchers with high home run to fly ball
rates will have lower xFIPs because this statistic would argue that chances are
some of those home runs should have simply been long fly ball outs, thus their
ERA should be lower.
BABIP: as discussed above, this is about the hitter’s
batting average when he puts the ball in play, as in not a strike out. There a
number of factors that go into BABIP, it isn’t necessarily simply about luck.
It could be about a team shifting against a player like Brian McCann. McCann
will have lower BABIP because he hits into a well-defensed area pretty often.
It could be about a pitcher who induces a lot of soft contact like Dallas
Keuchel last year, he had what appeared to be an unsustainable low BABIP until
you watched him pitch and looked into some of the underlying stats. Dallas
Keuchel will be making an appearance later in this article. Another factor into
BABIP is player speed. Someone like Dee Gordon that can beat out some of his
weakly hit ground balls will have a better BABIP in relation to their batting
average due to this.
OK so what do we do with this information? Think about the
premise here, all hits are not made equal. In terms of reproduce-ability, a
bloop is a not as good as a blast, a dribbler through the 5-6 hole is not as
good as a line drive over the 3rd baseman’s head. The idea here in
terms of producing and predicting future performance is to look at what a
player is doing now to see if they have the talent to hit the ball hard
consistently.
If you read fantasy articles at different points in the year
they will talk about these stats, FIP, xFIP, and BABIP to point out lucky and
unlucky players. That is what I am going to do here, and also try to dive into
why.
I’ll first cherry-pick a few examples to show you a
successful and unsuccessful case. Back on May 1st, I read a note
about players who have been unlucky 30 days in that you want to try to buy low
on. The first one was Kyle Seager, 3B for the Mariners. Seager has a career BABIP of .290 which had
led him to a career batting avg around 0.270, this .020 point difference is
pretty normal. However on May 1st his BABIP was .230 and his AVG was
correspondingly low. Had he lost his ability to see the ball? Had he lost his
talent as a hitter? Were pitchers learning how to pitch to him to get him out?
In this case it turns out no. Seager caught fire and has his BABIP for the
season up to .300 with his batting average even a little above his career
average at .287. Seager was simply getting unlucky back in April or was
suffering from some normal fluctuation in performance, this happens all the
time and sets up an opportunity to buy into what the fantasy community may
appear to be a market inefficiency on people panicking that Seager wasn’t good
anymore. Next we have the case of Jason Heyward, you all know who he is. Heyward also had a bad April, his BABIP was
around .270 with a corresponding AVG of .224. The problem here is nothing has
changed that much. Heyward has a career .310 BABIP with an AVG around .285. His
numbers have come back slightly from April, but they have not normalized to the
mean, nor has he even been producing at his career norms since April. There is
likely a fundamental change here that has gone on. It could be a higher ground
ball percentage, less hard contact, any number of things that this is not the
time to get into. The contrarian argument is just that it could be he needs
more time as a proven player to turn it around. You get the point though, not
all off-normal BABIPs mean that something is going to change…however it does
show opportunity to find a market inefficiency better than looking at AVG,
hits, and home runs.
Let’s dig in. Thanks to the help of Cory Meyer, I was able
to put together the following Tables. The first group of player’s we’ll dive
into is starting pitching. I asked Cory to gather the names of the top 20
preseason SP and the top 20 current SP to examine their fantasy numbers as well
as their sabermetrics (yes guys, FIP, xFIP, and BABIP are the sabermetrics you
always hear about). The following table resulted, note I simplified all these
tables a bit to take the names out of players that we don’t care about (i.e.
Kershaw because he’s awesome, and Joe Blanton because who cares).
|
Top 20 Preseason and Currently
Starting Pitchers
|
|||||||||
|
|
FIP
- ERA
|
xFIP
- ERA
|
proj
ERA
|
proj
FIP
|
ERA
- proj ERA
|
FIP
- proj FIP
|
|||
|
David Price
|
4.34
|
3.42
|
3.16
|
-0.92
|
-1.18
|
3.52
|
3.14
|
0.82
|
0.28
|
|
Dallas Keuchel
|
4.8
|
4.04
|
3.58
|
-0.76
|
-1.22
|
3.57
|
3.58
|
1.23
|
0.46
|
|
Sonny Gray
|
5.16
|
4.5
|
4.24
|
-0.66
|
-0.92
|
3.51
|
3.75
|
1.65
|
0.75
|
|
Corey Kluber
|
3.61
|
2.95
|
3.34
|
-0.66
|
-0.27
|
3.31
|
2.94
|
0.3
|
0.01
|
|
Noah Syndergaard
|
2.56
|
2.06
|
2.45
|
-0.5
|
-0.11
|
2.87
|
2.74
|
-0.31
|
-0.68
|
|
Jose Fernandez
|
2.52
|
2.13
|
2.35
|
-0.39
|
-0.17
|
2.62
|
2.49
|
-0.1
|
-0.36
|
|
Chris Archer
|
4.66
|
4.28
|
3.66
|
-0.38
|
-1
|
3.62
|
3.57
|
1.04
|
0.71
|
|
Zack Greinke
|
3.62
|
3.51
|
3.81
|
-0.11
|
0.19
|
2.99
|
3.25
|
0.63
|
0.26
|
|
Rich Hill
|
2.25
|
2.57
|
3.6
|
0.32
|
1.35
|
3.58
|
3.67
|
-1.33
|
-1.1
|
|
Gerrit Cole
|
2.77
|
3.1
|
4.11
|
0.33
|
1.34
|
3.18
|
3.05
|
-0.41
|
0.05
|
|
Jacob deGrom
|
2.61
|
3.25
|
3.37
|
0.64
|
0.76
|
3.01
|
2.96
|
-0.4
|
0.29
|
|
Danny Salazar
|
2.75
|
3.39
|
3.75
|
0.64
|
1
|
3.57
|
3.45
|
-0.82
|
-0.06
|
|
Drew Pomeranz
|
2.47
|
3.18
|
3.66
|
0.71
|
1.19
|
3.31
|
3.47
|
-0.84
|
-0.29
|
|
Kyle Hendricks
|
2.55
|
3.46
|
3.85
|
0.91
|
1.3
|
3.63
|
3.61
|
-1.08
|
-0.15
|
|
Julio Teheran
|
2.96
|
3.91
|
4.1
|
0.95
|
1.14
|
3.74
|
3.92
|
-0.78
|
-0.01
|
|
Jon Lester
|
3.01
|
3.97
|
3.6
|
0.96
|
0.59
|
3.31
|
3.37
|
-0.3
|
0.6
|
|
Madison Bumgarner
|
1.94
|
2.96
|
3.4
|
1.02
|
1.46
|
2.78
|
2.95
|
-0.84
|
0.01
|
|
Marco Estrada
|
2.93
|
4.14
|
4.51
|
1.21
|
1.58
|
4.04
|
4.5
|
-1.11
|
-0.36
|
|
Felix Hernandez
|
2.86
|
4.16
|
4.2
|
1.3
|
1.34
|
3.39
|
3.33
|
-0.53
|
0.83
|
|
Michael Fulmer
|
2.11
|
3.53
|
3.94
|
1.42
|
1.83
|
4.69
|
4.27
|
-2.58
|
-0.74
|
|
Carlos Carrasco
|
2.47
|
4.02
|
3.33
|
1.55
|
0.86
|
3.46
|
3.11
|
-0.99
|
0.91
|
In this blog post green implies players that have gotten
unlucky or are underperforming, red implies the player has gotten lucky, or is
out-performing. 'proj’ stands for projected, this is based on published
statistical projections by a system called ZIPs that is published on
fangraphs.com. We can talk another day about the glories of fangraphs if any of
you care.
NOTES: the most unlucky pitcher here according based on the
difference in his ERA to his FIP so far is David Price. Price has an extended
track record of elite level performance, but this year he appears to have lost
a step. Even his projected ERA and FIP are above his career norms. Price isn’t
this bad, but he isn’t what he was on draft day either. The same story applies
to Keuchel. He is no longer what he was on draft day, but he is not waiver-wire
fodder either. There are a few SP I want to note on the lucky end, elite
starter Madison Bumgarner has been fortunate this year but his projections are
catching up to him, his projected ERA and FIP are not far off his actual to
date. Michael Fulmer is shown to be one of the higher end sell-high candidates
(sorry Dave). His FIP and xFIP show significant luck here but the difference
between him and Bumgarner is that his projections have remained fairly steady.
He’s a rookie that is off to a hot start. ZIPs doesn’t think too highly of him.
Since this is my blog, I’m also going to point out Corey Kluber and Chris
Archer, damn I could really use their luck to turn around. On the Kluber front,
this is two straight years of putting up FIP weighted numbers with unusually low
strand rates (as in getting out of innings with runners on base). One theory
for this is he isn’t very good pitching out of the stretch, so if a runner gets
on base, he is more likely to score. FIP takes strand rate into account and
normalizes it, so Kluber’s bad strand rate may actually be not bad luck but a
bad skill.
|
Top 20 Preseason and Currently Relief
Pitchers
|
|||||
|
|
FIP
- ERA
|
xFIP
- ERA
|
|||
|
Wade Davis
|
1.23
|
2.7
|
4.07
|
1.47
|
2.84
|
|
Brad Brach
|
0.91
|
2.53
|
3.41
|
1.62
|
2.5
|
|
Mark Melancon
|
1.23
|
2.52
|
3.73
|
1.29
|
2.5
|
|
Fernando Rodney
|
1.04
|
2.83
|
3.47
|
1.79
|
2.43
|
|
Ryan Dull
|
2.01
|
3.13
|
3.97
|
1.12
|
1.96
|
|
AJ Ramos
|
2.25
|
3.04
|
4.17
|
0.79
|
1.92
|
|
Joe Blanton
|
2.09
|
3.47
|
3.98
|
1.38
|
1.89
|
|
Kenley Jansen
|
1.16
|
1.34
|
2.92
|
0.18
|
1.76
|
|
Jeanmar Gomez
|
2.59
|
3.66
|
4.17
|
1.07
|
1.58
|
|
Seung Hwan Oh
|
1.59
|
1.79
|
3.09
|
0.2
|
1.5
|
|
Raisel Iglesias
|
2.66
|
3.77
|
4.07
|
1.11
|
1.41
|
|
Jonathan Papelbon
|
2.83
|
3.04
|
4.24
|
0.21
|
1.41
|
|
Zach Britton
|
0.72
|
2.03
|
1.96
|
1.31
|
1.24
|
|
Will Harris
|
1.62
|
1.97
|
2.8
|
0.35
|
1.18
|
|
Alex Colome
|
1.69
|
2.68
|
2.7
|
0.99
|
1.01
|
|
Nate Jones
|
2.45
|
2.88
|
3.45
|
0.43
|
1
|
|
Roberto Osuna
|
2.27
|
2.42
|
3.26
|
0.15
|
0.99
|
|
Kelvin Herrera
|
1.77
|
1.92
|
2.69
|
0.15
|
0.92
|
|
David Robertson
|
3.22
|
3.18
|
4.13
|
-0.04
|
0.91
|
|
Cody Allen
|
2.79
|
3.54
|
3.63
|
0.75
|
0.84
|
|
Huston Street
|
5.09
|
5.27
|
5.89
|
0.18
|
0.8
|
|
Hector Rondon
|
1.72
|
2.22
|
2.42
|
0.5
|
0.7
|
|
Jeurys Familia
|
2.55
|
2.37
|
3.17
|
-0.18
|
0.62
|
|
Francisco Rodriguez
|
2.93
|
3.05
|
3.46
|
0.12
|
0.53
|
|
Craig Kimbrel
|
3.55
|
2.87
|
3.42
|
-0.68
|
-0.13
|
|
Aroldis Chapman
|
2.49
|
1.91
|
2.28
|
-0.58
|
-0.21
|
|
Andrew Miller
|
1.37
|
1.93
|
1.09
|
0.56
|
-0.28
|
|
Ken Giles
|
4.38
|
3.2
|
3.04
|
-1.18
|
-1.34
|
|
Trevor Rosenthal
|
5.4
|
4.18
|
3.94
|
-1.22
|
-1.46
|
|
Shawn Tolleson
|
7.31
|
5.3
|
4.09
|
-2.01
|
-3.22
|
|
Glen Perkins
|
9
|
1.68
|
5.04
|
-7.32
|
-3.96
|
NOTES: I simplified the table for relief pitchers. Projections
on them are less reliable since they only throw one inning at time. What is far
more reliable is FIP and xFIP on them. Notable on the above table is Zach
Britton, Mark Melancon, and Fernando Rodney. All of these pitchers are performing
far above their underlying sabermetrics and career norms. These players’ owners
don’t need to be told how good of seasons that these players appear to be
having, but the question is, is it repeatable? For Zach Britton, I may argue
that it is. His sinker induces such weak contact that his low BABIP is somewhat
sustainable and the large gap between ERA and FIP is sustainable, though,
probably not this far apart. His career numbers show this as well. Melancon and
Rodney do not have this track record, it is likely to go downhill quickly for
these two at some point in the second half. There weren’t too many unlucky
relief pitchers that mattered. Although if you look at the table long enough
you’ll wonder why Shawn Tolleson lost his job. I don’t have an overall takeaway
with the high end RPs, I do find it curious that so many are getting this lucky
or are out-performing their FIP so well, but they are. I just can’t know why
without more research.
|
Top 20 Current and Projected Hit Total Players
|
||||||
|
|
proj
BABIP
|
proj-actual
BABIP
|
||||
|
Ian Desmond
|
7.30%
|
24.50%
|
0.402
|
0.338
|
-0.064
|
0.322
|
|
Christian Yelich
|
11.00%
|
20.50%
|
0.392
|
0.360
|
-0.032
|
0.317
|
|
Carlos Gonzalez
|
7.20%
|
21.80%
|
0.370
|
0.328
|
-0.042
|
0.318
|
|
Xander Bogaerts
|
8.60%
|
14.70%
|
0.369
|
0.345
|
-0.024
|
0.329
|
|
Mike Trout
|
15.00%
|
18.40%
|
0.363
|
0.348
|
-0.015
|
0.322
|
|
Paul Goldschmidt
|
16.20%
|
22.30%
|
0.362
|
0.350
|
-0.012
|
0.297
|
|
Martin Prado
|
7.30%
|
11.20%
|
0.361
|
0.321
|
-0.040
|
0.324
|
|
Yunel Escobar
|
6.70%
|
12.20%
|
0.357
|
0.316
|
-0.041
|
0.317
|
|
Daniel Murphy
|
5.00%
|
10.50%
|
0.352
|
0.319
|
-0.033
|
0.348
|
|
Jean Segura
|
5.20%
|
14.10%
|
0.351
|
0.317
|
-0.034
|
0.311
|
|
Eric Hosmer
|
7.90%
|
19.70%
|
0.347
|
0.332
|
-0.015
|
0.299
|
|
Manny Machado
|
8.00%
|
17.30%
|
0.346
|
0.317
|
-0.029
|
0.318
|
|
Jose Altuve
|
10.10%
|
9.10%
|
0.345
|
0.334
|
-0.011
|
0.341
|
|
Corey Seager
|
8.50%
|
20.10%
|
0.338
|
0.316
|
-0.022
|
0.297
|
|
Charlie Blackmon
|
7.70%
|
14.60%
|
0.336
|
0.323
|
-0.013
|
0.31
|
|
Eduardo Nunez
|
3.60%
|
12.20%
|
0.335
|
0.313
|
-0.022
|
0.321
|
|
Dustin Pedroia
|
9.30%
|
11.90%
|
0.331
|
0.313
|
-0.018
|
0.304
|
|
Mark Trumbo
|
6.90%
|
26.40%
|
0.327
|
0.302
|
-0.025
|
0.288
|
|
Ender Inciarte
|
7.90%
|
11.40%
|
0.255
|
0.299
|
0.044
|
0.227
|
|
Ben Revere
|
5.10%
|
8.90%
|
0.241
|
0.309
|
0.068
|
0.224
|
NOTES: finally we come to the hitters, these are the top 20
preseason and actual hitters based simply on number of hits accrued or
projected. It is by no means an all-encompassing stat, but it IS the stat most
closely correlated to BABIP which we are talking about. The takeaways here are pretty simple. I hope
you have enjoyed the Ian Desmond, Christian Yelich, Carlos Gonzalez and Jean
Segura runs, because they are probably about to end. Prado and Escobar are due
for regression as well, but they aren’t owned. The outlier here may be Daniel
Murphy, he is over-performing too, but there is some sustainability in his numbers
to maintain a high BABIP as well as some demonstration of a new skill; to that
end Christian Yelich has some evidence for this as well. Ender Inciarte and Ben
Revere still sit on the waiver wire, but the stats say they are due for better
numbers in the second half. Revere’s job is in jeopardy and Inciarte hasn’t
looked like the player he was last year. I will not be rushing to the free
agent wire to grab them, they are all yours.
Thank you for your help, Cory. Best of luck in the second
half everyone…except for everyone ahead of me in the standings.
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