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Revenge of the Tandem: Why Starters and Relievers Need To Go (Part I)

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Starting pitchers getting worse each subsequent time through the order? Relief pitchers lacking consistency? Why tandem pitching should be the way of the future.

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"Tandem pitching makes my elbow hurt."
"Tandem pitching makes my elbow hurt."
Dale Zanine-USA TODAY Sports

Note: This pair of articles is intended to stoke discussions about tandem rotations, and the one in which I make the case for tandems should follow soon (this one is about starters and relievers as currently constructed). I started working on this pair of articles last spring, so all stats, unless specified otherwise, are 2015. The conclusion hasn't changed, however.

Back in 2013, the Astros employed tandem rotations for their minor league teams, an experiment previously used by the Colorado Rockies and previously employed by Tony La Russa in the early '90's. All three experiments were soon scrapped (the one used by the Astros was changed and then thrown out), and the same goes for other unidentified experiments with paired pitching. The first question is: why did they fail? One reason is the probable need of a thirteenth pitcher on every team (i.e., the mop-up guy). Another is more related to money: no one will be racking up wins or saves (but saves are obviously awful for baseball), leading to less money in the free-agent market. But we here at the Crawfish Boxes live in an ideal world. One where paired pitching is possible, J.D. Martinez is still an Astro, and Jeff Bagwell is in the Hall of Fame (man, I started this a while ago). So let's explore paired/tandem/piggyback pitching, beginning with why anyone in the world ever thought it was a good idea.

Starting pitchers get worse as games go on

Look no further than this chart of 2015 starters as they proceed through the order once, twice, and thrice (w/r/t tOPS+:<100=good for pitcher; >100=bad for pitcher, and it's performance relative to to doing better/worse than usual):

BA OBP SLG OPS tOPS+
1st PA in G, as SP 0.251 0.308 0.4 0.709 96
2nd PA in G, as SP 0.26 0.318 0.413 0.731 102
3rd PA in G, as SP 0.268 0.328 0.436 0.764 111

BA rises by .009 for the second PA and .008 for the third, OBP rises by .01 for each, SLG goes up by .013 and .023...there's obviously a tangible impact felt each time through the order.

But don't take my word (or, I guess BBREF's word) for it--check out this super amazing article by Mitchel Lichtman over at BP, where he discusses wOBA-against in a multitude of ways (this is 1999-2002):

Times Through the Order TBF wOBA
1 163,900 0.345
2 158,872 0.354
3 124,603 0.362
4 22,221 0.354

Again, similar results. wOBA rises by about .009, then .008, then drops by .008 when faced with a 4th. But even that is not as it appears.

As stated by Lichtman, pitchers who make their way through the order for a 3rd (and even 4th) time are, on the whole, better than their counterparts. Therefore, when he judged for pitcher quality, these results appeared:

TTO Pitcher quality Batter quality Expected wOBA Obs. wOBA
1 0.349 0.347 0.353 0.345
2 0.349 0.348 0.353 0.354
3 0.348 0.35 0.354 0.362
4+ 0.345 0.351 0.353 0.354

Judging by this chart, you can see that wOBA is below expected for the first time through the PA, and it jumps by .001 for the 2nd and 4th+ time. Yet for the third PA, it jumps by .008.

Now, 2000-2012 data:

TTTO Pitcher quality Batter quality Adj. wOBA Obs.
1 0.346 0.34 0.34
2 0.345 0.34 0.35
3 0.343 0.343 0.359
4+ 0.339 0.346 0.359

This data does have a larger sample size (but not necessarily more impactful, except for possibly 4+). As evidenced by this, wOBA jumps up to be more than expected by at least .01 for each time through the order.

Lichtman has quite a bit more to talk about, so check it out (along with his follow-up article on pitchers' repertoires). BP does in fact have more articles with similar topics. Ken Funck, for example, put up an article in 2009 about tandem pitchers, in which he referenced the performance of pitchers in trips through the order. The 2006-2008 stats of all starting pitchers:

TTO PA AVG/OBP/SLG OPS wOBA
1 130,882 .262/.326/.415 0.741 0.328
2 127,740 .273/.335/.436 0.771 0.339
3 96,999 .285/.347/.460 0.807 0.352

An obvious jump exists here as well, unsurprisingly. Both the wOBA and OPS jumps are pronounced. To put the OPS jump in context, it's like having Nick Markakis (2016 OPS: .744) turn into Carlos Correa (2016 OPS: .811) just by going through the order two extra times.

Funck wondered whether or not those stats were being influenced by "bad starters", so he checked out the stats while only including pitchers with 40+ total starts (143 of those pitchers) in the same era:

TTO PA AVG/OBP/SLG OPS wOBA
1 92,423 .258/.319/.406 0.725 0.322
2 90,893 .266/.327/.421 0.748 0.33
3 73,265 .279/.339/.449 0.788 0.344

This one's a little better, but take a look at the 1st & 3rd TTTO for both wOBA and OPS. They show a similar jump (.066 in the first chart and .061 in the second for OPS, and .024 in the first and .02 in the second for wOBA). This led to Funck's suggestion of implementing a tandem rotation (more on that later!)

Russell Carlton addressed a similar topic, this time wondering about how many pitchers can go late in games. First, he explored how deep starters went into games (in 2012):

Recorded 0-15 outs Recorded 16-18 outs Recorded 19-21 outs Recorded 22-24 outs Recorded 25 or more outs
30.60% 30.40% 27.00% 9.20% 2.80%

The mean number of outs is 17.4 and the median is 18, suggesting most pitchers make it through between 5-7 innings per outing. This is consistent with what most people assume about starters. Interestingly enough, Carleton discovered that, in 2012, more starts lasted four innings or fewer (12.7%) than lasted past the seventh inning (12.0%), suggesting that good starts are becoming less likely.

A tangent

Looking at pitcher quality and perseverance made me wonder about a point brought up in Scorecasting (and probably other places as well), which is that ace pitchers are usually defined as those who are able to make their way through more innings than the average pitcher without getting shelled. Here is how the pitchers with a SIERA of 3.00 and under performed in 2015 (sOPS+ has the same rules as tOPS+, except it's adjusted for the league):

Name Team xFIP SIERA BA OBP SLG OPS tOPS+ sOPS+
Clayton Kershaw
2.09 2.24 1st PA in G 0.179 0.221 0.229 0.45 74 29
Dodgers 2nd PA in G 0.21 0.246 0.333 0.579 121 58
3rd PA in G 0.197 0.249 0.287 0.536 106 42
Chris Sale
2.6 2.52 1st PA in G 0.226 0.255 0.342 0.598 83 68
White Sox 2nd PA in G 0.229 0.297 0.39 0.687 111 88
3rd PA in G 0.226 0.272 0.357 0.629 94 65
Max Scherzer
2.88 2.63 1st PA in G 0.194 0.229 0.362 0.591 96 65
Nationals 2nd PA in G 0.186 0.223 0.312 0.534 79 46
3rd PA in G 0.228 0.264 0.366 0.63 111 64
Carlos Carrasco
2.66 2.74 1st PA in G 0.257 0.285 0.438 0.723 122 102
Indians 2nd PA in G 0.202 0.267 0.309 0.576 80 59
3rd PA in G 0.231 0.292 0.367 0.659 105 73
Jake Arrieta
2.61 2.75 1st PA in G 0.16 0.212 0.255 0.466 84 32
Cubs 2nd PA in G 0.211 0.254 0.287 0.541 114 49
3rd PA in G 0.19 0.254 0.286 0.54 113 43
Dallas Keuchel
2.75 2.84 1st PA in G 0.236 0.287 0.322 0.61 112 73
Astros 2nd PA in G 0.199 0.249 0.308 0.557 93 53
3rd PA in G 0.21 0.242 0.311 0.553 91 45
Corey Kluber
3.05 2.98 1st PA in G 0.218 0.252 0.395 0.647 97 80
Indians 2nd PA in G 0.221 0.273 0.378 0.651 100 77
3rd PA in G 0.237 0.293 0.329 0.622 94 65
Jacob deGrom
2.92 2.99 1st PA in G 0.167 0.208 0.287 0.494 72 39
Mets 2nd PA in G 0.236 0.276 0.335 0.611 114 68
3rd PA in G 0.247 0.288 0.34 0.629 120 66
Madison Bumgarner
3.02 3 1st PA in G 0.196 0.248 0.306 0.554 82 57
Giants 2nd PA in G 0.227 0.26 0.352 0.611 100 67
3rd PA in G 0.266 0.296 0.43 0.726 136 89

And here's how they did (same order as above) relative to the rest of the league (as far as vs. OPS/tOPS+ for that trip through the order and league OPS/tOPS+ as a whole, respectively):

Lg. avg. for TTO vs. lg. avg. for PA Lg. avg. for TTO vs. lg. avg. for PA Lg. avg. vs. lg. avg. Lg. avg. vs. lg. avg.
-0.709 -0.259 -96 -22 -0.732 -0.282 -102 -28
-0.731 -0.152 -102 19 -0.732 -0.153 -102 19
-0.764 -0.228 -111 -5 -0.732 -0.196 -102 4
-0.709 -0.111 -96 -13 -0.732 -0.134 -102 -19
-0.731 -0.044 -102 9 -0.732 -0.045 -102 9
-0.764 -0.135 -111 -17 -0.732 -0.103 -102 -8
-0.709 -0.118 -96 0 -0.732 -0.141 -102 -6
-0.731 -0.197 -102 -23 -0.732 -0.198 -102 -23
-0.764 -0.134 -111 0 -0.732 -0.102 -102 9
-0.709 0.014 -96 26 -0.732 -0.009 -102 20
-0.731 -0.155 -102 -22 -0.732 -0.156 -102 -22
-0.764 -0.105 -111 -6 -0.732 -0.073 -102 3
-0.709 -0.243 -96 -12 -0.732 -0.266 -102 -18
-0.731 -0.19 -102 12 -0.732 -0.191 -102 12
-0.764 -0.224 -111 2 -0.732 -0.192 -102 11
-0.709 -0.099 -96 16 -0.732 -0.122 -102 10
-0.731 -0.174 -102 -9 -0.732 -0.175 -102 -9
-0.764 -0.211 -111 -20 -0.732 -0.179 -102 -11
-0.709 -0.062 -96 1 -0.732 -0.085 -102 -5
-0.731 -0.08 -102 -2 -0.732 -0.081 -102 -2
-0.764 -0.142 -111 -17 -0.732 -0.11 -102 -8
-0.709 -0.215 -96 -24 -0.732 -0.238 -102 -30
-0.731 -0.12 -102 12 -0.732 -0.121 -102 12
-0.764 -0.135 -111 9 -0.732 -0.103 -102 18
-0.709 -0.155 -96 -14 -0.732 -0.178 -102 -20
-0.731 -0.12 -102 -2 -0.732 -0.121 -102 -2
-0.764 -0.038 -111 25 -0.732 -0.006 -102 34

As a whole, that group performed rather well, and here are the averages of those groups versus the rest of the league:

Avg. for TTO (OPS) (1, 2, 3) Avg. for TTO (tOPS+) (1, 2, 3) Avg. vs. lg. (OPS) Avg. vs. lg. (tOPS+)
-0.1386666667 -4.666666667 -0.1616666667 -10.66666667
-0.1368888889 -0.6666666667 -0.1378888889 -0.6666666667
-0.1502222222 -3.222222222 -0.1182222222 5.777777778

Now, in the interest of fairness, I then chose the nine worst (as opposed to the above nine best) in 2015, at least as far as SIERA. Behold:

Name Team xFIP SIERA BA OBP SLG OPS tOPS+ sOPS+ Lg. avg. for TTO vs. lg. avg. for PA Lg. avg. for TTO vs. lg. avg. for PA Lg. avg. vs. lg. avg. Lg. avg. vs. lg. avg.
Yovani Gallardo 1st PA Rangers 4.31 4.59 0.263 0.34 0.385 0.726 99 106 -0.709 0.017 -96 3 -0.732 -0.006 -102 -3
2nd PA 0.25 0.299 0.368 0.667 83 83 -0.731 -0.064 -102 -19 -0.732 -0.065 -102 -19
3rd PA 0.298 0.356 0.466 0.822 124 115 -0.764 0.058 -111 13 -0.732 0.09 -102 22
John Danks 1st PA White Sox 4.65 4.63 0.27 0.319 0.463 0.783 95 119 -0.709 0.074 -96 -1 -0.732 0.051 -102 -7
2nd PA 0.263 0.316 0.437 0.753 88 105 -0.731 0.022 -102 -14 -0.732 0.021 -102 -14
3rd PA 0.312 0.374 0.513 0.888 122 132 -0.764 0.124 -111 11 -0.732 0.156 -102 20
Marco Estrada 1st PA Blue Jays 4.93 4.64 0.203 0.285 0.347 0.632 101 79 -0.709 -0.077 -96 5 -0.732 -0.1 -102 -1
2nd PA 0.212 0.275 0.398 0.674 112 83 -0.731 -0.057 -102 10 -0.732 -0.058 -102 10
3rd PA 0.204 0.23 0.365 0.595 86 54 -0.764 -0.169 -111 -25 -0.732 -0.137 -102 -16
Tom Koehler 1st PA Marlins 4.58 4.67 0.211 0.285 0.341 0.626 70 78 -0.709 -0.083 -96 -26 -0.732 -0.106 -102 -32
2nd PA 0.285 0.35 0.472 0.822 121 124 -0.731 0.091 -102 19 -0.732 0.09 -102 19
3rd PA 0.272 0.367 0.405 0.772 110 105 -0.764 0.008 -111 -1 -0.732 0.04 -102 8
Chris Tillman 1st PA Orioles 4.58 4.69 0.228 0.29 0.374 0.664 74 87 -0.709 -0.045 -96 -22 -0.732 -0.068 -102 -28
2nd PA 0.291 0.364 0.452 0.816 115 124 -0.731 0.085 -102 13 -0.732 0.084 -102 13
3rd PA 0.281 0.34 0.468 0.808 111 111 -0.764 0.044 -111 0 -0.732 0.076 -102 9
Mark Buehrle 1st PA Blue Jays 4.46 4.71 0.293 0.312 0.484 0.796 110 122 -0.709 0.087 -96 14 -0.732 0.064 -102 8
2nd PA 0.288 0.318 0.415 0.733 97 100 -0.731 0.002 -102 -5 -0.732 0.001 -102 -5
3rd PA 0.245 0.294 0.418 0.712 90 86 -0.764 -0.052 -111 -21 -0.732 -0.02 -102 -12
R.A. Dickey 1st PA Blue Jays 4.72 4.76 0.24 0.297 0.422 0.719 102 102 -0.709 0.01 -96 6 -0.732 -0.013 -102 0
2nd PA 0.223 0.292 0.346 0.637 82 76 -0.731 -0.094 -102 -20 -0.732 -0.095 -102 -20
3rd PA 0.262 0.312 0.432 0.744 110 94 -0.764 -0.02 -111 -1 -0.732 0.012 -102 8
Aaron Harang 1st PA Phillies 4.99 4.87 0.246 0.29 0.414 0.704 75 97 -0.709 -0.005 -96 -21 -0.732 -0.028 -102 -27
2nd PA 0.288 0.346 0.416 0.763 93 110 -0.731 0.032 -102 -9 -0.732 0.031 -102 -9
3rd PA 0.287 0.355 0.585 0.941 131 142 -0.764 0.177 -111 20 -0.732 0.209 -102 29
Alfredo Simon 1st PA Tigers 4.78 4.88 0.279 0.338 0.482 0.82 102 130 -0.709 0.111 -96 6 -0.732 0.088 -102 0
2nd PA 0.267 0.353 0.463 0.815 103 123 -0.731 0.084 -102 1 -0.732 0.083 -102 1
3rd PA 0.275 0.335 0.466 0.801 98 109 -0.764 0.037 -111 -13 -0.732 0.069 -102 -4

And now, the averages (exact same format as the chart above):

Avg. for TTO (OPS) Avg. for TTO (tOPS+) Avg. vs. lg. (OPS) Avg. vs. lg. (tOPS+)
0.009888888889 -4 -0.01311111111 -10
0.01122222222 -2.666666667 0.01022222222 -2.666666667
0.023 -1.888888889 0.055 7.111111111


Some of the above are, of course, subject to small sample size, but as a whole, the data seem to support the hypothesis that, in fact, better pitchers tend to perform better than lesser-skilled pitchers if left in longer. Now, back to the topic at hand.

Why relief pitchers are inconsistent

Everyone moans about it. Relief pitchers are inconsistent, don't succeed from year to year, and pop up out of nowhere to have shutdown seasons before disappearing into the ether. And that is what makes this line (from 2015) so tempting yet so awful:

BA OBP SLG OPS tOPS+
1st PA in G (RP) 0.244 0.315 0.384 0.699 94

Yup, that's an OPS which is .01 better than a starter's first TTTO, and .065 better than their third.

If only relief pitchers could somehow sustain this achievement. An OPS below .700! Only 38 starters matched or beat that mark, and yet the mark above is an average of all relievers in baseball (that includes pitchers like Tanner Scheppers and Jason Motte). Of course, as stated above, that is but a mirage. Relievers are less consistent than the Astros' ability to score runners from third base. Our sister site Athletics Nation summed it up rather succintly:

Relievers, as can be seen by the R^2 factor of 0.02 (a perfect correlation is 1), are wildly inconsistent from season to season. Even the top relievers are inconsistent. Twenty-five relievers earned 2+ fWAR in at least one season between 2011-2015:

2015 Name WAR 2014 Name WAR 2013 Name WAR 2012 Name WAR 2011 Name WAR
1 Cody Allen 2.6 1 Dellin Betances 3.1 1 Koji Uehara 3.1 1 Aroldis Chapman 3.3 1 Craig Kimbrel 3.2
2 Aroldis Chapman 2.5 2 Wade Davis 3 2 Greg Holland 3 2 Craig Kimbrel 3.3 2 Jonathan Papelbon 3
3 Dellin Betances 2.4 3 Aroldis Chapman 2.7 3 Mark Melancon 2.5 3 Fernando Rodney 2.4 3 Sean Marshall 2.6
4 Zach Britton 2.1 4 Jake McGee 2.6 4 Kenley Jansen 2.4 4 Greg Holland 2.1 4 David Robertson 2.6
5 Carson Smith 2.1 5 Sean Doolittle 2.4 5 Joe Nathan 2.3 5 David Hernandez 2.1 5 Mariano Rivera 2.4
6 Andrew Miller 2 6 Craig Kimbrel 2.2 6 Trevor Rosenthal 2.3 6 Jake McGee 2 6 Sergio Romo 2
7 Trevor Rosenthal 2 7 Andrew Miller 2.2 7 Craig Kimbrel 2.2 7 Kenley Jansen 2
8 Ken Giles 2 8 Kenley Jansen 2.2 8 Nate Jones 2
9 Wade Davis 2 9 Greg Holland 2.2
10 Mark Melancon 2
11 Steve Cishek 2

Of those twenty-five pitchers, only three made the top five twice (Craig Kimbrel, Aroldis Chapman, and Kenley Jansen), and none made it thrice. In fact, only three pitchers even reached 2 fWAR thrice.

Teams, not surprisingly, show no more consistency than do individual relievers. Bullpens fluctuate constantly, and BTBS put out an article in September 2014 detailing the fluctuation of team FIP- from month to month:

2014 Mar/April May Ch June Ch July Ch August Ch Average FIP-
ARI 83 102 19 101 -1 90 -11 89 -1 93
ATL 65 85 20 95 10 75 -20 97 22 83.4
BAL 117 104 -13 91 -13 62 -29 71 9 89
BOS 74 89 15 92 3 101 9 100 -1 91.2
CHC 115 74 -41 98 24 76 -22 81 5 88.8
CHW 107 112 5 102 -10 97 -5 124 27 108.4
CIN 129 101 -28 90 -11 77 -13 101 24 99.6
CLE 94 98 4 104 6 78 -26 85 7 91.8
COL 94 103 9 111 8 100 -11 102 2 102
DET 109 92 -17 104 12 106 2 117 11 105.6
HOU 134 81 -53 105 24 110 5 101 -9 106.2
KC 72 95 23 86 -9 101 15 94 -7 89.6
LAA 126 110 -16 113 3 65 -48 75 10 97.8
LAD 96 119 23 80 -39 101 21 89 -12 97
MIA 109 99 -10 80 -19 86 6 55 -31 85.8
MIL 78 110 32 98 -12 90 -8 117 27 98.6
MIN 92 89 -3 101 12 74 -27 135 61 98.2
NYM 124 104 -20 96 -8 105 9 123 18 110.4
NYY 98 74 -24 107 33 94 -13 91 -3 92.8
OAK 83 84 1 105 21 95 -10 97 2 92.8
PHI 129 103 -26 83 -20 81 -2 62 -19 91.6
PIT 102 120 18 86 -34 139 53 98 -41 109
SD 82 103 21 79 -24 105 26 82 -23 90.2
SEA 108 84 -24 64 -20 85 21 74 -11 83
SFG 98 98 0 96 -2 78 -18 117 39 97.4
STL 81 90 9 86 -4 117 31 118 1 98.4
TB 111 133 22 97 -36 52 -45 78 26 94.2
TEX 101 81 -20 86 5 97 11 97 0 92.4
TOR 111 96 -15 95 -1 109 14 101 -8 102.4
WAS 81 83 2 74 -9 88 14 84 -4 82

I mean, just take a look at the Astros from April through June. A drop of 53 from April to May, with a sudden jump of 24 in June? And poor Minnesota. After losing 27 FIP points in July, they jumped up a staggering 61 points in August. They had the fourth-best bullpen in July, and, ignominiously, were the flat-out worst in August.

To more easily understand the volatility, see this accompanying graph:

Fip-

I then decided to check out team SIERA in 2015, and sure enough, more change was at hand:

Team SIERA SIERA SIERA SIERA SIERA SIERA
Mar-Apr May May Change June June Change July July Change August August Change Sept-Oct Sept-Oct Change Average SIERA
Angels 3.63 3.34 -0.29 3.68 0.34 3.64 -0.04 3.91 0.27 3.08 -0.83 3.546666667
Athletics 3.71 3.78 0.07 3.24 -0.54 3.23 -0.01 3.46 0.23 4.41 0.95 3.638333333
Mariners 4.26 3.99 -0.27 3.32 -0.67 3.54 0.22 4.18 0.64 3.52 -0.66 3.801666667
Rays 3.87 3.56 -0.31 3.55 -0.01 4.1 0.55 2.71 -1.39 4.21 1.5 3.666666667
Rangers 4.09 3.94 -0.15 4.05 0.11 3.95 -0.1 3.28 -0.67 3.28 0 3.765
Blue Jays 3.29 3.34 0.05 3.03 -0.31 2.78 -0.25 2.86 0.08 3.53 0.67 3.138333333
Diamondbacks 4.06 3.34 -0.72 4.13 0.79 3.57 -0.56 3.67 0.1 3.58 -0.09 3.725
Braves 4.1 3.94 -0.16 3.76 -0.18 3.5 -0.26 4.3 0.8 4.41 0.11 4.001666667
Cubs 3.06 4.08 1.02 3.64 -0.44 3.54 -0.1 3.23 -0.31 2.66 -0.57 3.368333333
Reds 3.56 3.97 0.41 3.96 -0.01 4.25 0.29 3.92 -0.33 3.56 -0.36 3.87
Rockies 3.25 4.11 0.86 3.7 -0.41 4.26 0.56 4.78 0.52 3.82 -0.96 3.986666667
Orioles 4.06 3.02 -1.04 3.17 0.15 3.33 0.16 3.28 -0.05 3.41 0.13 3.378333333
Marlins 3.58 3.09 -0.49 3.46 0.37 2.88 -0.58 3.95 1.07 4.39 0.44 3.558333333
Astros 2.76 2.42 -0.34 3.7 1.28 3.75 0.05 2.9 -0.85 3.28 0.38 3.135
Dodgers 2.79 3.05 0.26 3.24 0.19 3.57 0.33 2.74 -0.83 2.95 0.21 3.056666667
Brewers 3.72 2.99 -0.73 2.92 -0.07 3.29 0.37 3.02 -0.27 3.29 0.27 3.205
Nationals 3.3 3.51 0.21 3.39 -0.12 2.89 -0.5 3.85 0.96 3.54 -0.31 3.413333333
Mets 2.93 3.2 0.27 4.6 1.4 3.64 -0.96 3.32 -0.32 3.33 0.01 3.503333333
Phillies 4.4 3.7 -0.7 3.47 -0.23 3.06 -0.41 3.63 0.57 3.94 0.31 3.7
Pirates 2.98 3.66 0.68 3.07 -0.59 4.24 1.17 3.09 -1.15 3.33 0.24 3.395
Cardinals 3.34 3.68 0.34 3.29 -0.39 4.04 0.75 3.65 -0.39 3.18 -0.47 3.53
Padres 4.51 2.99 -1.52 3.64 0.65 3.06 -0.58 3.12 0.06 3.28 0.16 3.433333333
Red Sox 3.77 4.17 0.4 3.7 -0.47 3.45 -0.25 4.59 1.14 3.97 -0.62 3.941666667
Giants 4.16 3.51 -0.65 3.61 0.1 3.24 -0.37 3.3 0.06 3.43 0.13 3.541666667
White Sox 3.04 3.77 0.73 3.63 -0.14 3.5 -0.13 2.86 -0.64 3.77 0.91 3.428333333
Indians 3.68 3.41 -0.27 3.05 -0.36 3.1 0.05 3.28 0.18 3.35 0.07 3.311666667
Tigers 4.17 3.8 -0.37 4.12 0.32 3.82 -0.3 4.26 0.44 4.39 0.13 4.093333333
Royals 3.16 4 0.84 3.42 -0.58 3.14 -0.28 3.1 -0.04 4.05 0.95 3.478333333
Twins 4.65 3.87 -0.78 4.38 0.51 3.73 -0.65 3.66 -0.07 3.88 0.22 4.028333333
Yankees 3.08 3.49 0.41 3.22 -0.27 2.67 -0.55 3.4 0.73 3.7 0.3 3.26

The Astros may have, by a hair, had the best bullpen in baseball, but that didn't stop them from, after having an average of 2.59 over the first two months, averaging 3.725 over the next two. And despite the Padres having the second-worst bullpen in April, theirs was suddenly tied for second-best in May.

So, to sum it all up, pitchers don't last that long and relievers are rather inconsistent. Now, how can paired pitching fix both of these problems? That's for the next installment.