The A’s and Hitting With Men On Base

Earlier this month I wrote about how the A’s front office is currently outpacing their competition when it comes to roster construction.  I focused primarily on how they’ve taken the platoon advantage to another level, loading up on defensively versatile players to allow for day-to-day lineup construction that maximizes the number of plate appearances where their hitters have the platoon advantage.  As a result of this, they get 70% of their PAs with the platoon advantage, as compared to the league average of 55%.  As part of my investigation into the platoon splits of A’s players, I also noticed another split of interest: offensive performance with runners on base as compared to with the bases empty.  After investigation, I’ve concluded that the A’s have identified and targeted players that have higher offensive production with runners on base.

League-wide trends
First, it should be noted that in general, everyone hits better with runners on base.  There are two primary reasons for this.  The first is sampling bias: if runners are on base, you’re more likely to be facing an inferior pitcher, as such pitchers allow more baserunners and hence face proportionally more batters with runners already on base.  Second, the defense is concerned with more than just the current batter.  With the bases empty, the defense presumably aligns themselves to maximize the chances of getting the batter out (or, more precisely, to minimize the overall output of the batter).  With runners on, there are other considerations – ensuring that the runners don’t steal, for example – that change the defensive alignment.  As a result, a given ball in play is more likely to be a hit if there are runners on base.  League-wide in 2014, the numbers look like this:

  PA OPS BAbip tOPS+
Bases Empty 80375 0.687 0.296 95
Runners on Base 61905 0.725 0.302 106

tOPS+ is a measure of the split, relative to average.  Roughly speaking, the above numbers mean that on average, hitters’ OPS is 6% higher (tOPS+ = 106) with runners on base compared to OPS in all scenarios.

Some teams have been better than others when it comes to hitting with runners on base:

Team OPS (Empty) OPS (RoB) OPS Diff BAbip (Empty) BAbip (RoB) BAbip Diff tOPS+
OAK 0.672 0.789 0.117 0.264 0.306 0.042 118
SEA 0.633 0.740 0.107 0.281 0.312 0.031 118
NYM 0.622 0.713 0.091 0.284 0.288 0.004 116
COL 0.740 0.820 0.080 0.319 0.332 0.013 112
CIN 0.648 0.719 0.071 0.291 0.288 -0.003 112
CLE 0.688 0.756 0.068 0.288 0.304 0.016 111
BAL 0.705 0.771 0.066 0.288 0.310 0.022 111
ATL 0.662 0.716 0.054 0.296 0.317 0.021 109
BOS 0.664 0.713 0.049 0.294 0.297 0.003 108
MIA 0.675 0.724 0.049 0.313 0.318 0.005 108
PHI 0.650 0.694 0.044 0.294 0.290 -0.004 107
CHW 0.700 0.743 0.043 0.308 0.311 0.003 107
LAA 0.717 0.752 0.035 0.290 0.327 0.037 106
PIT 0.710 0.744 0.034 0.302 0.313 0.011 106
CHC 0.666 0.700 0.034 0.300 0.279 -0.021 106
KCR 0.681 0.715 0.034 0.306 0.297 -0.009 106
MIL 0.710 0.740 0.030 0.299 0.295 -0.004 106
ARI 0.677 0.709 0.032 0.293 0.298 0.005 105
SFG 0.670 0.698 0.028 0.283 0.310 0.027 105
WSN 0.691 0.718 0.027 0.303 0.302 -0.001 104
MIN 0.691 0.710 0.019 0.293 0.308 0.015 103
HOU 0.696 0.711 0.015 0.292 0.294 0.002 102
NYY 0.686 0.697 0.011 0.282 0.293 0.011 102
DET 0.750 0.760 0.010 0.309 0.319 0.010 102
TBR 0.698 0.707 0.009 0.298 0.287 -0.011 102
SDP 0.637 0.644 0.007 0.278 0.274 -0.004 102
TEX 0.694 0.689 -0.005 0.308 0.299 -0.009 99
TOR 0.746 0.740 -0.006 0.303 0.291 -0.012 99
LAD 0.726 0.715 -0.011 0.313 0.310 -0.003 99
STL 0.699 0.688 -0.011 0.307 0.290 -0.017 98

Here, tOPS+ is the measure of the split relative to that team’s average.  So for example, the Tigers’ OPS with Runners on Base (RoB) is 0.760, vs. 0.750 with Bases Empty for a tOPS+ of 102.  The Reds on the other hand have a split of 0.648 vs. 0.719 for a tOPS+ of 112.  The Tigers are a better offensive team overall than the Reds, but the Reds’ split with runners on base is larger.

The A’s
The A’s and Mariners top the list as having the largest split with runners on base.  Let’s take a look at the A’s individual players and how they perform with RoB:

Name PA OPS BAbip tOPS+
Josh Donaldson 242 0.953 0.318 138
Brandon Moss 239 0.933 0.348 130
Yoenis Cespedes 208 0.798 0.310 114
Jed Lowrie 202 0.563 0.250 69
Alberto Callaspo 183 0.656 0.264 116
Derek Norris 147 0.878 0.316 109
John Jaso 144 0.842 0.351 120
Coco Crisp 143 0.857 0.333 130
Josh Reddick 134 0.837 0.283 122
Eric Sogard 115 0.587 0.258 108
Stephen Vogt 96 0.887 0.338 106
Nick Punto 94 0.679 0.368 135
Craig Gentry 85 0.676 0.333 116

Again, the tOPS+ column represents how well the player performs with runners on base relative to that player’s average performance.  We can see that across the board, with the notable exception of Jed Lowrie, all the A’s have been performing better with runners on this year.

Now typically this is where you’d say the A’s are just getting lucky, and expect them to regress to the mean.  Certainly some regression is expected, but I’m not sold on the idea that this is entirely luck-driven.  We know that there are some players who routinely and consistently perform better with runners on base – sometimes dramatically so.  Let’s take a look at these players’ career numbers to see if they might be such players:

Name PA OPS BAbip tOPS+
Donaldson – Empty 861 0.701 0.259 74
Donaldson – RoB 675 0.945 0.351 134
Moss – Empty 1084 0.737 0.263 85
Moss – RoB 944 0.864 0.348 117
Cespedes – Empty 844 0.746 0.277 90
Cespedes – RoB 768 0.824 0.304 111
Lowrie – Empty 1338 0.732 0.283 98
Lowrie – RoB 1096 0.756 0.299 104
Callaspo – Empty 2045 0.678 0.281 92
Callaspo – RoB 1580 0.741 0.287 110
Norris – Empty 471 0.694 0.292 87
Norris – RoB 390 0.813 0.309 116
Jaso – Empty 940 0.702 0.275 85
Jaso – RoB 697 0.835 0.308 120
Crisp – Empty 3609 0.742 0.298 100
Crisp – RoB 2237 0.739 0.291 100
Reddick – Empty 992 0.761 0.291 109
Reddick – RoB 820 0.692 0.249 89
Sogard – Empty 488 0.591 0.253 91
Sogard – RoB 362 0.654 0.274 112
Vogt – Empty 206 0.716 0.288 93
Vogt – RoB 183 0.773 0.300 107
Punto – Empty 2087 0.633 0.298 96
Punto – RoB 1627 0.664 0.298 106
Gentry – Empty 549 0.692 0.350 98
Gentry – RoB 432 0.709 0.325 103

Almost all of them have put up large splits with runners on.  Of course, it can take upwards of 1000 PAs for something like BABIP to stabilize (and even then you still need to account for regression to the mean), and many of these players aren’t at that threshold.  Nevertheless, taking these players’ careers in aggregate gives us 27,000 plate appearances; across these, the players show in an increase of 14 points of BABIP and 53 points of OPS with runners aboard.  When compared to league average (6 points of BABIP and 38 points of OPS), it really looks like the A’s are targeting players that have some inherent, non-random ability to perform better with runners on base (to a greater extent than average).

A quick look at the Mariners
The other team leading the league in the split is the Mariners.  What’s going on there?  A look at the individual players’ splits shows:

Name PA OPS BAbip tOPS+
Robinson Cano 221 1.032 0.327 137
Kyle Seager 219 0.905 0.336 120
Dustin Ackley 177 0.702 0.310 104
Mike Zunino 167 0.640 0.247 88
Brad Miller 139 0.619 0.293 108
Justin Smoak 117 0.697 0.268 119
James Jones 115 0.634 0.366 112
Logan Morrison 106 0.671 0.244 106
Corey Hart 101 0.580 0.269 97

The two biggest contributors, by far, are Cano and Seager.  If a genie were to give you one very specific wish which was, you get to pick 2 players on your team to magically perform dramatically better with runners on base, you’d want to pick the 2 guys who a) are clearly the best hitters on your team and b) get the most plate appearances.  For the Mariners, that’s Cano and Seager.

Here, I absolutely expect regression to the mean.  I don’t think the Mariners keep this up.  In fact, looking at Cano’s career numbers (over 6000 PA’s), he’s actually been better with the bases empty: OPS of .873 vs. 0.845, and BABIP of 0.335 vs. 0.313 — but for some reason so far this year he’s been far better with runners on.

What does it all mean?
The A’s have figured it out.  The Mariners have been lucky.  The Mariners will regress heavily to the mean for the remainder of the season.  The A’s might regress somewhat, but they’re on to something.  By building a roster of players that are more productive with runners on base, they score more runs.

This explains why the A’s are outperforming their Expected Runs, or BaseRuns.  BaseRuns predicts how many runs a team scores based purely on their aggregate totals (hits, homers, total bases, etc.), removing all sequencing from the picture entirely.  Based on BaseRuns, FanGraphs says they “should have” only scored 4.54 runs per game, when they’ve actually been scoring 4.82 runs per game.  If we can do a better job quantifying how much of this sequencing is luck-based versus skill-based, we can do a better job projecting run scoring, and by extension, win percentages.



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Strikethree
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Strikethree

Do they so it as well on the road as they do in their cavernous home park? If better at home, more ground balls at home, where foul balls in air more likely to be caught?

DavidKB
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DavidKB

First, it’s hilarious to see the Cardinals at the absolute bottom of this list after their RISP performance last year.

Second, my naive thought is that the tOPS+ difference should come mostly from ground balls, since the infielders’ positioning will change much more significantly with runners on. I wonder if that shows up in the data. That could be a good way to decide how much regression to expect for the A’s versus the Mariners.

MGL
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MGL

So many problems with this. One, OPS is a terrible stats to use for runners on base splits. It is not a particularly good stat in the first place, and even less so with runners on base. Your goal as a hitter is not to have a higher OPS with runners on base, but to adjust your approach to leverage those runners. You want to look at RE24 or WPA in order to see which teams/players do that well.

For example, if I get more walks with runners on base, thus raising my OPS, that is not such a great thing since walks with runners on base are not all that valuable (as compared to hits that is).

Second, is there any evidence that there is a significant skill with respect to players hitting more (or less) efficiently with runners on base? I don’t know of any. Without that being established first, identifying players who have “routinely and consistently (I don’t even know what those words means in this context) hit higher with ROB means nothing in terms of predictive value. We can similarly identify players who “routinely and consistently” perform better on weekends than weekdays, odd days than even days, etc. That doesn’t mean that it is anything more than extreme random variation (which will necessarily occur with any old “split” you can conjure).

So I see zero evidence that this is anything more than random variation (luck), other than the Bayesian prior that Oakland is a smart team and has presumably done similar things in the past (which is not worth nothing in this case).

And BTW, you cannot add up PA from different players and call that a large sample size. Here is an example of how and why you cannot do that: Say I have 100 batters with 10 AB each and they each have a BA of .400. That’s a total of 1000 AB, but that doesn’t mean that their collective true BA is near .400 because of the large sample size. You would regress each player individually which would obviously give you an estimated true BA for each batter of near average since you would regress like 95%.

Joe
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Joe

it would be interesting to split those stats up to find out the OPS/BAbip of the A’s before and after the Cespedes trade. Also, the A’s seem to have a lot of trouble getting those runners home when facing the Angels bullpen.

Shwan
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Shwan

The biggest difference to me between none on and baserunners is pitch-sequencing and olfaction changes according the situation. To cite an obvious example, bases loaded, when the pitcher cannot walk and is in even bigger trouble if he falls behind than he would normally. There are lots of more subtle situations where this goes on to a smaller degree.

Shwan
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Shwan

Read location for olfaction

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