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.