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.
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?
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.
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… Read more »
Awesome comment, thanks! You’re spot-on that the kind of metric we’re looking for is one that accounts for context-specific run values for given offensive events. RE24 would be a good example (I wouldn’t use WPA for a couple reasons). The reason I went with the less granular/accurate BABIP/OPS split by baserunners in this post is that it’s a bit clearer (and more intuitive) that there do exist players for whom this split is real and predictive. Perhaps rather than saying “consistently” performing better with runners on I should have said “predictably.” That’s not to say all (or even most) variation… Read more »
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.
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.
That’s a great point: players (both hitters and pitchers) have the ability to tailor their approach to a given PA based on the game situation to alter the probabilities of various outcomes. It stands to reason that this ability is a skill with tangible value and that some players might be better at it than others. Worth exploring further for sure.
Read location for olfaction