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.
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:
|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+|
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 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:
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:
|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:
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.