The most important big-picture trend in baseball right now is that offense is down (down, down, down). It’s a boring story, though. You know that offense is down. You know, for instance, that strikeouts occurred at the highest rate ever in 2014. You’ve probably heard that power is down, with the league posting its lowest Isolated Power since 1992 last year. If you’ve read any of my recent work around here, you probably know that walks are at their lowest rate since 1968. If you read me even before I started here, you probably know that, curiously, only BABIP is surviving the league-wide extinction event consuming all other kinds of offensive output. Even reciting these facts rapid-fire is boring to you. You want more information, more detail, more dimension.

Well, here’s something in that vein. See, a couple of weeks ago, I was re-reading The Book, that holiest of sabermetric do-it-yourself manuals, when I came to the page with the Run Expectancy table for all base-out states, drawn from the years 1999-2002. Here it is now!

Run Expectancy By Base-Out State, 1999-2002

In a league where, generally, each team scored nearly five runs per game, these are the number of runs they could expect to score from each state through the end of that inning. Though they’re only a jumble of numbers, really, they made me laugh (a rueful little laugh), because I realized right away how quaint those figures are now. Whereas offense was at an historic apex during the years covered by this chart, it’s now at something of a nadir. Wondering just how different things are now, I looked up the table for 2014 in the BP stat reports. Here it is:

Run Expectancy By Base-Out State, 2014

Just compare a few boxes; it’s immediately clear that a radical change took place. To make the changes easier to eyeball, here’s a third chart, this one showing the percentage of the 1999-2002 RE represented by the 2014 RE for each base-out state.

Run Expectancy Changes by Base-Out State

Well, now.

This chart is where things get interesting. Though the RE of every situation is lower than it was 10-15 years ago, the sagging isn’t uniform across the spectrum of possible situations. Expected runs are down by markedly more when there are no runners in scoring position, and when there are two outs, than at other times. In essence, the worse the odds of scoring were in the first place, the more they have plunged as offense has fallen.

Naturally, having an enigmatic statistical finding with which to deal, I emailed Russell Carleton to see what he made of it. Russell’s response:

This isn't very surprising. It tells us that since outs are more common than they once were … when we get to a point where we only need one more out to end the inning, it's easier to get. And the situations where runners are closer to home plate have more variance in them. An out in a situation with a runner on third and two out destroys the chance of a run. A non-out has a pretty good chance of driving him in (except a walk or HBP).

That accorded with my instincts about this phenomenon. With OBP down so significantly, chaining together positive events the way a team must in order to score runs with two outs is much, much more difficult than it once was. I was inclined, especially after hearing from Russell, to treat this as a fairly natural, minimally interesting finding.

It runs a little bit deeper, though. See, as it turns out, 2014 saw two-out offensive performance slide to its worst since 1976, and to its worst ever, relative to offensive performance with zero or one out. (We have this split data as far back as 1960, but only have consecutive seasons of it since 1974.)

Overall and Two-Out OPS, 1974-2014

Maybe you can easily see the steepness of the downturn the last two years, but in case it should appear to you that the lines are moving totally in lockstep, here’s two-out OPS as a percentage of the league’s overall OPS over the same span.

What we’re seeing, then, isn’t two-out scoring opportunities declining as the natural outgrowth of a lower overall scoring environment. It’s probably partially that, but it’s also a product of a historic offensive shortfall when there are two outs. That’s fascinating, but very difficult to explain.

To attempt to do so, though, we have to start by understanding what makes a two-out plate appearance different from one that comes with zero or one out. So what makes two-out situations special? How do they work?

Well, first of all, a two-out plate appearance is always going to have a lower expected OPS than one earlier in an inning. Some portion of fly balls hit with no outs or one get washed away from half of the OPS equation, because they don't count against slugging percentage. That’s why, in the chart above, we can see that two-out OPS has always come in lower than overall OPS.

More significantly to this discussion, though, there are different incentives for all the players involved in these plate appearances, too. Because the end of the inning is one well-handled batter away, pitchers are less willing to give in and groove pitchers. For that reason, walk rates rise in two-out spots.

This actually begins to explain our recent phenomenon; note the way two-out walk rates have sagged since 2012, while rates earlier in innings have held much more steady.

Now, pitchers will take any kind of out when they have two in hand, but because batters have more incentive to trade contact for power, strikeout rates also rise when there are two away.

Another small piece of the puzzle fits into place: Strikeout rate did spike with two outs last season, relative to at other times during innings, however small the gap.

Hitters trade contact for power, while pitchers nibble at the edges of the zone to avoid letting too much of that power be actualized. So who wins that tug-of-war? Do batters find more power with two outs?

No. Power drops with two outs, though the degree to which that’s true has varied over time. While two-out ISO has plummeted over the last few years, so has zero-out ISO. Nothing much here.

What about home runs, specifically? After all, pitchers are taking special care not to make two-out mistakes, handing out more free passes if that’s what they need to do. Are they successful in avoiding homers with that strategy?

Surprisingly, no, not really. There have been times—it was true less than 10 years ago, even—when home runs were more likely with two outs than with one. Two-out homer rates have fallen a little faster than zero- or one-out rates since 2012, but the difference is minute.

Finally, fielders can treat two-out situations differently, too. I suspect we saw the first glimpse of two-out fielding’s impact in the ISO chart: The no-doubles defense, or simply a risk-averse mentality, probably results in fewer balls making it into the gaps with two down. In general, though, defenses can play more optimally when there are two outs. Shifts are easier to implement. Defenders don’t need to adjust their positioning to set up double-play actions or to be ready to cut down a runner at the plate. Thus:

There, then, is the shape of two-out offense. More opportunities to get on base present themselves, but those opportunities have less value. Power becomes a premium commodity (the authors of The Book noted this in the context of the batting order; one reason to bat a power-over-OBP guy third is to maximize the leverage of the damage he does, since he’ll often come up with two outs), but is harder to access. Balls in play end in heartbreak. The three true outcomes ($1, Christina Kahrl) take over the game.

Which factors have most driven down overall two-out offense, relative to the league’s total production?

Two-Out OBP and SLG, Relative to Overall OBP and SLG, 1974-2014

It’s not a single factor; it’s everything. Batters just can’t hit with two outs right now, and it can’t be blamed solely on the offensive environment. Two more questions need to be answered, then: How can batters do better with two down? And how can teams use this information?

To answer the first one, let’s do a case study of two hitters whose situational results last season could hardly be more disparate. At one end of the spectrum: Mike Trout. But this isn’t the end of the spectrum you think! Trout was actually terrible with two outs last season:

Mike Trout, Offense by Outs in Inning, 2014











0 outs










1 out










2 outs










With zero or one out, Trout was Superman. With two outs, he was B.J. Upton. He hit only one of his 36 home runs with two outs.

On the other hand, Christian Yelich hit five of his nine bombs with two outs.

Christian Yelich, Offense by Outs in Inning, 2014











0 outs










1 out










2 outs










Both men struck out at a higher rate with two outs, but Yelich did so in order to accommodate a different swing designed to do different things. It appears as though Trout got nothing (except a few spare walks, the relative futility of which we noted above) in return for his lost contact.

These are small samples of plate appearances, affected heavily by BABIP fluctuations and random variation. Still, Yelich’s drastic differential in power output—no one in the league delivered a bigger ISO boost in two-out situations than the Marlins’ new $50 million man—demonstrates a modular approach, some mechanical and mental adjustments he can make to suit the situation in which he finds himself. Trout is a master adjuster; it’s a good bet that this will look like a fluke when his career is over. Still, for now, Yelich has figured out something that Trout hasn’t, and indeed, that the rest of the batters in the league haven’t. That ability to change one’s approach, to do drastically different things with the ball based on situation, is an extraordinarily rare one in the modern game, and it might just be the key to restoring smoother, more consistent production, especially with two outs.

Teams could use all the information above to better educate their hitters on what to expect with two outs, and just as importantly, what they should most strive to do in those spots. They could also, as I alluded to above, play with the batting order, given what we know about the current environment for two-out run production. The median team OBP from the top spot in the batting order last season was .321. The median for the second slot was .329. Given those numbers, the third hitter in the batting order will come to bat with two outs and no one on base in the first inning 45.5 percent of the time. From 1999-2002, this number was 43.7 percent. That’s three extra games a season in which the third hitter is coming up with a run expectancy of .086.

The Book found that the first, second, fourth and fifth hitters in the order should be better than the third. That’s even truer now. Every team has a batter (or two, or three, or four) who can’t do anything but hit for power. They strike out a ton, they never walk and they hit for a middling or poor average, but they mash extra-base hits. On most teams, this batter gets shoved down to the sixth or seventh slot in the order. I’m not sure, though, that they shouldn’t be batting third. That third hitter is going to see about as many base runners as anyone; their power won’t be wasted. Their outs just won’t hurt as much as they might in other places in the order. Flawed though they all are, Javier Baez, Pedro Alvarez and Oswaldo Arcia all have strong arguments for being the best third hitters their teams have. Certainly, given the way things shake out now, batting one’s best hitter third—a practice the authors of The Book counseled teams against, but which is still fairly ubiquitous—is an even more criminal waste than it once was.

Two-out at-bats are baseball’s third-down plays. They can provide tension and excitement, even in the midst of a game that won’t be decided until the final act. An underrated aspect of the problem with offense being so down lately has been the lack of intrigue in these particular spots. We can’t solve that problem here, and we probably can’t solve it overnight. At least, hopefully, we now understand it—and the discipline of two-out hitting—a bit better.

Thank you for reading

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Slightly off topic, but something that I've wondered for a while: run expectancy tables are often used to argue against certain managerial decisions (like bunting, for example), but since they are based on historical data that includes sub-optimal managerial decisions (which are at times ingrained in the accepted strategies of baseball), isn't the data flawed for that purpose? So when we say that bunting with no outs and a man on first is sub-optimal because the run expectancy is higher with no outs and a man on first than it is with one out and a man on second, the data for run expectancy in these situations is already inclusive of results that are the consequences of that very sub-optimal decision making.

So, while in that particular case we have to presume that the gap would be even wider if the historical data was only gathered using optimal decision making (and therefore the point about bunting in that situation still stands), it makes me wonder generally about the legitimacy of using the runs expectancy data. If certain sub-optimal strategies have been traditionally employed, isn't the whole data set flawed for the purpose of determining what *is* optimal decision making?
Great analysis
Seems 2014 is more off the chart than any other year. What was different in 2014? Over 60% increase in shifts. In your charts you show BABIB% was actually higher in 2014 with zero or one out. However lower with two outs. You also note that it is easier to utilize shift with 2 outs.

Its all about the shift!
It's certainly not *all* the shift, but yes, definitely a factor.
Great article! Of my favorites. My one question is, how confident can we be that Yelich's splits are a result of his efforts and not simply random noise due to sample size?
This. Exactly my thought when reading.
Yeah, I don't follow the conclusion of this article at all. Is he seriously suggesting teams "play with the batting order" based on tiny-sample #-of-outs splits? That's lunacy.
Would it be worth looking at how often the batter is facing pitchers of different types, e.g. Relief versus starting, same-hand vs. opposite-hand? Changes in the pattern of using relief pitchers could contribute to this.
If you dig deep into all the years of data you can see it has always been in the favor of the pitcher. Offense slips down a little each and every season unless there is a factor in play to change that.

If a team opens a new stadium that is more hitter friendly it can stop it or slow it down for a short time but today that not much of factor the building era in major league is pretty much over. The all pretty much have shiny new places to play ball

Other then that the only way to effect real change is with expansion. Expansion turn it in the favor of the hitters for a few years and you turn back pitching 15 years and have the kind of offense you had in the 90's if you go back to back with expansion and if you ask me Commissioner Manfred needs to get on that right away for it takes time to pick the cities and you got to give them a few years to build up the minor league systems.

He need to get on it right away for in the 4 or 5 years it's going to take the offense problem is only going to get worse and it is already borderline insine and by the time they get around to it nearly every game is going to be 1-0 or they will be playing 15 innings to get a 2-1 score.
Time flies. Some of those shiny new parks, like Camden Yards, are 20+ years old now. We're prob. within 10 years of another refresh cycle.

How are you able to isolate the effect of an aggressively shrinking strike zone from what you assert is an organic and permanent downward slope of offense over time?

The fact that you genuinely think games will average 2-3 total runs in ~5 years shows you really don't have a handle on how the game is (and isn't) changing.
"Still, for now, Yelich has figured out something that Trout hasn’t, and indeed, that the rest of the batters in the league haven’t." Interesting statement, but I wonder: is this the explanation, or is it that pitchers have not figured out that they have to be careful with Yelich in 2-out situations, while they obviously know that they do have to be careful (at all times) with Trout? This year's version of this chart may answer that question. Maybe.

Thanks for one of the most thought-provoking pieces I've seen here in a long time.
Thanks, Bill.

I didn't put this in the piece--widen a scope enough and even the landmarks look tiny and grainy--but the data suggest that Yelich's outperformance with two outs runs deeper than how pitchers pitched the two batters. Yelich actually saw a greater drop in the percentage of pitches thrown in the strike zone than did Trout, and had a lower raw zone percentage in those situations, too. Which is hard to explain. Some things remain mysteries.
That's very interesting. It would probably be better to look at this in terms of the "quality" of pitches thrown in the various circumstances rather than just zone percentages, but that probably exceeds our current ability to evaluate pitches unless someone has an ESP link to catchers and pitching coaches that I don't know about. That's kind of the point, actually: if it is something about the pitchers and they weren't taking the threat from Yelich seriously enough, they'll have wised up by now and his 2-out hitting will take a tumble in 2015 as they make adjustments. (Of course, that's also what to expect if there is absolutely nothing there with either Yelich or pitchers and it was all random fluctuations in 2014.)

There are many situations like this, probably more than we suspect even after being sensitized to the issue, where we can't really "know" what's going on without getting into the heads of the players and managers and knowing what was intended, not just what happened. May it ever be so; that's one of the things that makes baseball great.