As I was preparing this little post, the Dodgers scored a first-inning run against the Nationals. Interestingly, while the run was driven in by an extra-base hit, it was not driven in by a homer. So far in the playoffs, that’s been slightly unusual!

I think it was Joe Sheehan who came up with the “Guillen Number.” You take all the runs scored directly off homers, and then you divide by the total number of runs. I’ve taken care to calculate playoff Guillen Numbers during the wild-card era, stretching back to 1995. Here you are:

Now, this is coming into today, so this doesn’t include the Dodgers’ one run so far. And, yes, I know, it’s early, so very early, and the numbers still have time to sort themselves out. But in the playoffs to this point, more than half of all the runs have been driven in by dingers. We’re at 32 of 59, to be exact, even though the average before this year was 39%. Over the preceding decade, 37%. It’s easy to tell that this year stands out, and if you’ve been following the playoffs yourself, you’ve presumably noticed. It feels like the winners have been determined by who hits the long ball, or the biggest long ball, and that feeling is supported.

It’s not like home runs are necessarily out of control. Hitters in the playoffs are slugging just .378, which isn’t unusual. We aren’t really seeing an offensive resurgence. Rather, sort of the opposite?

Home runs are more responsible for offense right now because it’s less easy than it’s been to generate other kinds of offense. Batters have reached less than 27% of the time, and though that makes it more difficult to hit a multi-run homer, that also means there have been fewer opportunities to drive a run home with a single or a double. One interesting indicator: we’ve so far seen just two sac bunts. The sac-bunt rate is way down, and maybe that’s a coincidence, or maybe managers aren’t trying to play smallball because outs are already coming in such droves.

There are many games left to play. Importantly, so far we’ve mostly just seen the best starting pitchers. The numbers will change, to some degree. But if you’ve felt like offenses have leaned on homers — yeah. Yeah, that’s basically what’s been happening.

As I write this, the Indians lead the Red Sox 4-0, thanks primarily to a three-run homer from Lonnie Chisenhall. The home run from Chisenhall, off David Price, was his first home run off a left-hander this season. That would be notable, except that the first few days of this postseason have been filled with home runs from guys you don’t expect home runs from.

At this point, 15 different players have hit a home run in the postseason, Wild Card games included. Here are those 15 players, along with their regular season HR and wRC+ numbers.

Eight of the 15 players to hit one out so far this year had single-digit home run totals on the season; seven of the 15 had a wRC+ below 100. Besides Trumbo, Encarnacion, and Bautista, this is just not a group that you think of as big time sluggers. But facing some of the best pitchers in baseball, these are the guys muscling up and hitting the ball over the wall.

Of course, we’re talking about a half dozen games, so this isn’t really meaningful data. But it is a bit of a continuation of the trend we saw this year, where low-power guys started hitting for more power, and a reminder that the home run spike of 2016 has been led by the middle class of hitters.

The recipe for winning in the playoffs really shouldn’t be that complicated. You want your good players to do well, and you want the rest of your players to do well enough. It shouldn’t take any more than that, so, for the Rangers, they came into the ALDS against the Blue Jays hoping to lean on Cole Hamels and Yu Darvish, which is totally fine. Those are two outstanding pitchers; you expect them to have outstanding games. Hamels, yesterday, allowed seven runs, and couldn’t get out of the fourth. Darvish, today, allowed five runs, and couldn’t get into the sixth. Now the Rangers are probably screwed. What do you do? They just got baseballed.

Darvish’s problem was that he allowed four homers. And I’m not going to try to defend him — you have to work pretty hard to give up four homers. But we should at least be able to excuse him for one of them. Behold Kevin Pillar doubling what was, in the moment, a one-run lead. I’d argue that Pillar might’ve done a worse job of executing here than even Darvish did. No, that sounds stupid, but, you know what I mean. What is this!

Literally just the other day, Pillar knocked a double against a pitch that was similarly high, but that double could’ve been caught, and at least the pitch was somewhere over the plate. Pillar went up and Pillar went in to take Darvish yard, and I just honestly don’t know what you’re supposed to do with this. This isn’t the pitch that Darvish wanted to throw, but it should’ve just put him behind 3-1 in the count, not 3-1 on the scoreboard.

Here’s a plot of all the 2016 home runs, with data borrowed from Baseball Savant. I put righty and lefty home runs on the same scale, such that pitches on the left are inside, and pitches on the right are outside. You can easily see here that Pillar’s home run is exceptional, relative to the pack.

Maybe it’s not the single most exceptional home run. Maybe it is? I don’t know. But it’s out there. It’s above most of the group, and it’s to the left of most of the group, and it’s both of those things at the same time, which makes it bizarre. It doesn’t reflect well on Pillar that he swung at this pitch in the first place while ahead in the count. It’s probably no mystery why Pillar is the owner of a career 85 wRC+. But on the other hand, maybe that is a mystery, because if Pillar can go yard against a pitch like this, what’s to stop him from going yard against almost literally anything?

I am a professional baseball analyst, writing for a website founded upon baseball analysis. So often, around this time of year, my analysis comes down to a “welp.” Pillar put a…good? swing on the ball. I don’t even know, man. But it sure was a dinger, all right.

Last night, during Madison Bumgarner’s latest playoff masterpiece, someone asked me in our live blog whether Bumgarner’s postseason heroics have already been great enough to get him into the conversation about potential Hall of Fame pitchers, even though he’s obviously far short on regular season totals. And there’s no question that Bumgarner’s playoff numbers are staggering; he’s now thrown 97 1/3 innings with a 1.94 ERA, good for a 53 ERA-. He’s already thrown three complete game shutouts in his postseason career; the all-time Major League record is four. Bumgarner has already cemented himself as one of the best playoff pitchers in history, and he’s still just 27.

But while we’re all rightfully marveling at Bumgarner’s dominance today, it’s worth remembering that, for whatever reason, the current crop of Hall of Fame voters have shown no interest in enshrining quality starting pitchers, even ones with similarly unbelievable postseason numbers. For comparison, here’s Bumgarner’s career postseason numbers compared with those of Curt Schilling.

If Bumgarner throws another 40 innings at his current postseason pace, he’ll essentially match what Schilling did during his postseason career. Except Schilling also threw 3,261 excellent regular season innings, worth +80 WAR by FIP (#20 all-time) and +81 WAR (#32 all-time) by runs allowed. And the BBWAA members with Hall of Fame ballots haven’t cared at all.

In 2013, Schililng’s first year on the ballot, he got 39% of the vote. As the ballot got more crowded, he dropped to 29% in 2014, then bounced back to 39% in 2015. Last year, encouragingly, he got up to 52% of the vote, which puts him within striking distance of the 75% needed. Most guys who get over 50% within their first few years eventually get in, so Schilling has a shot at getting inducted.

But think about what we’d require from Bumgarner to get to Schilling’s career numbers. Not only does he have to throw 40 more amazing innings in the postseason, he’s almost 2,000 regular season innings behind; we’d basically need him to keep throwing 200 innings per year for the next ten years. And it’s not like Bumgarner got a late start on his career; he debuted at 19, and would still need to be throwing 200 innings per year at age 36 in order to get up to Schilling’s career innings total, without any injuries between now and then.

And if Bumgarner does that, his closest statistical comparison would be the kind of guy that voters kind of shrug their shoulders at, not sure whether he was worthy of the Hall of Fame or not. That’s just nuts.

Bumgarner, at this point, has more work to do to put himself in Hall of Fame consideration. He just hasn’t pitched enough at this point to be a strong candidate. But there’s no question that he’s put together one of the best postseason resumes we’ve ever seen, and if he remains a quality pitcher for most of the next decade, how could we possibly deny him entry?

But that’s what we’re doing to Schilling. If you’re awed by Bumgarner’s October dominance — and you should be — then you should support Curt Schilling for the Hall of Fame. I know he’s not the most likable guy in the world, but the Hall of Fame is for honoring the greatest players of all time, and Schilling was one of those. When you watch Bumgarner pitch, remember that Schilling was just as good for even longer, and then lobby your favorite Hall of Fame voter to recognize the guy who was doing this before Bumgarner came along.

The Blue Jays just walked off against the Orioles, with Ubaldo Jimenez giving up the game-losing home run to Edwin Encarnacion in the bottom of the 11th inning. The story, though, is that the Orioles used seven pitchers in their final game of the season, and potential Cy Young winning closer Zach Britton was not one of them. The Blue Jays beat the Orioles in large part because the Orioles didn’t use their best pitcher tonight.

At this point, there are two possible explanations for Buck Showalter’s decision.

1. Zach Britton wasn’t available, or felt something off when he warmed up in the 8th inning. Given that Buck Showalter seems like a reasonable human being, this should probably be our default assumption right now. Often times, when a manager does something inexplicable with their bullpen usage, there’s information asymmetry, and they know something we don’t know. That may very well be the case here.

OR

2. The “save” stat just cost the Orioles their 2016 season. If Showalter really used Brian Duensing and Ubaldo Jimenez before Zach Britton because he was waiting to get Britton a lead so that he could earn a save, then this is the craziest managerial decision that I can remember in my baseball-watching life.

I don’t see another possibility, really. Either Britton is hurt or Buck Showalter just screwed up in an historic way. It will be interesting to find out how honest the team is about Britton’s availability in postgame comments.

Update: It was option #2. A few quotes from Showalter.

Buck: "He was available"

— JJ Cooper (@jjcoop36) October 5, 2016

Buck: "You could (regret) afterwards, but we went 4 innings trying to get to that spot (of using Britton)."

— JJ Cooper (@jjcoop36) October 5, 2016

Buck: "Playing on the road had something to do with it too."

— JJ Cooper (@jjcoop36) October 5, 2016

"Nobody has been pitching better for us than Ubaldo."

— JJ Cooper (@jjcoop36) October 5, 2016

We’ve known for some time that the 2016 playoffs would begin on this evening. Many have known for less time that their favorite teams wouldn’t qualify for the tournament. My favorite team is done playing. This is a post and a poll for those of you who find yourselves in an identical circumstance. Of baseball’s 30 teams, 20 are finished. So for fans of those teams who’re finished, who from the remaining crop looks the most appealing, as a bandwagon?

I’ve run this once or twice before. I’ve been forced to acknowledge that there’s nothing I can actually do to prevent fans of playoff teams from also voting, presumably for their own playoff teams. I get that when one is overcome by the postseason spirit, irrational behavior can feel like rational behavior. But I would still ask you to please refrain. There is no benefit from a vote for your team. Literally nothing. Not even a moment of fleeting pleasure. I trust you’re not naturally inclined to be an asshole just because. You’re great! Keep it up!

Assuming you do indeed cheer primarily for a team of present-day golfers, who do you want to see do the best? Or can you not bring yourself to bandwagon at all? Vote below. And, vote, just.

Francisco Liriano has pitched his way into consideration for today’s American League Wild Card game by having been excellent since his return to the rotation — during which period he’s recorded 24.2 innings with 26 strikeouts, six walks, and four earned runs — but also by being on the right schedule to pitch today and by pitching with his left hand.

It’s easy enough to sort this list of teams and find that the Orioles have been the worst American League team against lefties this year. But that’s just what’s happened in the past. It doesn’t necessarily dictate what would happen in the future.

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You might’ve noticed that the regular season is over. As such, all the regular-season numbers are in the books, which gives us some good opportunities for evaluation on the outside. In this quick post, I would like to evaluate the preseason team projections. Last year, at least in the American League, the projections wound up being a mess. I remember there being a point about halfway through where it looked like one would’ve been smarter to bet the opposite of every AL team projection. How’d the numbers shake out in 2016? Below, see plots.

Several times before, I’ve used old team projections from seasons past. Not all team projections included here come from the same sources, because the same sources just haven’t always existed. For recent years, I’ve been able to use FanGraphs team projections. Going further back, I’ve had to search elsewhere, because FanGraphs just didn’t have projections. So I know that’s one potential source of error here, but I think it’s better than just not having data at all. And ultimately, all projection systems are built around similar foundations. You take recent numbers and weigh them and project them for the short-term future. There’s not a lot to change. So! Why don’t we just get to the information?

I have team projections stretching back to 2005. Here is a plot including actual team wins vs. projected team wins. What you see here is the average error per team per season:

This year, the projections fared much better than they did in 2015. I went with the last version of FanGraphs’ preseason team projections, and after the average projection missed by 8.1 wins a year ago, this time the average projection missed by 5.7 wins. That puts this year in line with 2014, as being fairly successful, math-wise. It would be the best-projected year since 2007. I have no idea if that means anything; I’m just putting it out there. This year’s biggest miss was the Twins, who fell an incredible 19 wins short of the March expectation. The word “Twins” has the word “wins” right in it. It also has a T, which looks like the symbol for perpendicularity. Other teams might be content to operate in parallel with winning. The Twins decided to challenge it head-on.

Another thing we can look at: What about BaseRuns wins vs. projected wins? We know there are elements that are just about un-projectable. What if we strip those away?

Last year, the average miss was 7.0 BaseRuns wins. This year, the average miss was 5.4 BaseRuns wins, standing again as the strongest year for the projections since 2007. You might say it’s strange that the projections haven’t improved on a set of projections from a whole decade ago, since that was so far back it was pre-PITCHf/x era. But this is at least evidence that last year’s weirdness was a blip. The biggest miss for 2016: The Red Sox, actually. They were projected for 89 wins, but they finished with 102 BaseRuns wins. Good team, the Red Sox. The Cubs were the second-biggest miss!

Just to close it out, we can leave out the projections entirely. Here are BaseRuns wins vs. actual wins. Are teams finding ways to beat their underlying statistics more often, or is that not the case?

This year, the average difference was 4.2 wins. That’s down from last year’s 5.1, but still, this is the second-biggest error in the sample. So that’s potentially of note. Last year it felt like the BaseRuns model was practically broken. This year has eased some of those concerns, but it’s still worth wondering about. The Rangers finished with 13 more actual wins than BaseRuns wins. The Rays finished with 13 fewer actual wins than BaseRuns wins. Go ahead and figure that one out. At least for the time being, I quit. What’s done is done!

Some time ago, in another InstaGraphs post, I mentioned something that could potentially become true about the 2016 Reds. We spend a lot of time writing about things that could potentially become true, and inevitably, a lot of those paces fall off. There are reasons for that, and that would be a subject for another post. But the 2016 numbers are official now, dammit. There’s no more “on pace for;” there only is. Here is something that is:

That’s bad! Boy, it gets worse. Here are the worst team pitching staffs since 1900, by our version of WAR:

To be absolutely clear about what we have here: By our numbers, the Reds just became the first pitching staff in modern history to finish with a negative WAR. It’s only slight, sure, and the difference between them and those 1915 Athletics is less than one win, but that negative symbol is conspicuous. It pushes the digits over to the right, so they can stand out. The Reds, as a collective, featured a major-league pitching staff that was a worse-than-replacement-level pitching staff. That’s almost unfathomable, is what that is.

Did the Reds really have the worst pitching staff ever? I mean, hell, I don’t know. Their pitching staff had one of the biggest home-run problems ever. By actual runs allowed, they were a little bit better than replacement-level, but you know where we stand on all that. There’s no way to actually compare across seasons or eras, not with the desired level of precision. We just don’t understand pitching that well yet, and we understand it even less the further back we go into history. We can say this: If you wanted to talk about the worst staffs of all time, you might use FanGraphs WAR as a starting point. It couldn’t make the Reds look any worse.

Reds pitchers, month by month:

• April: -1.3 WAR, 30th place
• May: -1.1, 30th
• June: -0.6, 30th
• July: +1.9, 20th
• August: +1.0, 26th
• Sep/Oct: +0.2, 30th

The first half is what did the Reds in. In the second half, they accumulated 3.2 WAR, good enough to edge out the Twins, Braves, and Diamondbacks. In each of the last three months, the Reds’ staff finished in the black. Yet it’s appropriate that, in the final stretch, they were dead last again. All they needed were one or two more good games, to avoid finishing in the…red. But a league-worst September and October locked the Reds into place. They had a chance to run away from history, but instead they suffer its embrace.

A negative WAR. It’s not that there weren’t some success stories. Anthony DeSclafani was pretty good. Dan Straily was all right. Raisel Iglesias adjusted well to the bullpen. Brandon Finnegan got stronger in the second half. But let me tell you: 32 Reds pitchers pitched. Twenty of them finished below 0.0 WAR. Alfredo Simon allowed more runs in 58.2 innings than Jon Lester allowed in over 200. J.J. Hoover allowed more runs in 18.2 innings than Zach Britton’s allowed the last two years combined. For the Reds, 2016 was never going to be about winning. Yet it also definitely wasn’t supposed to be about this.

To be clear, there are three postings here.

Position: Colorado Rockies Analyst – Baseball Research & Development

Location: Denver

Description:
This individual will collaborate with the Research and Development team and will assist in the development and maintenance of a player information and projection system along with other statistical analysis and on field strategy. This position requires strong statistical development skills and experience as well as a demonstrated ability for independent curiosity and a commitment to excellence while working within a team framework.

Responsibilities:

• Utilize advanced statistical techniques to analyze large datasets for actionable conclusions.
• Design and document development of new analytic applications to assist in player evaluation.
• Utilize existing Baseball Research and Development applications and databases in order to perform quantitative research related to baseball strategy and player evaluation.
• Work with Baseball Research and Development team to design and integrate new statistical ideas into existing analytical systems.
• Build automated solutions to import, clean and update datasets for use in downstream analyses.
• Complete ad-hoc database queries to answer specific questions from Front Office colleagues.

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