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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