Full-season team projections cause some heated arguments. If a team finishes the year with fewer wins than expected, fans want to know why their club underperformed projections. If a team overperforms its projections, meanwhile, those same fans will insist that forecasts in subsequent years lack the ability to detect their club’s particular strengths and are thus useless.

Here at FanGraphs, we have only been doing full-season projections for a couple years, but just about every week I see a mention of the 2015 World Champion Kansas City Royals’ projected record of 79-83. If I search Google for “79-83 Royals FanGraphs,” I get over 11,000 article links. Unsurprisingly, it’s a popular topic. Rarely does a club, following a pair of World Series appearances, then proceed to fail to break even. But that’s what the numbers suggest for 2016.

While FanGraphs has produced team win projections for only a couple seasons, Replacement Level Yankee Weblog (RLYW) has been publishing win projections for years. Since 2007, to be precise. Given this larger sample, I thought that it might be worthwhile to compare the projected win values produced by RLYW to the actual final win values produced by teams. So, with the permission of RLYW editor SG, that’s what I’ve done here.

I hate to disappoint anyone, but there are actually aren’t any great findings in the plethora of graphs to follow. I did find a couple interesting artifacts of the data, but no game changers. Instead, I see the following mainly as an additional data point in many past, present, and future discussions.

To start with, here is how projected and actual values have correlated.

The projections weren’t completely off, but a wide variance exists. Now, here is a graph illustrating how much each team’s projections have deviated from the projections over the years.

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I’m pretty sure each team’s fanbase has complained about projections in the past. This graph allows those fanbases to observe how justified their complaints have actually been. In the case of the Cardinals (whose median win totals have been six wins better than their projected totals), reasonably justified. In the case of the Brewers (who’ve finished within four wins of their projection in almost every season), not as much.

Of all the graphs included here, this one offers the biggest surprise. It compares the density of projected versus actual wins.

The results are pretty much as expected: a narrow band of projected results and a wider, flatter band of actual results. For me, though, the notable observation here is the small decline right at 80 and 81 wins. This could just be an artifact of a small sample. Or it may occur because teams late in the season see themselves as contenders and bump up their win total a bit with trades. The sellers could see their win total pushed down a bit. Or maybe something else.

Finally, here are two graphs showing the difference between projected and actual wins two different ways. First, the simple way.

This graph offers another interesting point. Look at the average slope line. The differences should have equal points above and below the 0 line. Instead, the low-win projected teams seem to outperform more often than underperform. The opposite is true for the high-win teams. I am not sure why this has been true since I don’t know the exact details of RLYW’s projection creation process. If I were to guess, I bet the stats used are not regressed enough.

With the simple graph of expected versus actual wins out of the way, here is the complex version.

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Besides just the individual results, the quartile, median, and extreme (whisker) values are available for each win total. Looking again at the Royals’ projected 79 win total for 2016, it can be seen that these teams overperform by two Wins, with the quartile range sitting at plus or minus eight wins. On the extreme ends, we find +16 wins by the 2015 Royals (and below that, +11 by the 2011 Rockies) and -15 Wins by the 2014 Diamondbacks. Well, the graphs are done and hopefully readers can find a way to use them in future projection discussions.

Preseason team projections are far from perfect, with players over- and underperforming, injuries, trades, and about 1000 other factors. The preseason projections do give everyone a beginning expectation level to which they can anchor their hopes. The expectations can change and teams can make the projections look silly at a season’s end. Hopefully, I was able to get a quick snapshot on how those projections have historically and the realistic chances of a team beating those projections.

Big thanks to Sean Dolinar for the graphs.