FanGraphs presents on its pages a few different projection systems (Steamer/ZiPS/Depth Charts-which is a combination of the prior two) and shows preseason projections for each system, rest of season projections, and update projections (which is simply actual year to date performance plus rest of season projections). One of the more unique versions, and the one I will be looking at today, is the Steamer600 projections. These simply take the Steamer projections and assumes every player has the same playing time (600 PA/200 IP for SP/65 IP for RP). The Steamer600 Update projections (found here) incorporate the up to date Steamer RoS projections, so I like to look at them from time to time to get a sense of how Steamer is estimating true talent level regardless of playing time (due to injury, a bench role, or being in the minor leagues). I have taken those Steamer600 Update projections and applied ottoneu FGPTs scoring to find some interesting and surprising names that might be underappreciated as potential keepers.

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My how time flies! We are already at the halfway point of the season, and the league races and overall power rankings are really starting to take shape. Here are links to the  April and May power rankings where I gave the history of these rankings and explained the methodology.

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The halfway point of the season is upon us, so I thought it would be fun to take a look at the ottoneu All Stars through the first half of 2016. I didn’t want to make things messy with calculated dollar values or points over replacement, so I’ve simply taken the player with the most points (FGpts) at each position in the ottoneu starting lineup. There are some surprising names on these lists, so let’s jump right in with the hitters:

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I have posted two articles earlier this season on the hitters with the largest changes to their Depth Charts rest of season projections versus the preseason, and today I’m going to take a look at the pitchers.

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Niv, creator and grand poobah of all things ottoneu, was gracious enough to share with me some data regarding the most added ottoneu players over the last 30 days(across all league types). I thought it would be interesting to take a look at the hot pickups in ottoneu, so here we go!

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With another month of the baseball season gone, it’s time once again to present the ottoneu power rankings. Much of the history and methodology of these rankings was explained with last month’s post, so be sure to read that if you haven’t already.

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As I did last month, I’m once again going to take a look at the Depth Charts rest of season projections for hitters, and compare them against the preseason projections. Once again, all points per game figures and positional rankings are shown using ottoneu points scoring.

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As we mentioned on a recent episode of OttoGraphs, May is traditionally the month where contenders and re-builders start buying and selling with gusto in ottoneu. With that in mind, I want to highlight a handful of players I would be looking to acquire as a contender and with a look to the future, specifically in ottoneu points leagues (although most of these players offer value in the rotisserie leagues as well).

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Starting in July of last season I began posting monthly power rankings for ottoneu FanGraphs points leagues, taking up the mantle of work previously done by Nate Emmerson (who was gracious enough to share his process and python scripts with me). With April in the books, it’s time for the first edition of the 2016 power rankings, and this time I am including rankings for all four ottoneu formats.

The first ranking I’m going to present shows the top individual teams in all FanGraphs points leagues based on their Pace, which is a metric I calculated by taking each team’s Pts/G and Pts/IP and multiplying them by their projected games (max of 1,944) and innings (max of 1,500).

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The season is only a little over two weeks old, and all the rote small sample size caveats apply, but some hitter performances in the early going have been enough to move the needle when looking at rest of season projections. I’m specifically going to be looking only at hitters in this article, mostly because rest of season projections aren’t quite as quick to adapt to changes in talent for pitchers. I’m also going to be presenting this information through the lens of ottoneu Fangraphs points leagues, which utilize linear weights scoring.

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