Last year around this time I took a look at the Steamer600 Update projections to try to identify potential keepers that were currently undervalued or overlooked. As a refresher, the Steamer600 Update projections represent the current Steamer rest of season projections, but scaled to 600 PA/200 IP (SP)/ 65 IP (RP) for all players. I like to look at these projections periodically 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 potential keepers.

Before I go any further, I thought it would make sense to highlight a few of the names I mentioned in last year’s article(these are the hits, there were many misses as well):

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Another month gone by in the ottoneu season, another edition of this article highlighting how desperate teams are for pitching. Seven out of the ten most added players in the last week are pitchers, though things are more even when looking at the most added players over the past month.

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‘Tis the season for reviewing bold predictions, let’s check in and see if my second year of predictions is going any better than my first did (1 for 10):

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A few weeks ago, I took an initial (and someone rudimentary) dive into tying Statcast data to Ottoneu. Specifically, looking at the Fangraphs Points (FGpts) format. Like in real baseball, when the ball leaves the bat with a higher exit velocity, it is more likely to produce more favorable results. This was to be expected. The way the scoring settings for FGpts was developed was through linear weights, so it would make sense that the distribution of points per batted ball ties closely to reality. Today, I am going to look at the batted balls hit though the all star break (actually, just before the break as I pulled this July 6th) and examine the expected points per ball in play on specific batted ball types. Let’s get started.

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With the All-Star break upon us, I wanted to take some time to notice several players who are putting up seasons worth your attention across Ottoneu. I guess we should probably consider what we mean by “worth attention.” Certainly many players are having great seasons. Chris Sale is pitching at career bests, Aaron Judge and Cody Bellinger have combined for 53 HRs, Justin Turner is walking more than he strikes out, and Jose Ramirez has picked up 2B eligibility when producing a career high ISO. None of these players will be included… Wait, what? I thought you said this was an All-Star team?

A couple key points that drive the Ottoneu format. First, it is an economic game with a defined budget. Second, players get kept year to year. For this reason, and because others can/have spilled virtual ink over the players listed above, I will exclude them. A couple additional stipulations in filling out this roster Read the rest of this entry »

We have now reached the halfway point in the ottoneu season, and at this point if your team isn’t squarely in the top four or five in your league you are in some trouble. To me the end of June is a watershed moment where teams are no longer just benefiting from a hot start or suffering from under performance, but rather the standings represent the realistic groupings of contenders, also-rans, and bottom dwellers.

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Monday, Tom Tango took to twitter, releasing two graphs.

Explanation here: https://t.co/fSoKBGymlH pic.twitter.com/lXpHEkTy7s

— Tangotiger (@tangotiger) June 26, 2017

This might be interesting, especially at the high-end speeds. @pobguy pic.twitter.com/LJcmO7l7RQ

— Tangotiger (@tangotiger) June 27, 2017

Like many of us who benefit greatly from Tom’s work, these got me thinking (specifically the second graph). What we have here is wOBA on each event type plotted against each event’s exit velocity. Like we would expect, the harder you hit the ball, generally, the better outcomes you have. So my thought is, how do we apply this to Ottoneu? While hardly revolutionary, the idea of converting all metrics into Fangraphs Points is an adjustment all players must make.

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As I did last year, today I’ll be taking a look at some players I would be targeting in ottoneu (specifically FGPts, but the advice works for all formats) as both a title contender and a rebuilding team. There are plenty more targets than just the ones I’m naming here, but this group is a good start.

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We are nearing the halfway point of the season, and this time of year is often peak prospect season. Top prospects are getting called up (or are about to be) now that the Super Two “deadline” has passed, fantasy teams are quickly shifting into rebuilding mode, and a new crop of draftees is being added to ottoneu. Let’s once again take a look at the top prospect performances (using the tool I put together that pulls from MLBfarm.com) year to date and over the past thirty days.

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A few weeks ago, I stole RotoGraphs contributor Joe Douglas’ idea (with his permission) as I pointed out that the “generic” Tommy Pham had provided surprisingly similar offensive production in his career to the “brand name” Michael Conforto. It was a fun exercise, and one that we’re going to do again today.

To set the stage, we’re going to talk about two rookies with outfield eligibility. One receives plenty of attention and hype; the other, not so much. Mr. Generic debuted in 2016 but is still considered a rookie this season, while Mr. Brand Name debuted in 2017. Here’s how they’ve fared so far this year:

The first thing that jumps out is the nearly 100-point difference in BABIP, and the fact that Mr. Generic’s BABIP is perhaps unsustainably high. More about that in a minute.

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