Most fantasy owners expect drafted players to under and over perform some amount. When a player overperforms, the owner looks and feels great because they “knew” a breakout was coming. Owners hope they didn’t pick too many players on the other end of the spectrum. The underperformers are the ones who drag down a team and owners find as escape goats for a bad season. I’m going to start laying the groundwork to determine a hitter’s disappointment chances.

The first major step in finding a disappointing hitter is to define what is a disappointment. After owning too many fantasy teams over the years, I’ve had my share of disappointments (e.g. Brandon Webb in a 2009 first round) and feel they are just part of the game. This ambivalence doesn’t mean I shouldn’t know the breakout chances. Even small changes in the odds can make a major difference after rostering 20 or more players.

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Chase Anderson had shown glimpses of upside over his first three seasons as a major leaguer, during which he netted a 4.26 ERA, 1.35 WHIP, and 12% K-BB in 418.7 IP. He even caught the eye of our very own Eno Sarris, who heralded him as a solid deep league option for those first two seasons.  Unfortunately, he (Anderson, not Sarris… though Sarris has given up some bombs) had a home run issue as well (1.3 HR/9) only exacerbated by playing first in Arizona then Milwaukee.

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I’m a little late to the Bold Prediction review party due to taking a brief hiatus this October. It’s time to play a little catch-up on end-of-season content.

This year, I decided to try a little experiment, I made 10 predictions using positive language and 10 more using negative language. It’s always been my impression that it’s easier to get a negative prediction right. Let’s see how I did this year. I’m going to organize this in the most confusing way possible, bundling by number. You’ll see what I mean.

Typically, I aim for a 20 percent success rate with these picks.

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Yesterday, I reviewed the 2017 starting pitcher walk rate improvers using my updated xBB% equation. Today, I’ll recap my walk rate regressers, which includes those starting pitchers whose actual walk rates were significantly better than their xBB% marks.

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Fantasy owners participate in mock drafts to practice for their actual league drafts. Without the internet, neither mock drafts nor average draft position (ADP) data would be possible or available to us. The union of the two allow owners to understand approximately when they can expect players they plan to target and devise an appropriate plan of action.

What ensues when you draft for the next baseball season before the current season has ended and without ADP data and having not refined/run your 2018 projections yet? Chaos, namely. For the second year, the Honourable Justin Mason organized the Too-Early (#2early) mock drafts for me and a few dozen other analysts, the final ADP “data” (in air quotes, because a sample size of four drafts is not particularly strong data, and, as aforementioned, it is way too early to be mock-drafting) for which can be found here.

I drafted out of the 11th spot (of 15) in draft #4. I made a concerted effort to draft a good team, but I also deliberately avoided certain players just to see where other owners would draft them. (Had I drafted these players for my team, I would have a weaker understanding of how other owners valued them.) The lack of ADP data made it incredibly difficult to implement this plan. In a nutshell, my plan consisted of drafting the best player with a track record still available on the draft board (mind-blowing, I know), with a strong preference for hitting. There’s an objective basis to this that, rather than trying to explain, I’ll let reveal and define itself as I go. This plan served me admirably in all rounds of the draft.

Here’s my team, by round. “Rk” indicates a player’s draft rank within his assigned position. Columns labeled “All” and “Pos” depict players’ end-of-season values overall and by position, respectively, each of the last four years. These ranks come courtesy of Rudy Gamble, who concocts them for Razzball. If you’re concerned that these might somehow be inferior to ESPN’s Player Rater, know that Rudy won this year’s Tout Wars Mixed by a landslide, so I thoroughly trust his process.

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The Graduate: Amir Garrett, LHP: Coming off of a strong 2016 season, Garrett positioned himself well to pitch some big innings for the Reds in 2017. He ended up throwing 70.2 innings but his ERA of 7.39 was down right ugly. He was hittable (9.42 H/9) and walked a lot of batters (5.09 BB/9), which is a terrible combination. Worse yet, he allowed 23 homers — good for an ugly 2.93 HR/9 (the worst rate in the Majors for pitchers with more than 50 innings pitched). Garrett, 25, will have to focus on improved command and control if he’s going to get another shot at starting for the Reds. If he can’t, though, his fastball-changeup combo could be enough to be an impact reliever.

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Ottoneu arbitration began on October 15 and runs through November 15. This omnibus is a one stop shop for all the strategy and tactics you need to get through the process. The following omnibus is a recreation of the one I published the last two years. We’ve mostly said everything there is to say over the course of more than 10,000 words. But first, some background.

Perhaps I jumped too quickly into the details. Ottoneu is the award-eligible fantasy platform hosted by FanGraphs. Think of it as dynasty-lite. You get a 40-man roster, a $400 payroll, and way too much freedom to manage your team your way.

Over the offseason, the price of every major league player increases by $2. Minor leaguers increase by $1. Then there is an arbitration process that can be done one of two ways. Most leagues use the allocation process which ultimately adds an additional $11 to $33 per team. This omnibus is intended for both forms of arbitration, but the allocation process does open more possibilities for strategery and thus has more words dedicated to it.

As I mentioned, there are two systems of arbitration: voting and allocation. An asterisk indicates that the article is intending for voting leagues. I’ve organized the omnibus into sections: intro, intermediate, and advanced. Read the rest of this entry »

In late January, I updated my pitcher xBB% was include and additional variable, 3-0%, which is the percentage of a pitcher’s plate appearances in which a 3-0 count is seen. Since Alex Chamberlain had already published such a formula, which built upon my original equation, I simply updated the variables of his. I then used the new formula to highlight a group of starting pitchers with an xBB% significantly lower their actual 2016 marks. This group was then discussed as potential walk rate improvers in 2017. Let’s see how they did.

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Last week I posted my calculated 2017 dollar values for ottoneu FanGraphs points leagues, and while they aren’t meant to be an exact representation of each player’s true value this past season, they should reasonably convey how much each player was worth. I also happen to have a set of data on the average auction price for all players in thirty first year ottoneu FGPts leagues from last spring. Today I’ll be constructing three hypothetical ottoneu teams using ’17 final results and those average auction salaries, with one of those teams serving as my best guess for the best $400 ottoneu team that could have been built in a first year league.

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I often struggle to come up with players and topics to write about this time of year. Thankfully, colleague Justin Mason has provided me with something to piggyback off of. He’s done an awesome job rounding up participants and conducting industry mock drafts, and you can check out the ADP from those here. The ADPs will change as players change teams and roles change for individuals, but this is a great starting point. Using that data, three pitchers stood out as enticing dice rolls in the 300-plus range. Read the rest of this entry »