Welcome to the Ottoneu keeper deadline, 2018 edition.  Today (11:59 PM EDT) is the final day to make that difficult decision about your on-the-bubble players before rosters lock and you set your sights on your upcoming league auction.  Per the rules:

Between the end of the Major League Baseball regular season and the end of arbitration, players may be cut. Between the end of arbitration and the keeper deadline, players may be cut or traded. After the keeper deadline and before the auction draft, teams may not cut or trade any players.

Since the keeper deadline also serves as a de-factor trade deadline, I’ve lined up a few final resources for you below and I’ve asked a handful of Ottoneu experts (Justin, Chad, Brad) to check your questions and comments periodically throughout the day to offer their input on your toughest decisions.  You don’t play this game? You should, but even if your non-Ottoneu keeper deadline is still a few weeks away, feel free to fire your questions below and we’ll do our best to give you feedback (for context, don’t forget to let us know details about your league format).

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“… pros were more likely to ride a wave of irrational exuberance than to fight it. One reason is that it is risky to be a contrarian. ‘Worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally’” – Richard Thaler in Misbehaving

At the root level, fantasy baseball is about acquiring more undervalued assists than your opponents. Everyone wants a first-round talent for a last round price (e.g. Aaron Judge). With teams clamoring to acquire every advantage, they are insistent on wasting away an early draft advantage. In early 2018 drafts I’ve participated in, an early emphasis on middle infielders is inflating their value way beyond their projected production. Is the observation wrong? If so why? If not, how can an owner take advantage of this mispricing?

Note: For this article, I will lump second basemen and shortstops together into one middle infield position. Neither position has more talent than the other and the bottom players will be used to fill a middle infield position.

For those who have recently created mixed-league valuations, positional scarcity doesn’t exist besides with catcher. I use the method outlined in Larry Schechter’s book, Winning Fantasy Baseball to determine my values. I’m not going to go into the process’s exact details but it’s the standard procedure used by fantasy experts to prep for auctions. Even a couple years ago a small amount of positional scarcity existed but a huge influx of good middle infielders has raised the group’s overall talent level up to the other positions.

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I was recently asked the following question by Werthless.

Jeff, what are you trying to accomplish here? Are you trying to estimate the volatility in an individual player’s projection? That’s an interesting question, and directly related to the risk of the player. Are you trying to do better than Steamer at predicting performance? That’s a big endeavor. Are you trying to predict injuries? Might be better to do that directly. Are you trying to better estimate number of plate appearances by estimating job security? Might be better to do that directly.

Then, you can combine the models to perhaps better quantify a player’s risk of meeting preseason performance objectives. You can apply your model onto a different year’s data to see how well your predictions match reality (ie. Do the higher risk players actually underperform more often than lower risk players).

I do have a plan I’m implementing but it wasn’t known to my readers. Sorry. I want to understand which hitter traits to concentrate on. If they don’t exist, I created some.

For a few season’s now, my hitters have steadily outperformed my pitchers. In my three main leagues, here are the pitching-hitting splits from this past season.
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Breakouts and busts. If there was a set procedure for finding both, it would have been found years ago and incorporated into projections. For now, all we have is the overall chances of either happening. Over the past few weeks, I’ve been trying to put a simple value on these chances. I’ve completed the underperforming calculations and will now finish the overperforming metric. Additionally, I will compare both metrics to get an overall idea of the projection’s volatility.

In my last article, I found the breakout thresholds for plate appearances (222 PA) and wOBA (.040) and won’t change these values. Besides these two values, I determined who had both thresholds crossed and when both were partially achieved. The overperformance needed to increase near to the threshold values.

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It’s probably been awhile since you’ve read a traditional “buy low, sell high” article.  In today’s golden age of baseball analytics where complex physics and statistics can be boiled down to a few simple indicators accessed instantly using one hand, it’s not very often that we (readers, fans, fantasy players) find ourselves in possession of knowledge before the masses.  For example, try “selling” Avisail Garcia and his recent .375 wOBA around your league without getting some type of response that includes “yeah, but he had a .392 BABIP”.

Thankfully, despite all the data available at our fingertips, the one ingredient that will always play a critical role in the mixture of value is the human element of perception, which can swing wildly in different directions depending who you’re dealing with.  Today I’d like to isolate a few players who’s perception may be suppressing their actual value a little more than it should be, which may represent a buying opportunity for savvy fantasy owners prepping for 2018.  The good news is you don’t have to stand in line to land these deals, but you will still need to get them early.

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A few days ago, Jake Leech asked me if Zack Cozart’s 2017 improved plate discipline would stick into 2018.

https://twitter.com/Stroke_19/status/931525718667943936

Cozart saw quite a bit of improvement with his K%-BB% dropping by 6% points.

Note: I like using K%-B% to get an overall value for a hitters plate discipline. Earlier this year, I investigated what early season stats point to a true breakout. K%-BB%, along with launch angle (FB%), were the two key factors to focus on.

The Steamer projection has his K%-BB% regressing closer to his 2016 values than the ones from 2017. This is how projections work with previous season stats having some weight along with some regression.

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I’ve done this before — compare similar players, one of whom is “name-brand,” the other “generic-brand,” using blind résumés — as have many others. Ben Kaspick carried the torch a while this year, but he credited Joe Douglas with the idea. So let’s say it’s a group effort to which I’ll contribute once again.

In anticipation of 2018 drafts, I wanted to carry out a “buying generic” style of analysis, borrowing in part from too-early mock draft average draft position (ADP) data. I do not intend to construe the following comparisons as rigorous analysis. I do, however, intend to highlight some potential bargains that, if the too-early mock ADP information is concerned, warrant your attention on draft day.

Comparison #1: Outfielders

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Max Kepler wasn’t an incredibly popular sleeper heading into the 2017 season, but I was certainly far from the only analyst who was high on the young German. The 24-year-old was coming off a productive yet unspectacular rookie campaign, and was just one year removed from a breakout year in Double-A that made him a fixture on top prospect lists.

Kepler’s 2016 wasn’t eye-popping, but there were many positive signs for the rookie. His power had just started showing up in games in that breakout Double-A season a year before, and now he was taking the next step and hitting the ball over the fence (17 HR in 447 PA). It certainly wasn’t out of the question to predict another step forward in that department, perhaps to a 20-25 HR season in 2017.

He stole just six bases in the majors in 2016, but the fact that he’d swiped 19 bags in the minors the year before was reason for optimism. Furthermore, his .235 batting average was held down by a .261 BABIP, which seemed far too low for a player with pretty good speed.

In short, it wasn’t hard to envision something like .275/25 HR/15 SB if everything came together in 2017. Despite being an unproven option at a deep position, Kepler was drafted in well over half of Yahoo leagues. Like I said, not a super-popular sleeper, but not flying under the radar either.

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This was supposed to be a quick paragraph on Max Fried but it turned into a borderline Quick Look. I was doing an article on pitchers who saw their ERA balloon because of starts at Coors. His non-Colorado ERA dropped stood at 3.09 vs 3.81 which made him seem like a borderline ace. I kept digging and found additional encouraging information. Here are some of my thoughts on my first 2018 deeper sleeper.

First, here’s how industry sources graded him including his pERA grades from his short MLB stint.

Some definite disparities exist. I will examine each pitch with a video from his September 9th start against the Marlins.

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If you’ve followed baseball over the course of of the past few seasons, you’ve probably noticed the new data available to us with the advent of Statcast. This has led to the development of new metrics to measure player performance, with xwOBA being one of the most notable. If you’re familiar with xwOBA, you have likely seen it used to examine the quality of contact made or induced by hitters or pitchers.

Today, I want to look at the pitcher side of things. While it is generally accepted that some pitchers are better at inducing weak contact than others, to this point, the baseball community is still working through the best ways to process the implications of the relatively new data available to us.  As Craig Edwards wrote yesterday on the main site, there isn’t a strong relationship between weak contact year to year.

Acknowledging all of this, I want to look at pitchers who have recently improved the quality of contact they have allowed. There are a couple assumptions to acknowledge here (included at the bottom of the following table). First, I am only looking at pitchers with over 1000 pitches in 2017 before the All-Star Game. Additionally, I am only including pitchers who have thrown 500 pitches since the All-Star Game.

My intent with this is to try to get a better look at starting pitchers, who have made more than a couple of starts, and remove relief pitchers. I have also limited the group to players who’s post All-Star Game expected wOBA is less than the sample average at the time of the break (this works out to be around .315, for reference). The last stipulation I have included is that I am only showing pitchers who have seen an improvement of .010 or greater in their expected results (10 points or greater). The reason for this is simple, I would rather show 25 results than 45.

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