Archive for Sleepers

When Plate Discipline Sticks

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.

Zach Cozart’s Plate Disciple
Season BB% K% K%-BB%
2016 7.3% 16.5% 9.2%
2017 12.2% 15.4% 3.2%
2018 (Steamer) 8.8% 15.6% 6.8%

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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Early 2018 Hitter Blind Résumés

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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Breakouts That Didn’t Happen: Max Kepler

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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Max Fried: 2018 Deep Sleeper

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.

Max Fried Prospect Grades
Season Source Fastball Curve Change Control
2018 BA 92-93 mph (55) Plus (60) Fringe Avg (45) Below Avg (40)
2017 pERA (MLB) 71 56 55 42
2017 FanGraphs 60 60 55 50
2017 MLB 60 60 50 45
2014 MLB 60 65 50 50
2013 MLB 60 60 60 60

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

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Pitchers Improving their Expected Results

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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Hitter Breakouts: Stickiness Of Stats

A few days back, I start the process of trying to find breakout hitters. I found some possible traits which point to hitters breaking out but didn’t get into the stickiness of the stats over different time frames. I’m back to see how the “breakout” stats main their values over time.

For a quick review, here are the claims I made in the previous article.

Overall, here are the rules.
• K%-BB% (plate discipline) changes by +/- 4.5%.
• Flyball rate (FB%) changes by +/- 3%.
If the above two items can’t explain the change move onto the following three points.
• Pull% change (only) by +/- 5% this value can good or bad depending on the hitter’s other traits.
• Raw power can start decline once a player reaches 30-years-old.
• BABIP changed by +/- 30 points. (A change in plate discipline can cause this change)

I will just start walking through the points comparing the results for the year after the breakout. Also, I will look for hitters breaking out in the season’s first month and how those stats carried forward.

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Profile Changes: Hitters Improving

For my next few articles, I am going to examine batters and pitchers who have changed their approach from the second half of 2016 to the first half of this season. Today, I will start with the hitters.

For hitters, I found how far their stats changed, in standard deviations, from the league average in these five categories:

  • Strikeouts (K%)
  • Walks (BB%)
  • Groundball Rate (GB%)
  • Pull Percentage (Pull%)
  • Isolated Power (ISO)

Then I binned the change as good or bad. I determined “pulling the ball” (can be shifted) and groundballs (fewer line drives and home runs) to be bad. If a person disagrees, they can change the values found in this spreadsheet and create their rankings.

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Respect the Elders: Three Underowned 30-Somethings

Every year, it seems like there’s a handful of veteran players who go overlooked by fantasy owners. Part of it is likely that these 30-somethings do not excite you anymore. You’ve been scrolling past some of these names for a decade, if not longer. Your eyes simply skim through them on their quest to find that young sleeper who’s about to break out.

Another part of the the puzzle may be that no analysts write about these guys anymore. What would anyone possibly have to say at this point about a player we’ve all been watching since 2005? “He’s still here”? That’s no fun — at least, it’s far less fun than projecting the next breakout performer.

As someone who understands that life isn’t always fun, I hereby declare my intent to remind you that the following three players are worth owning, despite their relatively high ‘old and boring’ levels.

Shin-Soo Choo (17% Yahoo, 17.5% ESPN, 46% CBS, 92.1% Ottoneu)

I understand there might not be anything sexy about owning Choo these days. The guy does turn 35 next month, and spent most of last year struggling with injuries. However, the fact that he’s owned in about 17% of Yahoo/ESPN leagues is entirely unforgivable. Check out these numbers and tell me why he’s on your waiver wire.

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The Mostly Legit Marwin Gonzalez

As a Marwin Gonzalez owner, I’ve considered writing about him several times over the season’s first two months. I took a $1 flier on him in a 4×4 ottoneu Classic league this offseason, and at the time my expectations could be roughly summarized as “There’s worse ways to fill out a roster.” In most fantasy formats, Marwin qualifies at every position except pitcher and catcher, and he was coming off a season that saw him produce 25 HR+SB. Like I said, there’s worse ways to spend a dollar on a bench player.

Every time I’ve thought previously about writing up Marwin this year, I got hung up on the fact that I had absolutely no idea if his breakout was for real, or if it was just a fluky hot start. Actually, if I’m being completely honest, the whole reason I decided to write this piece you’re currently reading is because I still don’t know. In the following paragraphs, I invite you to join me as I take a stab at figuring out Marwin Gonzalez.

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Buying Generic: An Underrated Comp for a Budding Star

As a RotoGraphs reader, I’ve always enjoyed Joe Douglas’s “Buying Generic” series. In past articles, he has compellingly compared “generic” players like Ryon Healy, Logan Forsythe, and Justin Bour to “brand name” ones like Jake Lamb, Jason Kipnis, and Carlos Santana.

The fantasy relevance of the exercise is obvious: Buying generic allows owners to acquire players cheaply and with relative ease, instead of paying a premium (whether in a trade or in a draft or auction) for a brand-name player.

For today’s comparison, we’re going to analyze two players who have played in parts of the last three seasons, but neither has eclipsed 350 plate appearances in any one year. Both appear on their way to full-time jobs in 2017 and beyond.

Here’s how Mr. Generic and Mr. Brand Name have fared since 2015:

Mr. Generic vs. Mr. Brand Name (2015-2017)
Name PA BB% K% AVG OBP SLG ISO BABIP wOBA wRC+
Mr. Generic 438 11.0% 29.9% .262 .350 .486 .223 .346 .357 124
Mr. Brand Name 715 10.6% 23.9% .258 .343 .497 .239 .298 .355 125

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