The current fantasy focus is evaluating the top-100 or so players, especially the first couple rounds. I’m going to deep dive and investigate some later round values.  I will group 10 players together and pick which one I would take earlier than the others.

201: Arodys Vizcaino
202: Jonathan Schoop
203: Kevin Gausman
204: Luke Voit
205: Chris Taylor
206: Jake Arrieta
207: Forrest Whitley
208: Ross Stripling
209: Odubel Herrera
210: Yusei Kikuchi

There are several players I really like in this group like Taylor, Stripling, and Herrera but easily the best bargain is Kikuchi.

Kikuchi isn’t on par with Asian imports Ohtani or Darvish but he’s still extremely talented. He’s considered to be a better talent than Miles Mikolas and Mikolas is going 86th overall. I believe once Kikuchi signs and everyone gets to know him, his ADP will be cut in half and around a top-100 pick, if not double digits.
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I’m a little weary of the Hot Stove season until about 80% of the free agents have signed. Team compositions change so fast that research done one day is irrelevant the next. Instead of wasting my energy, I normally focus on other topics and wait for everything to settle down and come into focus. Like, how much will Manny Machado’s value change if he signs with the Phillies or the Yankees? Or even the White Sox? Not much if his profile was written now or right after his season ended. He’s still going to be valued in the top-20 picks.

It’s hard sometimes for baseball fans, who play fantasy baseball, to differentiate the general excitement around a baseball move to one that makes a legitimate change in value.

That being said, some free agent signings do have a little more variance depending on where the player signs Jonathan Schoop and Nathan Eovaldi are two such players. Both maxed out their value with their recent signings. Here are my two cents on each.
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This study came about from a comment made in passing by Jeff Erikson and Scott Jenstad in a RotoWire podcast. They were discussing how they were worried more about hitters who were striking out on fastballs instead of breaking balls. They figured it was worse to strikeout on fastballs and it showed the hitter was in decline. I don’t remember the exact show but I agreed and now have time verify. And as usual, the pair was right with three-hitter groups differentiating themselves from the pack.

The first key was that I wasn’t interested in the batter ability to tell balls from strikes. Instead, I wanted to focus on pitches right down the heart of the plate while keeping the strike zone as big as possible to increase the sample size. In the end, the taken pitches in the zone used were called strikes 97% of the time.

Next, I found the swinging strike rate for pitches in the strike zone. I grouped the pitches into several groups.

• All fastballs
• Fastballs > 94 mph
• Fastball < 94 mph
• All non-fastball
• All changeups including splitters
• All breaking balls

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Merrill Kelly

The Diamondbacks signed Merrill Kelly after Kelly spent four years in the Korea Baseball Organization. I never heard of Kelly until the signing some it’s time to put together a profile for him.

First, here’s a tweet from Homin on Kelly’s profile:

KBO season ended. Merrill Kelly, former #rays prospect will play MLB next year. 92-94 mph FB, cutter, two-seam, CH, CV. Various pitches. No plus pitch, but reliable pitcher. Could be good long RP at mlb level. #rays are scouting him heavily.

— Homin Lee (@Homein22) November 13, 2018

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For fantasy baseball writers, this time of year means only one thing, player profiles. Dozens and dozens of player profiles to slog through. While writing up some hitters, I can to the realization I didn’t have a quick method to flag hitters who may be shifted. While I could navigate to the splits page, scroll down, and calculate each player’s shift rate, I wanted a quick and dirty method to flag them. While I went over several methods, I settled on one stat and two values.

I did some shift work for an older edition of a The Hardball Times Annual but I have stayed away from the topic over the last few years. My initial conclusions stand which are the shift really affected some hitters and the rate of effectiveness slowly declines more players are shifted. The shift isn’t going away though. It’s still effective against slow pull hitters.

I initially thought a hitter’s pulled groundball rate would be a major factor, but it was not. Just pull rate was enough.

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I’ve always been a week or two behind evaluating hitters so I continue trying to find ways to gain an edge. Today’s stab in the dark is trying to see if StatCast data can determine if a player is hurt. A recent study of mine found no correlation between playing through an injury and exceeding their projection in the next season. Instead of looking at preseason projections, I’m going to go a little more in-season today and determine how much of an impact an injury has on a hitter’s in-season production.

As Al showed, there are a ton of metric available StatCast batted ball metrics to use. I started down the path of using several of them but quickly found there is more to injuries than just power. A hitter’s plate discipline and speed results (e.g. turning singles into doubles) also matter. Instead of incorporation all of them in, I decided to use BaseballSavant’s xwOBA metric to measure a hitter’s production since it combines all of these factors.

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I heard in passing from a credible source:

“The effectiveness of a pitcher’s slider relies on it having the same spin as his fastball.”

I figured it would be an easy test and could help to immediately identify top-rated sliders. After looking at the data every conceivable way and came up with the following conclusion: publicly available spin information has near ZERO correlation to a slider’s effectiveness. But while rooting around, I did find two factors which do matter, fastball velocity and the difference in slider and fastball velocity.

The theory behind the quote is that a hitter has a tougher time differentiating a fastball and slider if they are spinning at the same rate. So, the closer the difference, a higher chance for a swing-and-miss.

I compared 2018 pitchers with at least 200 sliders and 200 four-seamers thrown. Then, I compared just the difference, the absolute value of the difference, square of difference. Nothing tangible. Nothing matched.

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One of the keys to fantasy success involves finding when projections can be systematically off. The hard part for fantasy owners is that most of these findings, like pitch velocity, get quickly incorporated into projections. Since it’s difficult to find these discrepancies, I was intrigued when I saw this quote by Mitchel Lichtman (MGL) in an article he wrote:

So, the substantial under-projections seem to occur when a player gains speed but his wOBA remains about the same.

And by substantial, it was a 22 point difference is wOBA. This is a major difference and could point owners to some nice upside plays. I decided to go ahead and dive in.

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The Process is done. After months of writing and disagreements, the fantasy baseball guide I wrote with Tanner Bell is for sale. Simply, we researched and created the guide we would want. It’s not going to contain player capsules or positional rankings. Dozens of sources provide them. Instead, it’s going to give the reader the ability to create their own and the 2017 NFBC Main Event Champion and my podcast mate, Rob Silver wasn’t happy about the end results as he states in his intro.

Jeff Zimmerman and Tanner Bell are both awful people.

Thanks Rob. Before Rob gets to explain himself, here is the general flow of he book.

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From now until some point before the season starts, the focus of most RotoGraph articles will be on getting the most useful information out of the StatCast information. It’ll include as many or as few of articles to create a good understanding of the data. With tons of available information much has been written about the subject especially here at RotoGraphs. The key with this series is to cut through all the different data points (e.g. launch angle, spin rate, sprint speed) and how they measured (e.g. max, average, weighted) to find which values to focus on and which ones to ignore.

The point of this article is to provide an initial forum, via the comments, to collect and try to answer the question with any previous research. There is no need to completely recreate everything.

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