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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Yesterday, I published an article on a few pitchers whose fastball velocity changed over the course of last season. And then my old buddy MGL showed up.

He’s right. I have so much on my plate right now, mainly my first book and a 2019 player previews, that I didn’t take it another step forward. Here are most of his answers.

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It may seem that fastball velocity gets over utilized for explaining a pitcher performance, but I don’t think it gets used enough. As I get going full steam ahead in my off-season research, I find myself always looking to see how a pitcher’s velocity held up over the season. Instead of looking up each pitcher individually, I decided to go ahead and collect 2018 fastball velocity reading for an easy reference.

For the data, I found the pitcher’s velocity for the whole season, April to June, July to October, and just September/October. The entire data dataset can be found here.

I went through the data and found some intriguing pitchers who gained and lost a few ticks. I divided up the analysis between first versus second half and first half and September. Many of the same pitchers would have made both lists. Here is the first to second half values.

Stephen Strasburg is the most concerning name on the list with his fastball down ~2 mph. The drop occurred after he went on the DL with shoulder inflammation.

His results also took a hit with his ERA going from 3.46 to 4.20 and his K%-BB% dropping from 23% to 19%.

Strasburg is getting in the danger zone where his fastball will start losing its effectiveness if he loses any more velocity. Here are its swinging-strike rates since he joined the league at different velocities.

The fastball starts to lose its effectiveness as it dips near 93 mph.

With the velocity drop, Strasburg will still be a good pitcher because his changeup is elite and curve and slider are decent. His issue is that he’s already been cutting his fastball usage from 73% when he entered the league to 52% last season. I think the chances of him having that elite season has passed.

One major consideration will be if he can get his walks under control. In the first half, they were at 2.1 BB/9 but jumped to 3.5 BB/9 in the second half. Spring training reports are going to matter quite a bit on how he gets valued.

Corey Kluber is the name which jumps off. For pitcher going in the second round of mock drafts, an ~1.5 mph drop in his fastball throws a major red flag.

He got hit around more in the second half with his BABIP jumping from .248 to .321 while list strikeout and walk rates remained constant (22.5 K%-BB% to 22.1 K%-BB%). Kluber found a way to be effective even with the velocity loss.

My gut says something is off but I can’t find it. His steamer projection has his ERA next season back in the 3.50 range (same as 2015). so it even sees that something is not right. I’ll read some more previews as the season nears and see if I can gain a better understanding of him.

A computer program and I are back for some more abuse. After lining up the Steamer hitter projections with the Standings Gain Points (SGP) for The Great Fantasy Baseball Invitational, the pitchers now take center stage. And boy can I see some conflicts to fill the comments.

The SGP formula is from the average of the 13, 15-team Roto leagues and will soon be available in The Process (looks like Monday at the latest).

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A while back, I ranked hitters if all their 2018 stats were prorated to 600 plate appearances. It’s now time for the pitchers. In all fairness, the rankings are a huge disappointment with no surprises coming through.

I adjusted the rankings for 180 innings for starters and 60 innings for relievers and no one seemed out of place. With the hitters, Raul Mondesi at the top was an attention-getter. Looking over both sets of top-25 pitchers, the biggest surprise was Joshua James and he’s not really a surprise since he dominated at the season’s end. Time to get bored.

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