Last week, the Cubs signed Tyler Chatwood, who has had the unfortunate luck of spending the majority of his Major League career pitching half his games in the most offense friendly home park. He has still managed to perform respectably given the circumstances, posting a 4.31 ERA and 95 ERA- (5% better than league average where lower is better) over his career, which includes 142 innings with the Angels in his 2011 debut. Now heading into his age 28 season, let’s see how the park factors compare between Wrigley Field and Coors Field and why the move makes him a prime sleeper.

Read the rest of this entry »

Spin rate is becoming ever more important to baseball analysis now that we have access to more reliable measurement devices. Namely, Trackman. But there are other technologies as well which are being used by high school, college, and minor league teams. Trackman is the big name, though, since it has been adopted by MLB, NPB and KBO along with many colleges and even a few high schools.

Trackman uses Doppler radar to measure the movement of the ball. I want to paint a picture in your mind of what this may look like, in the eyes of the radar. Remember, we’re trying to track the ball here. Read the rest of this entry »

Shohei Ohtani will unquestionably be the toughest rank for anyone all year just for the vast number of unknown variables, especially since we’re not even sure where’s going to play yet. In my October SP rankings, I didn’t include Ohtani because we weren’t even sure that he was coming over 100%. Now that the posting system issue is settled and he’s officially coming over, I’ve dropped him into the rankings at 31. I’ve made plenty of changes to the October rankings as I’m in full research mode and will have an update after the New Year, but here’s a look at those just above and below Ohtani:

Read the rest of this entry »

And it’s evident you should now pay attention to him. Jeff Sullivan wrote about Miles Mikolas here — Sullivan does a good job of summarizing Mikolas’ skill set and how he’ll succeed stateside. Which is helpful. But, for fantasy purposes, it doesn’t help us a whole lot in terms of exactly what we should expect. Not that that’s Sullivan’s fault. He doesn’t keep a cross-league projection system in his brain.

So when you see a Tweet like this — from NEIFI Analytics, which FanGraphs has featured previously — it’s hard to ignore:

Congrats, Cardinals. Per NEIFI, Mikolas handily outperforms Jake Arrieta over next three seasons. Massive bargain, fantastic bet to be the most underrated FA of 2017 class.

— NEIFI Analytics (@NEIFIco) December 5, 2017

Then again, there are Tweets like this from ZiPS’ own Dan Szymborski:

Read the rest of this entry »

Alas, we have finally reached our final 2017 preseason article recap! Welllll, this one shouldn’t have been the last one, but no one wants to read a recap of my David Dahl Pod Projection, right? So we wrap things up by reviewing my Pod Projection for curveball aficionado Lance McCullers, who was coming off around 200 innings of 3.22 ERA ball supported by strong skills over his first two seasons. Health was a question mark, but there was no doubting his talent. Let’s remind ourselves what I forecasted for his 2017 performance and how he actually performed.

Read the rest of this entry »

I have never been supportive of pitch clocks. In fact, the first ever thing I wrote about baseball (formally), was an article in the Journal of Sports Sciences, illustrating how pitch clocks could elevate muscle fatigue in pitchers, possible contributing to increased injury risk. I also came up with a workload metric which factors in the time between pitches when calculating the number of Fatigue Units a pitcher can accumulate. I was pleased to read Travis Sawchik’s article on pace of play solutions, focusing on how it may be more on the batters than the pitchers when it comes to speeding up the games. Well, I was pleased until the last paragraph, where he proposed the ol’ 15 second pitch clock – but we’ll get there.

Read the rest of this entry »

Pitching blisters were an afterthought just two years ago but the reported instances have jumped the past two seasons. Detailed accounts were written by Eno Sarris here at FanGraphs and Ben Lindbergh at the Ringer.

Throwing a curveball may be to blame according to Sarris:

But we can’t dismiss that chart completely. The players who have gone down with blister problems have thrown curves 14.9% of the time, far above the 10-11% baseball as a whole averaged over that timeframe. The players who ended up on the list more than once averaged 18.9% curveballs. Enough to say there’s some smoke here.

Read the rest of this entry »

It’s time to recap some of my 2017 Pod Projections! This preseason, I begun the series with one of 2016’s most surprising pitchers, Kyle Hendricks. We all figured that even backed by the historically strong Cubs defense, he was quite a bit fortunate en route to a sub-3.00 ERA. But how much regression was I projecting and how did that compare to his actual results? Let’s find out.

Read the rest of this entry »

Yesterday, I recapped my comparison of the starting pitcher ERA Pod Projections vs Steamer projections in which I was more bullish. Today I finish reviwing the Pod Projections vs Steamer projections series by looking at the group of starters I projected for worse ERA marks. Since I mentioned in yesterday’s article that I projected a lower ERA than Steamer for the vast majority of starters (which is one of the reasons I performed so poorly in the results comparison), I only had 21 pitchers whose ERA I was projecting a higher mark for. So this group to review is much smaller and the gap in ERA between the two projections is as well.

Read the rest of this entry »

We’re winding down the recaps comparing my Pod Projections to Steamer projections and will finish things off by moving on over to starting pitchers. We’ll begin by checking in on the group of hurlers in which I had forecasted a significantly better ERA than Steamer. Let’s see how these pitchers actually performed.

Read the rest of this entry »