Last week, I examined pitchers whose strikeout per nine (K/9) increased while the strikeout per plate appearance (K%) dropped. This article focuses on the pitchers who saw their strikeout rates go in the opposite directions. Besides the strikeout divergers, I’m going to include the walk rate divergers since both player sets aren’t long.
The season seems to never end for fantasy baseball writers. Once the regular season is over, it’s time to begin writing player previews for the next season. Pitchers who’ve had their strikeout (K% and K/9) and walk rates change in different directions spin me for a loop. Now, I query these schizophrenic pitchers to start the preseason previews. I’ll give a quick look at some of these pitchers. I’ll start with those pitchers who’ve seen their K% (strikeout per batter faced) drop while K/9 (strikeouts per nine innings) increase.
Two reasons exist for why the rates diverge. The key for both is increasing the number of plate appearances per innings. More plate appearances lead to their K% dropping if the strikeouts remain constant per inning. The other factor is how many hits a pitcher allows (basically BABIP). If a pitcher had good luck on balls in play and recorded more outs, they could quickly get through an inning and thereby raise their K%. Once the BABIP normalizes, the K% will drop.
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Yesterday, I reviewed my 2017 starting pitcher strikeout rate upsider list, which produced embarrassing results. Let’s hope the downsider results look much improved. Once again, this list was compiled by using my pitcher xK% equation to identify starters whose 2016 xK% marks were significantly above their actual K% marks.
At the beginning of January, I used my xK% equation to identify six starting pitchers with significant strikeout rate upside in 2017. Let’s see how these pitchers actually performed.
Alas, we reach the final position to dive into for this year’s recap of Pod’s Picks & Pans. We finish with the starting pitchers, a group that always garners varying opinions. Let’s see how the players I was most bullish and bearish on versus the RotoGraphs consensus actually performed.
Masahiro Tanaka and Jeff Samardzija had perplexing, enigmatic, and ultimately bad seasons. Many attempts were made to ascribe reasons or causes for their struggles. I think both will bounce back for very simple reasons; accordingly, I think both will be undervalued in 2018 for equally simple reasons.
Travis Sawchik and Eno Sarris discussed his various ailments, so to speak, long after I gave up trying to diagnose him. The heat maps are interesting, and the splits are interesting, albeit a bit of an archaism.
While 2017 was the year of the dinger, it also looks like it was the year of velocity. Has baseball changed (both the sport, and the physical ball itself)? The signs point to yes – but on the pitching from, the importance of velocity has never been higher. Exhibit A:
The slowest fastball the @Yankees threw tonight was 96 MPH… That is completely absurd.
— Daren Willman (@darenw) October 10, 2017
That’s not average. That’s not maximum. That’s … the slowest. Let’s borrow a little bit of math from the documentary “Fastball” to just put into context how crazy this is.
For all intents and purposes, Kevin Gausman had a bad season. Like, catastrophically bad, such that has ERA was 10th-worst among qualified starters. That he was allowed to see enough innings to become a qualified starter should be construed as nothing short of a blessing for him.
On paper, sure. This narrative works on the surface, at the macro level. But Gausman had himself a season of two incredibly different halves. An aside: if you don’t pay attention to FanGraphs’ community research, you should. User jkved10 wrote a post about Gausman on July 24 — about the time I reluctantly convinced myself to roster him in my primary home league — in which jkved10 noticed a sudden change in Gausman’s release point. Kudos to the author for doing all the heavy lifting for me. Click through to familiarize yourself with the events that unfolded and the ensuing analysis, or dig around Brooks Baseball for yourself.
The results weren’t immediately promising at the time of his/her writing: a 4.94 ERA across six starts. Everything else under the hood, however, had changed: 12.2 strikeouts and only 2.6 walks per nine innings (K/9, BB/9), good for a 3.19 xFIP. Sure, everything else stunk; he was still allowing home runs and hits on balls in play at astronomical rates. But the peripherals very dramatically improved, having essentially doubled his K’s and halving his walks in that span.
Such success continued. In his 19 starts from June 21 onward, Gausman struck out 10 hitters-per-nine and recorded a 3.39 ERA despite a still-inflated rate of home runs to fly balls (HR/FB). A tale of two halves, indeed: prior to June 21, his 6.60 ERA was almost exactly doubly large. He was a second-half ace, and this was more than just regression to the mean — his success correlated, if not directly resulted from, his adjustment.
Well, this is the earliest I’ve ever done SP rankings so let’s see how this goes! With the regular season wrapped, I figured it was worth putting my initial list together if only to see how it changes over the fall and winter leading into next March’s draft season. Heck, I’ve already done a mock draft hosted by the InThisLeague guys, which you can check out here and I’ll be on the clock for my annual NFBC draft at the BaseballHQ First Pitch Forum in a few short weeks so having an idea of the pitching pool will be very useful.
As usual, I went with tiers, but since we’re not in-season I simply numbered them instead of naming them. I think it really gets tough to differentiate sharply on guys in the middle of the pitcher pool. Trevor Bauer finished 36th on the Player Rater this year while Dinelson Lamet was 83rd, but are they really that far apart in talent? Maybe the point to make is that once you get to about 40 in the rankings, the separation with each ranking is a lot smaller than if you were comparing say #5 to #55.
Anyway, let’s get to the list. Let me know your thoughts in the comments. Was there anyone not in the top 111 that you really think has to be in there?
Once again, heading into the all-star break, I presented two lists of 10 pitchers each — both lists composed of pitchers on each side of SIERA-ERA differential. I then asked you to vote on which group, the SIERA overperformers or underperformers, would power a lower second half ERA, and which ERA range each group will land in as a group average. Amazingly, the results suggested these two groups of pitchers were identical! Both garnered between 44% and 45% of the vote to post the lower ERA, while nearly 11.5% of you couldn’t choose a side, predicting the groups would finish within .05 earned runs of each other. Furthermore, 29% voted that 4.00-4.24 would be the ERA range that both groups would settle into, with 3.75-3.99 the second highest vote-getter at 27% and 28%. Pretty crazy how close these votes were! Let’s see what actually happened.
