A week ago, I discussed the American League starting pitchers who have improved their Z-Contact% (batter contact rate on pitches inside the strike zone) most versus 2017. Today, let’s take a look at the pitchers on the other side — those who have induced fewer in-zone whiffs, meaning their Z-Contact% marks are well above what they posted last season.
The 26-year-old lefty is straight up dealing with a 10.7 K/9 and only a 1.6 BB/9. No pitcher with a strikeout as high as his has a lower walk rate. His K%-BB% is the 12th best among qualified starters.
Gonzales’s great start is being hidden by a .406 BABIP fueled 4.37 ERA. While he struggled giving up runs in his first three starts, he allows nothing in his last two. Gonzales may finally be living up to some of his prospect status from a few years back.
Monday night’s Marlins/Dodgers game in LA garnered a lot of attention, as it was the season debut and first ever start for Dodgers uber-prospect Walker Buehler, but his less-heralded counterpart put up six strong innings for the Marlins, too. Jarlin Garcia actually bumped his ERA up with a 6 IP/1 ER outing as he now sits at a 1.00 ERA through 27 innings. He opened the season with six one-hit innings of relief in that Cubs/Marlins 17-inning epic on March 30th, the second day of the season.
He allowed a pair of runs in another extended relief outing, this time four innings at Philly. The Marlins installed him in the rotation after that and he’s netted some insane results: one run on five hits and eight walks with 12 strikeouts in 17 innings of work. Obviously, we know the 1.00 ERA and 0.81 WHIP won’t sustain. But does he have the skills to remain fantasy relevant even as his 99% LOB and .121 BABIP regress?
Here in the final week of April, sample sizes are starting to build up, but there is still a lot of statistical weirdness out there. Just in the walk rate leaderboard alone there are some confounding data for fantasy owners to ponder. Jose Quintana and Michael Wacha with double digit rates? Vince Velasquez among the lowest one-fourth? It’s like I hardly know these guys.
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Every time an analyst uses the caveat “small sample size, but,” an angel gets its wings. And then that angel takes flight and also analyzes a small sample size.
I preach patience when it comes to the first few weeks of a Major League Baseball season, and I try to practice it, too, regarding both early-season breakouts and duds. Aside from transactions related to the disabled list, I have yet to drop any player I drafted who wasn’t legitimately dead weight (like my decaying shares of Melky Cabrera and John Lackey) or, in ottoneu, a roster burden, such as a hapless $7 share of a helpless Alex Cobb.
That said, I can’t simply wait until mid-May or whatever to make meaningful analyses of players. But I also can’t make knee-jerk reactions about 30 innings or 90 plate appearances. I try to reconcile this cognitive dissonance by engaging in what I called last year Small Sample Normalization Services (SSNS). The intent: first, to attempt to find similarly long and (un)productive streaks in a player’s past; second, to evaluate how similar or comparable those streaks actually are; and, last, to slap an appropriate level of excitement or panic to the performance in question. If we can’t say with absolute certainty that we’re watching a player do something sustainable, then maybe it helps to know if he had done something similar in the past. If not, what befell him afterward? And if so, how should we move forward with him?
The Kimbrel discussion starts with this graph:
Plenty of narratives can exist explaining the initial velocity drop. He missed a good part of spring training. The cold weather is keeping his velocity down. He’s still warming up.
The drop doesn’t bother me as much as the trend. If he’s still building strength for the season, shouldn’t his velocity start trending up at some point?
As it remains far too early to actually analyze results (like ERA and WHIP), let’s continue discussing the underlying skills driving those results. Those skills are significantly more important when projecting for the future. So let’s find out which American League starting pitchers have improved their in-zone whiff rate the most. In FG metric parlance, that means calculating who has reduce their Z-Contact% by the greatest rate.
When a pitcher starts off a season with a new look, it’s hard to know whether or not you can trust their success. That’s especially true when that “new look” involves a change that we generally assume to be negative.
This is precisely the situation we find ourselves in with regard to Alex Wood. The Dodgers’ lefty has compiled a career-high K-BB% (23.3 percentage points) through four starts, even though his average sinker velocity is more than 3 mph lower than where it was last April. He seems to be carrying a red flag with the words “SELL HIGH” boldly emblazoned on it, but could it be that he has found a new way to succeed?
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This is going to get ugly fast. Here is my current Tout Wars pitching staff:
That’s right, it contains three of the biggest disappoints so far this season. The results have my team posting a 4.88 ERA. The next closest is at 4.35. The destruction has my WHIP also in last place. It’s time to examine the trio to see if there is any hope.
Before getting to these three, owners should now start examining their pitchers in detail to see what is and isn’t working. Spending a minute or two on each pitcher can possibly unearth items such as lost velocity or a new pitch mix.
I knew Archer was a risk with his two-pitch arsenal which may get lit up later in games. This season, he can’t even make it to those later innings.
As a Twitter dork, I’m exposed to a lot of discussion about swinging strike rates (SwStr%), so much so that it almost feels like it has supplanted xFIP (or other comparable metrics) as a catch-all way to evaluate pitchers. Dude has a 12.5% whiff rate! Sweet. It’s not for naught — swinging strike rate bears a strong correlation to strikeout rate (K%), which comprises substantial portions of the regression equations that underpin the aforementioned xFIP and its counterparts. Swinging strike rate’s correlation to the following metrics (using data from the last five years of 714 pitchers who threw at least 100 innings in a given season):
(*See footnote.)
It also correlates strongly year over year (among 392 player-seasons during the same timeframe in which the pitcher threw 100 innings in the current and subsequent seasons):