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):
The 24-year-old righty has made two starts so far this season and I watched his start from the 5th because of a better camera angle. Since it was his first game, the Gameday’s pitch labeling was off with his slider and splitter getting mislabeled quite a bit.
• Fastball: 91-95 mph. Fairly straight pitch with a small bit of glove-side run and sink. His plus controls made this pitch play up. Also, he may have mixed in a handful of four-seamers.
It’s still far too early to analyze a lot of the metrics we typically use to evaluate players. But underlying skills like SwStk% are meaningful more quickly than strikeout rate because the denominator increases faster. So let’s peruse the American League starting pitcher SwStk% leaderboard and discuss.
Fastball velocity changes may be getting beat to death but they are one of the few stats in which stabilize early in the offseason. I’ve created a couple tools which I use during the season to help find which pitchers to evaluate further using velocity. I have one which grades fastballs and a new pitcher injury finder.
Fastball Grades
This tool simply gives an ERA and 20-80 grade to a fastball based on its projected results from its velocity and spin. The velocity component is simple to understand. It’s hard to hit fast flying small objects. It’s even harder to hit faster flying small objects. Understanding the spin component is a little tougher.
