Archive for Curve

It’s easy to dismiss Randy Dobnak, to turn him into a punchline. When 99.99% of baseball fans were introduced to Dobby last fall, they learned two things:

1. When he wasn’t pitching, he worked part-time as a ride-share driver to help pay the bills (an altogether separate indictment of MLB and its broad moral shortcomings), and
2. He has a handlebar mustache.

That’s just enough, but also plenty, to undercut a grown man’s legitimacy. It’s this very illegitimizing, I hypothesize, that has allowed Dobnak to fly under fantasy radars, even as he demonstrates nonzero aptitude on the mound.

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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?

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Maybe it’s just me, but it seems Michael Wacha’s solid season has largely gone unnoticed among a sea of excellent pitching performances this year. His value is largely buoyed by the win column (12) because of a relatively modest 7.61 strikeouts per nine innings (K/9), but his 3.09 ERA and 1.09 WHIP are nothing to sneeze at — they rank 21st and 18th, respectively, among all qualified starters.

Wacha has largely taken teammate Adam Wainwright’s path to success this year: walk very few batters, induce a lot of ground balls and limit hard contact. Wainright has historically been more effective in limiting baserunners — only recently did his strikeout rate fall below 8.0 K/9 — but Waino has also been around a while. He really didn’t hit his stride until his age-27 season.

Technicalities aside, Wacha and Wainwright don’t have a lot in common. Waino throws sinkers, cutters and curves; Wacha lives primarily off a four-seamer while peppering in the occasional cutter, curve and change-up. However, Wacha improved one of his secondary pitches this year, and it helped him join an elite, albeit somewhat contrived, group of pitchers.

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Any day now, Zach Sanders (@zvsanders) will come out with his end-of-season FAVRz/Fantasy Rankings. Look out for them.

For this post, I will provide three sets of rankings using that same approach (summed up z-scores) for our end-of-season “Bullpen Report: Expected Fantasy Rankings”. The Bullpen Report team should follow up with role reports for each division in the coming weeks as well.

The first set of rankings you will find almost anywhere: on the fantasy sites that you use, via player-raters, etc. It’s the standard 5×5 fantasy value (Wins, ERA, WHIP, SO and Saves). The second grid will be for 6×6 leagues (addition of Holds). The last grid will be for 5×5 and 6×6 leagues, but instead of standard ERA and WHIP, we’ll look at rankings if you were to use expected ERA (via SIERA) and adjusted (adj)WHIP through BABIP differential: I will explain below.

1) 5×5 Rankings (Wins, ERA, WHIP, SO and Saves) – actual 5×5 value in column 4; expected 5×5 value in column 5:

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