Archive for relief pitcher

Acquiring saves in fantasy baseball is becoming more and more of a headache. The Tampa Bay Rays had 12 different pitchers notch a save in 2020. Imagine if it was a season of normal length? With the league trending towards using their best pitchers in high leverage positions instead of the conventional only ninth-inning role, it seems like grabbing saves are only going to get more complicated. Below you will see some closers that likely won’t be too popular but could help you in the long run. A quick side note, there are a lot of free-agent relief pitchers (ie. Brad Hand) so things can definitely change.

If you would like to read parts one and two you can check them out here and here.

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Only a few games into the season and the relief pitcher body count has already started to mount. No, none of your fantasy arms died tonight but injuries to some top rated arms seems almost unavoidable in today’s game. Earlier today the Phillies placed Pat Neshek on the 10-Day disabled list with what the Phillies are calling “Right Shoulder Strain”. This only a day after Philadelphia placed late inning reviver Tommy Hunter on the 10-Day DL as well. Neither of those two were lined directly up for save opportunities but it leaves Hector Neris on a closer island all by himself with a somewhat secure ninth inning role, even after his implosion just a night ago.

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Two weeks ago, I wrote about the importance of evaluating expected strikeout rate (xK%) in the context of each pitcher’s respective histories. In other words, xK% on its own can only tell you so much about a pitcher’s chance and magnitude of regression toward the mean.

And last week, I refined the expected walk rate (xBB%) metric for pitchers by adding a proxy for pitch sequencing in the form of percentage of counts that reach 3-0 (“3-0%”). This helped better explain the model’s fit with respect to the data, as pitchers who worked into more 3-0 counts tended to walk more batters. (Who knew?)

The logical next step is to combine the two aforementioned analyses: 1) comparing xBB% to BB% 2) for each pitcher over time. I’ll reiterate a couple of key points. Calculating a pitcher’s xBB% can give us a decent idea of how lucky or unlucky he may have been during a given season. Calculating his xBB% and comparing it to his actual BB% on an annual basis can give us a better idea of truly how he typically performs against his xBB% — that is, if he consistently outperforms his xBB%, perhaps the difference between his xBB% and BB% is not a matter of luck at all but a skill or characteristic not captured by the variables specified in the xBB% equation.
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I’d like to talk to you about Dellin Betances.

Wait! Wait. No. No, I wouldn’t. I’d like to talk about Mike Podhorzer first. Mike has published a lot of great work covering the fundamentals of the xK% (and xBB%) metric for pitchers (and hitters), so if you are unfamiliar with or falling behind on his work, I recommend you first click here, here or here. But if you’re lazy, the short of it is: xK%, or expected strikeout rate, is an equation birthed from a linear regression that measures how a pitcher’s looking, swinging and foul-ball strike rates as well as overall strike percentage correlates with his strikeout rate. It doesn’t predict future strikeout rates as much as it retrospectively adjusts past strikeout rates; thus, it is a good tool for identifying pitchers who potentially benefited (or suffered) from good (bad) luck in a previous season – say, 2014.

Like many other metrics completely unrelated to xK%, however, there is evidence that certain players consistently out-perform (or under-perform) what their xK% rates predict their actual K% rates should be. (Mike alludes to this trend in his quip about Jeremy Hellickson, a xK% underachiever, in one of the articles linked above.) Similarly to how a power hitter will post consistently higher ratios of home runs to fly balls (HR/FB) than a non-power hitter, or how Mike Trout will probably post some of the highest batting averages on balls in play (babip) in the league for years to come, it appears there is some skill, or perhaps a particular characteristic, inherent to pitchers who consistently best, or fall short of, their xK% rates.

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