It’s time for our Depth Chart Discussions to begin. In an effort to suss out every team, we’ve divided them into four parts (infield, outfield, bullpen, and rotation) and will begin breaking them down for you over the next few weeks. You can find them gathered here.

With Yu Darvish likely to miss the entire season, the Rangers are staring into the face of another lost season. In some ways, it’s liberating to know you won’t contend. Texas won’t have to worry about the shaky quality of the bullpen. They can give their pitchers an opportunity to prove themselves without focusing solely on runs allowed.

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It’s time for our Depth Chart Discussions to begin. In an effort to suss out every team, we’ve divided them into four parts (infield, outfield, bullpen, and rotation) and will begin breaking them down for you over the next few weeks. You can find them gathered here.

In recent seasons, the Angels have been plagued by weakness in the bullpen. Failed experiments with Jordan Walden, Scott Downs, and Ernesto Frieri paved the way for their acquisition of Huston Street last season. The bullpen looks to be a strength in 2015 – or at least it probably won’t be a weakness. In addition to solid veteran depth, there are a few high profile youngsters to track.

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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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It’s time for our Depth Chart Discussions to begin. In an effort to suss out every team, we’ve divided them into four parts (infield, outfield, bullpen, and rotation) and will begin breaking them down for you over the next few weeks. You can find them gathered here.

I grew up playing against Sean Doolittle. He was even more feared as a hitter, but he was also a damned good pitcher. I talked to him once about his game plan and it was strikingly simple – use high fastballs to set up his hammer curve. These days, he’s still throwing that high fastball. The curve has gone extinct, but the lessons he learned as a kid have stuck with him. Of course, Doolittle is hurt, and the state of the bullpen is in flux until his return.

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After recording a 3.17 FIP and moving into the National League, all things were pointing up for Addison Reed following his trade to the Diamondbacks last winter. Unfortunately, not all worked as planned for Reed as he allowed 11 home runs, blew six saves, and recorded a 1-7 record with a 4.25 ERA.
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This past week, in the ottoneu Slack Community, (check this out if you are interested in joining, which you should be if you play ottoneu) there was a heated debate about paying for top RP, particularly in points leagues. One side was arguing that the elite RP put up a Pt/IP score that few other players can manage. That if you are willing to pay $35 more for Mike Trout than Ryan Braun (projected to have a 200 point gap between them), why not pay the same premium for Aroldis Chapman over a guy 200 points below him – Santiago Casilla?

The other side argued that spending $30+ on a relief pitcher would be crazy – there are always solid RP options on the wire, so why not just wait and see who will be the next Wade Davis?

Both sides are right.

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This post continues our Depth Chart Discussions. In an effort to suss out every team, we’ve divided them into four parts (infield, outfield, rotation, and bullpen) and will continue to break them down for you over the next few weeks. You can find the Depth Chart Discussion posts gathered here.

The San Diego Padres’ bullpen lacks name brand appeal, but it’s glorious. Friars’ relievers ranked just 14th in WAR (3.4), but boasted exceptional ranks in other categories ranking tied for third in SIERA (3.07), third in WHIP (1.14), and second in K-BB% (16.4%) and FIP (3.09). Those numbers are partly inflated by the brilliant 33 innings thrown by former closer Huston Street, who was dealt in July, but even in his absence this is a fantastic group. Read the rest of this entry »

I expected to follow up my xK% differential post from last week with a complementary xBB% differential post. For those who don’t enjoy surprises, I’ll let you know now that that didn’t happen. In its stead, I bring what I hope is good news — news that will not only influence a future xBB% differential post but also may impact general pitcher analysis henceforth and possibly international diplomacy.

The title of this post, however, is a tad misleading. I think I can say, with some degree of certainty — and I hope to demonstrate, with some degree of competency — that pitch sequencing indeed plays a role in a pitcher’s walk rate, as the devilishly handsome Mike Podhorzer has postulated. What I can’t describe, with any degree of certainty, is the magnitude of the role it plays. In truth, I desperately want to prove Mike wrong: there must be other factors, outside of pitch sequencing (and pitch framing, perhaps), that help explain a pitcher’s walk rate. For example, I have tried incorporating O-Swing% and Zone%, two PITCHf/x metrics provided by FanGraphs that I swore would fill in the cracks, but they offer little in the way of additional explanatory power.

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This post continues our Depth Chart Discussions. In an effort to suss out every team, we’ve divided them into four parts (infield, outfield, rotation, and bullpen) and will continue to break them down for you over the next few weeks. You can find the Depth Chart Discussion posts gathered here.

Despite lacking most of the elements classically associated with a sports dynasty, the Giants have been the most successful franchise in the last five years. Their specialized bullpen has played a big role in taking down powerhouses like the Phillies, Cardinals, Tigers, and Nationals. Surprisingly, there has been very little turnover in the unit.

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It’s time for our Depth Chart Discussions. In an effort to suss out every team, we’ve divided them into four parts (infield, outfield, bullpen, and rotation) and will begin breaking them down for you over the next few weeks. You can find them gathered here.

As Aroldis Chapman inches closer to free agency, decision makers in Cincinnati must be getting uncomfortable. Chapman is signed through 2016. Should he remain healthy over the next two seasons, he will sign a record breaking contract for a reliever. Since they’re unlikely to re-sign him, there will be increasing pressure to trade him while his value is highest.

The scariest part is the state of the Reds bullpen – they have no obvious alternative to Chapman. Cincinnati is lucky. We live in an era where at least 75 pitchers meet the basic requirements of a closer (and that excludes non-major league talent). Replacing Chapman is next to impossible, but finding a viable closer is easy.

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