The easiest way to tell you who I like is to actually tell you how I like every pitcher, I guess. So, by popular demand, here are my starting pitcher rankings. With a few toys that could be useful to you.

When making these rankings, I started with z-score style rankings based off of Steamer projections. You can find those yourself by using the Pauction Nalculator, for example. That’s a good way to keep your feet grounded in reality, since Steamer projects to the middle.

But breakouts happen. And so I’ve added a couple stats that help me spot breakouts. Strikeout minus walk rate was the backbone of the first ERA estimators ever put together (kwERA), so they aren’t new. And they might be a little better for in-season prediction versus season-to-season. Either way, they are clearly important and can give us a good snapshot of talent, even in a small sample.

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Last Friday, I wrote about some ways I plan on investigating a deeper into hitters. Here is the first weekly column on the information. I will pulish the data on Sunday night to help with setting weekly leagues. The format and information are not close to being 100%, so expect some changes for a few weeks as I iron out a few wrinkles.

Pitchers’ Approach Attacking Hitters

Robert Arthur at Baseball Prospectus has shown pitchers will change their approach depending on the hitter’s talent level. Here is a complete list of the number of fastballs (including sinkers) thrown to each hitter of the past two years divided into half seasons.  Also the number of pitches in the strike by half season is included along with the fastball percentage in the strike zone.  I will begin adding 2015 information as it becomes available.

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Along with the xK% formula I devised and updated last year, I also developed an xBB% equation. Unfortunately, it isn’t as good as the expected strikeout rate formula, as our community has really struggled to determine how the various underlying skill metrics should interact to result in an expected walk rate. That said, my version is still the best I’ve seen, so it’s better than nothing. But there are seemingly consistent underperformers and overperformers, so don’t take a pitcher’s xBB% as gospel.

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Last year, I shared my updated xK% equation, which blends a pitcher’s overall strike percentage with his called, swinging and foul strike rates to produce an expected strikeout rate. While its wonderfully high adjusted R-squared tells us how well it works, it’s even better used when dealing with a small number of innings since the metric uses pitches thrown, greatly alleviating sample size issues. It’s therefore a huge help when projecting young starting pitchers for my Pod Projections who were up in the Majors for just a grande sized cup of coffee.

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At the end of October, I asked a very serious, important question — Is There Any Hope For Mike Moustakas? I was quite negative, which is something that is hard not to come away feeling when staring at Mike Moustakas‘ statistical record. But then upon typing yesterday’s ESPN Home Run Tracker Analysis: The Upsiders and finding his name appear, I discovered a positive, and then another one and then another one. Behold, reasons for optimism! These new discoveries are tempting me to up my Moustakas forecast in my 2015 Pod Projections (available now!).

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Yesterday, I unveiled the xHR/FB rate equation I devised a year ago. So today I’ll begin my look back at 2014 and discuss a selection of hitters whose xHR/FB rates suggest serious HR/FB rate upside this year, assuming of course they sustain similar batted ball distances, average absolute angles and standard deviation of distances (SDD). Since my formula ignores home ballpark which absolutely plays a major role, I will mention it as a possible explanation for such underperformance as warranted.

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Last week I ran the 2015 Steamer projections for starting pitchers through Zach Sanders’ Fantasy Value Above Replacement system and compared them to early NFBC ADP data to identify some possible bust candidates. Today we’ll look at some sleeper candidates.

Tony Cingrani, Cincinnati Reds

After an impressive 100-ish inning debut in 2013, Cingrani struggled mightily last year. His season ended in mid-June after he was sent to the minors to work on his command, and he then revealed he had been dealing with a shoulder issue. Apparently he’s expected to come to Spring Training healthy and is a likely candidate for a rotation spot, but drafters aren’t buying a bounce back as his ADP among starters is 97. But Steamer does see a bounce back as he comes in 46th in the projection rankings. Read the rest of this entry »

On Monday I looked at bust candidates according to the 2015 Steamer projections. I ran the projections through Zach Sanders’ Fantasy Value Above Replacement system and then compared the rankings at each position to early ADP data from NFBC drafts. Today we’ll look at a sleeper candidate from each position. Read the rest of this entry »

I traded for Allen Webster and I’m not sure you should too. You see, I traded for Webster in a 20-team 28-keeper league, and he’s immediately my last keeper. Even if he’s the best last keeper, he’s probably no better than 540th. On the other hand, I spent a real-life asset to get him, so, yeah, I’m going to own some Allen Webster shares.

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You’ve heard me yammering about pitch-type peripherals for two years now, and we’ve made some advancements along the way. We established some good pitch-type peripheral benchmarks, and we took a first look at properly weighting each pitch. We’ve started to get a sense of how these things interact when it comes to the shape and speed of pitches. We’re making progress.

It’s worth stepping back and figuring out what the aim is at this point. Because we aren’t trying to rank the best starting pitchers overall, really. We’re trying to find undervalued pitchers before the market realizes that they’re good. So we have to move in the smallest possible samples. And we want to have a list of great pitchers that has some weird names on it as well. Those names, we hope, will soon start to make sense.

So, to that end, I’ve taken each pitch type and looked at only those pitchers that have thrown 100+ in each of those types. I’ve summed the ground-ball and swinging strike rates for each pitch, and then found the standard deviations. I’ve given each pitcher a z-score for his ground-ball rate and swinging strike rate on each pitch type. Then I’ve summed the z-scores for each pitch type, and then for each pitcher.

What we should be looking at is an Arsenal Score. With this way of looking at things, it’s possible to have one dominating pitch and still score well. Or a group of lesser pitches that are all positive.

What we haven’t done yet is nail down what the smallest sample for each pitch is. Or how to weight the pitches. Or how to weight the whiffs versus the grounders. So this may look different once we weight each pitch differently, and if we find a way to weight grounders and whiffs more correctly.

But at least we have a first attempt at it here.

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