When injury strikes, opportunity arises. This time, Michael Taylor is the beneficiary. Initially expected to step in for Jayson Werth as he recovers from right shoulder surgery, Taylor is suddenly set to enjoy more guaranteed playing time in the wake of Denard Span’s expected absence. Yesterday, Span underwent core muscle surgery, which figures to sideline him for at least four to six weeks. That means that Taylor will now fill in at center field, possibly for the entire first month of the season.

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For a different publication I have recently looked at the hitters that all three of Steamer, ZiPS, and Mike Podhorzer’s projections like more and like less than their current ADP. I like this little exercise as a tool to find value. It’s admittedly not a great way to identify sleepers in the sense of identifying breakout players. But it is a good way to identify sleepers in the sense of identifying guys who should give you good return on investment, even if the margins aren’t huge.

To do this little exercise, I simply ran the Pod and ZiPS projections through the old Zach Sanders z-score method, and I took the Steamer rankings from the Auction Calculator. I based the rankings on a 12-team mixed league with 25 roster slots (aka the standard ESPN setup). I then compared the rankings to the ESPN, Yahoo, and NFBC composite ADP from FantasyPros and found the 34 guys that all three projection systems like more than their ADP. Below I’ll discuss a few of the pitchers with the biggest gap between the rankings and ADP, and at the end of the post I’ll list all 34. On Wednesday I’ll look at the pitchers that all the rankings like less than their ADP. Read the rest of this entry »

I spend a lot of time looking at pitching in any given offseason. Doing the starting pitching guide every year makes me fully aware of the pitching pool from top to bottom. I don’t have a similar exercise for digging as deeply into hitters so one thing I like to do is take a look at the part-time players, level set them at 600 PA, and see what shakes out. Let me be clear: extrapolation is dangerous. There is a lot of noise in small samples as there just isn’t enough time for everything to stabilize.

The purpose of this exercise is to unearth some potential playing time breakout candidates. Sometimes opportunity is the only real thing fueling a player’s breakout. I don’t just pick from the top of every leaderboard and tab those as potential breakouts. First off, I use 200-400 PA as the threshold so this will include a lot of injury guys. I think Troy Tulowitzki is on it yearly. Obviously we know about those guys.

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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 »