If I recall I believe we did something like this earlier this year. I could be wrong though.

 I just want to get everyone’s take on who is going to be on the team next year, where they are going to be playing, and how much they are going to be playing. After we get plenty of responses we can consolidate all the answers and see what the members of DRaysBay think as a whole.

I’ll start it off, and the format should be pretty simple to follow.

For every position just give a players name and how much of the total time of the year they will spend at that position. Each position should add up to 100%. If you want to use “other” for a player we don’t yet know then go ahead and do that. For starting pitching lets say there are 162 starts so give me a % of those starts. For example if you think Shields will make 31 starts then give a % of 19%. Make sure that adds up to 100. For the “closer” role just give me a % of the total save opportunities you think the player we get. Don’t be afraid to use “other” if you don’t think the guy is on the roster

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I know we typically frown on ranting on this board, but I think I reached my tipping point.

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...one of the most despised metrics in baseball.

Yes I'm talking about ERA

And no ERA is not better than WAR. Or FIP. Or anything for that matter.

But I did find something that ERA does a better job at (well with some help) than both WAR and FIP...

That thing is only the most important thing in baseball.....

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A week or so ago I began a search to find a way to predict and/or determine expected strikeout rates. Jump to the bottom to see the new expected strikeout rates for all 2009 pitchers with 30 or more innings and continue reading on if the process and the statistics interests you.

I initially gathered data from 2003-2009 for all the plate discipline and pitch result categories on fangraphs and statcorner. I ran a regression against K%, and found the significant variables. The adjusted r-squared was very high, and it passed the general validity questions. I could essentially look at the results of a player’s pitches and tell you what his strike out rate would be. Very powerful stuff.

But here comes the obligatory but.

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Lately we've seen quite a few posts relating plate discipline and pitch results to walks and strikeouts. Intuitively this makes sense. The scenario that occurs after a pitch is thrown should have a strong link to strikeouts and walks.

This led me down the path of starting a project using these results, both plate discipline and pitch results, to formulate an equation via multiple regression that would predict expected strikeouts and expected unintentional walks. So far on this site we've only compared and contrasted a few of these results, and in reality there are quite a few. I'm sure some haven't even been measured yet that may have a strong impact, and I'm not even totally sure if I was able to grab them all.

This is essentially just the start of the project. I'm not totally sure if the end results will be good or bad. If someone wants to play around or offer suggestions or help in any way please do. I'm sure there are independent variables I missed and quite a few that may be removed. There are tons of possible combinations and tons of tests to check to make sure the model is actually okay to use. So if you want to play around and help please do.

That being said I did find two pretty solid equations. We certainly can improve, but I don't think the results will change that much.

Here are the results. I know many of you don't need or want to get into the statistical stuff and are just interested in what this really means. Essentially the eK and euBB is basd upon certain results (13 possible) ranging from call strikes, first pitch strikes, fouls, out of zone contact, etc

**Both models are pretty accurate, although eK% is very accurate. The euBB% also seems to be biased towards negative errors. This is something that would have to be fixed (hence why help would be great).

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I wanted to take a quick look at our starting pitching and how they were faring this year and over time. I decided to look at Shields, Jackson, Garza, Sonnanstine, and Kazmir. I picked these guys because they've been in the league a few years so we more data to look at.

I decided to look at FIP, tRA*, tRA+, and tRA.

Obviously Garza in 2007 was with Minnesota and Jackson in 2009 is with the Tigers.

For everyone who is curious Hickey has been around since 2007.

Now lets look at the graphs

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As many of you are aware there have been a couple of posts lately pertaining to the issue of pitching and volatility.

I initially took a look at it and one of the conclusions that I found was Jeff Niemann was the consistent, albeit definitely not the best, pitcher on staff

Sandy Kazmir then took their previous work a step further and estimated wins based upon pitcher volatility combined with run distribution (in the form of "FIH)

Those articles can be found here: Intro to Volatility and Updated Deserved Wins

Those articles as well as Niemann’s recent performance raised my interest into Mr. Niemann. If you browse around DRaysBay, The Heater, talk radio, and other outlets you’ll see quite a bit of praise on Niemann. Of course the level of knowledge is vastly different based upon the source, but its exists nonetheless. Some of the praise that you’ll hear is that Niemann is improving, he is consistent, one of our best pitchers, etc.

It is very easy to see why Niemann’s 2009 is perceived the way it is. All you have to do is look at the histogram of earned runs. ER is still a popular mainstream metric although it is obviously very flawed. He has given up 3 earned runs or less in 80% of his starts. Oh by the way he is leading the team in wins. For the public that is excellent.

via lh3.ggpht.com

Does the perception equal the reality? And if so is the reality sustainable?

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Given the return of Scotty Kazmir, the demotion of Sonny, and questions surrounding the starting pitching in general I figured I'd take a deeper look into our Super Six Starters.

By now we all know where each pitcher stands with their season long FIP and tRA as well as where they stand among nearly useless but popular metrics such as wins/losses and ERA. FIP and tRA are excellent, and they do paint most of the picture. However I do think they miss a key component: volatility.

Volatility is important because teams are not evaluation based upon cumulative 162 game statistics. Teams are judged based upon 9 inning incriments. There is a difference between two pitchers if one has a FIP/tRA in three games ranging from 0, 6, and 6 and another pitcher with a FIP/tRA of 4, 4, and 4. The team of the first pitcher likely goes 1-2 whereas the other team actually has a shot of winning three games. As we certainly all know W's/Ls in the public context in relation to pitchers is largely irrelevant, however, the number of times a pitcher pitches well enough to give his team a shot at winning is largely ignored and is ultimately very important.

For the time being all of this is another way at looking what has happened. I have no idea whether it has any predictive power so lets rather use at as another way to explain 2009 so far.

There are quite a few ways to look at volatility and I hope to do so in the future. In this piece we are just going to look at it in general with a simple introduction.

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Welcome to Part 2 of my Jim Hickey Analysis.

Quick Recap

In Part 1 we looked at how pitchers performed under Hickey compared to their performance a year before or after with a different pitching coach. I'm going to go back and make a "Part 3" adjusting some of that data while using the methodology in Part 2. I'm also going to try to use many more metrics. In Part 2 I look at batted ball types as well as pitch types. I'll eventually add that into Part 1 as part of Part 3.  We found that pitchers statistically significantly performed worse under Hickey than under a different PC for the following metrics: tRA, FIP, and HR/9.

You can find Part 1 here:

Jim Hickey Analysis Part 1

Part 2

We are going to look at the changes of metrics for a pitcher while they are under the Hickey reign. To do this I gathered the data two different ways and analyzed them.

The first method is "year over year". I took the 2005 Astros and compared to the 2006 Astros. The 2007 DRays to the 2008 Rays. The 2008 Rays to the 2009 Rays. In this a Rays pitcher can show up twice.

The second method is "first and last". I took the 2005 Astros to the 2006 Astros. And for some pitchers the 2007 Rays to the 2009 Rays, as well as some for '07 to '08 and '08 to '09.  In this a Rays pitch can show up once (07-09 or 07-08, 08-09)

The sample size for the first "year over year" method is 26. The sample size for "first and last" is 20. I wish we had bigger samples, but we can still have some fun with this!

Remember this is all per pitcher. This is important to note and remember. For example a 3% change means on average each pitcher exhibits a 3% change.

The Metrics that will be analyzed

Basic:   ERA

Advanced: FIP, BABIP

Performance Ratios:  K/9, BB/9, HR/9, HR/FB

Batted Ball Types:  LD%, FB%, GB%

Pitch Types: FB%, SL%, CT%, CB%, CH%, SF%

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