SB Nation User Blog: matthan

2010 Rays Roster Opinion and Survey

matthan — Fri, 04 Sep 2009 15:26:45 -0000

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

Survey

C-Navi (45%), Zaun (40%), Riggans (15%)

1B-Pena (90%), Aybar (7%), SRod (3%)

2B-Zobrist (70%), SRod (20%), Aybar (10%)

SS-Brignac (75%), SRod (20%), Zobrist (5%)

3B-Longoria (90%), Aybar (7%), SRod (3%)

LF-Crawford (50%), Jennings (30%), Other (20%)

CF-Upton (90%), Zobrist (5%), Other (5%)

RF-Joyce (70%), Zobrist (10%), Other (20%)

DH-Burrell (60%), SRod (15%), Aybar (10%), Longoria (3%), Pena (2%), Other (10%)

Starting Pitchers-Shields (19%), Garza (11%), Niemann (19%), Davis (14%), Hellickson (10%), Price (19%), Sonnanstine (8%)

Closer-Howell (40%), Wheeler (20%), Davis (15%), Sonnanstine (5%), Other (20%)

Opinions

I think we start with Shields, Garza, Price, Niemann, and Davis. I believe Hellickson dominates in AAA and and earns a promotion. I also think Garza will eventually be traded. We end the year with Shields, Price, Niemann, Hellickson, and Sonnanstine. Davis in the pen (some performance issues and some for just the playoff push; he moves back to the rotation in 2011).

So that is three trades I see next year; Bartlett then Garza and CC. I don’t see us getting anybody that would truly help in 2010 (other than a Sept call up). However I do see those pieces as future 2011/2012 full time replacements for Navi and Pena. I see us also netting a stud OF prospect as well as some stud arms.

Basically our team and our system is stacked. We are going to need a couple trades a year to fill to reallocate to fill whatever holes we have at whatever level.

I see the cost savings as a means to lock up players….most think CC or perhaps Upton or Garza. I think we will lock up Jennings, maybe Joyce, maybe Brignac, and Zobrist…as well as having some left overs to potentially lock up the guys we get in the trades.

Langoria to Longo, BJ, Attendance, Fire Merlot and Andrew lets hang some Durham Banners at the Trop!

matthan — Wed, 02 Sep 2009 13:40:06 -0000

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

Firstly with the good. Evan was/is my favorite player (BJ is #2, but I don’t think he really plays anymore). I just loved the way he played the game and his passion as well as his intensity. However this year was a bit different. His performance is fine, but not as good as we hoped. The main problem for me was that the way he played the game changed. Just like his quote prior to last nights game saying how this series vs the Red Sox was just like any other series and that they weren’t treating it differently. However Evan did something that turned his Langoria type 2009 into a Longoria. He bunted. I’m not a big fan of bunting, but I love the message and the attitude. It showed that deep down, in places Joe Maddon doesn’t allow the players to talk about, that Evan knew the importance of the game. That is the way Evan played last year. All out, every game. Keep that up.

Pena and BenZo are monsters. I wouldn’t be opposed to extending both of them. Pena for less years obviously.

What the heck happened to BJ Upton? Would Theo give us Ellsbury for Upton? Pretty please? God Ellsbury was a monster last night. He is arguably the worst “hitter” out of any ML starter I’ve ever seen. He isn’t nearly as bad offensively due to his speed and his eye. Speaking of his eye, he needs to realize robots aren’t behind the plate. BJ, umpires don’t call the book strike zone. Adapt. Back to his hitting. God is it awful. Would he be able to catch up to a baseball that was placed on a tee? Why does he feel the need to swing once the ball hits the catchers mitt? I don’t think hes suddenly lost all his talent to become Sandy Kazmir with the stick. So that leaves a few possibilities. The possible solutions in (). In fact the Rays should explore every one of these possible issues. BJ has too much talent to not cover every single potential problem. In no particular order:

His mechanics are screwed up (BenZos and MVBs hitting coach)
His shoulder is hurt (Dr. James Andrews)
His bat speed is too slow (#1, #2, + Victor Conti)
He has the Rick Ankiel mind disease (a shrink, a vacation, and a room full of aromatherapists)
He has eye problems (go see a freaking eye doctor)

Personally I think it is #1, #3, #4, but Andrew you should cover all the possibilities.

Attendance. What the hell Tampa Bay? This is the biggest series since the World Series and you don’t show up. How highly embarassing. St Pete does not deserve the team. Does Tampa? Does the area? I’m leaning towards no. This has been the most disappointing Rays season (due to expectations and major underperformance), but we are still pretty darn good. Way over .500 plus in the wild card hunt plus playing the team we were hunting. That is prime baseball time. If the area will only support the Rays when they are in 1st then the team should move. That was just a pitiful and sad showing. Tickets were really cheap. I got 2nd row in right field for 15 bucks (2 for 30). Why aren’t people showing up? I’m sure there is a bit of cannabalism with the concerts, but still. It is just embarassing. We live in the worst baseball market in America.

Sonny, go back to what you were doing last year. Pretty please.

Fire Joe Maddon. Seriously. I always thought he was bad, but most managers are poor so I didn’t want Maddon fired. It would have been tough to upgrade if we went the traditional route. However Maddon has proven to be one of the worst in the league. He is full of gimmicks, but is absolutely pitiful with game strategy. We could have won maybe 10 to 15 more games if our manager just did the correct move. Would we have won? Not all 15, but a handful. Three of our last four losses Maddon has had a major hand in. Ranging from the Niemann/Balfour/Granderson fiasco to the giant egg he laid last night. Lets talk about last night. Aki/BenZo instead of BenZo/Kapler. Mistake #1. That was fairly close, but the numbers clearly point to ben and gabe. Considering Aki’s error as well as his really slow turn on the double play……nice move. Oh lets throw multiple pitch outs when Andy is walking guys. You know the plan is to not allow Ellsbury to steal, but instead lets just let him walk to first. Nice move Joe. What about sending Sonny out for his last inning? That also was close, but the move again was to be yanked. Oh and the not pinch running for Navi in the 8th was a total brain fart. What are you thinking Joe? Throw in other things that a good, smart team would have done and we could have easily won (say pinch hitting for Upton).

I got more but I cant think now. All in all that was a total fail of a team effort last night.

Oh and I like the Kaz trade now.

Oh and Friedman….the major league playoffs are more important than the Durham playoffs. Can the Rays hang a banner for that champtionship? Would have been nice to pinch hit Joyce for Upton? Or perhaps allow Perez to run for Navi? Perhaps that’s why people don’t show up.

WAR, what is it good for? A lot, but not as much as....

matthan — Fri, 24 Jul 2009 21:49:20 -0000

...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.....

Wins!!

Basically after our little discussion earlier today I wanted to see the correlations between certain metrics and team wins. These are the following metrics I looked at.

WAR, FIP, ERA, wOBA, wRC, wRAA, UZR

Honestly I could have chosen more or less, but these metrics were easy to sort since they were next to each other on certain pages. I'm lazier than BJ Upton.

I looked at WAR against Wins, and then the six different combinations (3 independent variables) of offense, defense, and pitching.

These were the adjusted r-squares (or r-squared for WAR). FYI I kept the constant zero for the multiple regressions as having a constant wouldn't make sense. For WAR I did not do that.

This includes all teams from 2002-2008.

Independent Var		Adj R^2
wRC+FIP+UZR		47.89%
wRAA+FIP+UZR		0.00%
wOBA+FIP+UZR		78.21%
wRC+ERA+UZR		55.98%
wRAA+ERA+UZR		0.00%
wOBA+ERA+UZR		83.82%
WAR		75.58%

Wow. So the best combination is wOBA+ERA+UZR. Surprising? WAR is pretty strong itself, and I'll get to that later.

So I dug deeper into that formula, and I found that the UZR component was insignificant*. The t-value was very small so the p-value was large. I decided to drop it. This is my result

Independent Var		Adj R^2
wOBA+ERA		83.89%
wOBA+FIP		73.25%

Quite clearly out of these variables the wOBA+ERA is the strongest in predicting team wins. Fairly surprising.

Here are the formulas for the WAR model and the best model

Wins = 0. + ( (wOBA) * 473.05 ) + ( (ERA) * -16.92 )

Wins = 46.66 + ( (Total WAR) * 0.989533 )

What is interesting is that in "real life" an increase of one WAR really only translates to 1 win. Again fairly interesting. Of course this isn't surprising as that is exactly what we'd have hoped to see. Gaining 1 WAR equals about 1 win (slightly less)

Using the formula this is what we get for 2009 and for 2008. I cannot use this WAR formula for 2009 as it is based on 162 games. The other formula adjusts to fit for 162 games. The WAR one does not.

2009

Team	Wins	wOBA+ERA Wins
Dodgers	61	98.5404
Yankees	58	96.49225
Rays	52	95.4496
Red Sox	55	93.6956
Braves	49	90.3258
Blue Jays	47	88.5576
Phillies	54	88.5505
Rockies	52	88.2502
Cardinals	52	87.45295
Rangers	52	87.204
Mariners	51	86.7797
White Sox	50	86.327
Cubs	48	85.49165
Tigers	49	84.80775
Angels	56	83.9821
Twins	48	82.67015
Astros	49	82.6117
Giants	51	82.4567
Brewers	48	80.53965
Marlins	49	78.04685
Diamondbacks	41	78.0433
Pirates	42	77.8431
Mets	44	77.6048
Athletics	40	74.36255
Reds	44	73.51655
Royals	37	70.7748
Orioles	41	70.08735
Nationals	28	69.0376
Indians	38	68.28815
Padres	37	61.91795

2008

Team	Wins	wOBA+ERA wins	Error	WAR Wins	Error
Cubs	97	99.1	2.1	95.8	-1.2
Red Sox	95	98.7	3.7	104.3	9.3
Rays	97	94.3	-2.7	95.0	-2.0
Phillies	92	93.6	1.6	92.2	0.2
Blue Jays	86	93.3	7.3	90.0	4.0
Brewers	90	90.6	0.6	82.3	-7.7
White Sox	89	90.3	1.3	90.8	1.8
Cardinals	86	90.2	4.2	88.3	2.3
Dodgers	84	90.1	6.1	82.2	-1.8
Mets	89	89.1	0.1	85.4	-3.6
Yankees	89	87.5	-1.5	88.8	-0.2
Angels	100	86.1	-13.9	84.6	-15.4
Diamondbacks	82	85.8	3.8	82.5	0.5
Twins	88	84.4	-3.6	81.4	-6.6
Indiands	81	82.5	1.5	87.0	6.0
Marlins	84	81.0	-3.0	78.7	-5.3
Braves	72	80.9	8.9	79.0	7.0
Tigers	74	78.7	4.7	77.8	3.8
Astros	86	77.6	-8.4	76.1	-9.9
Athletics	75	77.4	2.4	77.3	2.3
Rangers	79	77.1	-1.9	83.3	4.3
Rockies	74	76.3	2.3	78.6	4.6
Reds	74	73.0	-1.0	66.5	-7.5
Padres	63	72.5	9.5	66.5	3.5
Royals	75	72.4	-2.6	78.6	3.6
Giants	72	72.1	0.1	73.7	1.7
Orioles	68	69.9	1.9	72.3	4.3
Nationals	59	67.3	8.3	63.8	4.8
Mariners	61	66.6	5.6	66.5	5.5
Pirates	67	63.7	-3.3	63.2	-3.8

*Before anyone says anything I fully know that defense is important and that FIP is better than ERA. However this suggests that UZR may not be significant in determing expected wins and losses, and that ERA may be better than FIP in that same exercise.

Updated Expected Strikeouts based on Pitch Result

matthan — Tue, 21 Jul 2009 15:15:14 -0000

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.

As FreeZorilla pointed out there were definitely some problems. Firstly there was some overlap, namely multicollinearity. This basically means that the independent variables are correlated to each other. It doesn’t ruin the model, but we can do better. Secondly the model had limited application use. It wasn’t essentially using apples to apples, but rather apples to oranges. For example one variable was saying a % of swinging strikes out of total pitches whereas another was the % of contact a hitter made when the pitch was thrown in the zone. I wanted to standardize it and make it easier to understand and apply without taking away predictive power.

To solve these problems I decided to only use the results that could happen once a pitcher throws a baseball.

A ball, a swinging strike, in play, foul, and called strike. For swinging strike, in play, and foul we can also break it down between in the zone and out of the zone. This makes up 100% of all possibilities when a pitcher throws a pitch. In my analysis I found it was better to just use “In Play” or “Foul” instead of In and Out of zone. For swinging strikes I found it better to differentiate between in the zone and out of the zone swinging strikes.

Secondly I made all these metrics as part of total pitches thrown. So now we can say if a pitcher throws 2% more called strikes and 2% less balls his strikeout rate should now be about 1.8% higher.

Also I made the “constant” in the regression formula zero. It is not logical to have a constant that is not equal to zero. If he throws no strikes, or no pitches at all then the pitcher will have a zero strike out rate. The constant must be zero.

Here is the formula. I rounded it to make it a bit easier:

K%=(ClStr%*.9)+(Foul%*.5)+(InPly%*-.9)+(InZSwStr%*1.1)+(OZSwStr%*1.5)

The Adjusted R-Squared is: 91.4%

This essentially means how strong the model is; how much the model explains K%. This is a very strong relationship as many consider just hitting 70% to be good. This figure takes into account how many variables are in the model.

All of the independent variables in the formula are significant at a 99% level of confidence. This is why I threw “Ball%” out. The model also passes the F-Test. Multicollinearity is now not really an issue. And most importantly it passes the simple logic test.

Again any and all help is appreciated. I’m definitely not done looking at this. For example I’ve begun to look at “In zone Contact%” and “Out of zone Contact%” in play of In play and Foul. Perhaps that will lead to a better model since the pitcher has a stronger control over those two metrics compared to in play and foul.

Here are the 2009 expected strikeouts. This is for all pitchers with about 30+ innings. Remember the model was based upon all qualified pitchers, so being able to predict this accurate with pitchers with this small amount of innings is very telling, basically saying it is holding true for even smaller sample sizes. Due to some constraints I'll just post the Rays, Yanks, and Red Sox here. If you want to see all pitchers with about 30+ IP go to this link. The eK's as well as all the components for all the pitchers are in the link.

I highly recommend you check out the link. Some of the best info is in there

Expected Strikeouts for 2009 pitchers with 30+ inning as well as component data

Name	K%	eK%	Difference
Andy Sonnanstine	13.81%	15.45%	1.64%
David Price	23.04%	21.46%	-1.58%
Grant Balfour	23.46%	20.75%	-2.71%
J.P. Howell	28.41%	29.85%	1.44%
James Shields	16.79%	15.27%	-1.52%
Jeff Niemann	13.49%	14.18%	0.69%
Joe Nelson	20.78%	21.48%	0.70%
Lance Cormier	13.26%	14.32%	1.06%
Matt Garza	21.17%	18.89%	-2.28%
Scott Kazmir	16.89%	17.88%	0.99%
Brad Penny	14.83%	14.70%	-0.13%
Daisuke Matsuzaka	19.21%	18.56%	-0.65%
Hideki Okajima	24.36%	23.65%	-0.71%
Jon Lester	27.41%	24.60%	-2.81%
Jonathan Papelbon	24.12%	22.76%	-1.36%
Josh Beckett	22.07%	21.08%	-0.99%
Justin Masterson	17.95%	19.02%	1.07%
Manny Delcarmen	15.79%	19.17%	3.38%
Ramon Ramirez	17.53%	22.33%	4.80%
Tim Wakefield	12.81%	12.30%	-0.51%
A.J. Burnett	21.91%	19.63%	-2.28%
Alfredo Aceves	21.80%	19.04%	-2.76%
Andy Pettitte	14.67%	16.15%	1.48%
CC Sabathia	18.13%	18.23%	0.10%
Chien-Ming Wang	12.72%	14.35%	1.63%
Joba Chamberlain	19.40%	19.01%	-0.39%
Mariano Rivera	30.07%	24.75%	-5.32%
Phil Coke	20.39%	19.90%	-0.49%
Phil Hughes	18.71%	19.92%	1.21%

Expected K% & uBB% based upon Pitch Results & Plate Discipline

matthan — Fri, 10 Jul 2009 15:09:40 -0000

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

	Years (qualified pitchers)		Adj R-Squared	MAPE	MSE	RMSE
eK% formula	2003-2008		92.7507%	5.8571%	0.0138%	1.1736%
euBB% formula	2003-2008		77.4111%	11.9994%	0.0101%	1.0045%

Last	K%	eK%	Error	uBB%	euBB%	Error
2009 Notable Rays Players
Sonnanstine	13.81%	16.03%	2.21%	5.80%	4.93%	-0.88%
Wheeler	17.21%	16.63%	-0.58%	4.10%	3.31%	-0.79%
Price	23.04%	22.53%	-0.51%	15.20%	10.45%	-4.75%
Garza	20.88%	19.27%	-1.61%	9.67%	9.41%	-0.26%
Balfour	23.43%	22.27%	-1.16%	11.43%	10.53%	-0.90%
Niemann	12.85%	13.96%	1.11%	9.78%	8.90%	-0.87%
Nelson	21.19%	22.20%	1.01%	12.58%	9.68%	-2.90%
Kazmir	16.89%	17.64%	0.75%	11.15%	8.65%	-2.50%
Shields	17.22%	17.05%	-0.17%	4.70%	4.16%	-0.53%
2009 Other League Notables
Baker	19.80%	20.14%	0.34%	4.82%	5.30%	0.48%
Beckett	22.06%	21.36%	-0.70%	7.28%	6.58%	-0.70%
Billingsley	23.52%	23.72%	0.20%	9.41%	8.62%	-0.79%
Braden	14.96%	15.75%	0.79%	5.80%	4.70%	-1.10%
Burnett	21.91%	20.05%	-1.86%	11.50%	9.67%	-1.82%
Cain	19.83%	20.16%	0.33%	8.96%	6.29%	-2.67%
Danks	21.14%	22.25%	1.11%	7.96%	8.04%	0.08%
Dempster	19.69%	20.67%	0.98%	9.07%	8.75%	-0.32%
Feldman	12.33%	13.55%	1.22%	8.22%	8.89%	0.67%
Galarraga	15.23%	17.51%	2.27%	10.07%	7.66%	-2.41%
Gallardo	26.14%	23.23%	-2.92%	10.68%	10.43%	-0.25%
Greinke	25.49%	20.71%	-4.79%	4.15%	6.95%	2.80%
Halladay	21.35%	20.48%	-0.88%	3.70%	3.42%	-0.28%
Hamels	20.20%	21.42%	1.22%	4.35%	5.59%	1.24%
Hammel	15.78%	14.63%	-1.15%	4.81%	5.70%	0.89%
Haren	25.87%	24.58%	-1.29%	3.26%	5.53%	2.27%
Hernandez	23.51%	22.39%	-1.11%	7.01%	6.70%	-0.31%
E Jackson	20.35%	19.87%	-0.48%	7.00%	7.57%	0.57%
Josh Johnson	20.65%	20.16%	-0.48%	5.87%	5.84%	-0.03%
Ra Johnson	20.46%	21.14%	0.68%	7.42%	6.49%	-0.93%
Jurrjens	16.63%	17.08%	0.45%	8.87%	6.68%	-2.19%
Kershaw	24.48%	22.85%	-1.63%	13.14%	11.24%	-1.90%
Cliff Lee	16.41%	15.40%	-1.01%	5.79%	5.09%	-0.71%
Lester	27.39%	24.85%	-2.54%	7.35%	8.50%	1.15%
Lilly	21.46%	21.26%	-0.20%	4.87%	4.76%	-0.11%
Lincecum	28.95%	24.38%	-4.57%	5.95%	6.67%	0.72%
Liriano	20.71%	20.41%	-0.30%	10.35%	9.09%	-1.27%
Lowe	11.97%	12.83%	0.86%	6.84%	6.70%	-0.14%
Oswalt	18.86%	19.36%	0.50%	5.93%	5.02%	-0.92%
Owings	13.83%	16.08%	2.25%	10.12%	7.92%	-2.20%
Pavano	16.67%	16.66%	-0.01%	4.69%	3.92%	-0.78%
Penny	15.40%	14.66%	-0.74%	7.07%	7.26%	0.19%
Pettitte	15.10%	16.10%	1.01%	8.97%	8.31%	-0.66%
Porcello	12.67%	12.91%	0.24%	8.36%	7.17%	-1.19%
Rodriguez	22.84%	22.02%	-0.83%	7.76%	8.03%	0.27%
Sabathia	17.98%	19.82%	1.84%	6.46%	6.50%	0.03%
Joh Santana	23.11%	24.14%	1.03%	7.34%	4.56%	-2.78%
Scherzer	23.15%	24.24%	1.09%	9.07%	7.93%	-1.14%
Vazquez	28.51%	27.12%	-1.39%	4.82%	5.56%	0.74%
Verlander	29.50%	27.45%	-2.05%	6.49%	6.58%	0.10%
Je Weaver	20.30%	19.45%	-0.85%	7.05%	5.89%	-1.16%
Zito	18.00%	19.09%	1.09%	8.88%	7.79%	-1.09%

* There is no JP Howell data for 2009 on StatCorner which is why he isn't here

**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).

All in all I've accumulated have 13 independent variables across 2003-2009 and the two dependent variables for each model, K% and uBB%. I ran my regression on data for qualified pitchers (+/- a few) between 2003-2008 (using 2009 as a test or holdout period).

I believe I found the highest Adj R-squared for both models. Both equations only use 11 of the 13 independent variables. I'll link the workbooks at the end so if you want to look over the models and statistics it will be there. Also I included the numbers for a bunch of different tests so feel free to check them out (I really haven't look real hard at them yet).

Here are the two equations that I believe had the highest Adj R-Sq:

K = 0.34523 + ( (Ball) * -0.092208 ) + ( (ClStr) * 0.642177 ) + ( (SwStr) * 1.35 ) + ( (Foul) * 0.981356 ) + ( (InPly) * -0.343883 ) + ( (Oswing) * -0.015719 ) + ( (Zswing) * -0.146531 ) + ( (Swing) * -0.42555 ) + ( (Ocontact) * -0.038438 ) + ( (Contact) * -0.184088 ) + ( (Fstrike) * -0.000762 )

uBB = 0.58193 + ( (Ball) * 0.05506 ) + ( (ClStr) * -0.443504 ) + ( (SwStr) * -0.303051 ) + ( (Foul) * 0.092248 ) + ( (InPly) * -0.352155 ) + ( (Oswing) * -0.055224 ) + ( (Swing) * -0.366769 ) + ( (Ocontact) * 0.005447 ) + ( (Contact) * -0.173878 ) + ( (Zone) * -0.043222 ) + ( (Fstrike) * -0.053383 )

Like I said before I'm sure there is a better equation out there. I'm sure something simpler as well. Feel free to mess around. I've mixed and matched a bit and in fact I did find another "K" equation from the 03-08 data that actually fits the 09 data better than the equation above. The difference isn't huge though.

These are the independent variables that I used (all in percent):

Balls, Cl Strike, Sw Strike, Foul, In Play, O Swing, Z Swing, Swing, O Contact, Z Contact, Contact, Zone, F Strike

Correlation matrix for K% (split up into two for easier viewing):

	K	Ball	Cl Str	Sw Str	Foul	In Play	O Swi
K	1.00	-0.27	-0.02	0.85	0.41	-0.80	0.34
Ball	-0.27	1.00	-0.22	-0.31	-0.54	-0.24	-0.38
Cl Str	-0.02	-0.22	1.00	-0.27	-0.34	0.16	-0.09
Sw Str	0.85	-0.31	-0.27	1.00	0.31	-0.65	0.44
Foul	0.41	-0.54	-0.34	0.31	1.00	-0.30	0.27
In Play	-0.80	-0.24	0.16	-0.65	-0.30	1.00	-0.14
O Swi	0.34	-0.38	-0.09	0.44	0.27	-0.14	1.00
Z Swi	0.01	-0.24	-0.67	0.22	0.51	0.05	-0.21
Swing	0.27	-0.80	-0.35	0.46	0.72	0.09	0.44
O Con	-0.57	0.07	0.15	-0.66	0.01	0.41	0.09
Z Con	-0.74	0.17	0.19	-0.78	-0.30	0.66	-0.02
Con	-0.85	0.13	0.22	-0.94	-0.15	0.71	-0.29
Zone	-0.01	-0.61	0.24	-0.05	0.34	0.28	-0.37
F Str	0.14	-0.79	0.29	0.17	0.36	0.25	0.31

	Z Swi	Swing	O Con	Z Con	Con	Zone	F Str
K	0.01	0.27	-0.57	-0.74	-0.85	-0.01	0.14
Ball	-0.24	-0.80	0.07	0.17	0.13	-0.61	-0.79
Cl Str	-0.67	-0.35	0.15	0.19	0.22	0.24	0.29
Sw Str	0.22	0.46	-0.66	-0.78	-0.94	-0.05	0.17
Foul	0.51	0.72	0.01	-0.30	-0.15	0.34	0.36
In Play	0.05	0.09	0.41	0.66	0.71	0.28	0.25
O Swi	-0.21	0.44	0.09	-0.02	-0.29	-0.37	0.31
Z Swi	1.00	0.63	-0.24	-0.29	-0.14	0.30	0.11
Swing	0.63	1.00	-0.13	-0.26	-0.23	0.46	0.61
O Con	-0.24	-0.13	1.00	0.44	0.75	-0.10	0.00
Z Con	-0.29	-0.26	0.44	1.00	0.81	-0.11	-0.01
Con	-0.14	-0.23	0.75	0.81	1.00	0.16	0.00
Zone	0.30	0.46	-0.10	-0.11	0.16	1.00	0.52
F Str	0.11	0.61	0.00	-0.01	0.00	0.52	1.00

What is notable:

The correlations pretty much make sense. Swinging strikes is highly correlated with K's. Anything to do with contact, especially in play, is highly negatively correlated. What is really interesting is call strikes,f-strikes, zone aren't what I originally thought. Firstly call strikes is barely negatively correlated. A bit strange, but perhaps that makes sense on some level. Or perhaps that may be a problem with the model. Thoughts? I would have thought Zone and F-Strike would have had a higher correlation. However in a way it makes sense. If you are throwing in the zone (esp on the first strike) you have a higher chance of a ball in play which eliminates the K potential.

Correlation matrix for uBB% (split up into two for easier viewing):

	uBB	Ball	Cl Str	Sw Str	Foul	In Ply	O Swi
uBB	1.00	0.75	-0.27	0.07	-0.19	-0.56	-0.27
Ball	0.75	1.00	-0.22	-0.31	-0.54	-0.24	-0.38
Cl Str	-0.27	-0.22	1.00	-0.27	-0.34	0.16	-0.09
Sw Str	0.07	-0.31	-0.27	1.00	0.31	-0.65	0.44
Foul	-0.19	-0.54	-0.34	0.31	1.00	-0.30	0.27
In Ply	-0.56	-0.24	0.16	-0.65	-0.30	1.00	-0.14
O Swi	-0.27	-0.38	-0.09	0.44	0.27	-0.14	1.00
Z Swi	-0.10	-0.24	-0.67	0.22	0.51	0.05	-0.21
Swing	-0.57	-0.80	-0.35	0.46	0.72	0.09	0.44
O Con	-0.17	0.07	0.15	-0.66	0.01	0.41	0.09
Z Con	-0.22	0.17	0.19	-0.78	-0.30	0.66	-0.02
Con	-0.25	0.13	0.22	-0.94	-0.15	0.71	-0.29
Zone	-0.51	-0.61	0.24	-0.05	0.34	0.28	-0.37
F Str	-0.70	-0.79	0.29	0.17	0.36	0.25	0.31

	Z Swi	Swing	O Con	Z Con	Con	Zone	F Str
uBB	-0.10	-0.57	-0.17	-0.22	-0.25	-0.51	-0.70
Ball	-0.24	-0.80	0.07	0.17	0.13	-0.61	-0.79
Cl Str	-0.67	-0.35	0.15	0.19	0.22	0.24	0.29
Sw Str	0.22	0.46	-0.66	-0.78	-0.94	-0.05	0.17
Foul	0.51	0.72	0.01	-0.30	-0.15	0.34	0.36
In Ply	0.05	0.09	0.41	0.66	0.71	0.28	0.25
O Swi	-0.21	0.44	0.09	-0.02	-0.29	-0.37	0.31
Z Swi	1.00	0.63	-0.24	-0.29	-0.14	0.30	0.11
Swing	0.63	1.00	-0.13	-0.26	-0.23	0.46	0.61
O Con	-0.24	-0.13	1.00	0.44	0.75	-0.10	0.00
Z Con	-0.29	-0.26	0.44	1.00	0.81	-0.11	-0.01
Con	-0.14	-0.23	0.75	0.81	1.00	0.16	0.00
Zone	0.30	0.46	-0.10	-0.11	0.16	1.00	0.52
F Str	0.11	0.61	0.00	-0.01	0.00	0.52	1.00

What is notable:

Well for the most part the obvious things hold true. Balls are highly correlated. Swings and contact for the most part are negatively correlated. A key to limiting BB would be throwing first pitch strikes. That is obviously very intuitive, but the huge negative correlation bares that out.

I think I'm going to limit this post to just this. I'll answer whatever question I can in the comment section. And I do have quite a few comments on the players themselves, but I wanted to save that for comments.

All of the data, as well as the audit for the regressions, will be linked just below. Check them out. Once I hear some suggestions, thoughts, and opinions I'll know what step should be taken next if any step at all.

If you want to play around with the notable and Rays pitchers. For example changing a value for any independent variable for a specific pitcher to see what would happen to their expected rates click here:

eK for 2009

euBB for 2009

If you want to look at the regression, the audit and all the regression statistics, as well as the results of the equation to the sample as well as the holdout period click here:

eK regression with highest Adj R sq

euBB regression w highest adj R sq

If you want to run your own regressions based upon the data set (if you want to add a variable you have to find the data and add it to the sheet, deleting is simple...click here:

2003-2008 data with 2009

For anyone interested the largest problem with this project was easily collecting the data between Fangraphs and Statcorner. Once I consolidated the data running the regressions and testing on the holdout period was quite easy. Of course with the sheer quantity of combinations testing everything would be highly time consuming.

My fantasy would be to be able to create an accurate eK or euBB based upon these sorts of variables and then be able to plug them in as part of an expectedFIP.

Steve Phillips tells Price to JUST THROW STRIKES!!

matthan — Thu, 09 Jul 2009 13:04:33 -0000

Steve Phillips tells Price to JUST THROW STRIKES!!

Basically he says Price's problem is with the his control and that he will figure it out because he is competitive. Although I'm competitive and left handed, but I'm posting a fanshot instead of pitching in the major leagues.... hmmmmm. Oh and there is your obligatory comment that the Rays were fleeced in the Tiger deal.

The Decline of Rays Starting Pitching

matthan — Mon, 06 Jul 2009 01:16:16 -0000

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

via lh6.ggpht.com

Remember higher is better for this statistic. 100 is average

For tRA+ I decided to throw them all on the graph. Here are the things I noticed.

All 4 of the current Rays pitchers are at their 3 year low in tRA+

Shields worst year was 2006, but he has been declining ever since from his high in 2007

Kazmir is on a major 4 year decline

Garza was best with Minnesota and has gotten worse since coming to TB

Sonnanstine was above average in 2006, improved in 2007, and tanked in 2008.

Jackson had his 2nd best year in 2006 where he was above average and his best year was a tremendous 2009 so far. Both years without Hickey.

via lh4.ggpht.com

Garza was better than what people think in Minnesota. His 2008 was pretty much the same as his 2007. He has gotten worse across all metrics in 2009.

via lh6.ggpht.com

The ugliest graph of them all. His 2006 and 2007 were really good and just about equal. However he has really tanked ever since. The decline has been steep and sudden.

via lh4.ggpht.com

He was slightly better than average in 2006, but not so good in 2007 and 2008. 2009 he is an all-star. 2006 and 2009 are no Hickey years.

via lh3.ggpht.com

Sonnanstines 2007 to 2008 is the only year over year improvement by a starter under Hickey.

Ever other Rays starter has gotten worse from 2007 to 2008 and 2008 to 2009.

Okay Edwin did go from really really bad to really bad from 2007 to 2008.

via lh3.ggpht.com

I love Shields although 2009 hasn't been as good as usual.

Basically here are my overall thoughts. Our pitchers are just not doing too hot in 2009. Obviously this doesn't even include the below average season of Niemann so far and the really horrific year of Price. The next thing I noticed is that there is definitely a downward trend for Rays pitchers. All 4 current Rays pitchers are at their 3 year low.

Jeff Niemann Mr. Consistently Inconsistently Consistent

matthan — Tue, 30 Jun 2009 21:08:43 -0000

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?

Earned runs just do not tell the whole story. Lets see if the reality matches using FIP. Firstly lets look at a good and interesting comparable to Niemann, Rick Porcello. As you can see by just looking at the graph Niemann has been more consistent than Porcello even using FIP. I also added a trendline for Niemann’s starts and as we can see his FIP is trending downward. That again is excellent.

via lh5.ggpht.com

Now lets really see if the reality matches the perception. I took a sample of a good handful of pitchers, as well as the 2009 Rays starters and measured their volatility. Niemann rates very well on this scale as he is 5th out of 22.. Niemann’s 1.795 Standard Deviation is in fact closer to the leader of this sample, Adam Wainwright at 1.60 than it is to the 6th most consistent at 2.01. As you can see Niemann does have a lower volatility than Porcello here confirming out suspicions founded in the previous graph. It appears the perception may be the reality.

via lh3.ggpht.com

On the other hand we see a few Rays trailing at the back of the group. Hopefully they can pick it up and become more consistent.

Out of this sample Niemann’s average start posts a FIP of 4.79 which puts him 14th out of 22 in this group. His "yearly" FIP on fangraphs is 4.69. I’ve been doing a bit of research on looking at volatility and FIP in this manner and I have found a correlation (looking at 2006/2007/2008/some 2009) of around ~.6-.7. In other words this means as the average FIP per start raises so does the volatility. Note this exercise is done for around 15 starts for each of these 22 so the sample size will have to bear out. What I’ve seen so far (in looking at 06/07/08/09 is that there is certainly a fair amount of year over year change in volatility for pitchers; however, generally there appears to be distinct ranges for pitchers. Some pitchers certainly seem more consistent than others when looking..

Either way this is the first anomaly for Niemann. Medium to high average FIP per start pitchers just cannot seem to sustain this low volatility. Niemann in fact has the highest spread between average FIP by start and volatility in this sample. This could be a bad sign in the future.

The next stage is to break down Niemann’s FIP to see what is really driving his consistent numbers. In this analysis we are just going to look at the basic components of FIP: HR, IP, K, BB. There has been some analysis on this site that has looked at how Niemann may improve those metrics. One way would be to throw more curves. However here we are just going to look at how those counting stats change over time by start.

via lh4.ggpht.com

Unfortunately we cannot take too much out of this graph. However it does begin to paint the picture that Niemann has in fact been all over the map (mainly IP) rather than the extremely consistent pitcher his start by start ER and FIP show. To further break this down we are going to look at the rates.

I decided to compare Niemann to Mr. Big Game James. I found this compelling because we all love Shields, think Shields is a great pitcher, he is a Ray, we think of him as consistent and yet he in fact has had higher volatility than Niemann. So let’s see how real that difference is or if it has been just a product of luck.

via lh4.ggpht.com

via lh3.ggpht.com

via lh4.ggpht.com

via lh3.ggpht.com

Out of everything in this post this is by far the most important. I’ve presented the data in both table and graph form. As you can see Shields has in fact been more consistent on a per start basis on each component than Niemann other than HRs (which Niemann is due for a regression). Shields has also flat out been better. The consistency and excellence of Shields over Niemann isn’t even close. Of course this Shields being better isn’t unexpected, but it does help us reach some not so great conclusions about Jeff.

This leads us to why Niemann has actually posted more consistent FIPs than Shields. The answer is simply luck. It appears that Niemann has little control over things like K, walks, or even the innings he pitches. He has simply been getting a "good combination" of those in each start equaling roughly the same FIP. However since each metric is very volatile it is only a matter of time before he has a "bad combination" such as high walks, low k’s, and low innings.

I’ll sum up with some pro’s and con’s

Pro’s:

Niemann’s FIP by start is trending downward
80% of Niemann’s starts have resulted in 3ER or less; 87% of 4 ER or less
He has not given up a home run in his last 6 starts
James Shields is rock solid. You know exactly what you are getting out of him every single start.

Con’s:

Niemann has been wildly inconsistent with his strikeouts, walks and innings pitched. It is really only a stroke of luck that he hasn’t produced wildly inconsistent (and bad) FIPs because of it.
His HR rate is due for a regression
Pitchers with high average per start FIP typically cannot sustain this low volatility
Sonny and Kaz haven’t been as bad as what people think (see my previous and Sandy Kazmir’s work), but wow they’ve been embarrassing as a whole. To have that type of volatility you really have to be all over the map.
We really have no idea what we are getting when Niemann takes the hill. He can give us a few innings or a complete game. He can give us literally 0 strikeouts and walks or he can do than at a rate of one per inning. The range is enormous and Niemann must be able to gain some consistency within his performance to become a reliable starter.

Hopefully Hickey and the crew can teach Niemann some new tricks in order to be consistent. Or perhaps they should take RJ’s advice and throw more breaking pitches. Or perhaps it will just take a bit more maturing and experience. However if all else fails it is very likely Niemann could be excellent trade bait at the end of this year. His "mainstream" numbers will be very good given that he is a young pitcher, but his underlying numbers may not.

Introducing volatility to the 2009 Rays starting pitching

matthan — Sat, 27 Jun 2009 04:45:43 -0000

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.

First and foremost I took the FIP of each game started for each pitcher**. Obviously each pitcher has a different number of starts, but here are the FIPs for eachs of our starters for each start.

Firstly I apologize for the graph being tough to read. Basically some general highlights. Price has the best start. Sonny has the worst start. Sonny and Kaz both have two starts that are far worse than their others. Shields worst start was his first and has been solid ever since.

Here are some basic statistics:

All three of these metrics are fairly important. Firstly I calculated the "Yearly FIP" by using the cumulative season counting stats for each pitcher. I also average the FIP for each start for each pitcher. They certainly are different. Both are important in their own ways. However what is even more interesting is the standard deviations. Sonny and Kaz have by far the most volatility. Neimann has the least, which is quite a bit surprising. However is a high standard deviation good or bad? I'll glimpse into that in a bit but here is what I see. Neimann is consistently below average. That is going to result in poor losses (with our elite offense a bit more wins) since he has no volatility resulting in excellent games. Kazmir and Sonny both have higher FIPs, but they have far higher volatility. Therefore they'll have more games falling into the "winning" category due to their volatility. Generally if you have a great FIP you want low volatility, if you have a high FIP then more volatility is good. All of this with the overall goal in mind, maximing wins and losses for the team.

This next chart we are going to look at the individual volatility for each pitcher. 0% is set as the average FIP per start for each pitcher. Anything over 0% is something higher than their individual average. Anything lower is less than their average. Of course 0% for Kazmir is far higher base FIP than it is for Shields.

The few things I take out of this chart. Sonny clearly is the most volatile around his average. Neimann is very consistent. Garza is remarkably inconsistently consistent. He bounces above and below his mean slightly but very often. Kazmir and Sonny have two starts that really crush them. Shields has been improving as compared to his average (as has Neimann).

The last thing I want to share is something I find very interesting. As you've seen have had quite a bit of W and L theme to this post, and this is ultimately why. I cannot emphasize enough how wins and losses in the context of MLB pitchers are irrelevant. However my modifed W and L are actually a fairly good indicator of what each pitchers record SHOULD BE given the team that they are on. Typical wins and losses are based a lot on luck, run support, bullpen, and distribution of runs. I take all of those variables out here.

I assume that the Rays RPG has no standard deviation. We score our runs per game average every game. After 74 games this is 5.66

I assume that all 9 innings of the game is performed at the FIP of the starting pitcher. Therefore taking the bullpen out of the question

Therefore if the players FIP for that game is less than the Rays average RPG then they put the team in position to win. On average they should have won the game. If it is over the Rays RPG then they deserve to have lost. Clearly the variability of runs scored is important. A 6 FIP clearly gives the team a better chance of winning than a 15 FIP, and this modified W/L ignores that. I'm merely counting the times above and below the Rays RPG

According to this Sonny should have the second most Ws on the team. Why? Because of volatility. He has had 10 games where he has put up a lower FIP than what the Rays score. Sure some of those 5 games he "lost" were pretty bad, but they all count the same. Kazmir fits the same bill. Now lets take a look at Neimann and Shields. They've been our two most consistent pitchers. However Shields has been consistent on the good side and Neimann on the below average side. You see this in the hypothetical W/Ls. Even though the Rays have the highest RPG in the league Neimann has only truly pitched to a .500 record. This is due to his low volatility at a FIP right around our RPG. Shields**** has low volatility, but has a low FIP resulting in tons of good results.

Clearly a lot more work can be done with this type of analysis. I'm particularly interested in downside risk. However hopefully this gave some folks a different angle onto how to look at our pitchers.

**My FIP will be a bit different than what you see on FanGraphs. I'm using a different constant and only starting pitching innings. It won't truly matter as this is more of a comparison between starters and I used the same formula for each starter. However be very cautious if you try to compare one of these guys in this analysis to numbers you pull off of FanGraphs or another reference

****Shields start on Friday June 26th is not included

Jim Hickey Statistical Analysis Part 2

matthan — Wed, 17 Jun 2009 17:56:46 -0000

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%

The Methodology

The goal here is to see if any of the changes in any of these metrics over the course of time is statistically significantly different from zero. In general if coaching is irrelevant than we would expect on average that the change in these metrics over the course of time to be 0.00. Of course there are some variables that impact these metrics other than coaching so do keep that in mind. However over time we would expect pitchers to strike guys out at the same rate, throw the same % of curveballs, give up about the same % of fly balls, and so forth.

Therefore the expected change for every one of these metrics is 0.00

To find the actual change I simply took Year 2-Year1

*Please note for some "actual changes" I took Year 3-Year 1 when applicable for "first and last"

Also note that a "negative" change indicates that the metric has decreased. For some metrics that is bad (K/9) and for some that can be good (BB/9)

	"Year over Year" average change per pitcher	"Year over Year" standard deviation	"Year over Year" P Value	"Year over Year" Is change Signifigant?
Basic
ERA	-0.048	2.198	91.24%	No
Advanced
FIP	0.209	0.846	21.39%	No
BABIP	-0.009	0.068	49.94%	No
Performance
K/9	-0.683	1.459	2.07%	YES
BB/9	0.208	1.012	30.02%	No
HR/9	-0.004	0.428	96.00%	No
HR/FB	-0.003	0.034	63.11%	No
Batted Ball
LD%	-0.001	0.038	89.48%	No
FB%	0.006	0.082	72.65%	No
GB%	-0.005	0.068	72.64%	No
Pitch Types
FB%	-0.010	0.072	47.61%	No
SL%	-0.007	0.053	50.37%	No
CT%	0.018	0.058	11.14%	No
CB%	0.015	0.035	3.26%	YES
CH%	-0.017	0.033	1.14%	YES
SF%	0.001	0.008	60.05%	No

	"First and Last" average change per pitcher	"First and Last" standard deviation	"First and Last" P Value	"First and Last" Is change Signifigant?
Basic
ERA	-0.062	1.950	88.77%	No
Advanced
FIP	0.272	0.942	20.50%	No
BABIP	-0.012	0.047	27.03%	No
Performance
K/9	-0.889	1.576	1.60%	YES
BB/9	0.270	0.878	17.72%	No
HR/9	-0.006	0.510	96.18%	No
HR/FB	-0.004	0.040	64.17%	No
Batted Ball
LD%	-0.001	0.038	87.98%	No
FB%	0.007	0.079	68.11%	No
GB%	-0.006	0.065	67.90%	No
Pitch Types
FB%	-0.013	0.079	46.17%	No
SL%	-0.009	0.039	31.16%	No
CT%	0.024	0.091	24.59%	No
CB%	0.020	0.047	7.06%	No
CH%	-0.022	0.033	0.49%	YES
SF%	0.001	0.009	59.29%	No

*Please remember this is based upon the change per pitcher. As in for "First and Last" each pitcher on average is going to throw 2% less changeups

Significant Results

"Year Over Year"

With this we see that the changes in K/9, CB%, and CH% are all statistically significant than zero.

The average change per pitcher is -.683 for K/9, 1.5% for CB%, and -1.7% for CH%

The P Values (two tailed test anything under 5% is sig) are .0207 for K/9, .0326 for CB%, and .0114 for CH%

"First and Last"

With this we see that the changes in K/9 and CH% are statistically significant than zero. CB% is no longer significant, but is very close to being so.

Interesting Tidbits

Besides for the metrics that have significant differences from zero there are some interesting things among the nonsignificant metrics. For example it appears the FIP under pitchers actually increase (get worse) when a guy is with Hickey. It isn't significant, but it was in Part 1 of my analysis. Pitchers on average also seem to walk more guys under Hickey. Also since HR/9 was a major topic of discussion in Part 1 it is important to note that HR/9 was nowhere near a significant. In other words pitchers gave up home runs at about the same rate at the start of their Hickey tenure as they did at the end. Batted ball types were very stagnant. Each pitcher on average gave up pretty much the same % of each batted ball the first year of Hickey as they did the 2nd year of Hickey.

I also removed Scott Kazmir from the data just to see the impact. The K/9 decline was still significant

The documents can be found here:

Year over Year

First and Last

My worries/concerns

Before I refine my data in Part 1 I'll hold off on my opinion on that data. However I find this info in Part 2 to be pretty bad for Hickey. The higher FIP is not significant so I'm not going to pin that on him, but pitchers are without a doubt (statistically significantly) averaging less K/9 as their time with Hickey increases. That is very bad sign for performance. Secondly he seems to be against the changeup and generally for the curveball. On average as his time with his pitchers increase pitchers seem to use the curveball more at the expense of the change up. I have no problem with him doing this in isolated cases if a specific pitchers skill set warranted such an action. However since the results were significant it appears he (well his pitchers) is systematically favoring the curveball at the expense of the change up.