SB Nation User Blog: matthan

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.

Jim Hickey Analysis Part 1

matthan — Wed, 17 Jun 2009 02:22:17 -0000

Lately there has been a bit of talk about the performance of our pitching staff and our bullpen. Naturally this leads to Jim Hickey. Is he a good pitching coach? Bad pitching coach? Are pitching coaches irrelevant? I'm going to attempt to shine some light to this dilemma. There just has to be some way to quantify the performance of pitching coaches....right?

I decided on two methods to analyze the performance of Hickey. Both methods involve looking at his time with both the Rays and Astros

1. Comparing the performance of a pitcher under Hickey to the same pitcher's performance under a different pitching coach. In order to do this I found a sample of pitchers (30 IP min) that had 1 year with Hickey and then the next MLB year w/o Hickey (or vice versa).

2. Comparing how a pitcher performs while under the eye of Hickey. Does pitchers improve or get worse over a year under the eye of Hickey?

If pitching coaches are irrelevant the metrics should remain static. Things such as declines due to age or improvements due to maturation should be filtered away due to sample size.

For Part 1 I am going to look at the first method comparing a pitchers performance with and without Hickey. My initial assumption (which is why I wanted to do this to begin with), is that performance suffers under Hickey.

Lets get right to the results

I've found a statistically significant decline in performance for the following metrics:

tRA, FIP, HR/9

I've found no statistically significant decline in performance under Hickey for the following metrics:

ERA, K/9, BB/9, and K/BB

The sample size is 37. In other words we have pairs of data (one portion of the pair is the performance under Hickey and the other is the performance under another pitching coach). Some of the pairs include Andy Pettittes 2006 with Hickey (Astros) and 2007 with Yankees. In fact nearly all of the 2006 Astros as they all had a new pitching coach the following year. Edwin Jackson from 2006 (no Hickey) to 2007 (w Hickey). Also Edwin Jackson from 2008 (w Hickey) to 2009 (no Hickey). James Shields 2006 (no Hickey) to 2007 (w Hickey).

*I did not break down the performance of the 2004 Astro pitchers compared to the 2005 Astros. It would have been too labor intensive for me to get the breakdowns for each metric for each pitcher under the previous PC in 2004 and under Hickey. Also it is questionable how much influence Hickey would have had in 2004 anyways. RJ made a post with some of these details Looking at Jim Hickey

I do wish there existed a bit larger of a sample, but this will have to do (the skewness and kurtosis have some issues). However I did want to post what I have now

Now onto the numbers

FIP Analysis
FIP with Hickey		FIP w/o Hickey

Mean	4.66	Mean	4.24
Standard Error	0.15	Standard Error	0.12
Median	4.79	Median	4.18
Standard Deviation	0.93	Standard Deviation	0.75
Kurtosis	-0.73	Kurtosis	-0.09
Skewness	0.04	Skewness	0.36
Minimum	3.02	Minimum	2.83
Maximum	6.73	Maximum	6.04
Confidence Level(95.0%)	0.31	Confidence Level(95.0%)	0.25
P Value	0.0131

tRA Analysis
tRA with Hickey		tRA w/o Hickey

Mean	4.95	Mean	4.45
Standard Error	0.21	Standard Error	0.18
Median	4.76	Median	4.49
Standard Deviation	1.25	Standard Deviation	1.03
Kurtosis	-1.03	Kurtosis	-0.37
Skewness	0.10	Skewness	-0.27
Minimum	2.79	Minimum	2.28
Maximum	7.52	Maximum	6.29
Confidence Level(95.0%)	0.44	Confidence Level(95.0%)	0.36
P Value	0.0236

ERA Analysis
ERA with Hickey		ERA w/o Hickey

Mean	4.89	Mean	4.49
Standard Error	0.27	Standard Error	0.23
Median	4.69	Median	4.43
Standard Deviation	1.67	Standard Deviation	1.40
Kurtosis	-0.73	Kurtosis	0.71
Skewness	0.29	Skewness	0.41
Minimum	2.04	Minimum	1.80
Maximum	8.13	Maximum	7.98
Confidence Level(95.0%)	0.56	Confidence Level(95.0%)	0.47
P Value	0.1019

K/9 Analysis
K/9 with Hickey		K/9 w/o Hickey

Mean	7.23	Mean	7.33
Standard Error	0.29	Standard Error	0.23
Median	6.78	Median	7.02
Standard Deviation	1.78	Standard Deviation	1.38
Kurtosis	0.83	Kurtosis	1.94
Skewness	0.95	Skewness	1.21
Minimum	4.54	Minimum	5.04
Maximum	12.48	Maximum	11.82
Confidence Level(95.0%)	0.59	Confidence Level(95.0%)	0.46
P Value	0.3238

BB/9 Analysis
BB/9 with Hickey		BB/9 w/o Hickey

Mean	3.41	Mean	3.33
Standard Error	0.17	Standard Error	0.19
Median	3.20	Median	2.88
Standard Deviation	1.02	Standard Deviation	1.18
Kurtosis	-0.59	Kurtosis	0.30
Skewness	0.24	Skewness	0.94
Minimum	1.51	Minimum	1.71
Maximum	5.63	Maximum	6.19
Confidence Level(95.0%)	0.34	Confidence Level(95.0%)	0.39
P Value	0.3644

K/BB Analysis
K/BB w/ Hickey		K/BB w/o Hickey

Mean	2.33	Mean	2.43
Standard Error	0.16	Standard Error	0.13
Median	2.00	Median	2.57
Standard Deviation	1.00	Standard Deviation	0.80
Sample Variance	0.99	Sample Variance	0.64
Kurtosis	0.82	Kurtosis	-1.07
Skewness	1.21	Skewness	-0.03
Minimum	1.22	Minimum	1.08
Maximum	5.11	Maximum	4.00
Confidence Level(95.0%)	0.33	Confidence Level(95.0%)	0.27
P Value	0.3226

HR/9
HR/9 w/ Hickey		HR/9 w/o Hickey

Mean	1.26	Mean	1.01
Standard Error	0.07	Standard Error	0.06
Median	1.24	Median	0.96
Standard Deviation	0.45	Standard Deviation	0.36
Kurtosis	-0.29	Kurtosis	-0.45
Skewness	-0.11	Skewness	0.37
Minimum	0.23	Minimum	0.41
Maximum	2.22	Maximum	1.78
Confidence Level(95.0%)	0.15	Confidence Level(95.0%)	0.12
P Value	0.0054

The data here FURTHER emphasizes how much of a crappy metric ERA is.

There are some outliers within this sample. I wish it was larger to smooth out some of the possible variables. Trever Miller has two entries that really help Hickey. Camp has two that really hurt Hickey. Percival really hurts Hickey. Jackson really hurts Hickey (had lower FIP w/o Hickey in 2006 also). Clemens did great with Hickey in 2006 and flamed in NY in 2007 etc.

Here is the file Google Doc

This is my first fanpost so pardon my crappy tables. I'm too busy watching the Rays dominate to figure that out

I'll take a look at whether players improve or decline under at least 2 years of Hickey next.

After 81 games...

matthan — Sun, 29 Jun 2008 21:10:27 -0000

This can speak for itself...

2008 Major League - Standings
MLB	W	L	PCT	GB	HOME	ROAD	RS	RA	DIFF	STRK	L10
Tampa Bay	49	32	.605	-	30-13	19-19	382	327	+55	Won 1	7-3
Chicago Cubs	49	32	.605	-	33-10	16-22	442	344	+98	Lost 3	4-6
Boston	50	33	.602	-	31-10	19-23	422	348	+74	Lost 1	6-4
LA Angels	48	33	.593	1	22-18	26-15	339	328	+11	Lost 3	6-4
Chicago Sox	45	35	.563	3.5	26-11	19-24	388	307	+81	Won 3	6-4
St. Louis	46	36	.561	3.5	23-17	23-19	379	354	+25	Won 1	4-6
Oakland	44	36	.550	4.5	27-20	17-16	356	287	+69	Lost 1	5-5
Minnesota	45	37	.549	4.5	27-17	18-20	394	385	+9	Won 1	9-1
Milwaukee	44	37	.543	5	25-13	19-24	364	366	-2	Lost 1	6-4
Philadelphia	44	38	.537	5.5	22-18	22-20	419	343	+76	Won 1	2-8
NY Yankees	44	38	.537	5.5	22-18	22-20	385	364	+21	Lost 1	5-5
Florida	42	39	.519	7	24-19	18-20	385	403	-18	Won 1	4-6
Baltimore	41	39	.513	7.5	22-12	19-27	361	364	-3	Lost 1	5-5
Detroit	41	40	.506	8	25-17	16-23	393	389	+4	Won 5	8-2
Arizona	41	41	.500	8.5	24-15	17-26	366	359	+7	Lost 1	3-7
Texas	41	41	.500	8.5	20-18	21-23	451	466	-15	Lost 1	5-5
NY Mets	40	41	.494	9	22-18	18-23	378	379	-1	Won 1	5-5
Toronto	40	43	.482	10	22-19	18-24	344	316	+28	Won 2	5-5
Atlanta	40	43	.482	10	28-14	12-29	374	334	+40	Lost 2	4-6
LA Dodgers	38	42	.475	10.5	22-19	16-23	331	327	+4	Won 2	6-4
Houston	38	43	.469	11	20-18	18-25	357	393	-36	Won 1	5-5
Pittsburgh	38	43	.469	11	25-19	13-24	394	446	-52	Lost 1	4-6
Cincinnati	38	45	.458	12	21-17	17-28	353	411	-58	Won 2	5-5
Kansas City	37	44	.457	12	19-20	18-24	329	373	-44	Lost 1	8-2
Cleveland	37	45	.451	12.5	22-22	15-23	361	350	+11	Lost 2	4-6
San Francisco	35	46	.432	14	14-24	21-22	324	371	-47	Won 1	4-6
Washington	33	50	.398	17	18-26	15-24	300	414	-114	Won 1	4-6
San Diego	32	50	.390	17.5	21-25	11-25	300	381	-81	Lost 7	1-9
Colorado	32	50	.390	17.5	20-19	12-31	341	417	-76	Lost 7	2-8
Seattle	30	50	.375	18.5	15-24	15-26	322	388	-66	Won 2	6-4

Hellickson's Night

matthan — Fri, 06 Jun 2008 02:01:56 -0000

From what I can tell he had a perfect game through 5 and got pulled in the 6th (prior to giving up a hit). Looks like he had numerous K's also before he got yanked. Vero won 5-1 and Sarasota mustered two hits.

From what I can tell this was his final line

5IP

7Ks

0BB

Not too bad for being overshadowed by David Price for the time being. They both should be in AA within a reasonable amount of time in my not so expert opinion.