What are the best MLB starting pitching free agents available for the 2009 season?

Question: What are the best MLB starting pitching free agents available for the 2009 season?

Note: I asked Tom Tango to comment on my methodology and here is his response: http://www.insidethebook.com/ee/index.php/site/comments/fip_extended/

Note: I did an update of pitcher that I believe the Royals will target:

FA Type	Player	Age	BB	K	PA	GB	PPQ
Target	Randy Wolf	32	547	1227	6303	1611	0.287
Target	Brad Penny	30	475	1032	6179	2082	0.326
Target	Braden Looper	34	309	569	4201	1637	0.335
Reclamation	Carl Pavano	32	301	692	4660	1625	0.328
Reclamation	Mark Prior	28	223	757	2771	655	0.358
Reclamation	Matt Clement	33	650	1217	6189	2056	0.324
Reclamation	Freddy Garcia	32	554	1264	7277	2370	0.326
Reclamation	Mark Mulder	31	412	834	5562	2303	0.366
Chance	Tony Armas	30	431	690	4055	1141	0.261
Chance	Jason Jennings	30	477	705	4760	1603	0.284

Target – Pitcher the Royals should go after – young and good enough to help, but not too good to break the bank.

Reclamation – Pitcher with major past injuries and will be more of a chance – 1 year deal to turn career around.

Chance – Not the greatest pitcher, but may be signed for AAA/Pen and can be moved to starter

Why I asked the question: I wanted to see what free agent starting pitchers were available and the quality of the pitchers

Analysis:

I have been looking for a while to find a stat that uses only those attributes that are not effected by external factors that tell the strength of a pitcher. This is very important when comparing pitchers on different teams. W-L records are heavily determined by the teams offense, not the pitcher. Pitchers ERA is effected by how their bullpen handles inherited runners. Home Runs are determined by stadium elevation, distance to fences, wind direction and strength.

I had in the past like FIP (http://en.wikipedia.org/wiki/Defense_independent_pitching_statistics) as a measure, but because it takes HR into account and these can vary from one stadium to the next.

Another problem is that the attributes need to also be correlated from one season to the next. Baseball Prospectus,in the book Base ball Between the Numbers, examined what usefullstats are correlated from one season to the next and here are their results

Statistic - Year-to-year correlation

Winning percentage - .204

Batting average on balls in play (BABIP) - .272

ERA - .380

Home runs per batter faced - .470

Hits allowed per batter faced - .499

Walks (W) per batter faced - .676

Strikeouts (SO) per batter faced - .790

Ground ball (GB) percentage - .807

With the preceding information, a formula that takes W, SO and GB% into account was created.

First good events for the pitcher, SO and GB, will be given positive values, while BB will be subtracted away. The only problem is that not all ground balls hit into play will be an out. I decided to use the data from the work of Voros McCracken on BABIP historical determined that on average 29% of all balls hit should be hits and the rest should be outs. So GB will be multiplied by .71, getting the number of the ground balls will turn into outs. Finally the extrapolated number of GB outs and SO for the season are added together, W are then subtracted from the total and then the total is divided by the total number of batter faced over the season (TBF). Here is the equation:

(SO – W + (.7 * GB))/TBF

Currently I call the total Predictable Pitcher Quality (PPQ) until I come up with a more inventive name.

This stat is great way to compare pitchers and see how the rank without outside effects on them, stadiums, bullpen strength, run support, quality of competition, etc. As always I would love to have any feedback on the subject.

Here on the results of the available free agents and their PPQ number. Also the main starting pitchers for the Royals next season are included

			2008					2007
PLAYER	Age	TEAM	GB	BB	SO	TBF	PQ	GB	BB	SO	TBF	PPQ
Derek Lowe	35	LAD	390	45	147	851	0.441	398	59	147	831	0.441
CC Sabathia	28	CLE/MIL	325	59	251	1023	0.410	326	37	209	975	0.410
Mike Mussina	39	NYY	306	31	150	819	0.407	217	35	91	656	0.317
Andy Pettitte	36	NYY	333	55	158	881	0.381	331	69	141	916	0.332
A.J. Burnett	31	TOR	302	86	231	957	0.372	243	66	176	691	0.405
John Lackey	29	LAA	223	40	130	675	0.365	307	52	179	929	0.368
Randy Johnson	45	ARI	215	44	173	778	0.359	56	13	72	233	0.421
Ryan Dempster	31	CHC	279	76	187	856	0.358	93	30	55	282	0.320
Ben Sheets	30	MIL	244	47	158	812	0.347	160	37	106	592	0.306
Braden Looper	33	STL	325	45	108	842	0.345	254	51	87	746	0.287
Jon Garland	29	LAA	347	59	90	864	0.317	289	57	98	883	0.276
Jamie Moyer	45	PHI	281	62	123	841	0.306	262	66	133	867	0.289
Randy Wolf	32	HOU/SDG	222	71	162	823	0.299	130	39	94	458	0.319
Brad Penny	30	LA	155	42	51	426	0.276	305	73	135	865	0.318
Paul Byrd	37	BOS/CLE	227	34	82	761	0.272	274	28	88	835	0.302
Oliver Perez	27	NYM	174	105	180	847	0.232	166	79	174	765	0.276
Zack Greinke	24	KAN	258	56	183	851	0.361	118	36	106	507	0.301
Gil Meche	30	KAN	246	73	183	886	0.319	319	62	156	906	0.350
Luke Hoechaver	25	KAN	228	47	72	566	0.326
Brian Bannister	27	KAN	237	58	113	811	0.272	230	44	77	683	0.284
Kyle Davis	25	KAN	144	43	71	487	0.264	174	70	99	628	0.240