Messing with Sortable Stats

So I finally signed up for a Baseball Prospectus account and I’ve been throwing data ranges in and playing around with spreadsheets for the last couple of hours, yeah I know I’m a total nerd.  Here’s one I just was looking at:

 

NAME	YEAR	IP	PITCHES	P/IP	Pitches per Out	BB9	SO9
Chien-Ming Wang	2007	199.3	2860	14.35	4.78	2.66	4.7
Mike Mussina	2007	152	2393	15.74	5.25	2.07	5.39
Ron Villone	2007	42.3	667	15.77	5.26	3.83	5.31
Andy Pettitte	2007	215.3	3395	15.77	5.26	2.88	5.89
Mariano Rivera	2007	71.3	1126	15.79	5.26	1.51	9.34
Roger Clemens	2007	99	1584	16	5.33	2.82	6.18
Philip Hughes	2007	72.7	1235	16.99	5.66	3.59	7.18
Luis Vizcaino	2007	75.3	1290	17.13	5.71	5.26	7.41
Kei Igawa	2007	67.7	1219	18.01	6	4.92	7.05
Brian Bruney	2007	50	918	18.36	6.12	6.48	7.02
Sean Henn	2007	36.7	741	20.19	6.73	6.63	6.87

A couple of things before I get to what I noticed, first of course this is just Yankee SP/RP.  Second, I cut out anyone who pitched less than 30 Innings as I wanted a larger sample than what that would provide. 

Now, look at the P/IP.  As I thought, Wang comes in with the lowest total; that heavy sinker mitigates long innings when you get the two for one DP.  Then the correaltion between P/IP and BB9 /  SO9 jumped out at me. 

There seems to be an inverse propotion of P/IP to BB9 /SO9.  Since it takes more pitches [minimum three] to record the K and even more when you give the free pass, I guess this holds out.  Let me know if you see flaws in the way I am using the numbers.  I’m new to the whole on demand stat reporting, and I’m positive this is extremely basic analysis and something you probably didn’t a spreadsheet for, but this is another reason I love baseball.  Have a theory that is commonly held?  Test it against the mountains of data that is recorded and stored for your use and prove it out.  

BTW, Mo looks even more badass just looking at this tiny fragment of data. 

~jboogz