FIP Darlings and Dastards

Last year, I spent quite a bit of time talking about FIP and xFIP, primarily in the context of Trevor Cahill and the regression arguments.

In light of the arguable predictive value of FIP/xFIP for future performance, I thought it would be interesting to track the pitchers with the highest positive and negative ERA/FIP differentials from last year to determine how they would do this year. 

The table below sets out the pitchers we will be looking at this year, along with the contributing statistics:

	K/9	K/BB	HR/9	Whip	Babip	Lob%	Era	Fip	E/F	xFip
Clay Buchholz	6.22	1.79	0.47	1.20	.261	79.0 %	2.33	3.61	-1.28	4.07
Tim Hudson	5.47	1.88	0.79	1.15	.249	81.2 %	2.83	4.09	-1.26	3.77
Trevor Cahill	5.40	1.87	0.87	1.11	.236	76.5 %	2.97	4.19	-1.21	3.99
Jon Garland	6.12	1.56	0.90	1.32	.265	75.9 %	3.47	4.41	-0.95	4.22
Jonathan Sanchez	9.51	2.13	0.98	1.23	.252	79.4 %	3.08	4.00	-0.92	3.94
Jason Hammel	7.14	3.00	0.91	1.40	.328	68.6 %	4.81	3.70	1.11	3.66
James Shields	8.32	3.74	1.51	1.46	.342	68.3 %	5.20	4.23	0.97	3.54
Francisco Liriano	9.44	3.47	0.42	1.26	.331	73.1 %	3.62	2.66	0.96	2.95
Paul Maholm	4.95	1.65	0.73	1.56	.327	64.8 %	5.10	4.18	0.92	4.41
Kyle Davies	6.17	1.58	0.98	1.56	.316	66.9 %	5.34	4.46	0.88	4.73
Zack Greinke	7.40	3.29	0.74	1.25	.305	65.3 %	4.17	3.34	0.83	3.60
Yovani Gallardo	9.73	2.67	0.58	1.37	.324	69.8 %	3.84	3.02	0.82	3.29

Since it is so early in the season, it really does not do us much good to take a look at two starts for most of these pitchers.  Without tables, however, we can see that the first two starts of the year clearly indicate FIP has predicted nothing.  Throw Garland and Greinke out of the mix since they have not pitched, and the majority of the lucky are still lucky, and the majority of the unlucky are still unlucky.


Come May 1, I will unveil our first E/F differential chart and see how these pitchers stack up compared to last year.