That’s a limitation of linear weights metrics, but you addressed the problem yourself, I think. No pitcher records 3 Ks and 3 BBs per inning. Pitchers are generally found in ranges that can be modeled very well by linear regression. I think there have been some conjectures as to how pitchers at the extremes might not be valued as well by linear scaling, but I haven’t seen a full study/article. Extrapolation (going outside the range of the bulk of the data) can always present problems. Perhaps there is a non-linear regression out there somewhere that fits pitchers even better…

If a pitcher pitches in a small ballpark and gives up a fly ball, that’s an undesirable outcome. If a pitcher pitches in a big ballpark and gives up a ground ball (with no one on), that is an undesirable outcome. The face that FIP excludes certain fly balls, but not others, makes it a flawed statistic.

You say that FIP is best to use because it doesn’t look at the performance of fielders. It however, also excludes the ballpark, and most pitchers pitch half their games at the same ballpark.

I say include ball/strike ratio, not just BB, HBP and K, and find a way to successfully include the line drive/grounder numbers too.

There’s no point in being a jerk on FanGraphs. There’s plenty of sites that cater to jerks, but a sabermetrics site shouldn’t be one of those places. Dave Cameron’s work on this site stands on its own; he has nothing but my respect and that of countless others.

That said, Dave / Tango, if you don’t think you’re being a jerk here, then you might want to reassess what being a jerk means. If you don’t care about being a jerk then I have nothing to add and please retract these comments. The thing that bugged me here is that both Dave and Tango seemed oblivious to their own condescending tone, as if they really had no clue that they were coming off as something less than friendly.

While I enjoy the “wisdom of the crowd ” or SME approach and I think it works best for this, it lacks the convience of an a codified structure that academia provides. I don’t think proofs are in order, but just showing how the formulas for more complicated metrics are dervived might help a sap like me. It’s not a question of validity. At first it was but as i stumbled around the site the last few months i concluded that, just by the site’s upkeep and the data volume, all done out a passion for quantifying baseball, you know what your doing. For me knowing how it ticks makes learning easier. Like how was FIP derived? Is there one book that I can buy, a sabrmetricians bible so to speak? Also it can be daunting when you read about one metric and find a slew of other metrics its based on…for a beginner its frustrating. It would be great to have a “beginners start here section” which is something the aforementioned structure provides albeit with all the tedium and rigidity academia can pile on.