You are right that there is zero opportunity cost to using another method if it is superior. I spent numerous hours judging specifications based on many factors. Usually, I do not like to use ratios as variables, but I found that when I did, the model predicted better and helped me avoid some collineariarity problems. I didn’t make the choice to piss you off, nor do I continue to do so for this reason. I haven’t merely dismissed your argument. I responded, and when you kept pressing an incorrect point, I decided it wasn’t worth continuing the discussion.

I make mistakes all the time, and I am always willing to admit them and change my mind. After all, it was you who pointed out to me a few weeks ago that a 10% growth in salary was in fact sustainable, and I changed my mind to agree with you. You were right, and I would be stupid to disagree with you on that. The reason I’m not bending here, is that you’re not telling me anything useful.

We can argue all day about what is the proper specification to use. It’s very easy to be the guy in the audience who says “did you control for X or Y?” I could do an infinite number of things to this model. I waded through all of the possible models, weighed the costs and benefits of different specification, and ultimately chose the one that I felt to be the best. I took the time to develop this and publish this with my name and reputation on the line. I don’t have the time to go back and show what happens every time someone things one little thing could make a big difference. No one does.

This is why it’s necessary for someone to take the next step and make PrOPS obsolete. There’s not much to be gained by obsessing over the minutia of potential imperfections in the model. Quit bitching about LPs and invent the CD.

All I’m saying is “here’s PrOPS, here’s how it predicts, let’s see someone do better.” You’ve got the data, you’ve got your own idea as to what ought to be done. Why not give it a shot? Generate TtOPS. If it predicts better than PrOPS, I’ll use it with glee. That’s how progress happens.

There is no justitication for using one ratio (GB/FB) over the other (FB/GB), even though they absolutely give you different results. In fact, you are not even justifying it. Just deciding to use it.

There is zero opportunity cost to changing from ratios to rates, since I was able to generate 3 different regression equations in 5 minutes.

Your thread started with a comment about fans being skeptical, and here I am, giving you a thoughtful and legitimate beef, and you are dismissing it with “if you don’t like it, don’t use it”.

PrOPS correlate more highly from year to year than OPS, about .1 more in terms of R2.

I may have posted about this before but looking at the top Props-OPS guys I wonder if the shift is playing a role and whether left handed sluggers tend to under perform their PrOPS. Is this something you’ve considered?

Jared

Ah, but the coefficient will not change accordingly. What will happen is this: mow the guys with the highest FB/GB ratio will move *more* than the high GB/FB ratio players.

This is incorrect. The coefficients (including the constant) will adjust in magnitude, not just in sign. That’s how multiple regression analysis works.

EDIT: Text Removed
[Part of a comment was marked as spam, so I was commenting on something that was previously confusing.]

In any event, I don’t mean to be rude, but I’m tired of discussing this with you. There is an opportunity cost to refining things, and I’m done with PrOPS until the data improves significantly. If you don’t see any value to it, don’t use it. I’m comfortable with it. I welcome you, or anyone else, to generate your own version/improvement. I have no doubt that the system could be improved, and I would be happy to see it done rather than quibble over minor details of the model that are irrelevant.

The 2006 Frank Thomas is the most extreme, with a FB/GB of 2.44. His resulting regression yielded results of: .287, .313, .298.

At the other end is the 2004 Ichiro, with a GB/FB of 3.55. His results are: .247, .261, .255.

The sample standard deviations are: .0057, .0072, .0071

In all cases, the mean was .276.

GPA is analogous to batting average. Those are some HUGE differences, don’t you think?

Think of it in an extreme situation: you have a guy with 100 GB and 1 FB. In your current PrOps, this guy has a 100.00 value, which you multiply by some coefficient, say “.002″. So, he moves +.20 points up. If on the other hand you used FB/GB ratio, your coefficient may be “-.002″, which multiplied to 1/100 (or .01) will be zero.

From where I sit, using GB/FB taints your process whereby the higher the GB, the more impact than the higher the FB.

If you create a FB/GB version of PrOps, show your results both way (old Props, new Props) for Frank Thomas and Derek Jeter, and you will see the impact of this bias.

As for using the ratio, then how to justify using GB/FB instead of the reverse?

There is nothing wrong with using the reverse. The coefficient will change accordingly.

Just because something best-fits better on the sample doesn’t mean that it’s the right thing. A best-fit analysis would give the run value of a double .66 and the single .52 (instead of the more true .77, .47).

That should read a mis-specified best-fit analysis. I don’t understand how pointing out that it’s possible to mis-specify a regression sheds any light on the situation. I’m certainly aware of this, and I always bend over backwards in an effort to avoid such problems.

In any event, any mis-specification in this model, and the corresponding omitted variable bias that would result, would apply when including GB and FB as a ratio or if I included only one.

All else being equal, I’ll pick the model with the better fit.