- Feb 26, 2004*******************************************************************

DeanO writes:

2. Your method for estimating crunch time is interesting. I need to

plot that up, but it looks like a pretty decent estimate on first

glance.

3. I'll echo Ed's plea to consider pace as it does have some impact.

I'm not sure it's all that big with what you're doing, but I'm not

sure it's small either.

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DanR replies:

I am working on a new version that incorporates pace, in fact it

possession-based rather than time-based. Like practically every

other seeminly minor change that I make with these data, it appears

that the results change quite a bit.

With these results, I can also compute offensive and defensive

ratings.

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DeanO writes:

4. I believe WINVAL had Scottie Pippen as the 2nd best player last

year. That is closer to your WV2. It's amazing to me how much

weighting by clutch time matters in your system, which makes me

reconsider whether it is a good measure. Perhaps it exaggerates a

bit. Regardless, it implies that perhaps the method -- KF vs

regression -- isn't as important as how you treat crunch time. (You

can say the same thing about human perception, of course. We seem to

give a lot of credit to players who hit clutch shots, perhaps more

than we should.)

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DanR replies:

Pretty much anything you do with this system changes the results

quite a bit. There just is not enough variation to estimate player

value precisely.

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DeanO writes:

5. What is the R2 for your estimates of wv1 and wv2? One thing I

tend to think of a KF doing is predicting the winner of a basketball

game about 2/3rds of the time. I wonder if there is a way of seeing

how your measure does on such a thing.

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DanR replies:

Were you wanting the R-squared from these regressions or the squared

correlation between these two measures? With the big standard

errors I am not sure there is a lot we could learn from constructing

the predictions you mention above.

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DeanO writes:

6. How did you develop this index? Why do you put the stats together

that way?

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DanR replies:

This is just a simple index I use for comparison purposes. I would

hate to spend a lot of time defending it, since I don't think there

is anything particularly special about it.

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7. Adding entirely new seasons may reduce the collinearity, but it

also weakens the inherent assumption in these methods that player

ability remains constant over the estimation period. If Gilbert

Arenas played so much more poorly early this season just because of

his injury, sheesh, that's a tough thing to deal with.

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DanR replies:

That is sure part of the tradeoff, but given how noisy the estimates

are currently, it is a tradeoff I am more than willing to pay.

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8. Your point 3 in message 3268 (below) -- I like what it's saying

but I'm not seeing how it comes out of the results. I don't see, for

instance, where you regress against field goal attempts, two pt fga

vs three pt fga, etc. The r2 on those regressions aren't very good

either. What's PTS_AST, FG2_TP, STL_TO?

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DanR replies:

The second regression has all three of those variables in it

(fg2p40, tpp40, ftp40). That regression has a higher R-squared than

the first regression, which is based upon made missed field goals

and free throws. And what is interesting is that the R-squared is

higher despite the fact that the second regression has one less

parameter.

Yes, you said this before that the R-squared isn't very good, but I

don't know why you say this. If I was regressing team outcomes on

team statistics, I would expect a much higher R-squared, but for an

individual-level regression like this, I don't have much of

expectation for what the R-squared should be. (BTW, economists tend

to place far less importance on R-squared relative to other folks

who use statistics.)

PTS_AST is a test in the second regression that the beta for points

per 40 minutes is the same as the beta for assists per 40 minutes.

(This hypothesis is not rejected.)

FG2_TP is a test in the second regression that the beta two point

field goal attempts per 40 minutes is the same as the beta for three

point attempts per 40 minutes. (This hypothesis is soundly

rejected. Three point attempts appear to be less costly, even after

accounting for points scored.)

STL_TO is a test in the second regression that the beta for steals

per 40 minutes is the negative of the beta for turnovers per 40

minutes. (This hypothesis is in the ballpark of being rejected, but

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