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3284Re: My version of WINVAL (my analysis of it)

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  • dan_t_rosenbaum
    Feb 26, 2004
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      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
      it is not.)
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