1. EvanZ

    Hey, Daniel. Glad I caught this, as I had just done a model in BUGS/JAGS and was thinking about estimating turnover weights from the model itself (e.g. as another parameter). Have you found the work by Ed Kambour who also does Bayesian NFL ratings? He’s got a lot of good ideas in some old PPT slides.

    • DanielM

      What is BUGS/JAGS? Okay, read up on it… Still have no clue 🙂

      I’ve done quite a bit with football ratings over the years (I think I started in that before working with basketball), so I had a background with many if the issues in football.

      I haven’t seen Ed Kambour’s work.

      I have (though not updated for this year) developed some more intricate NCAA football ratings based on the inter-year correlation of various stats (within-season out-of-sample stability). I used that to generate projected point and yards-per-play differential.

      Here are those correlations, for the NCAA again:
      In Season Correlation:
      Off YPP 65.6%
      Off Int% 20.1%
      Off Fum% 7.8%
      Def YPP 68.2%
      Def Int% 20.1%
      Def Fum% 1.3%

      • EvanZ

        Ok, well I’ll post my model in the next couple of days. It will just include point differential. Maybe you’ll have suggestions for me about adding turnovers to improve it.

        • DanielM

          If you’re doing a model for prediction out of sample, absolutely adjust for turnovers. Just not stable, particularly fumbles. Fumbles are pretty nearly random.

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