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Statistical methods for football

Discussion in 'Miami Dolphins Forum' started by cbrad, Feb 4, 2020.

  1. cbrad

    cbrad . Club Member

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    Yeah.. if they're using EPA, then the first two games last season where we lost 10-59 and 0-43 are going to skew the expected wins quite a bit because EPA doesn't care that all the drives in those games (both on offense and defense) came in just 2 games lol. So EPA would predict a lower win% than we actually had because of that alone.

    Now whether that explains the full 2.5 wins above expected I don't know, but I wouldn't be surprised if it explained a good portion of it. Something to keep in mind when merging data.
     
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  2. The Guy

    The Guy Well-Known Member

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    Would that be controlled for if they used EPA per play instead of just EPA?
     
  3. cbrad

    cbrad . Club Member

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    No, in fact it looks like they used EPA per dropback and per rush. If you have one or two absolutely horrid games, with tons of plays and bad average EPA per play, that obviously skews the average across a season.

    The way to "control" for that is to use a sampling distribution — a distribution of means — where in this case the mean is per game. In other words, use average EPA per game as a single statistic and look at the distribution of that. Problem there is you have so few data points with 16 games so there are drawbacks to that approach too, but it solves this one issue.
     
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  4. The Guy

    The Guy Well-Known Member

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    What do you think about the fact that in that article, when they use a random forest as opposed to regression, just about every variable pales in comparison in importance to offensive and defensive EPA per dropback?
     
  5. cbrad

    cbrad . Club Member

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    That first figure uses simple linear regression, so they did regression separately on each variable (how well you can predict wins based on each variable alone). That second graph they used a random forest model which takes into account all the possible predictors, so those two graphs aren't comparable.

    The proper comparison would be multiple linear regression vs. random forest. In general, you don't want to trust machine learning techniques like random forests because they tend to "overfit" the data, i.e., they work well on that one dataset but not on a slightly different one. Multiple linear regression is better, however I suspect EPA per dropback would also win out in multiple linear regression because we know passing efficiency matters more than rushing efficiency.
     
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  6. The Guy

    The Guy Well-Known Member

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  7. cbrad

    cbrad . Club Member

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    Yeah, whatever the effect of fans on home field advantage is being swamped by the effect of no fans on offensive production for both teams. Points per game, yards per game, passer rating, plays per drive, scoring percentage per drive.. all of these are at record levels this year with QB's having to whisper in the huddle lol.

    Two links worth checking every week:
    https://www.pro-football-reference.com/years/NFL/index.htm
    https://www.pro-football-reference.com/years/NFL/passing.htm

    We can also compare those stats at the end of the year to what they were a week ago (i.e., after 1 month of play), especially ppg at 25.6 and passer rating at 96.5, to see how much of the observed effect was due to lack of preseason. Right now, it's looking like most of the effect is stable and due to lack of crowd noise, but passer rating did decrease last week, taking the overall average to 95.6, possibly showing some effect of lack of preseason.
     
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  8. The Guy

    The Guy Well-Known Member

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    I have to wonder also if there is an effect of the home field crowd on inspiring defensive play. So you have perhaps this synergistic effect where offenses can function better and defenses are less inspired and invigorated.
     
  9. The Guy

    The Guy Well-Known Member

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  10. The Guy

    The Guy Well-Known Member

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  11. The Guy

    The Guy Well-Known Member

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  12. The Guy

    The Guy Well-Known Member

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  13. The Guy

    The Guy Well-Known Member

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