Re: Modeling of NFL game results
- To: mathgroup at smc.vnet.net
- Subject: [mg129271] Re: Modeling of NFL game results
- From: Scott Hemphill <hemphill at hemphills.net>
- Date: Fri, 28 Dec 2012 05:35:03 -0500 (EST)
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Ray Koopman <koopman at sfu.ca> writes: [snip] > 2. If win[x,m,n] is supposed to give the log of the probability > that m beats n then low ratings are better than high ratings, > which is fine if that's what you want. Right. Here are the ratings through week 16. 29 Texans -0.28 14 Falcons -0.16 4 Broncos -0.12 19 Packers -0.10 21 Patriots -0.02 0 49ers 0.00 27 Seahawks 0.16 1 Bears 0.23 31 Vikings 0.31 24 Ravens 0.42 10 Colts 0.44 25 Redskins 0.55 23 Rams 0.57 15 Giants 0.61 11 Cowboys 0.69 2 Bengals 0.81 26 Saints 0.89 12 Dolphins 0.91 7 Cardinals 0.97 17 Jets 1.00 28 Steelers 1.06 20 Panthers 1.09 6 Buccaneers 1.17 18 Lions 1.18 30 Titans 1.18 3 Bills 1.30 8 Chargers 1.32 5 Browns 1.42 13 Eagles 1.45 22 Raiders 1.81 16 Jaguars 1.87 9 Chiefs 2.20 > 3. The solution is finite only if every team has won at least > one game and lost at least one game. That doesn't happen until > after week 10. After that the problem is well conditioned and > you shouldn't need to mess with the precision. Yes, I was aware of this. However, "FindMaximum" doesn't step to infinity, and "prob[x,m,n]" returns sensible, if inaccurate, results. (It will always find that a team with a perfect record has an estimated 100% chance of beating a team with an imperfect record.) The predictions earlier in the season weren't really very good, but lately they've been outperforming those of Elliot Harrison on his nfl.com blog. Scott -- Scott Hemphill hemphill at alumni.caltech.edu "This isn't flying. This is falling, with style." -- Buzz Lightyear