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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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• Reply-to: hemphill at alumni.caltech.edu

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