
Re: Modeling of NFL game results
Posted:
Dec 28, 2012 5:32 AM


On Dec 27, 2:03 am, Scott Hemphill <hemph...@hemphills.net> wrote: > Scott Hemphill <hemph...@hemphills.net> writes: > > [snip] > >> I decided to rate each team with a single number, such that the >> probability that a team rated "r1" has a probability beating a team >> rated "r2" is given by: >> >> p = CDF[NormalDistribution[], r1r2]; > > Hmmm. I guess my code actually implements "r2r1" instead of "r1r2". > It doesn't matter, except one way high ratings are better than low > ratings, and the other is vice versa. > >> filename="matrix.m"; >> prec; > > I don't know how this line got shortened. My original has "prec". > > Scott >  > Scott Hemphill hemph...@alumni.caltech.edu > "This isn't flying. This is falling, with style."  Buzz Lightyear
Some suggestions. Keep a 32 x 32 data matrix in which data[[m,n]] = the # of times m beat n, m,n = 1...32. The diagonals don't matter. The easiest way to eliminate infinite solutions is to initialize it as data = Table[eps,{32},{32}], where eps is some small positive value. (Use reals.) Update every week: win[m_,n_] := data[[m,n]]++; tie[m_,n_] := (data[[m,n]] += .5; data[[n,m]] += .5); Maximize Tr[LL[x,#]&/@Subsets[Range@32,{2}]]. Fix x[32] = 0, maximize with respect to x[1]...x[31]. Or define x[32] := Tr[x/@Range@31], to fix the mean at 0. If[logit === True, LL[x_,{m_,n_}] := (data[[m,n]]*Log[1. + Exp[x@n  x@m]] + data[[n,m]]*Log[1. + Exp[x@m  x@n]]) LL[x_,{m_,n_}] := data[[m,n]]*Log[.5 + .5*Erf[.42(x@m  x@n)]] + data[[n,m]]*Log[.5 + .5*Erf[.42(x@n  x@m)]] ] The .42 puts the two solutions on approximately the same scale for .10 < p < .90 .
Caveat user: I haven't tried any of that.

