Hi,
I have weed through some thread on math here, and let me quote of them them in a wee bit before I put a question:
from here http://www.sportsbookreview.com/forum/handicappe...ml#post1216316Define the logit function of a probability p as the log of the "fair" fractional payout odds p/(1-p) so that:lg(p) = ln(p) - ln(1-p)inverted:p = exp(lg(p)) (1+exp(lg(p)))This gives us:lg(A) = ln(35%) - ln(1-35%) ≈ -0.6190392084So from Bayes' Theorem:
lg(B) = ln(49%) - ln(1-49%) ≈ -0.0400053346
lg(H*) = ln(50%) - ln(50%) ≈ 0lg(P) = lg(A) - lg(B) - lg(H*)Solving for P we have:
lg(P) ≈ -0.6190392084 - -0.0400053346 - 0 ≈ -0.5790338738exp(lg(P)) ≈ exp(-0.5790338738) ≈ 0.5604395604
P = 0.5604395604 (1+0.5604395604) ≈ 35.915%
or
http://www.sportsbookreview.com/forum/handicappe...ml#post1253669Method 1:
So from Bayes' theorem:P( Xh | H ) = P( H | Xn ) * P ( Xn ) / P(H)Directly from Bayes' Theorem:P(H) = P(Xn) * P( H | Xn ) + (1- P(Xn) * ( 1 - P( H | Xn ))Substituting in to the P( Xh | H ) equality above gives us:P( Xh | H ) = P( H | Xn ) * P ( Xn ) / (P(Xn) * P( H | Xn ) + (1- P(Xn) * ( 1 - P( H | Xn ) ) )So in the first case we have:P(Xn) = 1/7Which yields:
P( H | Xn ) = 61%P( Xh | H ) = 61% * 1/7 / ( 61% * 1/7 + (1- 61%) * ( 1 - 1/7 ) ) ≈ 20.678%
My issue: I can't get this math here but badly want to be able to set correct odds. What math theories (not sure this is a word I am looking for) do I have to handle to understand these calculations? I bare in mind which exact math section from the whole Math do I need to adopt to get the understanding?