Hi,
I am looking to model the evolution of odds overtime in the 1X2 market. What I have done so far is create a table with various scorelines up to 6-6 and then used a decaying expected goals (scaled by the proportion of time remaining) as the lambda for the poisson distribution for every minute of the game.
How would I go about turning these expected goals per minute into odds that move like the actual market. I have tried various methods but none seem to work.
What I had in mind is to make a new table with columns minutes (0-90), home goals, away goals p(home), p(away) and p(draw) then convert the probabilities to odds and plot them. As the game is happening I would input the scoreline in the corresponding minute and the probabilities would dynamically adjust, as would the odds.
Has anyone had any success doing something similar?
Perhaps I am going about this all wrong, though.
I would really appreciate the help. Thanks in advance.
Modelling the evolution of odds overtime
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- Joined: Wed Feb 12, 2025 12:19 pm
For what it's worth you want to look into something called poisson probability mass function, you'd have something like this in python
Code: Select all
def poisson_pmf(k, lambda_):
return (lambda_**k * math.exp(-lambda_)) / math.factorial(k)
This link probably explains it best, down to the fact more goals are scored in the second half.
https://thepowerofgoals.blogspot.com/20 ... t-ten.html