Markov chain Monte Carlo and Soccer Matches

Posted on 2012-09-03
Last Modified: 2012-09-04
I'm trying to learn about a statistical simulation called Markov chain Monte Carlo. It is part of Bayesian statistics and is used when the posterior distribution is complicated.

I understand that it can be used to predict the results of soccer matches - can anyone explain exactly what it is about a soccer match that makes the posterior distribution complicated and not for example able to be simulated through a standard probability distribution?
Question by:purplesoup
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    Well of course the article suggests it is possible through a Poisson model - which is one of the standard statistical models - but then adds the MCMC at the end as a "novel" approach used by Rue and Salvesen. I don't think it really explains why they used MCMC.
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    The reason they used MCMC is because it is designed in such a way that it has the time dependent stuff built in. From the article, "Mark Dixon (statistician) and Coles [10] tried to solve this trade-off by assigning a larger weight to the latest match results. Rue and Salvesen [12] introduced a novel time-dependent rating method using the Markov Chain model."

    So two guys tried to solve the issue with changing team skill by just adding weight to later games, while Rue and Salvesen used a statistical model that had those elements built in.

    The table under "Football Prediction Methods" also shows that it has high performance which would be another reason to choose it.

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