In the last few weeks, I have come across two posts and discussions on modeling football (soccer) games with Monte Carlo simulation. Thought it might be interesting for our readers. Yes, I am more used to calling this game football, but let's just skip that part, shall we?
The first one is a blog post detailing a simple model which tries to simulate the number of goals scored by each competing team in a match. The blog discusses quite a bit on the inner details of finding out distribution parameters using optimization, which may not be that relevant, but the post is interesting. Find the blog post here. The post models the world cup final of 2011.
The second model is from our own Dave Hammal, who has posted the model in our LinkedIn group. He models the ongoing English Premier league, and predicts the match-by-match results. To access the post and the model, use this direct link.
Both models are similar, in that, they try to use discrete distributions to model the number of goals that are going to be scored in the game, and they find out the parameters of the discrete distribution from the past data for that team. The first post also discusses a few interesting improvements to the model. Note that, the first post is a static analysis of an old event (something similar to what I mentioned about the World Cup Cricket final in this post), whereas the model in the second post is dynamic, in that the model predictions are updated with the new data as the league progresses.
Finally, here are two websites (I am sure there are more of these), which claim to contain stats related to soccer games: Squawka [link to TechCrunch coverage] and FTBPro [link to TechCrunch coverage]. None of these websites contain predictive modeling though, just stats and user generated contents.